{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "

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\n", "\n", "## Interactive Variogram Calculation and Modeling Demonstration\n", "\n", "\n", "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n", "\n", "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n", "\n", "\n", "### The Interactive Workflow\n", "\n", "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n", "\n", "* setting the variogram calculation parameters for identifying spatial data pairs \n", "\n", "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n", "\n", "I have more comprehensive workflows for variogram calculation:\n", "\n", "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n", "\n", "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n", "\n", "#### Spatial Continuity \n", "\n", "**Spatial Continuity** is the correlation between values over distance.\n", "\n", "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n", "\n", "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n", "\n", "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n", "\n", "#### The Semivariogram\n", "\n", "Function of difference over distance.\n", "\n", "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n", "\n", "\\begin{equation}\n", "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2 \n", "\\end{equation}\n", "\n", "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n", "\n", "* Calculated over a suite of lag distances to obtain a continuous function.\n", "\n", "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n", "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n", "\n", "\\begin{equation}\n", "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n", "\\end{equation}\n", "\n", "Note the correlogram is related to the covariance function as:\n", "\n", "\\begin{equation}\n", "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n", "\\end{equation}\n", "\n", "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$. \n", "\n", "\\begin{equation}\n", "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n", "\\end{equation}\n", "\n", "#### Variogram Observations\n", "\n", "The following are common observations for variograms that should assist with their practical use.\n", "\n", "##### Observation \\#1 - As distance increases, variability increase (in general).\n", "\n", "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n", "\n", "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n", "\n", "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n", "\n", "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n", "\n", "We scan through the entire data set, searching for all possible pair combinations with all other data. We then calculate the variogram as one half the expectation of squared difference between all pairs.\n", "\n", "More pairs results in a more reliable measure.\n", "\n", "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n", "\n", "**Sill** is the variance, $\\sigma^2_x$\n", "\n", "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n", "\n", "we can define the covariance function:\n", "\n", "\\begin{equation}\n", "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n", "\\end{equation}\n", "\n", "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n", "\n", "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n", "\n", "\\begin{equation}\n", "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n", "\\end{equation}\n", "\n", "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n", "\n", "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n", "\n", "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n", "\n", "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing. This is known as **nugget effect**.\n", "\n", "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n", "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n", "\n", "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n", "\n", "1. generate a 2D model with sequential Gaussian simulation\n", "2. sample from the simulation\n", "3. calculate and visualize experimental semivariograms\n", "\n", "#### Variogram Calculation Parameters\n", "\n", "The variogram calculation parameters include:\n", "\n", "* **azimuth** is the azimuth of the lag vector\n", "\n", "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n", "\n", "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n", "\n", "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n", "\n", "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n", "\n", "* **bandwidth** is the maximum offset allowable from the lag vector \n", "\n", "\n", "#### Variogram Modeling\n", "\n", "Spatial continuity can be modeled with nested, positive definate variogram structures:\n", "\n", "\\begin{equation}\n", "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n", "\\end{equation}\n", "\n", "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms $\\gamma_i(\\bf{h})$.\n", "\n", "The types of structure commonly applied include:\n", "\n", "* spherical\n", "\n", "* exponential\n", "\n", "* gaussian\n", "\n", "* nugget\n", "\n", "Other less common models include:\n", "\n", "* hole effect\n", "\n", "* dampenned hole effect\n", "\n", "* power law\n", "\n", "these will not be covered here.\n", "\n", "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions. In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction).\n", "\n", "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n", "\n", "\\begin{equation}\n", "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2} \n", "\\end{equation}\n", "\n", "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n", "\n", "Some comments on modeling nested variograms:\n", "\n", "* we can capture nugget, short and long range continuity structures\n", "\n", "* we rely on the geometric anisotropy model, so all structures must inform the same level of contribution (porportion of the sill) in all directions.