diff --git a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb index b5ce880..98a0711 100644 --- a/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb +++ b/docs/transient/04pumpingtests/confined1_oude_korendijk.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n" + "" ] }, { @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -99,9 +99,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "# timflow model\n", "ml = tft.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", @@ -119,9 +128,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 30\n", + " # data points = 69\n", + " # variables = 2\n", + " chi-square = 0.17291363\n", + " reduced chi-square = 0.00258080\n", + " Akaike info crit = -409.245801\n", + " Bayesian info crit = -404.777588\n", + "[[Variables]]\n", + " kaq_0_0: 66.0893536 +/- 1.65499500 (2.50%) (init = 10)\n", + " Saq_0_0: 2.5409e-05 +/- 2.4016e-06 (9.45%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_0_0, Saq_0_0) = -0.8553\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml)\n", @@ -134,9 +166,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_0_0066.0893541.6549952.504178-infinf10.0000None[[66.08935356677974]]
Saq_0_000.0000250.0000029.451954-infinf0.0001None[[2.5408597145414816e-05]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial inhoms \\\n", + "kaq_0_0 0 66.089354 1.654995 2.504178 -inf inf 10.0000 None \n", + "Saq_0_0 0 0.000025 0.000002 9.451954 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq_0_0 [[66.08935356677974]] \n", + "Saq_0_0 [[2.5408597145414816e-05]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.05005991006296439\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"RMSE:\", cal.rmse())" @@ -144,9 +258,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(ro1, 0, to1)\n", "hm2 = ml.head(ro2, 0, to2)\n", @@ -177,27 +302,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow66.092.54e-050.050
AQTESOLV66.092.54e-050.050
MLU66.852.40e-050.051
K&dR55.711.70e-04-
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", - " index=[\"timflow\", \"AQTESOLV\", \"MLU\", \"K&dR\"],\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], \n", + " index=[\"timflow\", \"AQTESOLV\", \"MLU\", \"K&dR\"]\n", ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", "t.loc[\"AQTESOLV\"] = [66.086, 2.541e-05, 0.05006]\n", "t.loc[\"MLU\"] = [66.850, 2.400e-05, 0.05083]\n", - "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, \"-\"]\n", + "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, \"-\" ]\n", "\n", - "t_formatted = t.style.format(\n", - " {\n", - " \"k [m/d]\": \"{:.2f}\",\n", - " \"Ss [1/m]\": \"{:.2e}\",\n", - " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.2f}\",\n", - " }\n", - ")\n", + "t_formatted = t.style.format({\"k [m/d]\": \"{:.2f}\", \n", + " \"Ss [1/m]\": \"{:.2e}\", \n", + " \"RMSE [m]\": lambda x: \"-\" if x == \"-\" else f\"{float(x):.3f}\"})\n", "t_formatted" ] }, @@ -222,9 +394,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -236,7 +408,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined2_grindley.ipynb b/docs/transient/04pumpingtests/confined2_grindley.ipynb index cd3cf73..f842cfe 100644 --- a/docs/transient/04pumpingtests/confined2_grindley.ipynb +++ b/docs/transient/04pumpingtests/confined2_grindley.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "editable": true, "slideshow": { @@ -58,7 +58,9 @@ "tags": [] }, "source": [ - "This example is a pumping test conducted in 1953 in Gridley, Illinois, US. It was reported by Walton (1962). The aquifer is an 18 ft thick sand and gravel layer under confined conditions. The pumping well fully penetrates the formation, and pumping was conducted for 8 hours at a rate of 220 gallons per minute. The effect of pumping was observed at observation well 1, located 824 ft away from the well.\n", + "This example is a pumping test conducted in 1953 in Gridley, Illinois, US. It was reported by Walton (1962). \n", + "\n", + "The aquifer is an 18 ft thick sand and gravel layer under confined conditions. The pumping well fully penetrates the formation, and pumping was conducted for 8 hours at a rate of 220 gallons per minute. The effect of pumping was observed at observation well 1, located 824 ft away from the well.\n", "\n", "The time-drawdown data for the observation well were obtained from the AQTESOLV documentation (Duffield, 2007), while data from the pumping well were obtained from the original paper from Walton (1962). Following AQTESOLV documentation (Duffield, 2007), radii of 0.5 ft were used for both the pumping and observation wells. " ] @@ -85,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "editable": true, "slideshow": { @@ -121,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "editable": true, "slideshow": { @@ -133,13 +135,9 @@ "source": [ "# known parameters\n", "H = 18 * 0.3048 # aquifer thickness in m (18 ft = 5.48 m)\n", - "Q = (\n", - " (220 * 0.00378541) * 60 * 24\n", - ") # constant discharge in m^3/d (220 gallons/minute = 1199.22 m^3/d)\n", - "r = (\n", - " 824 * 0.3048\n", - ") # distance between observation well to test well in m (824 ft = 251.16 m)\n", - "rw = 0.5 * 0.3048 # screen radius of test well in m (0.5 ft = 0.15 m)" + "Q = (220 * 0.00378541) * 60 * 24 # constant discharge in m^3/d (220 gallons/minute = 1199.22 m^3/d)\n", + "r = 824 * 0.3048 # distance between observation well to test well in m (824 ft = 251.16 m)\n", + "rw = 0.5 * 0.3048 # screen radius of test well in m (0.5 ft = 0.15 m)" ] }, { @@ -157,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "editable": true, "slideshow": { @@ -165,10 +163,19 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(kaq=10, z=[0, -H], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", - "w = tft.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], rc=rw, layers=0)\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)],rc=rw , layers=0) \n", "ml.solve()" ] }, @@ -200,15 +207,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...........................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 24\n", + " # data points = 36\n", + " # variables = 2\n", + " chi-square = 2.41063000\n", + " reduced chi-square = 0.07090088\n", + " Akaike info crit = -93.3307093\n", + " Bayesian info crit = -90.1636715\n", + "[[Variables]]\n", + " kaq_0_0: 38.0863413 +/- 0.50173392 (1.32%) (init = 10)\n", + " Saq_0_0: 1.1935e-06 +/- 1.8556e-07 (15.55%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_0_0, Saq_0_0) = -0.7708\n" + ] + } + ], "source": [ "# unknown parameters: kaq, Saq\n", "cal = tft.Calibrate(ml) # create Calibrate object\n", - "cal.set_parameter(\n", - " name=\"kaq\", initial=10, layers=0\n", - ") # setting the parameters for calibration\n", + "cal.set_parameter(name=\"kaq\", initial=10, layers=0) # setting the parameters for calibration\n", "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=1e-7, layers=0)\n", "cal.series(name=\"obs1\", x=r, y=0, t=to1, h=ho1, layer=0) # setting the observation data\n", "cal.seriesinwell(name=\"obs2\", element=w, t=to2, h=ho2) # setting the observation data\n", @@ -217,9 +245,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_0_0038.0863415.017339e-011.317359-infinf10.0000None[[38.08634128836749]]
Saq_0_000.0000011.855625e-0715.5472401.000000e-07inf0.0001None[[1.1935399729656737e-06]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax \\\n", + "kaq_0_0 0 38.086341 5.017339e-01 1.317359 -inf inf \n", + "Saq_0_0 0 0.000001 1.855625e-07 15.547240 1.000000e-07 inf \n", + "\n", + " initial inhoms parray \n", + "kaq_0_0 10.0000 None [[38.08634128836749]] \n", + "Saq_0_0 0.0001 None [[1.1935399729656737e-06]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.25876996507559114\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"RMSE:\", cal.rmse())" @@ -227,15 +337,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm1 = ml.head(r, 0, to1)\n", "hm2 = w.headinside(to2)\n", - "plt.semilogx(to1, hm1[0], \"C0\", label=\"timflow piezometer\")\n", + "plt.semilogx(to1, hm1[0], 'C0', label=\"timflow piezometer\")\n", "plt.semilogx(to1, ho1, \"C0.\", label=\"obs piezometer\")\n", - "plt.semilogx(to2, hm2[0], \"C1\", label=\"timflow well\")\n", + "plt.semilogx(to2, hm2[0], 'C1', label=\"timflow well\")\n", "plt.semilogx(to2, ho2, \"C1.\", label=\"obs well\")\n", "plt.title(\"Model Results\")\n", "plt.xlabel(\"time [d]\")\n", @@ -267,12 +388,12 @@ "tags": [] }, "source": [ - "The performance of `timflow` with two datasets simultaneously was evaluated by comparison with AQTESOLV (Duffield, 2007), and MLU (Carlson and Randall, 2012). Results from `timflow` with added wellbore storage and MLU are identical, while those from AQTESOLV show small deviations." + "The performance of `timflow` with two datasets simultaneously was evaluated by comparison with AQTESOLV (Duffield, 2007), and MLU (Carlson and Randall, 2012). Results from `timflow` with added welbore storage and MLU are identical, while those from AQTESOLV show small deviations." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "editable": true, "slideshow": { @@ -282,10 +403,56 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow38.091.19e-060.259
AQTESOLV37.801.36e-060.270
MLU38.091.19e-060.259
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], index=[\"timflow\", \"AQTESOLV\", \"MLU\"]\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], \n", + " index=[\"timflow\", \"AQTESOLV\", \"MLU\"]\n", ")\n", "\n", "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", @@ -293,7 +460,9 @@ "t.loc[\"AQTESOLV\"] = [37.803, 1.356e-06, 0.270]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.2f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \n", + " \"Ss [1/m]\": \"{:.2e}\", \n", + " \"RMSE [m]\": \"{:.3f}\"}\n", ")\n", "t_formatted" ] @@ -323,9 +492,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -337,7 +506,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/confined3_sioux.ipynb b/docs/transient/04pumpingtests/confined3_sioux.ipynb new file mode 100644 index 0000000..387d63f --- /dev/null +++ b/docs/transient/04pumpingtests/confined3_sioux.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# 3. Confined Aquifer Test - Sioux Flats" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (5, 3) # default figure size" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Introduction and Conceptual Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "This example is a pumping test done in Sioux Flats, South Dakota, USA. The data comes from the AQTESOLV documentation (Duffield, 2007). \n", + "\n", + "The aquifer is 50 ft thick and is bounded by impermeable layers. The test was conducted for 2045 minutes (~34 hours), with a constant pumping rate of 2.7 $ft^3/s$. Drawdown data has been collected at three piezometers located 100, 200 and 400 ft away, respectively. The well radius is 0.5 ft. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# time and drawdown of piezometer 100ft away from pumping well\n", + "data1 = np.loadtxt(\"data/sioux100.txt\")\n", + "t1 = data1[:, 0] \n", + "h1 = data1[:, 1] \n", + " \n", + "# time and drawdown of piezometer 200ft away from pumping well\n", + "data2 = np.loadtxt(\"data/sioux200.txt\")\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "\n", + "# time and drawdown of piezometer 300ft away from pumping well\n", + "data3 = np.loadtxt(\"data/sioux400.txt\")\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# known parameters\n", + "Q = (2.7 * 0.3048**3) * 60 * 60 * 24 # constant discharge in m^3/d (2.7 ft^3/s = 6605.754 m^3/d)\n", + "b = -50 * 0.3048 # aquifer thickness in m (50 ft = 15.24 m)\n", + "rw = 0.5 * 0.3048 # well radius in m (0.5 ft = 0.1524 m) \n", + "r1 = 100 * 0.3048# distance between obs1 to pumping well (100 ft = 30.48 m)\n", + "r2 = 200 * 0.3048 # distance between obs2 to pumping well (200 ft = 60.96 m)\n", + "r3 = 400 * 0.3048 # distance between obs3 to pumping well (400 ft = 121.92 m)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "# timflow model\n", + "ml = tft.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Estimate aquifer parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 35\n", + " # data points = 77\n", + " # variables = 2\n", + " chi-square = 0.00121634\n", + " reduced chi-square = 1.6218e-05\n", + " Akaike info crit = -847.289903\n", + " Bayesian info crit = -842.602292\n", + "[[Variables]]\n", + " kaq_0_0: 282.795208 +/- 1.13789472 (0.40%) (init = 10)\n", + " Saq_0_0: 0.00420855 +/- 3.3461e-05 (0.80%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_0_0, Saq_0_0) = -0.8113\n" + ] + } + ], + "source": [ + "# unknown parameters: k, Saq\n", + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", initial=10, layers=0)\n", + "cal.set_parameter(name=\"Saq\", initial=1e-4, layers=0)\n", + "cal.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0) # Adding well 1\n", + "cal.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0) # Adding well 2\n", + "cal.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0) # Adding well 3\n", + "cal.fit(report=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_0_00282.7952081.1378950.402374-infinf10.0000None[[282.7952077845511]]
Saq_0_000.0042090.0000330.795070-infinf0.0001None[[0.004208548562555498]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial inhoms \\\n", + "kaq_0_0 0 282.795208 1.137895 0.402374 -inf inf 10.0000 None \n", + "Saq_0_0 0 0.004209 0.000033 0.795070 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq_0_0 [[282.7952077845511]] \n", + "Saq_0_0 [[0.004208548562555498]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.003974499249718837\n" + ] + } + ], + "source": [ + "display(cal.parameters)\n", + "print(\"RMSE:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = ml.head(r1, 0, t1)\n", + "hm2 = ml.head(r2, 0, t2)\n", + "hm3 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t1, hm1[0], label=\"timflow result 1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t2, hm2[0], label=\"timflow result 2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", + "plt.semilogx(t3, hm3[0], label=\"timflow result 3\")\n", + "plt.title(\"Model Results\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Comparison of results\n", + "The performance of `timflow` was evaluated by comparison with AQTESOLV (Duffield, 2007), and MLU (Carlson and Randall, 2012). In both software, the model was calibrated with observations. `timflow` achieved similar results as the other software. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow282.804.21e-030.004
AQTESOLV282.664.21e-030.004
MLU282.684.21e-030.004
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"], \n", + " index=[\"timflow\", \"AQTESOLV\", \"MLU\"]\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"AQTESOLV\"] = [282.659, 4.211e-03, 0.003925]\n", + "t.loc[\"MLU\"] = [282.684, 4.209e-03, 0.003897]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## References" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "* Carlson, F. and Randall, J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510\n", + "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M., Stensitzki, T., Allen, D.B., Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/transient/04pumpingtests/figs/Dalem.png b/docs/transient/04pumpingtests/figs/Dalem.png index 05ba9e5..d28b157 100644 Binary files a/docs/transient/04pumpingtests/figs/Dalem.png and b/docs/transient/04pumpingtests/figs/Dalem.png differ diff --git a/docs/transient/04pumpingtests/figs/Dalem_model1.jpeg b/docs/transient/04pumpingtests/figs/Dalem_model1.jpeg deleted file mode 100644 index 7b4c892..0000000 Binary files a/docs/transient/04pumpingtests/figs/Dalem_model1.jpeg and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Dalem_model1.png b/docs/transient/04pumpingtests/figs/Dalem_model1.png deleted file mode 100644 index d28b157..0000000 Binary files a/docs/transient/04pumpingtests/figs/Dalem_model1.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Dalem_model2.png b/docs/transient/04pumpingtests/figs/Dalem_model2.png deleted file mode 100644 index 2b7ac1e..0000000 Binary files a/docs/transient/04pumpingtests/figs/Dalem_model2.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Hardinxveld.jpeg b/docs/transient/04pumpingtests/figs/Hardinxveld.jpeg deleted file mode 100644 index a5e38cf..0000000 Binary files a/docs/transient/04pumpingtests/figs/Hardinxveld.jpeg and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Multi_well.png b/docs/transient/04pumpingtests/figs/Multi_well.png new file mode 100644 index 0000000..2668c51 Binary files /dev/null and b/docs/transient/04pumpingtests/figs/Multi_well.png differ diff --git a/docs/transient/04pumpingtests/figs/Nevada.png b/docs/transient/04pumpingtests/figs/Nevada.png deleted file mode 100644 index fc333b5..0000000 Binary files a/docs/transient/04pumpingtests/figs/Nevada.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/NevadaTTim.png b/docs/transient/04pumpingtests/figs/NevadaTTim.png deleted file mode 100644 index cc4353e..0000000 Binary files a/docs/transient/04pumpingtests/figs/NevadaTTim.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Schroth.jpeg b/docs/transient/04pumpingtests/figs/Schroth.jpeg deleted file mode 100644 index 3f8c6e6..0000000 Binary files a/docs/transient/04pumpingtests/figs/Schroth.jpeg and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Schroth.png b/docs/transient/04pumpingtests/figs/Schroth.png deleted file mode 100644 index 075ecac..0000000 Binary files a/docs/transient/04pumpingtests/figs/Schroth.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/Schroth_New.png b/docs/transient/04pumpingtests/figs/Schroth_New.png deleted file mode 100644 index 8823895..0000000 Binary files a/docs/transient/04pumpingtests/figs/Schroth_New.