\n", "\n", "* the geometric anisotropy model is based on azimuth of the major direction of continuity, range in the major direction and range in the minor direction (orthogonal to the major direction). The range is interpolated between the major and minor azimuths with a ellipse model\n", "\n", "* we can vary the type of variogram, direction or azimuth of the major direction, and major and minor ranges by structure\n", "\n", "In this workflow we will explore methods to:\n", "\n", "1. detect directionality from a spatial dataset\n", "2. calculate the directional variograms in the major and minor directions \n", "3. build a consistent 2D model fit to the major and minor directions\n", "\n", "#### Objective \n", "\n", "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n", "\n", "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods. \n", "\n", "#### Getting Started\n", "\n", "Here's the steps to get setup in Python with the GeostatsPy package:\n", "\n", "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n", "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n", "3. In the terminal type: pip install geostatspy. \n", "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n", "\n", "You will need to copy the data file to your working directory. They are available here:\n", "\n", "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n", "\n", "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n", "\n", "#### Load the required libraries\n", "\n", "The following code loads the required libraries." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geostatspy.GSLIB as GSLIB # GSLIB utilies, visualization and wrapper\n", "import geostatspy.geostats as geostats # GSLIB methods convert to Python " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also need some standard packages. These should have been installed with Anaconda 3." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import os # to set current working directory \n", "import sys # supress output to screen for interactive variogram modeling\n", "import io\n", "import numpy as np # arrays and matrix math\n", "import pandas as pd # DataFrames\n", "import matplotlib.pyplot as plt # plotting\n", "from matplotlib.pyplot import cm # color maps\n", "from ipywidgets import interactive # widgets and interactivity\n", "from ipywidgets import widgets \n", "from ipywidgets import Layout\n", "from ipywidgets import Label\n", "from ipywidgets import VBox, HBox\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### Set the working directory\n", "\n", "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#os.chdir(\"c:/PGE383\") # set the working directory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading Tabular Data\n", "\n", "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XYFaciesPorosityPermAI
050.0900.01.022.076146140.0212663413.063944
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" ], "text/plain": [ " X Y Facies Porosity Perm AI\n", "0 50.0 900.0 1.0 22.076146 140.021266 3413.063944\n", "1 50.0 850.0 1.0 23.715447 39.837129 3074.562617\n", "2 50.0 800.0 1.0 23.435152 84.992437 2292.783358\n", "3 50.0 750.0 1.0 24.455309 90.632307 2494.848885\n", "4 50.0 700.0 1.0 23.178661 811.547979 2522.063995" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = 3\n", "\n", "if data == 1:\n", " df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\") # load from Prof. Pyrcz's GitHub repository\n", " df = df.rename(columns = {'Por':'Porosity'}) # rename feature(s)\n", " df['Porosity'] = df['Porosity']*100.0\n", " df = df.iloc[:,1:] # remove first column\n", "elif data == 2:\n", " df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v3.csv\") # load from Prof. Pyrcz's GitHub repository\n", " df = df.rename(columns = {'Por':'Porosity'}) # rename feature(s)\n", " df = df.iloc[:,1:] \n", "elif data == 3:\n", " df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/12_sample_data.csv\") # load from Prof. Pyrcz's GitHub repository\n", " df = df.rename(columns = {'Por':'Porosity'}) # rename feature(s) \n", " df['Porosity'] = df['Porosity']*100.0\n", " df = df.iloc[:,1:] \n", "elif data == 4:\n", " df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v5_sand_only.csv\") # load from Prof. Pyrcz's GitHub repository\n", " df = df.rename(columns = {'Por':'Porosity'}) # rename feature(s) \n", " df = df.iloc[:,1:] \n", "else:\n", " df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v1.csv\") # load from Prof. Pyrcz's GitHub repository\n", " df = df.rename(columns = {'Por':'Porosity'}) # rename feature(s)\n", " df = df.iloc[:,1:] \n", " \n", "df.head() # we could also use this command for a table preview " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The features:\n", "\n", "* **X** - x coordinate in meters\n", "* **Y** - y coordinate in meters\n", "* **Porosity** - rock porosity averaged over a specific rock unit from a vertical well\n", "* **Perm** - rock permeability averaged (scaled up) over a specific rock unit from a vertical well \n", "* **AI** - acoustic impedance from a seismic cube assigned at a specific rock unit and at the location of a vertical well \n", "* **facies** - facies, 0 - shale, 1 - sandstone\n", "\n", "Concerning facies:\n", "\n", "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit. \n", "\n", "You are welcome to repeat this workflow on a by-facies basis. The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n", "\n", "```p\n", "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index() # copy only 'Facies' = sand records\n", "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n", "```\n", "\n", "Let's look at summary statistics for all facies combined:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
X480.0430.187500263.8326920.000000200.000000390.000000630.000000980.000000
Y480.0522.166667284.29342019.000000279.000000539.000000759.000000999.000000
Facies480.00.6166670.4867060.0000000.0000001.0000001.0000001.000000
Porosity480.018.9439893.17016411.75619616.58839518.54433921.65126626.109068