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/figs/TexasHill.png b/docs/transient/04pumpingtests/figs/TexasHill.png deleted file mode 100644 index f88f5ae..0000000 Binary files a/docs/transient/04pumpingtests/figs/TexasHill.png and /dev/null differ diff --git a/docs/transient/04pumpingtests/index.rst b/docs/transient/04pumpingtests/index.rst index 2fcb1ec..8225042 100644 --- a/docs/transient/04pumpingtests/index.rst +++ b/docs/transient/04pumpingtests/index.rst @@ -35,15 +35,18 @@ Confined Pumping Tests 1. :doc:`Oude Korendijk ` - One layer confined pumping test with two observation wells 2. :doc:`Grindley ` - One layer confined pumping test with data from both observation well and pumping well. +3. :doc:`Sioux ` - One layer confined pumping test with three observation wells. Leaky Pumping Tests (Semi-confined) ----------------------------------- 1. :doc:`Dalem ` - One layer, semi-confined aquifer test with four observation wells. 2. :doc:`Hardixveld ` - Four layers (Aquitard - Aquifer - Aquitard - Aquifer) semi-confined pumping test with data from the pumping well. +3. :doc:`Texas Hill ` - Two layers, semi-confined aquifer test with three observation wells. Slug Tests ---------- 1. :doc:`Pratt County ` - One layer partially penetrated slug test with data from the test well. 2. :doc:`Falling Head ` - One layer partially penetrated slug test with data from the test well. +3. :doc:`Multi well ` - One layer fully penetrated slug test with data from both observation well and pumping well. diff --git a/docs/transient/04pumpingtests/leaky1_dalem.ipynb b/docs/transient/04pumpingtests/leaky1_dalem.ipynb index c97544c..e0b1ac9 100644 --- a/docs/transient/04pumpingtests/leaky1_dalem.ipynb +++ b/docs/transient/04pumpingtests/leaky1_dalem.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "editable": true, "slideshow": { @@ -26,13 +26,13 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", - "import timflow.transient as tft\n", + "import timflow.transient as tft \n", "\n", - "plt.rcParams[\"figure.figsize\"] = [5, 3]" + "plt.rcParams['figure.figsize'] = [5, 3]" ] }, { @@ -60,7 +60,7 @@ ] }, "source": [ - "\n" + "" ] }, { @@ -72,14 +72,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "# data of observation well 30 m away from pumping well\n", "data1 = np.loadtxt(\"data/dalem_p30.txt\", skiprows=1)\n", - "t1 = data1[:, 0] # days\n", - "h1 = data1[:, 1] # m\n", + "t1 = data1[:, 0] \n", + "h1 = data1[:, 1] \n", "\n", "# data of observation well 60 m away from pumping well\n", "data2 = np.loadtxt(\"data/dalem_p60.txt\", skiprows=1)\n", @@ -106,10 +106,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ + "# known parameters\n", "H = 37 # aquifer thickness in m\n", "zt = -8 # top boundary of aquifer in m\n", "zb = zt - H # bottom boundary of the aquifer in m\n", @@ -131,12 +132,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ - "# unknown parameters: kaq, Saq, c\n", - "ml = tft.ModelMaq(\n", + "# unkonwn parameters: kaq, Saq, c\n", + "ml= tft.ModelMaq(\n", " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.01, tmax=1\n", ")\n", "w = tft.Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", @@ -152,14 +153,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".....................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 34\n", + " # data points = 51\n", + " # variables = 3\n", + " chi-square = 0.00178546\n", + " reduced chi-square = 3.7197e-05\n", + " Akaike info crit = -517.255127\n", + " Bayesian info crit = -511.459650\n", + "[[Variables]]\n", + " kaq0_0_0: 45.3318750 +/- 1.18522014 (2.61%) (init = 10)\n", + " Saq0_0_0: 4.7623e-05 +/- 3.1043e-06 (6.52%) (init = 0.0001)\n", + " c0_0_0: 331.164978 +/- 76.1868842 (23.01%) (init = 500)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_0_0, Saq0_0_0) = -0.7707\n", + " C(kaq0_0_0, c0_0_0) = +0.7616\n", + " C(Saq0_0_0, c0_0_0) = -0.2992\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", - "cal.set_parameter(name=\"kaq\", initial=10, layers=0)\n", - "cal.set_parameter(name=\"Saq\", initial=1e-4, layers=0)\n", - "cal.set_parameter(name=\"c\", initial=500, pmin=0, layers=0)\n", + "cal.set_parameter(name=\"kaq0\", initial=10, layers=0)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4, layers=0)\n", + "cal.set_parameter(name=\"c0\", initial=500, pmin=0, layers=0)\n", "\n", "cal.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", "cal.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", @@ -170,9 +197,105 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq0_0_0045.3318751.1852202.614540-infinf10.0000None[[45.33187498569421]]
Saq0_0_000.0000480.0000036.518552-infinf0.0001None[[4.762262320317127e-05]]
c0_0_00331.16497876.18688423.0057190.0inf500.0000None[[331.1649782944002]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial \\\n", + "kaq0_0_0 0 45.331875 1.185220 2.614540 -inf inf 10.0000 \n", + "Saq0_0_0 0 0.000048 0.000003 6.518552 -inf inf 0.0001 \n", + "c0_0_0 0 331.164978 76.186884 23.005719 0.0 inf 500.0000 \n", + "\n", + " inhoms parray \n", + "kaq0_0_0 None [[45.33187498569421]] \n", + "Saq0_0_0 None [[4.762262320317127e-05]] \n", + "c0_0_0 None [[331.1649782944002]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005916843158052398\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"RMSE:\", cal.rmse())" @@ -180,9 +303,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "hm_1 = ml.head(r1, 0, t1)\n", "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", @@ -218,16 +352,72 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The performance of `timflow` was evaluated by comparison with other numerical models and with parameter values reported in Kruseman and de Ridder (1970). The published values were obtained by graphical matching to the Hantush family of type curves (Hantush, 1955). In addition to the `timflow` results and the literature values, results from AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012), as reported by Yang (2020), are included for comparison.\n", + "The performance of `timflow` was evaluated by comparison with other numerical models and with parameter values reported in Kruseman and de Ridder (1970). The published values were obtained by graphical matching to the Hantush family of type curves (Hantush, 1955). In addition to the `timflow` results and the literature values, results from AQTESOLV (Duffield, 2007) and MLU (Carlson & Randall, 2012) are included for comparison.\n", "\n", "Overall, all models yield similar estimates of the aquifer hydraulic conductivity. However, the `timflow` results differ substantially from the MLU and AQTESOLV solutions with respect to aquitard storage and hydraulic resistance." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]c [d]RMSE [m]
timflow45.334.76e-053310.006
AQTESOLV49.294.56e-057450.007
MLU45.193.94e-057690.006
Hantush45.334.76e-053310.006
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", @@ -240,7 +430,12 @@ "t.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, 0.005917]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"c [d]\": \"{:.0f}\", \"RMSE [m]\": \"{:.2f}\"}\n", + " {\n", + " \"k [m/d]\": \"{:.2f}\", \n", + " \"Ss [1/m]\": \"{:.2e}\",\n", + " \"c [d]\": \"{:.0f}\",\n", + " \"RMSE [m]\": \"{:.3f}\"\n", + " }\n", ")\n", "t_formatted" ] @@ -261,9 +456,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -275,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb index 981be67..9068d66 100644 --- a/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb +++ b/docs/transient/04pumpingtests/leaky2_hardixveld.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -85,10 +85,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ + "# known parameters\n", "H = 27 # aquifer thickness, m\n", "zt = -10 # upper boundary of aquifer, m\n", "zb = zt - H # lower boundary of the aquifer, m\n", @@ -99,9 +100,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.ModelMaq(\n", " kaq=[50, 40],\n", @@ -121,18 +131,44 @@ "metadata": {}, "source": [ "### Estimate aquifer parameters\n", - "The parameters to be calibrated are the hydraulic conductivity and specific storage of the first layer, and the skin resistance of the well. The parameters of the aquitards and the second aquifer are kept fixed. " + "The parameters to be calibrated are the hydraulic conductivity and specific storage of the first layer, and the skin resistance of the well. The parameters of the aquitards and the second aquifer are kept fixed." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................................................................................................................................................................................................................................................................................................................................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 352\n", + " # data points = 35\n", + " # variables = 3\n", + " chi-square = 0.00106332\n", + " reduced chi-square = 3.3229e-05\n", + " Akaike info crit = -358.059623\n", + " Bayesian info crit = -353.393579\n", + "[[Variables]]\n", + " kaq0_0_0: 44.5281176 +/- 0.65971465 (1.48%) (init = 50)\n", + " Saq0_0_0: 6.3899e-06 +/- 9.5244e-07 (14.91%) (init = 0.0001)\n", + " res: 0.01620398 +/- 5.7461e-04 (3.55%) (init = 1)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq0_0_0, res) = +0.9766\n", + " C(Saq0_0_0, res) = +0.9502\n", + " C(kaq0_0_0, Saq0_0_0) = +0.8636\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", - "cal.set_parameter(name=\"kaq\", initial=50, pmin=0, layers=0)\n", - "cal.set_parameter(name=\"Saq\", initial=1e-4, pmin=0, layers=0)\n", + "cal.set_parameter(name=\"kaq0\", initial=50, pmin=0, layers=0)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0, layers=0)\n", "\n", "cal.set_parameter_by_reference(name=\"res\", parameter=w.res[:], initial=1, pmin=0)\n", "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", @@ -141,9 +177,105 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq0_0_00.044.5281186.597147e-011.4815690.0inf50.0000None[[44.52811759942783]]