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" ], "text/plain": [ " count mean std min 25% \\\n", "X 480.0 430.187500 263.832692 0.000000 200.000000 \n", "Y 480.0 522.166667 284.293420 19.000000 279.000000 \n", "Facies 480.0 0.616667 0.486706 0.000000 0.000000 \n", "Porosity 480.0 18.943989 3.170164 11.756196 16.588395 \n", "Perm 480.0 520.932093 1226.207190 0.005776 6.539988 \n", "AI 480.0 3758.879653 779.990582 1746.387548 3212.900121 \n", "\n", " 50% 75% max \n", "X 390.000000 630.000000 980.000000 \n", "Y 539.000000 759.000000 999.000000 \n", "Facies 1.000000 1.000000 1.000000 \n", "Porosity 18.544339 21.651266 26.109068 \n", "Perm 49.451463 369.470756 10319.904849 \n", "AI 3719.883000 4236.160395 6194.573653 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe().transpose() # summary table of sand only DataFrame statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set the Model Parameters\n", "\n", "See the the following model parameters:\n", "\n", "* **xmin**, **xmax**, **ymin** and **ymax** - extents of the dataset for plotting\n", "* **feature** and **feature_units** - feature of interest and associated units\n", "* **vmin** and **vmax** - minimum and maximum of the feature of interest" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "xmin = 0.0; xmax = 1000.0 # spatial extents in x and y\n", "ymin = 0.0; ymax = 1000.0\n", "feature = 'Porosity'; feature_units = 'percentage' # name and units of the feature of interest\n", "vmin = 0.0; vmax = 25.0 # min and max of the feature of interest\n", "cmap = plt.cm.inferno # set the color map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's transform the feature to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n", "\n", "Let's look at the inputs for the GeostatsPy nscore program. Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geostats.nscore # see the input parameters required by the nscore function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following command will transform the Porosity and Permeabilty to standard normal. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#Transform to Gaussian by Facies\n", "df['N' + feature], tvPor, tnsPor = geostats.nscore(df, feature) # nscore transform for all facies porosity " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XYFaciesPorosityPermAINPorosity
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" ], "text/plain": [ " X Y Facies Porosity Perm AI NPorosity\n", "0 50.0 900.0 1.0 22.076146 140.021266 3413.063944 0.808592\n", "1 50.0 850.0 1.0 23.715447 39.837129 3074.562617 1.403672\n", "2 50.0 800.0 1.0 23.435152 84.992437 2292.783358 1.324260\n", "3 50.0 750.0 1.0 24.455309 90.632307 2494.848885 1.720087\n", "4 50.0 700.0 1.0 23.178661 811.547979 2522.063995 1.155424" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head() # preview sand DataFrame with nscore transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values. e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity. Also, the normal score transform of values close to the mean value should be close to 0.0 \n", "\n", "Let's also check the original and transformed sand and shale porosity distributions." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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i2SgsAQBZKZVK6cLnPqeKlRWlkkk1MFkPACADxscnNT+/rIGBaa2t1aqoqET19c9RRUWVxsfndOgQ/c9OUFgCALLO4sKCei5fVsXsrJ57/HjoOACAPJJMJrW+vq6enkFNTCwrFkuptPSIysvvVkNDfeh4OYvCEgCQVZaXl3X9scfUtLKiY0eOhI4DAMgTy8vLWlpaUnf3kKaniySVqqnpflVXl2bttSFzCS0IAMgq6+vrqovHdZKiEgCwD9bW1nT1ar8mJxe1vHxIFRWHdfhwO+ft7zMKSwBAVlmYnZW5335FAAC2sba2ptnZWV2+PKx4XFpbq1Fj42k1NDCTa6ZQWAIAssbq6qrmxsfVUeDXqgQA7M76+rpGRsbV1zehxcUKVVefUEtLc+hYBYHCEgCQFRYXF3X98cdVtbSk2tbW0HEAADkkHo9rYmJK16+PaGamUvX1J3XkSFPoWAWFwhIAEJS7K5lM6voTT6hpaYlzKwEAO5ZKpTQ2NqbLl0e1ulqkqqoTOnGiSWYWOlrBobAEAAQ11NuriatXVbq2ppNcWgQAsEMDAyMaHZ3U0FBSDQ0coQyNwhIAEMzM9LQmR0d1qqJCjW1toeMAAHLA4uKinnzyuubmpJqakzp27JBKSkpDxyp4FJYAgCBWV1fV+/jjak+lVN/MxAoAgFtbX1/XwMCArl2bV3Fxs44c6eCSIVmEwhIAcOBmZ2d14/x5NSaTOsqRSgDALbi7xscnde3aoMbHy3X48GlVVXHZkGxDYQkAOFC93d2auXJFhysqdISiEgBwG+fPX9Pg4LLKy4/p1KkWjlJmKQpLAMCBSCQSWlhY0Exnp57T1KQqrlUJANhGKpVSf/+wEomUpqaW1dj4gCoqKkPHwi1QWAIAMi6VSun85z6nosVFHauspKgEAGxrfX1djz9+SSMjroaGEyouPkRRmQMoLAEAGTc/P6/S2Vk9v6MjdBQAQJZKpVIaGRlRZ+e4VlYqdfz4aRUXF4eOhR2isAQAZFQymdSNCxfUwlFKAMA2UqmUnnrqsnp742pqOqWjRxtDR8IdorAEAGTUysqKtLCgjsOHQ0cBAGShqakpnT/fr7k5U0fH87gmZY6isAQAZNT83JzqUimZWegoAIAsMzExqaeeGlBp6QmdPMk1jXMZhSUAIOOqGQYLANgkmUzq/PlrGh1dUUXFCdXVUVTmOgpLAEBGxWIxcRlrAMCG9fV1nTt3UWNjRTpy5EGVlZWHjoR9QGEJAMiYtbU1Ldy4oWO1taGjAACywOrqqs6du6zJyUodP36/ioqKQkfCPqGwBABkzMrKiqoSCVUyFBYACt7c3JyefrpHc3O1On78Xs69zzMUlgCAjIktL6uEa5ABQEFzd/X09Kizc15lZa06duwYRWUeorAEAGREMpnUaFeXjpcybTwAFCp31+XLXbp+fVFtbc9RRUVV6EjIEApLAEBGXH78cZXMzKjlxInQUQAAAaRnfu1UX9+aDh9mkp58R2EJANh3yWRS8cVFPe/IkdBRAAABJBIJnT9/Rb29KXV0vEAlJZQd+Y5pmAAA+254YEAl8/MqYxgsABQcd9f16zfU05NUR8eDFJUFgn9lAMC+WV9f1+LiomYnJ3W0vj50HADAAXN3dXWlz6k8evQ5FJUFhH9pAMC+uXb+vDQwoLqqKtU1NoaOAwA4QO6uCxeuqrs7pvb2B1RRURk6Eg4QhSUAYM/W1tZ0+dFH5dPTetHx41zwGgAKTCKR0BNPXNbQUEJHjz5PpaVloSPhgFFYAgD2rK+nR5UzM7rv6FGKSgAoQJcvX9fQkKuj44Uq5vrFBYneHwCwJ2tra1rq79fd7e1sTABAgXF3dXf3q69vRR0dz6MfKGAcsQQA7FoqldK1S5d0aH2dGWABoMCkUil1dl7X9evLamm5n6KywFFYAgB2ZXl5Wb09PYr39ek5x46FjgMAOGB9fQPq7FzW0aMPqqysPHQcBEZhCQC4Y6lUSlcfe0yH5ud139GjKmU6eQAoKIODQ+rsnFZr630UlZBEYQkA2IW+3l5pakr33nVX6CgAgAM2Njam8+fHVVt7j6qra0PHQZZg8h4AwB0ZGxvT7JUrOt3eHjoKAOCALSws6OLFYVVUHFdNTV3oOMgiFJYAgB2bn5/X8MWL6qioUE1VVeg4AIADtL6+rvPnu7S+3qqGhpbQcZBlGAoLANiR7s5Ozff2qk1Sa1tb6DgAgAO0urqqc+cua3q6UseYsA1boLAEANzWysqK5vv79bzGRi4rAgAFJpVK6cKFq5qYqNaJE6dlZqEjIQsxFBYAcFszMzM6tLZGUQkABejSpesaHEyqo+Neikpsi8ISAHBLCwsLGrtyRY21zPwHAIVmcHBEAwPLOnbsBSouLg4dB1mMwhIAcEvTo6NqXl9XUx2z/wFAIZmfn9elS6Oqrb1XJVyvGLdBYQkA2FY8Hlc8HldNRUXoKACAA+TuOneuW6WlR1VTcyh0HOQAdj0AALa0urqqy5/9rIrX1nSssTF0HADAAYnH41peXlEyeUzHjnHNYuwMhSUAYEvJZFLV7rr/yJHQUQAAB6inp0+JhOno0btDR0EOYSgsAGBLczMzSq6uho4BADhAk5PT6utbUGlphYqKKBWwc3xbAABbSqytqaWqKnQMAMABicVieuqpPpWUHOeyIrhjFJYAgG2xtxoACkMymdRjj3UqmWxSQ0NL6DjIQWwxAAAAAAWut3dAMzPlam09GToKchSFJQBgS7FYLHQEAMABGBoaVmfnjFpaToaOghxGYQkA2FJsdFR1NTWhYwAAMiiRSOjatTHV1d2nqir+5mP3KCwBAM8yPT0trayotISrUgFAvkomk3rkkfNaWqpSdXVt6DjIcRSWAIBncHf1Xbmio8wICwB5bXBwUCMjpiNHToeOgjxAYQkAeIbJiQlpYkJtjY2howAAMmR6elbXrk2rvf1eZgDHvuBbBAB4hrWVFR2uruYaZgCQp5LJpM6fvyHpKENgsW8oLAEAz1JSXBw6AgAgQwYGBjQ3V6bm5vbQUZBHMlpYmtmrzOyamXWb2U9t8Xidmf29mZ03s8tm9uZM5gEA3N706CjDorIYfSuAvRgfn4wuLXIqdBTkmYxtOZhZsaQ/lPRqSQ9IeqOZPXDTaj8s6Yq7v0DSGUm/aWZlmcoEALg1d1dyYUGNhw6FjoIt0LcC2ItkMqlLlwZUXn6CS4tg32Vyl/TLJXW7+w13X5f0QUmvvWkdl1Rr6RN5aiTNSEpkMBMA4BZmZ2dVurLCEcvsRd8KYNd6e/u1sFCh+vrm0FGQhzJ5gbKjkgY33R6S9Iqb1vkDSR+TNCKpVtJ3uHvq5hcys7dIeosktbS06OzZs5nIm/eWlpZou12g3XaPttudkO2WSCSUKirSzPh4kPffi3iiIGon+tYM4m8WbbAhH9shHo8rFourtLRC4+OTt10/kVjS+PjZzAfLcoXeDqlUcsfrZrKw3Go6Qb/p9r+X9LSkV0q6W9I/mdm/uvvCM57k/pCkhyTp9OnTfubMmX0PWwjOnj0r2u7O0W67R9vtTqh2