Saq0_0_00.00.0000069.524444e-0714.9054650.0inf0.0001None[[6.389900564895967e-06]]
resNaN0.0162045.746117e-043.5461160.0inf1.0000NaN[[0.016203975748327215]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial \\\n", + "kaq0_0_0 0.0 44.528118 6.597147e-01 1.481569 0.0 inf 50.0000 \n", + "Saq0_0_0 0.0 0.000006 9.524444e-07 14.905465 0.0 inf 0.0001 \n", + "res NaN 0.016204 5.746117e-04 3.546116 0.0 inf 1.0000 \n", + "\n", + " inhoms parray \n", + "kaq0_0_0 None [[44.52811759942783]] \n", + "Saq0_0_0 None [[6.389900564895967e-06]] \n", + "res NaN [[0.016203975748327215]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.005511867666330363\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"RMSE:\", cal.rmse())" @@ -151,9 +283,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAFBCAYAAAAhTjWiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABKVUlEQVR4nO3deVxU9f7H8ddhmGGVRUEFRVFzARMwl9zFssyt1FSuWu7+WjAyytK6pbao1TVtwRYrt+pm5tJ110w0zQW31NRMw9REFElAEBhmzu8PZHQEkcEZZkY+z8djHjJnzvIZZHjz/Z7v+R5FVVUVIYQQQtw2F3sXIIQQQtwpJFSFEEIIK5FQFUIIIaxEQlUIIYSwEglVIYQQwkokVIUQQggrkVAVQgghrERCVQghhLASCVUhhBDCSiRUhXByiqIwefJki7c7efIkiqIwb948q9dU0UJDQxk+fLi9yxBCQlUIa5g3bx6KoqAoClu3bi32uqqqhISEoCgKvXr1skOF5ZeYmGh6b4qioNFoqF69Ov379+fIkSP2Lq9Ehw8fZvLkyZw8edLepYhKRkJVCCtyd3fnm2++KbZ88+bNnDlzBjc3NztUZR1xcXEsXLiQzz//nCFDhrBq1So6duzIuXPn7F1aMYcPH2bKlCkSqqLCSagKYUU9evRg8eLFFBQUmC3/5ptvaNGiBTVr1rRTZbevY8eOPPbYY4wYMYKZM2cyc+ZMLl68yIIFC+xdmhAOQ0JVCCsaNGgQFy9eZMOGDaZl+fn5fP/99wwePLjEbbKzs3n++ecJCQnBzc2Nxo0b85///IcbbyCVl5fHc889R2BgIFWqVOHhhx/mzJkzJe7z77//ZuTIkdSoUQM3NzeaNm3Kl19+ab03SmHIApw4caJcx/7www9p2rQpnp6e+Pv707JlS7NW/vDhwwkNDS223eTJk1EU5aZ1zZs3jwEDBgDQpUsXU7d1YmIiALt376Zbt24EBATg4eFBvXr1GDlypKVvX4gSudq7ACHuJKGhobRt25b//ve/dO/eHYA1a9aQkZHBv/71Lz744AOz9VVV5eGHH2bTpk2MGjWKqKgo1q1bx/jx4/n777+ZOXOmad3Ro0fz1VdfMXjwYNq1a8dPP/1Ez549i9WQmppKmzZtUBSFsWPHEhgYyJo1axg1ahSZmZmMGzfOKu+1qGvV39/f4mPPmTOHuLg4+vfvz7PPPktubi4HDhxg586dN/3jo6w6depEXFwcH3zwAS+//DJhYWEAhIWFcf78eR588EECAwOZMGECfn5+nDx5kqVLl97WMYUwUYUQt23u3LkqoCYlJakfffSRWqVKFTUnJ0dVVVUdMGCA2qVLF1VVVbVu3bpqz549TdstX75cBdQ333zTbH/9+/dXFUVRjx8/rqqqqu7fv18F1KefftpsvcGDB6uAOmnSJNOyUaNGqUFBQWpaWprZuv/6179UX19fU13JyckqoM6dO7fU97Zp0yYVUL/88kv1woUL6tmzZ9W1a9eqd911l6ooirpr1y6Lj/3II4+oTZs2LfW4w4YNU+vWrVts+aRJk9Qbf3XVrVtXHTZsmOn54sWLVUDdtGmT2XrLli0z/T8JYQvS/SuElQ0cOJArV66wcuVKsrKyWLly5U1bX6tXr0aj0RAXF2e2/Pnnn0dVVdasWWNaDyi23o2tTlVVWbJkCb1790ZVVdLS0kyPbt26kZGRwd69e8v1vkaOHElgYCDBwcE89NBDZGRksHDhQlq1amXxsf38/Dhz5gxJSUnlqqW8/Pz8AFi5ciV6vb5Cjy0qBwlVIawsMDCQrl278s0337B06VIMBgP9+/cvcd2//vqL4OBgqlSpYra8qMvyr7/+Mv3r4uJCgwYNzNZr3Lix2fMLFy5w6dIlPvvsMwIDA80eI0aMAOD8+fPlel+vvfYaGzZsYNmyZQwdOpSMjAxcXK79CrHk2C+99BLe3t60bt2ahg0bEhsby7Zt28pVlyU6d+7Mo48+ypQpUwgICOCRRx5h7ty55OXl2fzYonKQc6pC2MDgwYMZM2YM586do3v37qYWkq0ZjUYAHnvsMYYNG1biOhEREeXad7NmzejatSsAffr0IScnhzFjxtChQwdCQkIsOnZYWBi///47K1euZO3atSxZsoTZs2fz2muvMWXKFICbDkYyGAzlqr9on99//z07duxgxYoVrFu3jpEjRzJjxgx27NiBt7d3ufctBEioCmETffv25YknnmDHjh0sWrTopuvVrVuXH3/8kaysLLPW6tGjR02vF/1rNBo5ceKEWev0999/N9tf0chgg8FgCkBbmT59OsuWLeOtt97ik08+sfjYXl5exMTEEBMTQ35+Pv369eOtt95i4sSJuLu74+/vz6VLl4ptV9R6L01po4MB2rRpQ5s2bXjrrbf45ptvGDJkCN9++y2jR4++5b6FKI10/wphA97e3nz88cdMnjyZ3r1733S9Hj16YDAY+Oijj8yWz5w5E0VRTCOIi/69cfTwrFmzzJ5rNBoeffRRlixZwqFDh4od78KFC+V5OyVq0KABjz76KPPmzePcuXMWHfvixYtmr+l0OsLDw1FV1XSus0GDBmRkZHDgwAHTeikpKSxbtuyWtXl5eQEUC+V//vmn2KVKUVFRANIFLKxCWqpC2MjNukCv17t3b7p06cIrr7zCyZMniYyMZP369fzwww+MGzfOdA41KiqKQYMGMXv2bDIyMmjXrh0bN27k+PHjxfY5ffp0Nm3axL333suYMWMIDw8nPT2dvXv38uOPP5Kenm619zh+/Hi+++47Zs2axfTp08t87AcffJCaNWvSvn17atSowZEjR/joo4/o2bOnqcX+r3/9i5deeom+ffsSFxdHTk4OH3/8MY0aNbrlYKuoqCg0Gg1vv/02GRkZuLm5cd999/HNN98we/Zs+vbtS4MGDcjKymLOnDn4+PjQo0cPq31fRCVmx5HHQtwxrr+kpjQ3XlKjqqqalZWlPvfcc2pwcLCq1WrVhg0bqu+++65qNBrN1rty5YoaFxenVqtWTfXy8lJ79+6tnj59utglNaqqqqmpqWpsbKwaEhKiarVatWbNmur999+vfvbZZ6Z1LL2kZvHixSW+Hh0drfr4+KiXLl0q87E//fRTtVOnTmq1atVUNzc3tUGDBur48ePVjIwMs32vX79evfvuu1WdTqc2btxY/eqrr8p0SY2qquqcOXPU+vXrqxqNxnR5zd69e9VBgwapderUUd3c3NTq1aurvXr1Unfv3l3q90CIslJU9Ya+ECGEEEKUi5xTFUIIIaxEQlUIIYSwEglVIYQQwkokVIUQQggrkVAVQgghrERCVQghhLASmfyhFEajkbNnz1KlSpVbTnsmhBDizqWqKllZWQQHB5vdSOJGEqqlOHv2LCEhIfYuQwghhIM4ffo0tWvXvunrEqqlKJou7fTp0/j4+Ni5mtuj1+tZv349Dz74IFqt1t7lCOE05LMjADIzMwkJCSl2m8YbSaiWoqjL18fH544IVU9PT3x8fOQXgxAWkM+OuN6tTgXKQCUhhBDCSiRUhRBCCCuRUBVCCCGsREJVCCGEsBIJVSGEEMJKZPSvEEKUICXjCslp2dT2dbNo/XoBXgT5elTYesKxSKgKIazGkiAoy7rWCJby7GNR0ikmLj2IUQUXBQbWU+hR5vVVpvcJY2DzIDDqwWgAgx6Melbu/4uZ6w6jUQ3oFAPPdgnlgcbVwFhwdR0D1GzGoqN5Zsef1q8ZMa3qlOv9i4qlqKqq2rsIR5WZmYmvry8ZGRl3xHWqq1evpkePHnKtnRO63XAp7/aWbHdjEJUWBCWu2zKkMFSMejDks2zPSaavPIirWoBOMTD+gQb0CK92NXwKwJB/3df6q9vpzb7em3yeNQdOXw2xAro1qUbTmp7F1rt+H7l5eWw/dg4NBrQYcFUK0GIgvIYHOsV4ddv8wm2MBRgL9GRfuYIrBlwxoFUMFv3f3KjApzaNLryD8brfzBpFYeuELtJitaOy5oG0VEWlUlFdatY8jiVhBVxtGeVffRTww95k/rP6EBoKcFMMxN9Xj25NqpnCyzykCrfBkM/uP1NZue8UrhSGWvfwajS7PpAM+Vf3UcCV3Ct4/fY3s10NaCkofKwwkL/PG51iMAuxAn0+nTIus1NXGFZaCnBdaUBdZUDhWpL0Bfpe3/O65erDAvcA91z/W+741Ucp3IEumhJeuFDy+i5AlVtODa5gdNFyxaBgQIMeDQVo0ONKgI8X7m5uoGjgwhFcM8+gUQswXvfr2aCqnEzLkVB1AhKqJUhISCAhIQGD4fb+4hSOdV7I4nCCa113ReFjCp7rQ+X61/X8/PvffLv9BJqrLayYe2rQKqSK+XrGkvZZUGyfeXl51D15nsXaAlwxoKMA1xUGCrZqcVULbtjP1a8x73x6BHjk+nDaevVxCy2Bltd3ahy7+iiBB9CrpCA6W3yRKxBUxvtTGFQFPa7ocaUADV4e7uh07qBxBRctaHTXfa0FF9ery7RcvGJk+8nMwgBTi4LMlQfurkVQVZ+r62sLt9foTPu4lKcyde0f5KuuV4NPgwENUx+Norq/z3XH0oCLlgtXjMR8vpt81RW9WhiWRsWVNfH3UdO/SmFNLhpSM67QfvpPxVugo6+2QA16eCMAAC8ll0uqt9l6oQGeZfumCbuSUC1BbGwssbGxpub+7XKkYKlIN+/iKygeTqV+fav1Swo483Vzc3OpcfwcC10LcFUKw0m7ogD9Dje0akHx7YrCSjVa/L47Ah2vD6ODVx/l4Aa0KWmMfkbZ91GgulCAhvzrwsnX+2rr6LowuRYyWtLzIOlUFvqrrSn91YC5r2ktavr7XAsyVzdwcSVTr/DexmT0V49ToGowKFqm9I3Ev4qX2THSrhgZsWA/+erVfaNBRcuSZzpTw88bNFpSLhvo8J+fMajX3rxGUdgaX/Yu0PyMK8SVEGIPdO8CpezDD2jhfoqXlx7CoKpXz6ka8I/oDiWcOgkEnuhbtXB9VDSKwtR+d1MzMMBsvSBfD6b1a2bab9F6pvdT9P036nm9ez2eW5NW8nrCoUmo2li5WkfloaolBEretWV5Ofhnn0A59QtgLB5EBXklhNb1+7rZ8pK/LtDn0eGfLHboCq51B64sgFUF1n/vZeAORJcUThct3JGiudoS0l1r4Wi0pnC6bHDhj7S8wnBRXSm4GmT31Aukmm+V61pGbjcEme7ac42bad+X8lT+veLYdQFUeObuw8dbE+DjdYv96Ei5XED7txOLt47+r/Rwysu4wlMlBFKXHiUHkg8QVuWUeWD0vRv/lsV/1gOAx/rWKRYuNYKvrRvkBlP7Rd48gMrgliFWiphWdejUKJCTaTnU8tWxb9tPZV4/NMDzpse45Xo6T8jN4OFwf1pFNrvl/oTjkYFKpbjdgUopV7t7BrhsQoceHQW4KQU81TGEKq7qDeF3Q8urxJC7ISivX9eot8F3oIIUtXiKwumGoML16jIX16vr6UoIEV3J21/9+lKeyptrTxR20V1tTRnQMmNwS6pV8S4WRiUfXwul3EcRrv2fW3OQyaKkU8WCwZI/zMq7fXm2S8m4UuYgKMu6luzPGjWVpEIH+c0Ig6yz8H+bITjKtscSFpGBSg4gOS0bowqvu87FTbmuhba9Io6uXBdAOlQXV3LyDXh6+6BodFeDouj161tOOlOXnlm4uLqZB4zptRtaSldfT7tiZPRXB8hTXU3djsbru/iKanNxhQq4Abwf0Mq9eEhUu9u6vQa30zq6mbK2gqy9fXm2C/L1KPP+y7KuJfuz5T4qjO7qeVN9jn3rEOUmoWpD9QK8cFFgg7ElCkbyC8c50iOqLl6eniW0hm5srV0NrqJWnFmLrrR1dYWDKK5ToNfzYwVeUhMADOpbv9Quvop2u+Fkz+PcbjCUd3unCqQ7gfZqqOZLqDorCVUbKmq1PLv0WbNg8aokF3FXVIhZoqJCQsJIlIvOq/BffbZ96xDlJqFqY44YLBVJwkUIC0hL1elJqFYACRYhRJkUnVPNv2zfOkS5yV1qhBDCUWiLun+lpeqsJFSFEMJRFJ1Tle5fpyWhKoQQjsJ0SY0MVHJWEqpCCOEotNJSdXYSqkII4Shk8genJ6EqhBCOwnRJjXT/OisJVSGEcBQ6Gf3r7CRUhRDCUcjkD05PQlUIIRyFTFPo9CRUhRDCUUhL1elJqAohhKPQyUAlZyehWoKEhATCw8Np1aqVvUsRQlQmWun+dXYSqiWIjY3l8OHDJCUl2bsUIURlopPuX2cnoSqEEI6iaKCSUQ8GvX1rEeUioSqEEI6iqPsX5Lyqk5JQFUIIR+GqA5ert7mWCSCckoSqEEI4EplU36lJqAohhCOR2785NQlVIYRwJDIBhFOTUBVCCEciLVWnJqEqhBCORM6pOjUJVSGEcCRyo3KnJqEqhBCOxHRO9bJ96xDlIqEqhBCORCfdv85MQlUIIRyJVrp/nZmEqhBCOBJTS1VG/zojCVUhhHAkRaEqLVWnJKEqhBCORCZ/cGoSqkII4Uh0cqNyZyahKoQQjkRaqk5NQlUIIRyJTP7g1CRUhRDCkWhl9K8zk1AVQghHIi1VpyahKoQQjsR0TlVaqs5IQlUIIRyJTP7g1CRUhRDCkcg0hU5NQlUIIRxJUUvVkA+GAvvWIiwmoSqEEI6kKFRBJoBwQhKqQgjhSDQ6UDSFX8sEEE7njg/VlStX0rhxYxo2bMjnn39u73KEEKJ0iiKT6jsxV3sXYEsFBQXEx8ezadMmfH19adGiBX379qVatWr2Lk0IIW5O6wl5mTIC2And0S3VXbt20bRpU2rVqoW3tzfdu3dn/fr19i5LCCFKJxNAOC2HDtUtW7bQu3dvgoODURSF5cuXF1snISGB0NBQ3N3duffee9m1a5fptbNnz1KrVi3T81q1avH3339XROlCCFF+MlWh03LoUM3OziYyMpKEhIQSX1+0aBHx8fFMmjSJvXv3EhkZSbdu3Th//nwFVyqEEFYkLVWn5dDnVLt370737t1v+vp7773HmDFjGDFiBACffPIJq1at4ssvv2TChAkEBwebtUz//vtvWrdufdP95eXlkZeXZ3qemZkJgF6vR6/X3+7bsaui+p39fQhR0ezx2dG4euACFFzJRJXPrEMo6/+/Q4dqafLz89mzZw8TJ040LXNxcaFr165s374dgNatW3Po0CH+/vtvfH19WbNmDa+++upN9zlt2jSmTJlSbPn69evx9PS0/puwgw0bNti7BCGcUkV+dlqnZxEE/LZvFydPe1fYccXN5eSUrdegTKF6zz33WHRwRVH43//+Z3Y+09rS0tIwGAzUqFHDbHmNGjU4evQoAK6ursyYMYMuXbpgNBp58cUXSx35O3HiROLj403PMzMzCQkJ4cEHH8THx8c2b6SC6PV6NmzYwAMPPIBWq7V3OUI4DXt8djQ//A8y9nB3o3qEt+lRIccUpSvqubyVMoXq/v37ef755/H2vvVfTKqqMn36dLNuVHt6+OGHefjhh8u0rpubG25ubsWWa7XaOyaI7qT3IkRFqtDPjlvh71qNIQ+NfF4dQln/78vc/Tt+/HiqV69epnVnzJhR1t2WW0BAABqNhtTUVLPlqamp1KxZ0+bHF0IIm9FdbcDINIVOp0yjf5OTkwkMDCzzTg8fPkzdunXLXVRZ6HQ6WrRowcaNG03LjEYjGzdupG3btjY9thBC2JTpnqoy+tfZlKmlamlAhoSElKuYG12+fJnjx4+bnicnJ7N//36qVq1KnTp1iI+PZ9iwYbRs2ZLWrVsza9YssrOzTaOByyshIYGEhAQMBsPtvgUhhLCcXFLjtMo1+jc3N5cDBw5w/vx5jEaj2WtlPX9ZFrt376ZLly6m50WDiIYNG8a8efOIiYnhwoULvPbaa5w7d46oqCjWrl1bbPCSpWJjY4mNjSUzMxNfX9/b2pcQQlhMJn9wWhaH6tq1axk6dChpaWnFXlMUxaqtu+joaFRVLXWdsWPHMnbsWKsdUwgh7E5aqk7L4hmVnnnmGQYMGEBKSgpGo9HsId2lQghhBXJO1WlZHKqpqanEx8ffdherEEKImzDd+k26f52NxaHav39/EhMTbVCKEEII4LqWqoSqs7H4nOpHH33EgAED+Pnnn2nWrFmxC2Lj4uKsVpy9yOhfIYRd6aT711lZHKr//e9/Wb9+Pe7u7iQmJqIoiuk1RVHuiFCV0b9CCLvSSvevs7I4VF955RWmTJnChAkTcHFx6DvHCSGEc5KWqtOyOBXz8/OJiYmRQBVCCFspaqka8sAop6GcicXJOGzYMBYtWmSLWoQQQsC10b8gg5WcjMXdvwaDgXfeeYd169YRERFRbKDSe++9Z7XihBCiUnJ1A8UFVGPhBBDuzn3rycrE4lA9ePAgzZs3B+DQoUNmr10/aEkIIUQ5KUphF3B+lrRUnYzFobpp0yZb1OFQ5JIaIYTd6TwLQ1WmKnQqMtqoBLGxsRw+fJikpCR7lyKEqKxkqkKnVKZQ7devH5mZmWXe6ZAhQzh//ny5ixJCiEpPpip0SmXq/v3hhx+4cOFCmXaoqiorVqzgjTfeoHr16rdVnBBCVFrSUnVKZQpVVVVp1KiRrWsRQghRRCfz/zqjMoVqeQYn1apVy+JthBBCXCVTFTqlMoVq586dbV2HEEKI68lUhU5JRv8KIYQjKjqnKpfUOBUJ1RIkJCQQHh5Oq1at7F2KEKKy0nkX/pt/2b51CItIqJZArlMVQthdlRqF/2b8bd86hEUkVIUQwhFVu6vw34vH7VuHsIiEqhBCOCJTqJ4AVbVvLaLMLA7V1NRUHn/8cYKDg3F1dUWj0Zg9hBBCWIF/aOGdavKz4LLMUOcsLJ5Qf/jw4Zw6dYpXX32VoKAguTONEELYgqsb+NWBf05C+olr51iFQ7M4VLdu3crPP/9MVFSUDcoRQghhUu2uwlC9eBzqtrN3NaIMLO7+DQkJQZX+fSGEsL2qDQr/lcFKTsPiUJ01axYTJkzg5MmTNihHCCGEyfWDlYRTsLj7NyYmhpycHBo0aICnpydardbs9fT0dKsVZy9yk3IhhEOoJi1VZ2NxqM6cOfOOH5wUGxtLbGwsmZmZ+Pr62rscIURlVdRSTf8TjAZwkSssHF25Rv8KIYSoAL61QeMGhjzIOAP+de1dkbgFi8+pDh06lLlz53LihPTxCyGETblooGr9wq+lC9gpWByqOp2OadOm0bBhQ0JCQnjsscf4/PPP+eOPP2xRnxBCVG6m86rSkHEGFofq559/zrFjxzh9+jTvvPMO3t7ezJgxgyZNmlC7dm1b1CiEEJWXDFZyKuWe+9ff359q1arh7++Pn58frq6uBAYGWrM2IYQQMrG+U7E4VF9++WXatWtHtWrVmDBhArm5uUyYMIFz586xb98+W9QohBCVl4SqU7F49O/06dMJDAxk0qRJ9OvXj0aNGtmiLiGEEHAtVDNOQ0Fe4ZzAwmFZ3FLdt28fr7zyCrt27aJ9+/bUqlWLwYMH89lnn3Hs2DFb1CiEEJWXVyC4+YBqLJwHWDg0i0M1MjKSuLg4li5dyoULF1i9ejU6nY7Y2FjCwsJsUaMQQlReiiKX1TgRi7t/VVVl3759JCYmkpiYyNatW8nMzCQiIoLOnTvbosYKJ9MUCiEcSrW7IGW/hKoTsDhUq1atyuXLl4mMjKRz586MGTOGjh074ufnZ4Py7EOmKRRCOJSr51VTkw9hvPsKQb4edi5I3IzFofrVV1/RsWNHfHx8bFGPEEKIG2zP8KMtcPLYAQZN/4lp/ZoR06qOvcsSJbD4nGrPnj1NgXrmzBnOnDlj9aKEEEIUSsm4wvRdBQCEKucwqvDy0kOkZFyxc2WiJBaHqtFo5PXXX8fX15e6detSt25d/Pz8eOONNzAajbaoUQghKq3ktGz+NNYEoIZyiSrkYFBVTqbl2LkyURKLu39feeUVvvjiC6ZPn0779u0B2Lp1K5MnTyY3N5e33nrL6kUKIURlVS/Ai2zFkz+NNanvco4OLgdZr7YhNMDT3qWJElgcqvPnz+fzzz/n4YcfNi2LiIigVq1aPP300xKqQghhRUG+Hkzr14wNP7TiCZcVdNckEf3waBms5KAsDtX09HSaNGlSbHmTJk1IT0+3SlFCCCGuiWlVh7QqT8K3K+jlcQCX5jXsXZK4iXJN/vDRRx8VW/7RRx8RGRlplaKEEEKYC2jUDqoE4ZJ/Gf7cbO9yxE1Y3FJ955136NmzJz/++CNt27YFYPv27Zw+fZrVq1dbvUAhhBCAiws06QVJc+DoCmj0oL0rEiWwuKXauXNnjh07Rt++fbl06RKXLl2iX79+/P7773Ts2NEWNQohhAAI61X479FVYJQZ3xyRxS1VgODgYBmQJIQQFa1ue/Dwh5yLcGo7hHawd0XiBmUK1QMHDpR5hxEREeUuRgghRCk0WmjcA/Z/DUdWSqg6oDKFalRUFIqioKoqiqKYlquqCmC2TCahF0IIG2rS62qoroCHphXexUY4jDKdU01OTubPP/8kOTmZJUuWUK9ePWbPns3+/fvZv38/s2fPpkGDBixZssTW9VaIhIQEwsPDadWqlb1LEUIIcw26gNYLMs/wa9Imma7QwZSppVq3bl3T1wMGDOCDDz6gR48epmURERGEhITw6quv0qdPH6sXWdHkLjVCCIel9eB0tXaEnNvAtv/N5T+GKzLBvgOxePTvwYMHqVevXrHl9erV4/Dhw1YpSgghRMlSMq4w/XQYAAM0m3FV9TLBvgOxOFTDwsKYNm0a+fn5pmX5+flMmzaNsLAwqxYnhBDCXHJaNusMLUlRqxKoZNDTZYdMsO9ALL6k5pNPPqF3797Url3bNNL3wIEDKIrCihUrrF6gEEKIa+oFeGFUXFlY8AAvahcx0nUN/9N3lAn2HYTFodq6dWv+/PNPvv76a44ePQpATEwMgwcPxsvLy+oFCiGEuKZogv13ll4mTl1KM5eTfBatlwn2HUS5Jn/w8vLi//7v/6xdixBCiDKIaVWHTo0CyVjxE+7Hv+X+S0uAPvYuS1COc6p16tRh6NChfPHFF/z555+2qEkIIcQtBPl6UOOBZwufHF0Jl07ZtyABlCNUp06diru7O2+//TZ33XUXISEhPPbYY8yZM4c//vjDFjUKIYQoSY1wqNcZVCOXf/6YX06kyShgO7O4+/exxx7jscceAyAlJYXNmzezcuVKnn76aYxGo8yoJIQQFanN05C8GcPueYze1pJcxV2uW7Wjcp1TzcnJYevWrSQmJrJp0yb27dvH3XffTXR0tJXLE0IIUZqUGh3JNdaknss5hmvWMdvwCC8vPUSnRoEyeMkOLO7+bdeuHdWqVWPChAnk5uYyYcIEUlJS2LdvHzNnzrRFjUIIIW4i+eIV3i/oB8ATrivw4bJct2pHFofq0aNH8fLyokmTJjRp0oSwsDD8/f1tUZsQQohbqBfgxUq1HUeNIfgqOTzpuhKNosh1q3ZicahevHiRn376iTZt2rBu3Trat29PrVq1GDx4MHPmzLFFjUIIIW4iyNeDt/pF8p5hIAAjNGt5r0cN6fq1E4tDVVEUIiIiiIuL4/vvv2fNmjU88MADLF68mCeffNIWNQohhChFTKs6TBn/AlmB9+Ch5PNI5jf2LqnSsjhU9+7dy3vvvcfDDz9MtWrVaNu2LQcOHOCZZ55h6dKltqhRCCHELQT5eVKlx+uFT/bMg/Rku9ZTWZVrmsLmzZvTuXNnxowZQ6dOneT2aEII4QjqdYT6XeDPTbDpLXj0c3tXVOlYHKrp6en4+PjYohYhhBC3q+sk+CwRDi6GliOhbjt7V1SpWNz9K4EqhBAOLLg5tBhW+PWq58Ggt289lYzFoWowGPjPf/5D69atqVmzJlWrVjV7CCGEsLP7J2F0rwrnD5OxZba9q6lULA7VKVOm8N577xETE0NGRgbx8fH069cPFxcXJk+ebIMSK15CQgLh4eG0atXK3qUIIYTFFv12mZezHgXAJXEaP/y8284VVR4Wh+rXX3/NnDlzeP7553F1dWXQoEF8/vnnvPbaa+zYscMWNVa42NhYDh8+TFJSkr1LEUIIi6RkXGHi0oMsMnRmv7EBVZQrsP41mWi/glgcqufOnaNZs2YAeHt7k5GRAUCvXr1YtWqVdasTQghhkeS0bIwqqLjwb/0IjKrCI5pt/HNwvb1LqxQsDtXatWuTkpICQIMGDVi/vvA/KikpCTc3N+tWJ4QQwiL1ArxwUQq/PqTWZ4HhAQAa7XoZ8rLsWFnlYHGo9u3bl40bNwLwzDPP8Oqrr9KwYUOGDh3KyJEjrV6gEEKIsgvy9WBav2ZolMJknWEYxGWPWrhmnuHc9+OlG9jGLL5Odfr06aavY2JiqFu3Lr/88gsNGzakd+/eVi1OCCGE5WJa1aFTo0BOpuUQGuDJru0q9+0YSc0//svQt0Pp2Xew3G/VRixqqer1ekaOHEly8rXpr9q0aUN8fLwEqhBCOJAgXw/aNqgGwOjN7swvKOwGnqb9jKlLd0mL1UYsClWtVsuSJUtsVYsQQggrKxq49HbBIE4ZA6mlXGSC5itWHUiRYLUBi8+p9unTh+XLl9ugFCGEENZWNHApB3fG6wvvJDbIdRNJa+bTfvpPLEo6ZecK7ywWn1Nt2LAhr7/+Otu2baNFixZ4eXmZvR4XF2e14oQQQtyeooFLLy89xE41jE8KevOk6wre1s7hQF4DXl56iE6NAuX+q1Zicah+8cUX+Pn5sWfPHvbs2WP2mqIoEqpCCOFgigYurTqQwturBtDG5TeiXP5kli6BQfn/5mRajoSqlVgcqtcPUhJCCOEcgnw96BkRxNTVR4jTP8Mq3cvc63KUZ1x/IDSgq73Lu2NYfE5VCCGEcyrqCv6bmryqHwHAs65L0J7ZwS8n0mTgkhWUqaUaHx9f5h2+99575S5GCCGEbV27hvVecnafx/PI9xi+G05c3jTSFV+m9Wsm17DehjKF6r59+8ye7927l4KCAho3bgzAsWPH0Gg0tGjRwvoVCiGEsKogXw+CfD045/M2Z37bTiOXv/lQ+yGP6SfKwKXbVKbu302bNpkevXv3pnPnzpw5c4a9e/eyd+9eTp8+TZcuXejZs6et6xVCCGElf2bCU/pxXFbdaas5zAuu32FQVU6m5di7NKdl8TnVGTNmMG3aNPz9/U3L/P39efPNN5kxY4ZVixNCCGE79QK8SKYWL+r/D4CnXFfwkGY3oQGedq7MeVkcqpmZmVy4cKHY8gsXLpCVJXdAEEIIZ1E0cGmd2pbPC7oD8KHHpwTly4QQ5VWuu9SMGDGCpUuXcubMGc6cOcOSJUsYNWoU/fr1s0WNQgghbCSmVR22TujC3cNmkVerDdqCbK4sGMi51BR7l+aULA7VTz75hO7duzN48GDq1q1L3bp1GTx4MA899BCzZ8+2RY1CCCFsKMjXgzYNa7ImbDp/qwF4ZJ3k94SBfLdL5iWwlMWh6unpyezZs7l48SL79u1j3759pKenM3v27GJTFgohhHAOKRlXiF91ljH58VxRdXR2OUDm/16Ra1ctVO7JH7y8vIiIiCAiIkLCVAghnFzR3WwOq6G8cHXi/dGuq8jeudDOlTkXmVFJCCGE6W42AKuMbfiwoA8A9be/zMXfNtqvMCcjoSqEEMI0ElijFCbrzIL+rDLci4uqR/Pd46zatMXOFToHCVUhhBDAtZHAHw1qDooL8fqn2Ge8Cz8lm6abxnDu3N/2LtHhSagKIYQwCfL1oKq3DqMKeegYk/88p42BhLqcQ/12CCkXL9m7RIcmoSqEEMLM9edX0/BlhH48maoHQZf2sWdWDIt2nbRrfY5MQlUIIYSZG8+vHldr84Q+nnxVQy/NDrL+N1EutbmJShGqffv2xd/fn/79+9u7FCGEcApF51f/3TMMgO3GptddarOavC0f2rM8h1UpQvXZZ59lwYIF9i5DCCGcSpCvBz0jgkxdwf8ztmeafhAAoXvegkNL7FidY6oUoRodHU2VKlXsXYYQQjidG7uCPzf25ljo4MIXlz7BxQNr+eVEmnQHX1Wmm5Tb0pYtW3j33XfZs2cPKSkpLFu2jD59+pitk5CQwLvvvsu5c+eIjIzkww8/pHXr1vYpuAQGgwG9Xm/vMkql1+txdXUlNzcXg8Fg73KEA9JqtWg0GnuXIRxQTKs6dGoUyMm0HEIDPAmq8hAsuQK/LcN9yVDezn+Fg9zFtH7NiGlVx97l2pXdQzU7O5vIyEhGjhxZ4l1uFi1aRHx8PJ988gn33nsvs2bNolu3bvz+++9Ur14dgKioKAoKCoptu379eoKDg21Wu6qqnDt3jkuXLtnsGNaiqio1a9bk9OnTKFf/4hTiRn5+ftSsWVN+RkQxQb4eBPl6mJ6n3DeLEwf/oIPLIebq3mFA/iReXqrQqVGg2XqVjd1DtXv37nTv3v2mr7/33nuMGTOGESNGAIV3yVm1ahVffvklEyZMAGD//v1WqSUvL4+8vDzT88zMTKCwlVdSSzQ1NZXMzEwCAwPx9PR06F9EqqqSnZ2Nl5eXQ9cp7ENVVXJycrhw4QIGg4EaNWrYuySHUfTZd/TeqIp2PC2PJ/Lj+Ub3JlEuf7JAN50BeZM4kZpJgKfdo8Xqyvr/79DvPD8/nz179jBx4kTTMhcXF7p27cr27dutfrxp06YxZcqUYsvXr1+Pp6en2TJFUQgKCqJmzZpotVqn+MDpdDqnqFPYh1arpUqVKqSkpLB3715UVbV3SQ5lw4YN9i7BoVzKgyu4MSL/RRbrXucul7N8rZvKrt0Kq4/42rs8q8vJySnTeg4dqmlpaSX+1VyjRg2OHj1a5v107dqVX3/9lezsbGrXrs3ixYtp27ZtsfUmTpxIfHy86XlmZiYhISE8+OCD+Pj4mK2bl5fHqVOnqFq1Kh4ejt/VoaoqWVlZVKlSRVqq4qa0Wi1ZWVncd999uLm52bsch6DX69mwYQMPPPAAWq3W3uU4FG2dM/z7h8M8nj+R792mUM/lHKGXPqGg1w/g4Wfv8qyqqOfyVhw6VK3lxx9/LNN6bm5uJf4i0Wq1xT5MBoMBRVHQaDS4uDj+IGqj0QgUtrCdoV5hHxqNBkVRcHV1lQC5QUm/Byq7wW3q0SWsJifTctC6toTFfVDO/4b2u0Hw+HJw87Z3iVZT1v97h/7tGhAQgEajITU11Wx5amoqNWvWtFNVQgghigT5etC2QTWq1w2Hx5eBux+cSYL//gv0le8yG4cOVZ1OR4sWLdi48dq9/IxGIxs3biyx+1ZYR2JiIoqiOMWo5oo0fPjwYpd7OYPQ0FBmzZpleq4oCsuXL7dbPeIOVqMpPLYUdN5w8mdY9BgU5N16uzuI3bt/L1++zPHjx03Pk5OT2b9/P1WrVqVOnTrEx8czbNgwWrZsSevWrZk1axbZ2dmm0cC2kJCQQEJCglzPKcy8//77MnhHiFup3QKGLMa4sB8ux38k979DcR/8FWgqR9e53Vuqu3fvpnnz5jRv3hyA+Ph4mjdvzmuvvQZATEwM//nPf3jttdeIiopi//79rF271qZD/mNjYzl8+DBJSUk2O4ZwPr6+vvj5+dm7DCEc3qLztRma8xx5qhb3E2s59fkQMBSfS+BOZPdQjY6ORlXVYo958+aZ1hk7dix//fUXeXl57Ny5k3vvvdd+BdtISsaVCpvqKy8vj7i4OKpXr467uzsdOnQo8Q+Ibdu2ERERgbu7O23atOHQoUOm1/766y969+6Nv78/Xl5eNG3alNWrV99WXUXdq1OmTCEwMBAfHx+efPJJ8vPzTevc2JUJhZN/TJ482fRcURQ+/fRTevXqhaenJ2FhYWzfvp3jx48THR2Nl5cX7dq148SJE6ZtJk+eTFRUFJ9++ikhISF4enoycOBAMjIyitVXJDo6mri4OF588UWqVq1KzZo1zeoAOHr0KB06dMDd3Z3w8HB+/PHHUrtfV65ciZ+fn6mXZP/+/SiKYromG2D06NE89thjpudbt26lY8eOeHh4EBISQlxcHNnZ2bf6dgthEykZV5i49CBbjXfzhH4c+aqGOinruLL4/8B45/f+2T1UBSxKOkX76T8xeM5O2k//iUVJp2x6vBdffJElS5Ywf/589u7dy1133UW3bt1IT083W2/8+PHMmDGDpKQkAgMD6d27t+k619