i8Vi6nz4YXVUV6u1vv7A33+vBqemQkc4CPStGcTfLNpgQ761QzKZ1NmzT6u+/h7V1NTt6Dnj42fV1nYms8FyQKG3w9LS/I7XzeRYpyFJHZtuH1N67+lmb5b0N57WLalX0v0ZzAQA2MbM1JTqlpdzsqgsIPStAO5YT0+f5ucrdlxUAruRycLynKR7zexUNGnAG5QemrPZgKSvkSQza5N0WtKNDGYCAGxhcXFR411daqhhMocsR98K4I6srKxodHROTU0dt18Z2IOMDYV194SZvU3SJyUVS3qPu182s7dGj79L0i9L+jMzu6j08J63u3tBjGUCgGxyo7NTjauramrnmmbZjL4VwJ1IJBJ6+OErWls7pKamqtBxkOcyeY6l3P0Tkj5x033v2rQ8IunrM5kBAHBr8XhciclJHW9pCR0FO0DfCmCnurp6NT9fqRMn7g0dBQWA+eQBoIAlEgldfOwxlcbjKi4uDh0HALBPYrGYBgYW1NZ2KnQUFAgKSwAoYANdXSoeG9ODR4+GjgIA2Cfr6+s6f/6q1tbqVVFRGToOCgSFJQAUKHdXKpnUiaYmFRXRHQBAvrh27YaGh0t0+DBHK3Fw2JIAgALV+eSTmu/rU2lJRk+3BwAcoOXlZQ0PL+vo0dPsNMSBYmsCAArQ0tKSVubm9GBTk8rLykLHAQDsk66uPq2t1ausrDx0FBQYdmMAQAEaHxhQ3eIiRSUA5JG5uTkNDq6qvf1E6CgoQBSWAFBgksmk5kZHdbi5OXQUAMA+WV1d1cWLPSopaVUJpzggAApLACggS4uLunL+vCoWFlRdyUyBAJAvLl26romJKrW0HAsdBQWKwhIACkQqldLowIDKent17+HDoeMAAPZJLBbT6Ghcx47dKzMLHQcFisISAApE18WLWuju1tHWVpWVloaOAwDYB+6urq5+FRfXMAQWQVFYAkCB8PV13d/UpJqqqtBRAAD7ZHJyUj09MTU2cs1KhMVuDQDIc8vLy7r22GPyWEzFDQ2h4wAA9kkqlVJn56AOHTqu0lJm+UZYHLEEgDw3Njam2lhML2huVkU51zUDgHwxNDSiiQmpvp5ZvhEehSUA5LGZmRnNXbqk9tpazr0BgDwSi8XU3T2u1tZ7mLAHWYHCEgDyWO+VKzpcXq7a6urQUQAA+ySVSumxxzq1stKompq60HEASRSWAJDXitbW1Mp5lQCQVwYGhjQ5KbW3M2EPsgeFJQDkqbW1NaXW1xkiBQB5JJlM6saNSbW13Rc6CvAMFJYAkKemp6dVlUiouLg4dBQAwD7p6enX3FyFqqtrQ0cBnoHCEgDy0MTEhEYvXlRrTU3oKACAfbK6uqre3lm1tTEEFtmHKQIBII8kk0n1XLmixYEBnaioUNOhQ6EjAQD2SXd3n1ZXD6m5uSp0FOBZKCwBIE9MTU2p/+mnVbywoOe2t3PNSgDII8vLy+rvX1Zb2/NCRwG2RGEJAHkglUqp/6mndKK4WM0nToSOAwDYZ5cvd0tqVGlpWegowJY4xxIAclw8HteVxx+XYjE113E9MwDIN3NzcxodTail5XjoKMC2KCwBIMctLS0pMTqq57e3h44CAMiAy5d7VFraxizfyGoUlgCQw6YnJ3XjyhXVl5SotISzGwAg34yPj2tyUmppORI6CnBLbIUAQA4bvn5dLXNzOtbaGjoKAGCfpVIpdXYOqbq6Q0VFHA9CduMbCgA5amlpSfH5eR1ubmaDAwDy0PDwsCYnpcZGdh4i+7ElAgA5aH19XdfOn1djMskQWADIQ4lEQtevT6il5e7QUYAdobAEgBw0NTWlsvFxnWDCHgDISzdu9GturlS1tfWhowA7QmEJADlmcnJSo5cu6XB9PUNgASAPra+vq79/Tu3t94SOAuwYWyQAkGMGzp/X8eJirlkJAHmqu7tPy8s1qqioCh0F2DEKSwDIEalUShMTE9LKipoOHQodBwCQAUtLS+rrW1Rb28nQUYA7wowPAJAjxsfGNPLoozpeV8cQWADIU9ev98m9UWVl5aGjAHeEwhIAspy7q6ezU/NTUzpWXa0WhsACQF6an5/X4OCajhx5IHQU4I6xyxsAstzo6KjmOzt1n6SWhobQcQAAGXLpUrfKytoYlYKcxLcWALJYKpXSaFeXTjU0qLa6mo0NAMhTExOTmpiQmpuPhI4C7ApbKACQxYYHBlQ8NaVGhr8CQF7r7BxQTU0HOxCRs/jmAkCWSqVSmujp0V3NzaGjAAAyaHh4WFNTpsbG1tBRgF2jsASALOXuKorHdaimJnQUAECGJJNJXb06poaGk6GjAHtCYQkAWSyVSoWOAADIoN7efs3OFquurjF0FGBPKCwBIEuNjY3JVlZCxwAAZEg8Hldv76za2+8NHQXYMwpLAMhCC/PzGrt6VSe5vAgA5K1r125oaalSlZXVoaMAe0ZhCQBZqOvpp9WyuspssACQp2KxmIaGltTWdlfoKMC+oLAEgGy0sqLj7e2hUwAAMmRmZlZra3UqL68IHQXYFxSWAJBl3F1KJkPHAABk0PLysioqGAKL/EFhCQBZZm11VXWJROgYAIAMGRkZ1bVri6quPhQ6CrBvKCwBIIu4u1LJpA43NYWOAgDIgFQqpWvXRlRVdZxJe5BXKCwBIItMTkxIiYQqy8tDRwEAZMDg4KCmpkwNDS2howD7isISALLI4uysSouKVFTEn2cAyDeJREJdXVNqbb0ndBRg37HlAgBZIpFIaG54WMXFxaGjAAAyoLu7T/PzZaqp4dxK5B8KSwDIAslkUhfPnVPVwoKKzELHAQDss7W1NQ0MzOvw4XtDRwEygsISALLAYH+/UoODuu/o0dBRAAAZcP16r2KxWq5bibxFYQkAWWC6t1enDx9mGCwA5KHFxUUNDCyrvf1k6ChAxlBYAkBgKysr0uqqqirYiw0A+WhkZEpSk0pLy0JHATKGwhIAAuu6fFn1qRQzwQJAHnJ3jY3NqKamIXQUIKPYigGAgNbX1xUfH9eJ1tbQUQAAGTAyMqKpKammpi50FCCjKCwBIKCFhQUVx+MqKSkJHQUAsM+SyaSuXh1TQ8Op0FGAjKOwBICA1lZX1VzGOTcAkI9u3OjXzEyR6uoaQ0cBMo7CEgACWpybYyZYAMhD8Xhc/f2zOnLkdOgowIGgsASAgJbHxtRQWxs6BgBgn12/3qvFxSpVVFSFjgIciIwWlmb2KjO7ZmbdZvZT26xzxsyeNrPLZvaZTOYBgGyyuLgoLS+rrLQ0dBTkEPpWIPstLy9rcHBRbW2cW4nCkbHZIsysWNIfSvo6SUOSzpnZx9z9yqZ16iX9kaRXufuAmTEtIoC8F4/HNdDVpeWlJdWVlXGZEewYfSuQGyYnZ7S+Xq/ycq5PjMKRya2Zl0vqdvcb7r4u6YOSXnvTOt8p6W/cfUCS3H0ig3kAILhUKqULn/+8Vq5e1fFYTMcbuK4Z7gh9K5ADBgcnVF1dHzoGcKAyOb/9UUmDm24PSXrFTevcJ6nUzM5KqpX0u+7+vptfyMzeIuktktTS0qKzZ89mIm/eW1paou12gXbbPdru2dbX15WMxVRZUaGpWEyKxZ61zlIiobPj4wHS5bZ4IhE6wkGgb80g/mbRBhv20g7xeFzLy3GVlS0ql/+UJxJLGh8/GzpGcIXeDqlUcsfrZrKwtC3u8y3e/yWSvkZSpaTPm9kj7n79GU9yf0jSQ5J0+vRpP3PmzP6nLQBnz54VbXfnaLfdo+2eaXZ6Wjcef1z3NjToUE3NtuudHR/Xmba2A0yWHwanpkJHOAj0rRnE3yzaYMNu2yGVSukzn3lKNTXH1dDQsv/BDtD4+Fm1tZ0JHSO4Qm+HpaX5Ha+bycJySFLHptvHJI1ssc6Uuy9LWjazz0p6gaTrAoA8MjU1pf5z53S0qOiWRSVwG/StQBYbGBjU5KR09925XVQCu5HJcyzPSbrXzE6ZWZmkN0j62E3r/J2krzCzEjOrUno4T2cGMwFAEGM3buhYSYnam5tDR0Fuo28FslQikVBPz5Ta2+8LHQUIImNHLN09YWZvk/RJScWS3uPul83srdHj73L3TjP7v5IuSEpJere7X8pUJgAIIZFIaG1+XnXV1aGjIMfRtwLZq7u7T/Pz5TpxgmsTozBlciis3P0Tkj5x033vuun2r0v69UzmAICQYrGYildWVNHYGDoK8gB9K5B9EomE+vvn1db2QOgoQDBcPA0AMmyot1e1ttWcKwCAfLC8vKzVVVNFRWXoKEAwFJYAkEFLS0taGRjQSWZ5BYC8delSt0pKmLAHhY3CEgAyaKCrS01FRSouLg4dBQCQARMTE5qaklpbj4aOAgRFYQkAGbK8vKyV2Vm11NWFjgIAyAB3V2fnoCorj6moiM1qFDb+BwBAhsxMTqp2bk7VlZxzAwD5aHh4WJOTUlMTpzsAFJYAkCGrq6uqr2XaeQDIV72902pquit0DCAr7KiwNLMHMx0EAPLNwtCQaquqQsdAlqJvBXLbysqKlpcTqqqqCR0FyAo7PWL5LjN7zMz+s5nVZzIQAOSD2dlZaWVFFeXloaMge9G3Ajns2rUbisfrVFJSGjoKkBV2VFi6+5dL+i5JHZIeN7MPmNnXZTQZAOSo5eVl3Xj8cR2rrJRx/Upsg74VyF1LS0saHFxVa+vJ0FGArLHjcyzdvUvSz0l6u6SvkvR7ZnbVzL41U+EAIBf1d3erPhZTW0ND6CjIcvStQG7q7OxVUVGTSkpKQkcBssZOz7F8vpn9tqROSa+U9E3u/pxo+bczmA8AcsrCwoJWBgd1or09dBRkOfpWIDctLi5pYWFd9fXMBAtsttPdLH8g6U8k/Yy7r2zc6e4jZvZzGUkGADmo9+pVtRQVsRcbO0HfCuSgp566pqWlBtXXcykpYLOdbvm8RtKKuyclycyKJFW4e8zd/zxj6QAgB7i7bnR2am15WYmRER09ciR0JOQG+lYgx6RSKa2sSMeOcYkR4GY7Pcfy05I275apiu4DgII3Pj6uuc5Odays6MG2NhUXF4eOhNxA3wrkmBs3+rW8zGzfwFZ2esSywt2XNm64+5KZcXE2AJA03tOjk/X1qq2uDh0FuYW+Fcgha2tr6umZUUvL6dBRgKy00yOWy2b24o0bZvYSSSu3WB8ACoK7q8hd1ZWca4M7Rt8K5JDu7l6trdWqqqomdBQgK+30iOWPSfqwmY1Etw9L+o6MJAKAHDLc36/1yUmVcF4l7tyPib4VyAmxWEx9fctqbX1e6ChA1tpRYenu58zsfkmnJZmkq+4ez2gyAMgB8dVVnaqrYxZY3DH6ViB3dHcPyL1BpaVloaMAWetOtoReJulk9JwXmZnc/X0ZSQUAOcDdNTM+roainZ5VADwLfSuQ5VKplGZnYzp06GjoKEBW21FhaWZ/LuluSU9LSkZ3uyQ6PwAFK5lMSrGY6ltbQ0dBDqJvBXLD6OiYpqZcJ05wbiVwKzs9YvlSSQ+4u2cyDADkkoG+PlWsroaOgdxF3wpkuVQqpa6uUTU0nJKZhY4DZLWdjt+6JKk9k0EAIJfEYjHN9vToRHNz6CjIXfStQJbr6xvUzEyp6uoaQ0cBst5Oj1g2S7piZo9JWtu4092/OSOpACDLdT72mJrdVVPFZQexa/StQBZLJpO6cWNKTU33ho4C5ISdFpbvyGQIAMg5a2s60dISOgVy2ztCBwCwvVgsppUVU0PDodBRgJyw08uNfMbMTki6190/bWZVkoozGw0AstPi4qLEuZXYI/pWILtdu9Yr94bQMYCcsaNzLM3sByX9taQ/ju46KumjGcoEAFltbW1NjaWloWMgx9G3AtkrmUxqZCSu9vbjoaMAOWOnk/f8sKR/J2lBkty9SxLz6wMoOMlkUsM3bqiYa1di7+hbgSzk7lpdXVdZWbuKixlEAOzUTreM1tx9feOGmZUofa0tACgoQwMDSvT16UgDw6OwZ/StQBaamJhQPO5qbj4SOgqQU3ZaWH7GzH5GUqWZfZ2kD0v6+8zFAoDsk0wmNXXjhk4fPqySkp3OfQZsi74VyEI9PWMqLi7jupXAHdppYflTkiYlXZT0Q5I+IennMhUKALKNu6v/xg2Vz81xiRHsF/pWIMusr69rcTGhoiKGwAJ3aqezwqYk/Un0AwAF58IjjygxOan729pCR0GeoG8Fsk9XV69isUqVly+GjgLknB0VlmbWqy3O+3D3u/Y9EQBkIV9Z0Qva2hgCi31D3wpkl5WVFQ0MLOnw4edpZmYidBwg5+x0C+mlm5YrJL1eUuP+xwGA7OTOnCrYd/StQBbp7OzR+nqdSkvLQkcBctKOzrF09+lNP8Pu/juSXpnZaACQHWZmZpRaWlIRlxjBPqJvBbLH3NycRkfX1N5+InQUIGftdCjsizfdLFJ6L2ttRhIBQBZxd/Vfvar28nIKS+wr+lYge0xMzElqVklJaegoQM7a6VDY39y0nJDUJ+nb9z0NAGSZmZkZpcbGdOT48dBRkH/oW4Es4O4aGZlWTc29oaMAOW2ns8J+daaDAEA2WltbU2t5Odczw76jbwWyw8DAoGZmSnTixKHQUYCcttOhsD9+q8fd/bf2Jw4AZJfFuTnVFnM9M+w/+lYgvHg8ruvXJ9XUdE/oKEDOu5NZYV8m6WPR7W+S9FlJg5kIBQDZYnl0VMdrOe0NGUHfCgR240a/FhfLdeJEXegoQM7baWHZLOnF7r4oSWb2DkkfdvcfyFQwAMgGJSUlXLsSmULfCgQ2MDCv9vbnho4B5IWdTnF4XNL6ptvrkk7uexoAyCLxeFzx1dXQMZC/6FuBgObn57WyIpWVlYeOAuSFne6G/3NJj5nZ30pySa+T9L6MpQKALLC4uKjytTWVcsQSmUHfCgTi7rp8uUdlZW1Mzgbsk53OCvtOM/tHSV8R3fVmd38qc7EAILzh3l7VUlQiQ+hbgXAmJ6c0Nubq6DgSOgqQN+7kat9Vkhbc/XclDZnZqQxlAoDgEomE1sfG1NHcHDoK8ht9K3DA3F1Xrw6opuaYioruZFMYwK3s6H+Tmf2CpLdL+unorlJJf5GpUAAQ2tTUlLS6qmIuNYIMoW8FwhgeHtHUVJEaG1tDRwHyyk5307xO0jdLWpYkdx+RxPz7APJSKpXS8LVrOllfHzoK8ht9KxDA+PiCDh06xrmVwD7baWG57u6u9OQCMrPqzEUCgLCWl5dVPDurJgpLZBZ9K3DAVlZWNDUVU3X1odBRgLyz08LyQ2b2x5LqzewHJX1a0p9kLhYAhDM1NqYqhsAi8+hbgQPW1dWn1dU6LjECZMBtpzu09DiBv5J0v6QFSacl/by7/1OGswFAGMmkmqs5eITMoW8FDt7CwoIGB2Nqb39+6ChAXrptYenubmYfdfeXSKLDA5DX3F0z4+NqYKZAZBB9K3Dwrl7tlVmLSkpKQ0cB8tJOt5weMbOXZTQJAGSB2dlZaXlZ9bXMoYKMo28FDsjMzIxGRhJqaTkWOgqQt3Z65e+vlvRWM+tTevY6U3qHK2MJAOSNyYkJDTzyiA5XVISOgsJA3wockGvXBlRRcZjrVgIZdMvC0syOu/uApFcfUB4ACGZxZkbtpaU60tQUOgryGH0rcLASiYSWlpKqr28OHQXIa7c7YvlRSS92934z+4i7/4cDyAQAQcyOjOh+hsAi8z4q+lbgwPT2DmhhoVSNjWWhowB57XbjATZfOfauTAYBgJDW1taklRVVV1aGjoL8R98KHJD19XX19c2qvf2e0FGAvHe7wtK3WQaAvLGysqLrly/rUCIROgoKA30rcEC6u/u0vFyjioqq0FGAvHe7obAvMLMFpfeuVkbL0hcnGDiU0XQAkGFjY2MafuIJlSeTOtneHjoOCgN9K3AAlpaW1Ne3qLa2B0NHAQrCLQtLdy8+qCAAEMLs0JA6ysrU2tgYOgoKBH0rcDBGR6eUSjWqrKw8dBSgIDDnMoCCNToyotjYmA5VV4eOAgDYZ6Oj06qpaQgdAygYFJYACtbItWu6u6ZGFeXszQaAfDIyMqLJSam2tj50FKBgUFgCKFjF7qqtYkIHAMgnyWRSV6+Oqr7+ZOgoQEHJaGFpZq8ys2tm1m1mP3WL9V5mZkkz+7ZM5gGAzZLJZOgIwB2jbwVurb9/UNPTpvr6ptBRgIKSscLSzIol/aGkV0t6QNIbzeyBbdb7VUmfzFQWALjZ3NycbGlJRUUM3EDuoG8Fbi2RSKi3d1qHD58OHQUoOJnconq5pG53v+Hu65I+KOm1W6z3XyR9RNJEBrMAwDPMTU+rrrxcZnb7lYHsQd8K3EJ3d5/m5ytVWcmkbMBBu911LPfiqKTBTbeHJL1i8wpmdlTS6yS9UtLLtnshM3uLpLdIUktLi86ePbvfWQvC0tISbbcLtNvuZXPbra2sqCSZ1OD4eOgoz7KUSOhsFubKdvFEInSEg0DfmkHZ/DfroORyG7i7FhdXVF5eqfHxve1TSSSWND5+dn+C5SjaIK3Q2yGV2vlpQ5ksLLc6DOA33f4dSW939+Stjhq4+0OSHpKk06dP+5kzZ/YpYmE5e/asaLs7R7vtXra23crKiq489pjulXSopiZ0nGc5Oz6uM21toWPknMGpqdARDgJ9awZl69+sg5TLbTAzM6OHHx5Se/vz9/xa4+Nn1dZ2Zu+hchhtkFbo7bC0NL/jdTNZWA5J6th0+5ikkZvWeamkD0YdX7Ok15hZwt0/msFcAAqYu+v6pUuqnppS7YkToeMAd4q+FdjGlSu9KipipxwQSiYLy3OS7jWzU5KGJb1B0nduXsHdT20sm9mfSfo4HR+ATOrv6lJiYEAPdnRwfiVyEX0rsIXx8XFNTUnHjh0NHQUoWBkrLN09YWZvU3pGumJJ73H3y2b21ujxd2XqvQFgKysrK5oeHdXdTU0qLi4OHQe4Y/StwLOlUil1dg6pupodhkBImTxiKXf/hKRP3HTflp2eu78pk1kAoKezU9VTU6pjCCxyGH0r8ExDQ8OanJTuuqs1dBSgoHEBNwAFwd21NjurU0eOsEcbAPJEMplUd/eEWlvvCR0FKHgUlgAKwtjoqGx+XmWlpaGjAAD2SU9Pv2ZnS1VTUxc6ClDwKCwB5D1318i1azpRV8fRSgDIE8lkUn19s2pruzt0FACisARQANxdtrqqpvr60FEAAPtkeXlZKytSZWV16CgARGEJoADEYjF5IhE6BgBgH1282KXi4ubQMQBEKCwB5L3eq1fVxBBYAMgbk5OTmpqS2to6QkcBEKGwBJD3itbX1dbQEDoGAGAfuLuuXBlQRcURFRWxKQtkC/43AshriURCq8vLbHwAQJ4YHR3V1JTU3Hw4dBQAm7ClBSBvubsmJiZUurqq8rKy0HEAAHuUTCZ17dqoGhpOhY4C4CYUlgDy1vT0tEYffVTt1cwYCAD5oK9vUNPTRaqrawwdBcBNKCwB5K1EIqHmykq11nHhbADIdYlEQn1902pruzd0FABboLAEkJfW1tY0fPmyKktLQ0cBAOyDnp5+zc9XqqqqJnQUAFugsASQl5LJpKoSCbXW14eOAgDYo/X1dfX2zqmtjXMrgWxFYQkAAICstri4qFSqQhUVlaGjANgGhSWAvBSPx5VMpULHAADsg87OPpnVh44B4BYoLAHkpfnpaVUlEqFjAAD2aHx8XJOTUkvLkdBRANwChSWAvLS2tqbaSoZMAUAuS6VS6uwcUk1Nh8wsdBwAt0BhCSDvzM3NaaGnR5Xl5aGjAAD2YGhoWJOTUmNja+goAG6DwhJA3kkmk2oqKVFNVVXoKACAXUomk+rpmVBrK9etBHIBhSWAvDM2OCgxcQ8A5LTe3gHNzJSppuZQ6CgAdoDCEkBeWVxc1OrIiI42NYWOAgDYpbW1Nd24MaO2trtCRwGwQxSWAPJKf1eXGhMJlZaUhI4CANilnp4+razUqrKyOnQUADtEYQkgbywvL2ttaEjH29tDRwEA7NLy8rJ6e5fU2noidBQAd4DCEkDeWF9fV0NZmYqLi0NHAQDsUmdnj9wbVFbGzN5ALqGwBJA3UkzYAwA5bX5+XiMjcY5WAjmIwhJAXkgkEuq7cEHlRfxZA4BcdelSt4qLWxl5AuQgtsAA5IVUKqWytTVmgwWAHDUxMaGpKam19WjoKAB2gcISQF7ovXZNqfX10DEAALvg7ursHFRl5TEVMfIEyEn8zwWQ85aXl7XU26vntLaGjgIA2IXh4WFNTkpNTW2howDYJQpLADkvkUiorqxMZaWloaMAAO5QMpnU9evjamq6K3QUAHtAYQkg581OTsoTidAxAAC70Ns7oJmZYh061BA6CoA9oLAEkPPisZiaq6tDxwAA3KF4PK6+vhkdPnxf6CgA9ojCEkBOc3ctLSww2QMA5KDu7j4tLlaroqIqdBQAe8SWGICcNjMzo9T4uA5xxBIAcsry8rL6+hbU1nYydBQA+4DCEkBOW19fV1tVlcwsdBQAwB3o6upTMtmg8vKK0FEA7AMKSwA5j6ISAHLL4uKiBgZW1dp6InQUAPuEwhJATpsYGFAxhSUA5JSLF7tUUtKi4uLi0FEA7BMKSwA5K5VKKbG0pIZDh0JHAQDs0OTklCYmXC0tx0JHAbCPKCwB5KylpSUVLy6qrLQ0dBQAwA51dvarsvIos3kDeYb/0QBy1vT4uKqKiznHEgByxMjIiCYnpaamttBRAOwzCksAOcsTCbXU1ISOAQDYgWQyqc7OUdXXn2SHIJCHKCwB5CR31+zEBEOpACBHDAwMana2SPX1TaGjAMgAtsgA5KRUKiUtL6uOI5YAkPVmZmbV1TWttrZ7Q0cBkCEUlgByFtPUA0D2SyaTeuqpG3I/oqoqdgYC+YrCEkDOSqVSoSMAAG6jv79fc3Nlam4+HDoKgAyisASQk/r7+6VYLHQMAMAtrK+vq6trVq2tp0JHAZBhFJYAcs7S0pJmL13Sfa2toaMAAG6ht3dIsVglQ2CBAkBhCSDnpFIpVZWWqqaqKnQUAMA21tbWNDExq/p6hsAChYDCEkDOmZmYUGkyGToGAOAWrlzp0vh4pWpq6kJHAXAAKCwB5JyF6Wk1VFaGjgEA2MbY2JiGh9fU0XEf1xsGCgT/0wHklHg8rvjMjBrr2AMOANkoFovpySeHVVNzl0pKSkLHAXBAKCwB5Ax318XHHlPl+rrMLHQcAMAWLl68rmSyQbW1DaGjADhAFJYAcsbKyop8akr3HzkSOgoAYAuzs7OanEyqtfV46CgADhiFJYCcMTM1pQqJ83UAIEtdvnxDRUVtDIEFChBbZwByxuLcnFqrq0PHAABsYXBwUJOTUlvbsdBRAARAYQkgZ8RGR1VfWxs6BgDgJtPTMzp/fkJNTfeGjgIgEApLADlhenpaWl1VKcOrACDrXLnSq5KSw6quPhQ6CoBAKCwBZL2VlRX1Xb6so1y7EgCyztjYmKampObmw6GjAAiIwhJAVltZWdGVRx9V7dSU2hobQ8cBAGySSqXU2Tms2trjXAYKKHAUlgCy2srKiqoWFnTvcTZaACDbPPzwU5qZKVZDQ0voKAACy2hhaWavMrNrZtZtZj+1xePfZWYXop+HzewFmcwDILe4uyZGR1VeWkpRCUToW5EthodHNDlpOnGCrxiADBaWZlYs6Q8lvVrSA5LeaGYP3LRar6SvcvfnS/plSQ9lKg+A3DMxPq7lq1d1pK4udBQgK9C3IlvE43FduzaqQ4cYTQIgLZNHLF8uqdvdb7j7uqQPSnrt5hXc/WF3n41uPiKJCx8BkJQ+Wjl07ZqO19Wporw8dBwgW9C3Ijh31+c/f0GLixWqr28OHQdAlsjkvP1HJQ1uuj0k6RW3WP8/SvrHrR4ws7dIeosktbS06OzZs/sUsbAsLS3RdrtAu+3eXtoumUxqfXFRi2Vl0vj4/gbLckuJhM4W2GfeD/FEInSEg0DfmkH8vd9ZG8TjcS0vJ1RWVqnx8Vuvm6sSiaW8/Ww7RRukFXo7pFLJHa+bycJyq3ERvuWKZl+tdOf35Vs97u4PKRrKc/r0aT9z5sw+RSwsZ8+eFW1352i33dtt2109f17LU1NqqqjQyZbCmxDi7Pi4zrS1hY6RcwanpkJHOAj0rRnE3/vbt8Ha2po+97lLOnTolOrq8nem7vHxs2prOxM6RlC0QVqht8PS0vyO181kYTkkqWPT7WOSRm5eycyeL+ndkl7t7tMZzAMgBywvL2u5v1/PbW5WOedWAjejb0UwqVRKjz56SbHYIR0+nL9FJYDdyeQ5luck3Wtmp8ysTNIbJH1s8wpmdlzS30j6Hne/nsEsAHLE3MyMaktKVFFezoQQwLPRtyKYoaERTU4Wqb39ntBRAGShjB2xdPeEmb1N0iclFUt6j7tfNrO3Ro+/S9LPS2qS9EfRBmTC3V+aqUwAst/qwoLqSjI5mALIXfStCGVlZUVdXeNqarqbnX4AtpTRrTd3/4SkT9x037s2Lf+ApB/IZAYAuWVlZUWNpaWhYwBZi74VBy2ZTOrRR69oba1RbW31oeMAyFKZHAoLAHckmUxqbWJCh6qrQ0cBAET6