jYWPLy8tiyZQsHDx7k7bffxtv79kf6bdy4kSNHjpCYmMh///tfli5dWuK1w7fyxhtvMHToUPbv30+TJk0YPHgwTzzxBBMnTmT37t2oqsrYsWPNtjl+/DjfffcdK1asYO3atezbt4+nn3661OPMnz8fLy8vdu7cyTvvvMPrr79uup7RYDDQp08fPD092blzJ5999hmvvPJKqfvr2LEjWVlZ7Nu3D4DNmzcTEBBAYmKiaZ3NmzcTHR0NwIkTJ3jooYd49NFHOXDgAIsWLWLr1q3F3psQFSU5LRvj1bMkicbmjNXHoVc1eBxdQs7iJ/jlj9Q7ep5gCVU7K/qrruiH0KjCy0sP2eyHLjs7m48//ph3332X7t27Ex4ezpw5c/Dw8OCLL74wW3fSpEk88MADNGvWjPnz55OamsqyZcsAOHXqFO3bt6dZs2bUr1+fXr160alTp9uuT6fT8eWXX9K0aVN69uzJ66+/zgcffGC6JKisRowYwcCBA2nUqBEvvfQSJ0+eZMiQIXTr1o2wsDCeffZZs6ACyM3NZcGCBURFRdGpUyc+/PBDvv32W86dO3fT40RERDBp0iQaNmzI0KFDadmypWlg3YYNGzhx4gQLFiwgMjKSDh068NZbb5Vat6+vL1FRUabaEhMTee6559i3bx+XL1/m77//5vjx43Tu3BkonLBkyJAhjBs3joYNG9KuXTs++OADFixYQG5urkXfMyGs4fobnAOsN7ZiXMEzGBUNnkcWc3r+/9Fh+o82bzzYi4SqnV3/V10Rg6pyMq1ss3dY6sSJE+j1etq3b29aptVqad26NUeOHDFb9/oR1lWrVqVx48amdeLi4njzzTdp3749kyZN4sCBAzc95tSpU/H29jY9Tp26+YcpMjLSbPaqtm3bcvnyZU6fPm3R+4yIiDB9XXT+vVmzZmbLcnNzzS7orlOnDrVq1TI7ttFo5Pfffy/TcQCCgoI4f/48AL///jshISFml3+V5UYQnTt3JjExEVVV+fnnn+nXrx9hYWFs3bqVzZs3ExwcTMOGDQH49ddfmTdvntn3t1u3bhiNRpKTk295LCGs7ca72mgUhYgHh/Js/tMYVIUY10Te0HzBK0sP3JEtVruP/nVEFTn6t+ivuuuDVaMohAZ43nwjBzB69Gi6devGqlWrWL9+PdOmTWPGjBk888wzxdZ98sknGThwoOn57dzkwMXFpdgI3JKmXrz+Qu2iGaRKWmZpC7i04xTt93b3GR0dzZdffsmvv/6KVqulSZMmREdHk5iYyD///GNqpULh6PknnniCuLi4YvupU6dy3y1E2M+Nd7VJTstmmqEtimpkpnY2g103AXDyQuH4mOS0bOoFeN0RE/FLS7UEFTn6t6S/6qb2u9tmP1wNGjRAp9Oxbds20zK9Xk9SUhLh4eFm6+7YscP09T///MOxY8cICwszLQsJCeHJJ59k6dKlPP/888yZM6fEY1atWpW77rrL9HB1vfnfcr/++itXrlz763XHjh14e3sTEhICQGBgICkpKabXMzMzrdYiO3XqFGfPnjU7touLC40bNy7X/ho3bszp06fNJi8py89U0XnVmTNnmgK0KFQTExNN51MB7rnnHg4fPmz2/S166HS6ctUthDUUTQoR5Othajz8z9ie5/VPYVAVBrtuwu+n8XSY/mOFjSepCBKqDiCmVR22TujCf8e0YeuELja9H6GXlxdPPfUU48ePZ+3atRw+fJgxY8aQk5PDqFGjzNZ9/fXX2bhxI4cOHWL48OEEBASYRr+OGzeOdevWkZyczN69e9m0aZNZ4JZXfn4+o0aN4vDhw6xevZpJkyYxduxY09SK9913HwsXLuTnn3/m4MGDDBs2zGr3AHV3d2fYsGH8+uuv/Pzzz8TFxTFw4MByz971wAMP0KBBA4YNG8aBAwfYtm0b//73vwFKnX/Z39+fiIgIvv76a1OAdurUib1793Ls2DGzlupLL73EL7/8wtixY9m/fz9//PEHP/zwgwxUEg7l+sbDcmMHXih4GiMuhJ1dxhuaL1Aw2nw8SUWR7l8HceO9Cm1p+vTpGI1GHn/8cbKysmjZsiXr1q3D39+/2HrPPvssf/zxB1FRUaxYscLU+jEYDMTGxnLmzBl8fHx46KGHmDlz5m3Xdv/999OwYUM6depEXl4egwYNMrtMZeLEiSQnJ9OrVy98fX154403rNZSveuuu+jXrx89evQgPT2dXr16MXv27HLvT6PRsHz5ckaPHk2rVq2oX78+7777Lr1798bd3b3UbTt37sz+/ftNoVq1alXCw8NJTU01azlHRESwefNmXnnlFTp27IiqqjRo0ICYmJhy1y2ELZh3Cd/H8V0NaLD1eQa7bkKDyoSC0RhUF06m5Th1N7CiyhQxN5WZmYmvry8ZGRnF7lKTm5tLcnIy9erVu+UvSEdgNBrJzMzEx8fHYSfUHz58OJcuXbLLFHqTJ09m+fLlVrs3781s27aNDh06cPz4cRo0aGDTY5WHs/1cVwS9Xs/q1avp0aOHTKhvRSkZV3j7nTeY4TobjaLyvaETEwueYMuE+x0yVEvLg+tJS1UIG1q2bBne3t40bNiQ48eP8+yzz9K+fXuHDFQhKlKQrwdt+zzJc8s1vOf6Ef01W2gZUgWMnfnlRJrTDlySUBXChrKysnjppZc4deoUAQEBdO3alRkzZti7LCEcQmGX8Muc2NOYRlufJfTsKla815/n9E9jVFyZ1q8ZMa3qkJJxxWlGCEv3bwmuv6Tm2LFj0v0rKg1n+7muCNL9WzHS9yzF+3+j0SkG1hpa8Yz+GYyKlhe7N+btNUcxquCiYArailbW7l/57VoCmVBfCCEq1lG/Tjypf4481ZWHNEl8op2Jq5rH9KuBCrafcc4aJFSFEELYXb0ALxLVexitf4FcVcv9mn3M0c7ATTW/H6stZ5yzBglVIYQQdld0LesvaiTD9S+RrbrRSXOQubp38eJay1SjKHjqXCrsrl6WkoFKQgghHMK1a1nv5Upea7yWD6Ft/mEW6qYzPP9FshVv+jQPpu/sX+x+jvVmpKUqhBDCYRRNbxgQ3hmG/QDuftzj8ge/BM/ih5GNWbbvb4c+xyqhKoQQwjHVagHDV4JnAN7pv1F/ZQzV1EtmqzjaOVYJ1RIkJCQQHh5Oq1at7F1KhUlMTERRFC5dunRb+8nJyeHRRx/Fx8fHtL/Q0FBmzZpllTodWWV5n0JUqJrNYMRq8K6JZ8YfLNZNIZg008uOdlcvCdUS3OmX1ERHRzNu3DizZe3atSMlJQVfX9/b2vf8+fP5+eef+eWXX6yyP2emKIpdplwU4o4T2BhGrgHfOoS6pLLY7XXqKudMd/UCHGbgkgxUEgDodLpy343leidOnCAsLIy7777bClVZR35+vtwGTQhnV7V+YbAueIRaF4+zwW8aGf0X81M6tJ/+k8MMXJKWaiUzYsQINm/ezPvvv4+iKCiKwsmTJ4t1/86bNw8/Pz9WrlxJ48aN8fT0pH///uTk5DB//nxCQ0Px9/cnLi7OdDP36OhoZsyYwZYtW1AUxey+n9c7deoUjzzyCN7e3vj4+DBw4EDTPUczMjLQaDTs3r0bKJwJqmrVqrRp08a0/VdffWW6v2pJoqOjGTt2LOPGjSMgIIBu3boBcOjQIbp37463tzc1atTg8ccfJy3tWjfS999/T7NmzfDw8KBatWp07dqV7Oxs0z5vbN336dOH4cOHl1hDaGgoAH379kVRFNPzX3/9lS5dulClShV8fHxo0aKF6b0KIW7BtzaMWAPVm6K7coFqi/vx9bIfHGrgkoSqNakq5Gfb51HG2SZnzZpF27ZtGTNmDCkpKaSkpNw0oHJycvjggw/49ttvWbt2LYmJifTt25fVq1ezevVqFi5cyKeffsr3338PwNKlSxkzZgxt27YlJSWFpUuXFtun0WjkkUceIT09nc2bN7Nhwwb+/PNP063KfH19iYqKIjExEYCDBw+iKAr79u3j8uXLAGzevNnsnqIlmT9/vulm7J988gmXLl3ivvvuo3nz5uzevZu1a9eSmprKwIEDAUhJSWHQoEGMHDmSI0eOkJiYSL9+/SjvLJ5Fpw7mzp1LSkqK6fmQIUOoXbs2SUlJ7NmzhwkTJsjUd0JYwrt64eClWi1wyU3na+1btFKOml6298Al6f61Jn0OTA22z7FfPgs6r1uu5uvri06nw9PT85bdvXq9no8//th0R5X+/fuzcOFCUlNT8fb2Jjw8nC5durBp0yZiYmKoWrUqnp6epXYlb9y4kYMHD5KcnGwK8wULFtC0aVOSkpJo1aoV0dHRJCYm8sILL5CYmMgDDzzA0aNH2bp1Kw899BCJiYm8+OKLpdbesGFD3nnnHdPzN998k+bNmzN16lTTsi+//JKQkBCOHTvG5cuXKSgooF+/ftStWxeAZs2a3fL7eTOBgYEA+Pn5mX0vTp06xfjx42nSpImpTiGEhTyrwtAfyFs4gCpntrNAN50n9M+xxRhp94FL0lIVN+Xp6Wl2i7IaNWoQGhqKt7e32bLz58+XeZ9HjhwhJCTErHUcHh6On58fR44cAQpv0L1161YMBgObN28mOjraFLRnz57l+PHjN+1aLtKiRQuz57/++iubNm3C29vb9CgKthMnThAZGcn9999Ps2bNGDBgAHPmzOGff/4p8/sqq/j4eEaPHk3Xrl2ZPn06J06csPoxhKgU3KrgNmwZZwM74KHk87n2P/TQ7LL7wCVpqVqT1rOwxWivY1t7lzd0SyqKUuIyo9Fo1eN26tSJrKws9u7dy5YtW5g6dSo1a9Zk+vTpREZGEhwcfMsWnpeXeav98uXL9O7dm7fffrvYukFBQWg0GjZs2MAvv/zC+vXr+fDDD3nllVfYuXMn9erVw8XFpVhXsF6vt/i9TZ48mcGDB7Nq1SrWrFnDpEmT+Pbbb+nbt6/F+xKi0tN6EPzEMq4sGonHHytI0H3ArjMBtF9a324Dl6Slak2KUtgFa4+HopS5TJ1OZxpcVNHCwsI4ffo0p0+fNi07fPgwly5dIjw8HCjsMo2IiOCjjz5Cq9XSpEkTOnXqxL59+1i5cuUtz6eW5J577uG3334jNDSUu+66y+xRFMCKotC+fXumTJnCvn370Ol0LFu2DCjszk1JSTHtz2AwcOjQoVKPqdVqS/w+N2rUiOeee47169fTr18/5s6da/H7EUJc5arDY9B8aP4Yimrk3l//zeMu6wD7DFySUC3BnT75Q2hoKDt37uTkyZOkpaVZvaVZmq5du9KsWTOGDBnC3r172bVrF0OHDqVz5860bNnStF50dDRff/21KUCrVq1KWFgYixYtKleoxsbGkp6ezqBBg0hKSuLEiROsW7eOESNGYDAY2LlzJ1OnTmX37t2cOnWKpUuXcuHCBcLCwgC47777WLVqFatWreLo0aM89dRTt5woIzQ0lI0bN3Lu3Dn++ecfrly5wtixY0lMTOSvv/5i27ZtJCUlmY4hhCgnFw30/pCzTUYAMEU7n7GaZYBa4QOXJFRLcKdP/vDCCy+g0WgIDw8nMDCQU6dOVdixFUXhhx9+wN/fn06dOtG1a1fq16/PokWLzNbr3LkzBoPB7NxpdHR0sWVlFRwczLZt2zAYDDz44IM0a9aMcePG4efnh4uLCz4+PmzZsoUePXrQqFEj/v3vfzNjxgy6d+8OwMiRIxk2bJjpD4D69evTpUuXUo85Y8YMNmzYQEhICM2bN0ej0XDx4kWGDh1Ko0aNGDhwIN27d2fKlCkWvx8hxA1cXFAemsr7Bf0AeEG7mJddv0GjUKEDlxS1vNcMVAKl3ek9NzeX5ORk6tWrh7u7u50qLDuj0UhmZiY+Pj64uMjfUqJkzvZzXRH0ej2rV6+mR48ecvmTE1iUdIo/fniHf7suBODPkH7UH/F5YWv2NpSWB9eT365CCCHuGDGt6jBq/LscbzsdVXGh/uml8P1IKMivkONLqAohhLijBPl6cFe3p1D6z0V10cLh5WSverlCji2hKoQQ4o60KOcehufGc9hYly477mFRku3Hj8h1qkIIIe44KRlXmLj0IEY1ki35zVBx4eWlh+jUKJAgXw+bHVdaqrdJxnmJO4n8PIs7RXJatmmiffVq1FXE5TUSquVUNAowJ8dx7jgvxO0q+nmWUa7C2dUL8MLlhjlxKmJeYOn+LSeNRoOfn59p3ltPT08UC2Y1qmhGo5H8/Hxyc3PlkhpRjKqq5OTkcP78efz8/NBobu/yAyHsLcjXg2n9mvHy0kMYVNV0Q3Nbdv2ChOptKbr7iCUTytuLqqpcuXIFDw8Phw5/YV833lVHCGcW06oOnRoFcjIth9AAT5sHKkiolighIYGEhIRbzo+rKApBQUFUr169XJOrVyS9Xs+WLVvo1KmTdO2JEmm1WmmhijtOkK9HhYRpEQnVEsTGxhIbG2uaQeNWNBqNw/8y0mg0FBQU4O7uLqEqhBA2IifXhBBCCCuRUBVCCCGsREJVCCGEsBI5p1qKogvhMzMz7VzJ7dPr9eTk5JCZmSnnVIWwgHx2BFzLgVtNkCKhWoqsrCwAQkJC7FyJEEIIR5CVlVXqAFa5n2opjEYjZ8+epUqVKmbXdrZq1apcNzC3dLuyrl+W9TIzMwkJCeH06dOl3gvwTlTe/y9bqqiarH2c292fo312yrJuZf7sgON9fuz12VFVlaysLIKDg0udQEdaqqVwcXGhdu3axZZrNJpyfbgs3a6s61uyXx8fn0r3i6G8/1+2VFE1Wfs4t7s/R/vsWLJuZfzsgON9fuz52SnLJZYyUKkcYmNjK2S7sq5f3noqC0f8/lRUTdY+zu3uz9E+O+XZd2XjaN8fR//sSPdvJVE0kUVGRoZD/dUphKOTz46whLRUKwk3NzcmTZqEm5ubvUsRwqnIZ0dYQlqqQgghhJVIS1UIIYSwEglVIYQQwkokVIUQQggrkVAVQgghrERCVQghhLASCVVh5tKlS7Rs2ZKoqCjuvvtu5syZY++ShHAap0+fJjo6mvDwcCIiIli8eLG9SxIVTC6pEWYMBgN5eXl4enqSnZ3N3Xffze7du6lWrZq9SxPC4aWkpJCamkpUVBTnzp2jRYsWHDt2DC8vL3uXJiqIzP0rzGg0Gjw9PQHIy8tDVdVb3upICFEoKCiIoKAgAGrWrElAQADp6ekSqpWIdP86mS1bttC7d2+Cg4NRFIXly5cXWychIYHQ0FDc3d2599572bVrl0XHuHTpEpGRkdSuXZvx48cTEBBgpeqFsK+K+PwU2bNnDwaDQW4dWclIqDqZ7OxsIiMjSUhIKPH1RYsWER8fz6RJk9i7dy+RkZF069aN8+fPm9YpOl964+Ps2bMA+Pn58euvv5KcnMw333xDampqhbw3IWytIj4/AOnp6QwdOpTPPvvM5u9JOBhVOC1AXbZsmdmy1q1bq7GxsabnBoNBDQ4OVqdNm1auYzz11FPq4sWLb6dMIRySrT4/ubm5aseOHdUFCxZYq1ThRKSlegfJz89nz549dO3a1bTMxcWFrl27sn379jLtIzU1laysLAAyMjLYsmULjRs3tkm9QjgSa3x+VFVl+PDh3HfffTz++OO2KlU4MAnVO0haWhoGg4EaNWqYLa9Rowbnzp0r0z7++usvOnbsSGRkJB07duSZZ56hWbNmtihXCIdijc/Ptm3bWLRoEcuXLycqKoqoqCgOHjxoi3KFg5LRv8JM69at2b9/v73LEMIpdejQAaPRaO8yhB1JS/UOEhAQgEajKTawKDU1lZo1a9qpKiGcg3x+hDVIqN5BdDodLVq0YOPGjaZlRqORjRs30rZtWztWJoTjk8+PsAbp/nUyly9f5vjx46bnycnJ7N+/n6pVq1KnTh3i4+MZNmwYLVu2pHXr1syaNYvs7GxGjBhhx6qFcAzy+RE2Z+/hx8IymzZtUoFij2HDhpnW+fDDD9U6deqoOp1Obd26tbpjxw77FSyEA5HPj7A1mftXCCGEsBI5pyqEEEJYiYSqEEIIYSUSqkIIIYSVSKgKIYQQViKhKoQQQliJhKoQQghhJRKqQgghhJVIqAohhBBWIqEqxB0kMTERRVG4dOlShR9bURQURcHPz6/U9SZPnkxUVJTZ86JtZ82aZdMahbA1CVUhnFR0dDTjxo0zW9auXTtSUlLw9fW1S01z587l2LFjFm3zwgsvkJKSQu3atW1UlRAVRybUF+IOotPp7HqbMj8/P6pXr27RNt7e3nh7e6PRaGxUlRAVR1qqQjih4cOHs3nzZt5//31T1+nJkyeLdf/OmzcPPz8/Vq5cSePGjfH09KR///7k5OQwf/58QkND8ff3Jy4uDoPBYNp/Xl4eL7zwArVq1cLLy4t7772XxMTEctU6ffp0atSoQZUqVRg1ahS5ublW+A4I4ZgkVIVwQu+//z5t27ZlzJgxpKSkkJKSQkhISInr5uTk8MEHH/Dtt9+ydu1aEhMT6du3L6tXr2b16tUsXLiQTz/9lO+//960zdixY9m+fTvffvstBw4cYMCAATz00EP88ccfFtX53XffMXnyZKZOncru3bsJCgpi9uzZt/XehXBk0v0rhBPy9fVFp9Ph6el5y+5evV7Pxx9/TIMGDQDo378/CxcuJDU1FW9vb8LDw+nSpQubNm0iJiaGU6dOMXfuXE6dOkVwcDBQeN5z7dq1zJ07l6lTp5a5zlmzZjFq1ChGjRoFwJtvvsmPP/4orVVxx5KWqhB3OE9PT1OgAtSoUYPQ0FC8vb3Nlp0/fx6AgwcPYjAYaNSokel8p7e3N5s3b+bEiRMWHfvIkSPce++9Zsvatm17G+9GCMcmLVUh7nBardbsuaIoJS4zGo0AXL58GY1Gw549e4oNHro+iIUQxUmoCuGkdDqd2eAia2nevDkGg4Hz58/TsWPH29pXWFgYO3fuZOjQoaZlO3bsuN0ShXBYEqpCOKnQ0FB27tzJyZMn8fb2pmrVqlbZb6NGjRgyZAhDhw5lxowZNG/enAsXLrBx40YiIiLo2bNnmff17LPPMnz4cFq2bEn79u35+uuv+e2336hfv75VahXC0cg5VSGc1AsvvIBGoyE8PJzAwEBOnTpltX3PnTuXoUOH8vzzz9O4cWP69OlDUlISderUsWg/MTExvPrqq7z44ou0aNGCv/76i6eeespqdQrhaBRVVVV7FyGEcH6KorBs2TL69OlTru1DQ0MZN25csVmihHAm0lIVQljNoEGDLJ5ucOrUqXh7e1u1pS2EvUhLVQhhFcePHwdAo9FQr169Mm+Xnp5Oeno6AIGBgXabt1gIa5BQFUIIIaxEun+FEEIIK5FQFUIIIaxEQlUIIYSwEglVIYQQwkokVIUQQggrkVAVQgghrERCVQghhLASCVUhhBDCSiRUhRBCCCv5f6kRpmz6RDnjAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", "hm = w.headinside(tm)\n", @@ -182,12 +325,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]resRMSE [m]