+gY0NWVqazsVOgqALEZhCSBrTE1NSSsrKi4uDh0FACBpZGREV67MqLX13tBRAGQ5CksAWSGVSmno6lWdqK8PHQUAoPQokqtXR1VVdVLV1bWh4wDIchSWALLC6PCwbHJSTVxiBACCc3c9/PDTmp8vU319U+g4AHIAhSWA4FKplMa6unRXczOzDQJAFhgaGtbkZJE6Oh4MHQVAjqCwBBDc9PS0yubnVV/LUCsACG16ekZXroyrvv4EO/sA7BiFJYDgUqmU6iorQ8cAgILn7rp0qVfSUdXVNYaOAyCHUFgCCG5ycFCl7BUHgKDcXcvLK5qbq1VTU1voOAByTEnoAAAK2+jQkNbm51VfUxM6CgAUtKGhISUSUkfHfaGjAMhBHLEEEEwqldLI5cs6ZabKiorQcQCgYE1MTOjChQmVlJSHjgIgR1FYAgim+9IlFS8tqZFLjABAMEtLS7p4cVAlJUdVVFQcOg6AHEVhCSCIVCqlxelp3dvGeTwAEEo8HtfIyIgWFw+pubk9dBwAOYzCEkAQI0ND0vS0qpkNFgCCOX/+qi5dWlFT0+HQUQDkOCbvAXDglpeXNd7To3taWkJHAYCCdeVKtwYH19XR8SIVFXGsAcDe8FcEwIFKpVK6eu6cGpeWVMdMsAAQxNjYmHp759Xe/iBFJYB9wRFLAAdqbW1NWljQqaNHQ0cBgII0MjKup54aVnX1XSorYxZYAPuDwhLAgUmlUurp7la1e+goAFCQFhYWdPHikKqr71ZtbX3oOADyCGMfAByYhYUFrV+/rruYCRYADlwikdC5c10qKjpMUQlg31FYAjgw8Xhch6qqVFZaGjoKABSUeDyuRx89r8XFajU2HgkdB0AeorAEcCDW1tY0cP26KsxCRwGAguLu+vznL2hqqlJHj94XOg6APEVhCeBATE5OqmJ4WEeamkJHAYCC4e66dOmqpqdLdPToc5gBFkDGMHkPgIxaXV3VzMyMxjs7daq5mY0aADggyWRSjz9+SUNDCR058qCMESMAMojCEkBGXX/iCZXOzOhkTY0aDx0KHQcACsb5850aHEyqo+MFKilhkw9AZvFXBsC+m5qY0Mj161pdXlZc0v0tLUzYAwAHqLu7X4ODazp+/IUqLi4OHQdAAaCwBLDvpicm1LiwoGlJz2tqoqgEgAOSSqXU2dmlrq4lNTffT1EJ4MBwshOAfbc0MqKWhgaZGUUlAByQVCqlS5c61dm5pPb256mysjp0JAAFhCOWAPbV7OystLqqcmZ/BYADk76kyNMaGzMdO/Y8lZaWhY4EoMBQWALYN0tLS7px7pyOVVWFjgIABaW3t1djY66Ojhcw/BVAEAyFBbBvZiYm1LC6qraGhtBRAKBgdHZ26+LFWbW2nqaoBBAMRywB7Ju1tTXVVVaGjgEABSF9TuV19fQsq63tuSovrwgdCUAB44glgH1x48YNLXR3q5rCEgAyLplM6uLFK7p+fVmHDz+PohJAcByxBLBn01NTmr14UacbGyksASDDpqdndOlSryYmSnT8+PNVUsLs2wDCo7AEsGd9nZ06UlGhGibtAYCMGh0d1YULI3I/rBMn2jinEkDWoLAEsCfz8/PS1JTaOzpCRwGAvDY0NKILF0ZVVXWXamuZJA1AdqGwBLBr7q7ZqSnVl5fLzELHAYC8lEgk1N8/rCtXpnTo0D2qqakLHQkAnoXCEsCuXT53Tmvj47qvjo0cAMiElZUVPf74FU1PF6mp6bSqqmpCRwKALVFYAtiVxYUFrc3P6/mtrSot4U8JAOy3WCymxx7r1OxsrTo67mVkCICsxtYggB1zd12/cEGp1VXFFhbUvLZGUQkA+8zd1dV1QzduzCkeb1BHxymKSgBZjy1CADuWTCa1NDys59TVyaqqVNnYGDoSAOSVWCymnp4+dXevqLX1AVVUcAknALmBwhLAjg309alibU1VFVyIGwD2k7urt3dQvb2Tmp+vVXv7c1Vezt9aALmDwhLAjkyOj2v22jWdbm4OHQUA8srq6qr6+vp19eqSGhru0YkTTIgGIPdQWALYViqV0uzsrCRpbHhYR0pKVFNVFTgVAOSP4eExXb8+rLm5SrW1cZQSQO6isASwrcH+fk09+aSaqqtV666mBi7IDQD7YWlpSU8+eU2zs1JNzUkdP94UOhIA7AmFJYAtJRIJzY+N6VRDgxq5TiUA7It4PK6BgQFduzYns0YdOXJCRUVFoWMBwJ5RWAJ4llQqpfOf+5xK5+dV09oaOg4A5Dx31+TktDo7+zU+Xqb29vtUXV0bOhYA7BsKSwDPMj8/r9LZWT2/oyN0FADIae6u+fl5Xb/er4mJhEpLj+rUqVaOUgLIOxSWAJ5ldXVV1eXloWMAQM6bnZ3VZz7Tq4qKRrW2HldxcXHoSACQERSWAJ5hampKI5cu6TiFJQDsSiwW09NPX1ciUax4fF1VVa1qbmYECID8RmEJQFJ6hsLh69e1ND6u46WlamHCHgC4I4uLi5qamtLVqzNKperU1HRcZWVSSUlp6GgAkHEUlkCBc3dNTU1p4MIFNcVi6mhsVFUF11EDgJ2KxWLq6hrS2Nii4vEG1dbeq5qaQ6FjAcCBorAEClgsFlNfb69WurrUceiQWo8cCR0JAHLCysqKZmZmdPnymNyltbUGNTber8rK6tDRACAICkugQC0vL+vqY4+pZmZGzz18WBWcUwkAtzU5OaWVlVV1dY1rfb1aVVV3q6amPnQsAAiOwhIoUH3XrqluYUH3nDwZOgoAZK1UKqVkMqmurn7Nz69pampVZWVHVFZ2l1pbG0LHA4CsQWEJFKBUKqXV+Xmdam4OHQUAslIsFtPKyoo6O/s0P29aXze1tNynlpZSlZaWhY4HAFmHwhIoMLFYTFfPnVPRzIwqT5wIHQcAssr1631KpVzDwzOKxWpVVtaqtrZjMrPQ0QAgq1FYAgVmfHxcVfPzuucYG0oAClcymdTq6qok6eLFbi0vpxSLxXTx4pwaGo6rpKRRR45w2SUA2CkKS6CArK6uaubKFd1TV6eSEv77Aygs8XhcU1NTkqSBgTGNjUnl5RVKJCrU0nKXVlfndeLEC9jpBgC7wJYlUEBmpqdVmkiorqYmdBQAyJhUKvWF5atXe7W6mpAkLS0taWysTHV1TZLadfhwq4qLi7+wrpkoKgFglygsgQKxsrKi0e5unaqtDR0FAPbN6urqF4a0StLU1LSuX59TUVGRJGl5OaXW1tOS0oXjqVNVX3gMALB/KCyBPBePxzV044ZmRkZUt7ioxqNHQ0cCgF1ZXl7W6OjUM44q9vdPanm56gsztbq76uqeo4qKKklSS0uQqABQcDJaWJrZqyT9rqRiSe9291+56XGLHn+NpJikN7n7k5nMBOSLRCKhCw8/rOJk8tbrJZMqX1jQ3Y2NqqeoBHJePvStqVRK8Xh828e7uwc0NRV7xjBVSVpdXdPi4iHV1tZ/4b6iolM6cqQxU1EBADuUscLSzIol/aGkr5M0JOmcmX3M3a9sWu3Vku6Nfl4h6X9Hv4GCND8/r7W1tR2tOzkxobKpKd2/g2KxuL6e84aAPJDNfWssFtPS0tKO1u3vH9XoaEJlZeVbPr62llJz870qKnpmYVldLTU0bP0cAEBYmTxi+XJJ3e5+Q5LM7IOSXitpc+f3Wknvc3eX9IiZ1ZvZYXcf3e5FU6mUnnjiiQzGzl+xWIy224UDbbfFRWlmZserV5SUqG92NoOB9mYtkVD35GToGDmHdtud+VgsdISDkJG+1d33/HdudVWamNjZuu5SRUWN0gddn628XFpcHNlTnt1IJNY0Odl94O+bTWiDNNqBNthQ6O2wurrzvjWTheVRSYObbg/p2XtMt1rnqKRndH5m9hZJb4lurr30pS+9tL9RC0azpKnQIXIQ7bZ7tN3u0G67dzp0gAyjb80s/u/RBhtoB9pgA+2ww741k4XlVuPufBfryN0fkvSQJJnZ4+7+0r3HKzy03e7QbrtH2+0O7bZ7ZvZ46AwZRt+aQbQDbbCBdqANNtAOO+9bMznf9pCkjk23j0m6eVzLTtYBAABp9K0AgKyUycLynKR7zeyUmZVJeoOkj920zsckfa+lfYmk+VudAwIAQIGjbwUAZKWMDYV194SZvU3SJ5U+O/897n7ZzN4aPf4uSZ9Qejr0bqWnRH/zDl76oQxFLgS03e7QbrtH2+0O7bZ7ed129K0ZRzvQBhtoB9pgA+2wwzaw9KRxAAAAAADsTiaHwgIAAAAACgCFJQAAAABgT3KqsDSzV5nZNTPrNrOfCp0nV5hZn5ldNLOnC2Aq/j0xs/eY2YSZXdp0X6OZ/ZOZdUW/G0JmzFbbtN07zGw4+u49bWavCZkxG5lZh5n9PzPrNLPLZvaj0f18727hFu3Gd26XzOyXzexC1G6fMrMjoTOFYGa/bmZXo7b4WzOrD53poJnZ66P/VykzK6jLLLCtuXV/Xmi262MKjZlVmNljZnY+aodfvOX6uXKOpZkVS7ou6euUnkr9nKQ3uvuVoMFygJn1SXqpuxf6xV1vy8y+UtKSpPe5+4PRfb8macbdfyXqZBrc/e0hc2ajbdruHZKW3P03QmbLZmZ2WNJhd3/SzGolPSHpWyS9SXzvtnWLdvt28Z3bFTM75O4L0fKPSHrA3d8aONaBM7Ovl/Qv0URJvypJhfZ/z8yeIykl6Y8l/Td3L4id0mxrpm3Vnxea7fqYAvwumKRqd18ys1JJ/ybpR939ka3Wz6Ujli+X1O3uN9x9XdIHJb02cCbkGXf/rKSZm+5+raT3RsvvVXrjFTfZpu1wG+4+6u5PRsuLkjolHRXfu1u6RbthlzaKyki1pNzY87zP3P1T7p6Ibj6i9HVAC4q7d7r7tdA5AmBbU/TnEn3MBk9bim6WRj/b9g25VFgelTS46faQCvAfeJdc0qfM7Akze0voMDmobeMacNHv1sB5cs3boiFl72E4562Z2UlJL5L0qPje7dhN7Sbxnds1M3unmQ1K+i5JPx86Txb4fkn/GDoEDgzbmniWLfqYgmJmxWb2tKQJSf/k7tu2Qy4VlrbFfQW5N3UX/p27v1jSqyX9cDTEATgI/1vS3ZJeKGlU0m8GTZPFzKxG0kck/dhNR45wC1u0G9+5WzCzT5vZpS1+XitJ7v6z7t4h6f2S3hY2bebcrh2idX5WUkLptsg7O2mDAsS2Jp6Bvlly96S7v1Dp0RsvN7Nth0eXHFiqvRuS1LHp9jFJI4Gy5BR3H4l+T5jZ3yo91OOzYVPllHEzO+zuo9GY+4nQgXKFu49vLJvZn0j6eMA4WSs6b+Ejkt7v7n8T3c337ja2aje+c7fm7l+7w1U/IOkfJP1CBuMEc7t2MLPvk/SNkr7Gc2Uyijt0B9+FQsK2Jr5gm765YLn7nJmdlfQqSVtO7JRLRyzPSbrXzE6ZWZmkN0j6WOBMWc/MqqOTjmVm1ZK+Xtt8GbCtj0n6vmj5+yT9XcAsOSUqiDa8Tnz3niU6Mf5PJXW6+29teojv3S1s125853bPzO7ddPObJV0NlSUkM3uVpLdL+mZ3j4XOgwPFtiYk3bJvLihm1rIxM7aZVUr6Wt2ib8iZWWElydLTxv+OpGJJ73H3d4ZNlP3M7C5JfxvdLJH0Adpte2b2l5LOSGqWNK703vqPSvqQpOOSBiS93t0L+qT2rWzTdmeUHpLokvok/dDGeYNIM7Mvl/Svki4qPQujJP2M0udy8L3bxi3a7Y3iO7crZvYRSaeVbs9+SW919+GwqQ6emXVLKpc0Hd31SKHNjmtmr5P0+5JaJM1Jetrd/33QUAeEbc2t+3N3/9OgoQ7Ydn2Mu38iXKqDZ2bPV3oCwWKlD0h+yN1/adv1c6mwBAAAAABkn1waCgsAAAAAyEIUlgAAAACAPaGwBAAAAADsCYUlAAAAAGBPKCwBAAAAAHtCYYmCZGZJM3vazC6Z2YfNrCpD7/NSM/u9aPmMmX3ZDp/3O2b2lZnItFtm9i1m9kAGXrfMzD5rZiX7/doAgDQzazOzD5jZDTN7wsw+H11WJNPv+4V+cI+v87dRv91tZvPR8tM77Vfv8L3KzezT0et/x36//g4zvMPM/ts29w9v2ob55gxmePdGv29mP5Op90H+oLBEoVpx9xe6+4OS1iXt6Bpld1r8uPvj7v4j0c0zkm7bAZpZo6QvcffP3sl7bfNa+1msfYukfS8s3X1d0j9LCtJ5A0C+iy72/lFJn3X3u9z9JZLeIOlYpt/7pn5wL6/zOnd/oaQfkPSvUR/+