timflow44.536.39e-060.020.006
MLU51.538.16e-040.020.008
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", - " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res [d]\", \"RMSE [m]\"],\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\", \"RMSE [m]\"],\n", " index=[\"timflow\", \"MLU\"],\n", ")\n", "\n", @@ -195,7 +380,7 @@ "t.loc[\"MLU\"] = [51.530, 8.16e-04, 0.022, 0.00756]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"res [d]\": \"{:.2f}\", \"RMSE [m]\": \"{:.2f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"res\": \"{:.2f}\", \"RMSE [m]\": \"{:.3f}\"}\n", ")\n", "t_formatted" ] @@ -210,13 +395,20 @@ "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", "* Newville, M., Stensitzki, T., Allen, D.B. and Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -228,7 +420,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/leaky3_texashill.ipynb b/docs/transient/04pumpingtests/leaky3_texashill.ipynb new file mode 100644 index 0000000..dbc5311 --- /dev/null +++ b/docs/transient/04pumpingtests/leaky3_texashill.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Leaky Aquifer Test - Texas Hill" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams['figure.figsize'] = [5, 3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction and Conceptual Model\n", + "\n", + "This example, taken from the AQTESOLV examples (Duffield, 2007), is a pumping test done in the location of 'Texas Hill'. \n", + "\n", + "A pumping well was screened at an aquifer located between 20 ft and 70 ft depths. The aquifer is overlain by an aquitard. The formation at the base of the aquifer is considered an aquiclude.\n", + "\n", + "Three observation wells are located at 40, 80 160 ft distance. They are denominated OW1, OW2 and OW3, respectively. Pumping lasted for 420 minutes at a rate of 4488 gallons per minute. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# data of OW1 \n", + "data1 = np.loadtxt(\"data/texas40.txt\")\n", + "t1 = data1[:, 0] #days\n", + "h1 = data1[:, 1] #meters\n", + "r1 = 40 * 0.3048 # distance between obs1 to pumping well in m (40 ft = 12.191 m)\n", + "\n", + "# data of OW2\n", + "data2 = np.loadtxt(\"data/texas80.txt\")\n", + "t2 = data2[:, 0]\n", + "h2 = data2[:, 1]\n", + "r2 = 80 * 0.3048 # distance between obs2 to pumping well in m (80 ft = 24.383 m)\n", + "\n", + "# data of OW3\n", + "data3 = np.loadtxt(\"data/texas160.txt\")\n", + "t3 = data3[:, 0]\n", + "h3 = data3[:, 1]\n", + "r3 = 160 * 0.3048 # distance between obs3 to pumping well in m (160 ft = 48.766 m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# known parameters \n", + "Q = (4488 * 0.00378541) * 60 * 24 # constant discharge in m^3/d (4488 gallons/minute = 24464.06 m^3/d)\n", + "b1 = 20 * 0.3048 # overlying aquitard thickness in m (20 ft = 6.096 m)\n", + "b2 = 50 * 0.3048 # aquifer thickness in m (50 ft = 15.24 m )\n", + "zt = -b1 # top boundary of aquifer, m\n", + "zb = -b1 - b2 # bottom boundary of aquifer, m\n", + "rw = 0.5 * 0.3048 # well radius in m (0.5 ft = 0.15 m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The overlying layer is modeld as an aquitard without storage (```Sll```, the storage parameter, is set to zero in ModelMaq)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml= tft.ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Saq=0.001,\n", + " Sll=0,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters\n", + "The hydraulic parameters of the aquifer (```kaq``` and ```Saq```) and for the aquitard (```c```) are estimated. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "......................................................\n", + "Fit succeeded.\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq, c\n", + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", layers=0, initial=10)\n", + "cal.set_parameter(name=\"Saq\", layers=0, initial=1e-4)\n", + "cal.set_parameter(name=\"c\", layers=0, initial=100)\n", + "cal.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "cal.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "cal.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", + "cal.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
optimal
kaq_0_0224.628844
Saq_0_00.000213
c_0_043.882032
\n", + "
" + ], + "text/plain": [ + " optimal\n", + "kaq_0_0 224.628844\n", + "Saq_0_0 0.000213\n", + "c_0_0 43.882032" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.060243076833231254\n" + ] + } + ], + "source": [ + "display(cal.parameters.loc[:, ['optimal']])\n", + "print(\"RMSE:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1_1 = ml.head(r1, 0, t1)\n", + "hm2_1 = ml.head(r2, 0, t2)\n", + "hm3_1 = ml.head(r3, 0, t3)\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_1[0], label=\"timflow OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_1[0], label=\"timflow OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_1[0], label=\"timflow OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", + "plt.legend(bbox_to_anchor=(1.05, 1))\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of Results " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, the aquifer responses obtained with `timflow` are compared to the results based on Hantush’s analytical solution (Hantush, 1955), implemented in the software AQTESOLV (Duffield, 2007). The results show almost identical values." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]c [d]RMSE [m]
timflow224.632.13e-0443.880.06
AQTESOLV224.732.12e-0443.960.06
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"RMSE [m]\"],\n", + " index=[\"timflow\", \"AQTESOLV\"],\n", + ")\n", + "\n", + "t.loc[\"AQTESOLV\"] = [224.726, 2.125e-4, 43.964, 0.059627]\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"c [d]\": \"{:.2f}\", \"RMSE [m]\": \"{:.2f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Carlson F. and Randall J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510.\n", + "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Newville, M.,Stensitzki, T., Allen, D.B. and Ingargiola, A. (2014), LMFIT: Non Linear Least-Squares Minimization and Curve Fitting for Python, https://dx.doi.org/10.5281/zenodo.11813, https://lmfit.github.io/lmfit-py/intro.html (last access: August,2021)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb index bb4150f..a0052d1 100644 --- a/docs/transient/04pumpingtests/slug1_pratt_county.ipynb +++ b/docs/transient/04pumpingtests/slug1_pratt_county.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "editable": true, "slideshow": { @@ -26,13 +26,13 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "import timflow.transient as tft\n", "\n", - "plt.rcParams[\"figure.figsize\"] = [5, 3]" + "plt.rcParams['figure.figsize'] = [5, 3]" ] }, { @@ -55,7 +55,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n" + "" ] }, { @@ -67,13 +67,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "data = np.loadtxt(\"data/slug.txt\", skiprows=1)\n", "to = data[:, 0] / 60 / 60 / 24 # convert time in seconds to days\n", - "ho = data[:, 1] # m" + "ho = data[:, 1] #m " ] }, { @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -107,9 +107,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slug: 0.00863 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc**2 * H0\n", "print(\"slug:\", round(Q, 5), \"m^3\")" @@ -128,9 +136,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", "w = tft.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", @@ -146,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "editable": true, "slideshow": { @@ -154,7 +171,30 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".......................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 20\n", + " # data points = 61\n", + " # variables = 2\n", + " chi-square = 4.9800e-04\n", + " reduced chi-square = 8.4407e-06\n", + " Akaike info crit = -710.662504\n", + " Bayesian info crit = -706.440756\n", + "[[Variables]]\n", + " kaq_0_2: 6.04867256 +/- 0.02486885 (0.41%) (init = 10)\n", + " Saq_0_2: 2.1319e-04 +/- 1.0494e-05 (4.92%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_0_2, Saq_0_2) = -0.6526\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq\", layers=[0, 1, 2], initial=10)\n", @@ -165,9 +205,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_0_2[0, 1, 2]6.0486730.0248690.411146-infinf10.0000None[[6.048672558447974, 6.048672558447974, 6.0486...
Saq_0_2[0, 1, 2]0.0002130.0000104.922186-infinf0.0001None[[0.00021318989015063131, 0.000213189890150631...
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial inhoms \\\n", + "kaq_0_2 [0, 1, 2] 6.048673 0.024869 0.411146 -inf inf 10.0000 None \n", + "Saq_0_2 [0, 1, 2] 0.000213 0.000010 4.922186 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq_0_2 [[6.048672558447974, 6.048672558447974, 6.0486... \n", + "Saq_0_2 [[0.00021318989015063131, 0.000213189890150631... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.002857267940528677\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"RMSE:\", cal.rmse())" @@ -175,9 +297,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", "hm = ml.head(0, 0, tm, layers=1)\n", @@ -202,14 +335,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here, we reproduce the work of Yang (2020) to check the `timflow` performance in analysing slug tests. We compare the solution in `timflow` with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007). Both models show similarly low RMSE values. However, the estimated hydraulic conductivity and specific storage parameters differ substantially." + "The solution in `timflow` is compared with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007). Both models show similarly low RMSE values. However, the estimated hydraulic conductivity and specific storage parameters differ substantially." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow6.052.13e-040.003
AQTESOLV4.033.83e-040.003
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", @@ -238,15 +410,22 @@ "## References\n", "* Butler Jr., J.J. (1998), The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", - "* Hyder, Z., Butler Jr, J.J., McElwee, C.D., Liu, W. (1994), Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D. and Liu, W. (1994), Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -258,7 +437,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug2_falling_head.ipynb b/docs/transient/04pumpingtests/slug2_falling_head.ipynb index dd936cb..8fbf197 100644 --- a/docs/transient/04pumpingtests/slug2_falling_head.ipynb +++ b/docs/transient/04pumpingtests/slug2_falling_head.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "editable": true, "slideshow": { @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -86,16 +86,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "rw = 5 * 0.0254 # well radius in m (5 inch = 0.127 m)\n", - "rc = 2 * 0.0254 # well casing radius in m (2 inch = 0.0508 m)\n", - "L = 13.8 * 0.3048 # screen length in m (13.8 ft = 4.2 m)\n", - "b = -32.57 * 0.3048 # aquifer thickness in m (-32.57 ft = -9.92 m)\n", - "zt = -0.47 * 0.3048 # depth to top of the screen in m (-0.47 ft = -0.14 m)\n", - "H0 = 1.48 * 0.3048 # initial displacement in the well in m (1.48 ft = 0.4511 m)\n", + "rw = 5 * 0.0254 # well radius in m (5 inch = 0.127 m)\n", + "rc = 2 * 0.0254 # well casing radius in m (2 inch = 0.0508 m) \n", + "L = 13.8 * 0.3048 # screen length in m (13.8 ft = 4.2 m) \n", + "b = -32.57 * 0.3048 # aquifer thickness in m (-32.57 ft = -9.92 m) \n", + "zt = -0.47 * 0.3048 # depth to top of the screen in m (-0.47 ft = -0.14 m) \n", + "H0 = 1.48 * 0.3048 # initial displacement in the well in m (1.48 ft = 0.4511 m)\n", "zb = zt - L # bottom of the screen in m" ] }, @@ -108,9 +108,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slug: 0.00366 m^3\n" + ] + } + ], "source": [ "Q = np.pi * rc**2 * H0\n", "print(f\"slug: {Q:.5f} m^3\")" @@ -125,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -142,9 +150,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 8\n", + "solution complete\n" + ] + } + ], "source": [ "ml = tft.Model3D(\n", " kaq=10, z=zlay, Saq=Saq, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", @@ -171,22 +188,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................\n", + "Fit succeeded.\n", + "[[Fit Statistics]]\n", + " # fitting method = leastsq\n", + " # function evals = 32\n", + " # data points = 27\n", + " # variables = 2\n", + " chi-square = 0.00108532\n", + " reduced chi-square = 4.3413e-05\n", + " Akaike info crit = -269.286376\n", + " Bayesian info crit = -266.694703\n", + "[[Variables]]\n", + " kaq_0_21: 0.49527891 +/- 0.02256667 (4.56%) (init = 10)\n", + " Saq_0_21: 4.0641e-04 +/- 1.0404e-04 (25.60%) (init = 0.0001)\n", + "[[Correlations]] (unreported correlations are < 0.100)\n", + " C(kaq_0_21, Saq_0_21) = -0.9593\n" + ] + } + ], "source": [ "cal = tft.Calibrate(ml)\n", - "cal.set_parameter(name=\"kaq\", layers=list(range(nlay)), initial=10, pmin=0)\n", - "cal.set_parameter(name=\"Saq\", layers=list(range(nlay)), initial=1e-4, pmin=0)\n", + "cal.set_parameter(name=\"kaq\", layers= list(range(nlay)), initial=10, pmin=0) \n", + "cal.set_parameter(name=\"Saq\", layers= list(range(nlay)), initial=1e-4, pmin=0) \n", "cal.seriesinwell(name=\"obs\", element=w, t=to, h=ho)\n", "cal.fit(report=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_0_21[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...0.4952790.0225674.5563550.0inf10.0000None[[0.49527890522676343, 0.49527890522676343, 0....
Saq_0_21[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...0.0004060.00010425.6012280.0inf0.0001None[[0.00040640566742622397, 0.000406405667426223...
\n", + "
" + ], + "text/plain": [ + " layers optimal \\\n", + "kaq_0_21 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 0.495279 \n", + "Saq_0_21 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 0.000406 \n", + "\n", + " std perc_std pmin pmax initial inhoms \\\n", + "kaq_0_21 0.022567 4.556355 0.0 inf 10.0000 None \n", + "Saq_0_21 0.000104 25.601228 0.0 inf 0.0001 None \n", + "\n", + " parray \n", + "kaq_0_21 [[0.49527890522676343, 0.49527890522676343, 0.... \n", + "Saq_0_21 [[0.00040640566742622397, 0.000406405667426223... " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.0063401121553331085\n" + ] + } + ], "source": [ "display(cal.parameters)\n", "print(\"RMSE:\", cal.rmse())" @@ -194,9 +320,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tm = np.logspace(np.log10(to[0]), np.log10(to[-1]), 100)\n", "hm = w.headinside(tm)\n", @@ -214,25 +351,64 @@ "metadata": {}, "source": [ "### Comparison of results\n", - "Here, we reproduce the work of Yang (2020) to check the `timflow` performance in analysing slug tests. We compare the solution in `timflow` with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007). AQTESOLV parameters are quite different from the set parameters in `timflow`. Furthermore, AQTESOLV also has a better RMSE performance." + "Here, the `timflow` performance in analysing slug tests is checked. The solution in `timflow` is compared with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007). AQTESOLV parameters are quite different from the set parameters in `timflow`. Furthermore, AQTESOLV also has a better RMSE performance." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow0.504.06e-040.006
AQTESOLV2.627.89e-050.001
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t = pd.DataFrame(\n", " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", - " index=[\"timflow-multi\", \"AQTESOLV\"],\n", + " index=[\"timflow\", \"AQTESOLV\"],\n", ")\n", "\n", - "t.loc[\"timflow-multi\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", "t.loc[\"AQTESOLV\"] = [2.616, 7.894e-5, 0.001197]\n", "\n", "t_formatted = t.style.format(\n", - " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", ")\n", "t_formatted" ] @@ -257,9 +433,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -271,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/docs/transient/04pumpingtests/slug3_multiwell.ipynb b/docs/transient/04pumpingtests/slug3_multiwell.ipynb new file mode 100644 index 0000000..df425bf --- /dev/null +++ b/docs/transient/04pumpingtests/slug3_multiwell.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Slug Test for Confined Aquifer - Multi-well" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import timflow.transient as tft\n", + "\n", + "plt.rcParams['figure.figsize'] = [5, 3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction and Conceptual Model\n", + "\n", + "This Slug Test, taken from the AQTESOLV examples, was reported in Butler (1998). \n", + "\n", + "A well (Ln-2) fully penetrates a sandy confined aquifer, with 6.1 m thickness. Additionally, a fully penetrating observation well (Ln-3) is placed 6.45 m away from the test well. The slug displacement is 2.798 m. Head change was recorded at the slug well and the observation well. The well and casing radii of the slug well are 0.102 and 0.051 m, respectively. For the observation well, they are 0.051 and 0.025 m, respectively." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = np.loadtxt(\"data/ln-2.txt\")\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h1 = data1[:, 1]\n", + "\n", + "data2 = np.loadtxt(\"data/ln-3.txt\")\n", + "t2 = data2[:, 0] / 60 / 60 / 24\n", + "h2 = data2[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Parameters and model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# known parameters\n", + "H0 = 2.798 # initial displacement, m\n", + "b = -6.1 # aquifer thickness, m\n", + "rw1 = 0.102 # well radius of Ln-2 Well, m\n", + "rw2 = 0.071 # well radius of observation Ln-3 Well, m\n", + "rc1 = 0.051 # casing radius of Ln-2 Well, m\n", + "rc2 = 0.025 # casing radius of Ln-3 Well, m\n", + "r = 6.45 # distance from observation well to test well, m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Converting slug displacement into volume" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Slug: 0.02286 m^3\n" + ] + } + ], + "source": [ + "Q = np.pi * rc1**2 * H0\n", + "print(\"Slug:\", round(Q, 5), \"m^3\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "self.neq 1\n", + "solution complete\n" + ] + } + ], + "source": [ + "ml = tft.ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w = tft.Well(ml, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", + "ml.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimate aquifer parameters\n", + "The hydraulic conductivity and specific storage are calibrated, as in the KGS model (Hyder et al. 1994)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "...................................\n", + "Fit succeeded.\n" + ] + } + ], + "source": [ + "# unknown parameters: kaq, Saq\n", + "cal = tft.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq\", layers=0, initial=10)\n", + "cal.set_parameter(name=\"Saq\", layers=0, initial=1e-4)\n", + "cal.seriesinwell(name=\"Ln-2\", element=w, t=t1, h=h1)\n", + "cal.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", + "cal.fit()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
layersoptimalstdperc_stdpminpmaxinitialinhomsparray
kaq_0_001.1661062.927703e-030.251067-infinf10.0000None[[1.1661060121106153]]
Saq_0_000.0000091.158603e-071.234870-infinf0.0001None[[9.382385664277816e-06]]
\n", + "
" + ], + "text/plain": [ + " layers optimal std perc_std pmin pmax initial inhoms \\\n", + "kaq_0_0 0 1.166106 2.927703e-03 0.251067 -inf inf 10.0000 None \n", + "Saq_0_0 0 0.000009 1.158603e-07 1.234870 -inf inf 0.0001 None \n", + "\n", + " parray \n", + "kaq_0_0 [[1.1661060121106153]] \n", + "Saq_0_0 [[9.382385664277816e-06]] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 0.010236345953569054\n" + ] + } + ], + "source": [ + "display(cal.parameters)\n", + "print(\"RMSE:\", cal.rmse())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hm1 = w.headinside(t1)\n", + "hm2 = ml.head(r, 0, t2, layers=0)\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1[0] / H0, label=\"timflow ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2[0] / H0, label=\"timflow ln-3\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"Normalized Head: h/H0\")\n", + "plt.title(\"Model Results - Single layer model\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of results\n", + "\n", + "The values in `timflow` are compared with the KGS analytical model (Hyder et al. 1994) implemented in AQTESOLV (Duffield, 2007), and to the MLU model (Carlson & Randall, 2012). The parameters in every model closely match each other. The error was also very similar." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 k [m/d]Ss [1/m]RMSE [m]
timflow1.179.38e-060.010
AQTESOLV1.179.37e-060.009
MLU1.318.20e-060.010
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE [m]\"],\n", + " index=[\"timflow\", \"AQTESOLV\", \"MLU\"],\n", + ")\n", + "\n", + "t.loc[\"timflow\"] = np.append(cal.parameters[\"optimal\"].values, cal.rmse())\n", + "t.loc[\"AQTESOLV\"] = [1.166, 9.368e-06, 0.009151]\n", + "t.loc[\"MLU\"] = [1.311, 8.197e-06, 0.010373]\n", + "\n", + "t_formatted = t.style.format(\n", + " {\"k [m/d]\": \"{:.2f}\", \"Ss [1/m]\": \"{:.2e}\", \"RMSE [m]\": \"{:.3f}\"}\n", + ")\n", + "t_formatted" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### References\n", + "\n", + "* Butler Jr., J.J. (1998), The Design, Performance, and Analysis of Slug Tests, Lewis Publishers, Boca Raton, Florida, 252p.\n", + "* Carlson F. and Randall J. (2012), MLU: a Windows application for the analysis of aquifer tests and the design of well fields in layered systems, Ground Water 50(4):504–510.\n", + "* Duffield, G.M. (2007), AQTESOLV for Windows Version 4.5 User's Guide, HydroSOLVE, Inc., Reston, VA.\n", + "* Hyder, Z., Butler Jr, J.J., McElwee, C.D. and Liu, W. (1994), Slug tests in partially penetrating wells, Water Resources Research 30, 2945–2957." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}