Qnd/WNr3/u5Fkkqj1/+rnTzBzIr38f1v57ejtni9pPeY2Y625+80o7v/gLtfiW5SWOK2KCyB9AVw7zGzRjP7qJldMLNHoovCbuwdfMjMPiXpfWZ2wsz+OVrvn83seLTe66O9h+fN7LPRfWfM7ONmdlLp4vW/RnsZv8LMes2sNFrvkJn1Rbe/TdL/3QgX3f+rZvZY9HNPdH+LmX3EzM5FP/9um7xt0Z7e89HPl0XrfXf0ek+b2R9vdDhmtmRm74zWfSR6/pdJ+mZJvx6tf7eZ/WD0vuejHFXR8++OnnfOzH7JzJY2fZb/Ht1/wcx+cdO/wUclfdc+/7sCANJeKWnd3d+1cYe797v770uSmZ00s381syejn41+4oyZfXzjOWb2B2b2pmj5V8zsSvT3/Dei+7btB6Pll5vZw2b2VPT7dHT/m8zsb8zs/5pZl5n92k4+VPS8D5vZ30v6lJnVRP3yk2Z20cxeu+nzdZrZn5jZZTP7lJlVRo/9yKbP8UEza5X0F5JeuKm/+5oo80Uze4+ZlUfP7TOznzezf5P0+uj2/7T00eDHzezFZvZJM+sxs7duyr1lX2hmP2tm18zs05JO3+7zu3unpISkZjN7Y5Tvkpn96qbXXIr64kclfamZ/Xi0ziUz+7FonWoz+4fo3+2SRUdpzeyspY84/4qkyqg93m9mv2xmP7rpPd5pZnveeYA84O788FNwP5KWot8lkv5O0n+S9PuSfiG6/5WSno6W3yHpCUmV0e2/l/R90fL3S/potHxR0tFouT76fUbSxze9zn/blOH/SPqWaPktkn4zWn6vpG/atF6fpJ+Nlr930+t9QNKXR8vHJXVuk/evJP1YtFwsqU7Sc6LPURrd/0eSvjda9o33l/Rrkn4uWv4zSd+2KVfTpuX/Iem/RMsfl/TGaPmtm9r66yU9JMmU3qn1cUlfuSnXZOjvBT/88MNPPv5I+hGlj3Jt93iVpIpo+V5Jj0fLX+jDott/IOlNkholXZNk0f310e/b9YOHJJVEy18r6SPR8psk3Yj6pwpJ/ZI6tsm6+fXeJGlIUmN0u0TSoWi5WVJ31OecVLoAe2H02IckfXe0PCKp/BaZKyQNSrovuv2+TX1qn6Sf3JStT9J/ipZ/W9IFSbWSWiRNRPdv2RdKeknUflVRO3Vr0zbDpvd4x8b9kl4R5T8qaSB6nxJJ/6Ivbl+4pG+Pljfeo1pSjaTLSh+d/Q+S/mTTe9RFv89Kemm0vLTp8ZOSnoyWiyT1aNM2AT+F+8MRSxSqSjN7WtLjSv8x/lNJXy7pzyXJ3f9FUpOZ1UXrf8zdV6LlL1W6qFO0/pdHy5+T9Gdm9oNKF0q3825Jb46W36x0oSlJhyVN3rTuX276/aXR8tdK+oPoc3xM0iEzq90i7ysl/e/ocyXdfV7S1yjdwZyLnv81ku6K1l9XuqOT0gXqyW3yPxjt4b6o9NHG50b3f6mkD0fLH9i0/tdHP09JelLS/UpvwMjdk5LWN+UHAGSImf1hdHTqXHRXqaQ/if6ef1i3P+1hQdKqpHeb2bdKikX3364frJP0YTO7pHTh9dxNj/2zu8+7+6qkK5JO7PDj/JO7z2x8NEn/08wuSPq00gVXW/RYr7s/HS1v7tsuSHq/mX230sXnzU5Hz70e3X6v0oXghpuHyn4s+n1R0qPuvujuk5JWzaxe2/eFXyHpb9095u4Lm15nK/816rt/Q+nTSF4q6ay7T7p7QtL7N2VMSvpItPzl0Xssu/uSpL+J3veipK+19Oior4i2E7bl7n2Sps3sRRufxd2nb/UcFAYmy0ChWvH0+QlfYGa2xXoe/V6+xWuldwm6v9XMXiHpGyQ9bWYvvMVz5O6fi4bnfJWkYne/tJFN6T2kW+XYvFwk6Us3FZAbn+N2eaV05/ted//pLR6Lu/vGeyS1/d+JP1N6j+j5aGjUmR285/9y9z/e5vFypTdUAAD767LSR6UkSe7+w2bWrPTOVUn6r5LGJb1A6b5l429xQs88baoien7CzF6u9E7JN0h6m6RX7qAf/GVJ/8/dX2fpU0TObnpsbdPyrfqem23u775L6aN2L3H3uJn16Yv96c2vXxktf4PSRdg3S/r/zGxzsSul+66dvv/m90nd9J4ppT/Tln1hNCx1c19/K7/t7r+x6bnfcot1V6Odt9I2n8Xdr5vZSyS9RtL/MrNPufsv3SbDu5U+Ytwu6T07zI08xxFL4Is+q+g8PzM7I2kq2mt4s4eV7kgVrf9v0XPudvdH3f3nJU1J6rjpeYtKD4nZ7H1KH4X8P5vu65R0z03rfcem35+Plj+ldGeu6P1fuM3n+melh/rKzIrN7FB037dF55LI0ueX3m7v8M35ayWNWvq80M3nRz6iL27AvGHT/Z+U9P1mVhO959FN79+k9FDY+G0yAADu3L9IqjCz/7Tpvs2zoddJGnX3lKTv0RePNvZLesDSs6TWKV1IKvo7Xufun5D0Y5JeGN1/u36wTtJwtPym/floz3r9iaio/Grd5qinpSe96XD3/yfpJyXVKz1EdLOrkk5aNL+B0u3zmT1k3K4v/Kyk15lZZTR655vu4DUflfRVZtZs6fkS3rhNxs9K+hYzqzKzakmvk/SvZnZEUszd/0Lpo6Av3uK58ai/3/C3kl4l6WXRZwIoLIFN3iHppdEQml+R9H3brPcjkt4crfc9kjZOYP/1jRPnlf7jff6m5/290p3G02b2FdF975fUoC8OdZWkf9Czj/6VRyfe/6jSe5Y3crw0Ovn/iraf2fZHJX11NMTpCUnP9fQsbz+n9GQHFyT9k9JDcG/lg5L+u6UnMLhb0v+ndGf2T0p3vBt+TNKPm9lj0WvOS5K7f0rpobGfj7L8tb5YqH61pE/c5v0BALsQjUL5FqWLj97o7/N7Jb09WuWPJH2fmT0i6T5FR+HcfVDp8xEvKN1fPRWtXyvp41H/8Rl9sV+6XT/4a0ofEfucdnbKyJ16v9L94uNK7/C8epv1iyX9RdQnPaX0kcC5zStEQ3PfrPQQ3otKH3l8180vtFPb9YXu/qTSw2qfVnro6r/ewWuOSvppSf9P6TZ/0t3/bov1nlR6tNFjSvff73b3pyQ9T9Jj0fDan1V63oSbPSTpgpm9P3qt9ej9PrTpiCgK3MZJ1wACMLNvk/Rad/+em+7/N0nf6O5z0VCel7r7VIiMd8rSs8OuuLub2RuUnsjntbd5zt9I+ml3v3YgIQEAwK5FR3uflPR6d+8KnQfZgXMsgUDM7PclvVrpcxpu9hNKz/Q6d5CZ9slLlJ5UyJTO//23WtnMypSeWZeiEgCALGdmDyg9yd/fUlRiM45YAgAAAAD2hHMsAQAAAAB7QmEJAAAAANgTCksAAAAAwJ5QWAIAAAAA9oTCEgAAAACwJ/8/pDuOJlKX+AYAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(121) # plot original sand and shale porosity histograms\n", "plt.hist(df[feature], facecolor='red',bins=np.linspace(vmin,vmax,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n", "plt.xlim([vmin,vmax]); plt.ylim([0,1.0])\n", "plt.xlabel(feature + '(' + feature_units + ')'); plt.ylabel('Frequency'); plt.title('Porosity')\n", "plt.grid(True)\n", "\n", "plt.subplot(122) \n", "plt.hist(df['N'+feature], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n", "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n", "plt.xlabel('Gaussian Transformed ' + feature); plt.ylabel('Frequency'); plt.title('Guassian Transformed ' + feature)\n", "plt.grid(True)\n", "\n", "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the normal score transform has correctly transformed the feature to standard normal.\n", "\n", "#### Inspection of Posted Data\n", "\n", "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n", "\n", "* This data visualization will also be important to assist with parameter selection for the variogram calculation search template.\n", "\n", "Let's plot the location maps of the original feature and the normal score transforms of the feature." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(121) # location map of normal score transform of porosity\n", "GSLIB.locmap_st(df,'X','Y',feature,0,1000,0,1000,vmin,vmax,feature,'X (m)','Y (m)',feature,cmap)\n", "\n", "plt.subplot(122) # location map of normal score transform of porosity\n", "GSLIB.locmap_st(df,'X','Y','N'+feature,0,1000,0,1000,-3,3,'Gaussian Transformed ' + feature,'X (m)','Y (m)','Gaussian Transformed ' + feature,cmap)\n", "\n", "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.5, hspace=0.3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do you see? Here's my observations:\n", "\n", "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n", "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n", "* suspect a 045 azimuth major direction of continuity (up - right)\n", "* there may be cycles in the 135 azimuth \n", "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n", "\n", "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit!\n", "\n", "#### Experimental Variograms\n", "\n", "We can use the location maps to help determine good variogram calculation parameters. For example:\n", "\n", "```p\n", "tmin = -9999.; tmax = 9999.; \n", "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n", "```\n", "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n", "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n", "* **nlag** is number of lags - set to extend just past 50 of the data extent\n", "* **bandh** is the horizontal band width - set to have no effect\n", "* **azi** is the azimuth - it has not effect since we set atol, the azimuth tolerance, to 90.0\n", "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n", "\n", "#### Dashboard for Interactive Variogram Calculation and Modeling\n", "\n", "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating and modeling experimental variograms.\n", "\n", "* allowing you to calculate and model the variogram of the normal score transformed variogram interactively while changing (and exploring) the search template parameters.\n", "\n", "* first calculate the isotropic or directional variogram(s)\n", "\n", "* then fit the same isotropic or directional variogram(s)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# interactive calculation of the experimental variogram\n", "l = widgets.Text(value=' Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "lag = widgets.FloatSlider(min = 10, max = 500, value = 10, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'))\n", "lag.style.handle_color = 'gray'\n", "\n", "lag_tol = widgets.FloatSlider(min = 5, max = 500, value = 5, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'))\n", "lag_tol.style.handle_color = 'gray'\n", "\n", "nlag = widgets.IntSlider(min = 1, max = 100, value = 100, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'))\n", "nlag.style.handle_color = 'gray'\n", "\n", "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n", "azi.style.handle_color = 'gray'\n", "\n", "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 10, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'))\n", "azi_tol.style.handle_color = 'gray'\n", "\n", "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n", "azi_tol.style.handle_color = 'gray'\n", "\n", "\n", "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting \n", "ui = widgets.VBox([l,ui1],)\n", "\n", "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth): # function to take parameters, calculate variogram and plot\n", "# text_trap = io.StringIO()\n", "# sys.stdout = text_trap\n", " global lags,gammas,npps,lags2,gammas2,npps2\n", " tmin = -9999.9; tmax = 9999.9\n", " lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n", " lags2, gammas2, npps2 = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi+90.0,azi_tol,bandwidth,isill=1.0)\n", " \n", " plt.subplot(111) # plot experimental variogram\n", " plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi), alpha = 0.8)\n", " plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi+90.0), alpha = 0.8)\n", " plt.plot([0,2000],[1.0,1.0],color = 'black')\n", " plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n", " plt.ylabel(r'$\\gamma \\bf(h)$')\n", " if azi_tol < 90.0:\n", " plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi) + ', Azi. Tol.' + str(azi_tol))\n", " else: \n", " plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n", " plt.xlim([0,1000]); plt.ylim([0,1.8])\n", " plt.legend(loc=\"lower right\")\n", " plt.grid(True)\n", " \n", " plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n", " plt.show()\n", " \n", " return\n", " \n", "# connect the function to make the samples and plot to the widgets \n", "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n", "interactive_plot.clear_output(wait = True) # reduce flickering by delaying plot updating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interactive Variogram Calculation Demonstration\n", "\n", "* calculate omnidirectional and direction experimental variograms \n", "\n", "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n", "\n", "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n", "\n", "### The Problem\n", "\n", "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data. Note, size of the experimental point is scaled by the number of pairs.\n", "\n", "* **azimuth** is the azimuth of the lag vector\n", "\n", "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n", "\n", "* **unit lag distance** the size of the bins in lag distance\n", "\n", "* **lag distance tolerance** - the allowable tolerance in lage distance\n", "\n", "* **number of lags** - number of lags in the experimental variogram\n", "\n", "* **bandwidth** - maximum departure from the lag vector" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "411cc95bfdcc42f785a4e9aa44e5c885", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Text(value=' Variogram Calculation Interactive Demonstration, Mich…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2e9ffe8d4e5241968d2c67a13fe84cc7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '
', 'i…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(ui, interactive_plot) # display the interactive plot" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n", "l = widgets.Text(value=' Variogram Modeling, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n", "nug = widgets.FloatSlider(min = 0, max = 1.0, value = 0.0, step = 0.1, description = r'$c_{nugget}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "nug.style.handle_color = 'gray'\n", "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n", " description=r'$Type_1$:',disabled=False,layout=Layout(width='200px', height='30px'))\n", "c1 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.1, description = r'$c_1$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "c1.style.handle_color = 'gray'\n", "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{1,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "hmaj1.style.handle_color = 'black'\n", "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 25.0, description = r'$a_{1,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "hmin1.style.handle_color = 'red'\n", "\n", "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n", " description=r'$Type_2$:',disabled=False,layout=Layout(width='200px', height='30px'))\n", "c2 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0, description = r'$c_2$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "c2.style.handle_color = 'gray'\n", "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{2,maj}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "hmaj2.style.handle_color = 'black'\n", "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = r'$a_{2,min}$',orientation='vertical',layout=Layout(width='60px', height='200px'))\n", "hmin2.style.handle_color = 'red'\n", "\n", "ui1 = widgets.HBox([nug,it1,c1,hmaj1,hmin1,it2,c2,hmaj2,hmin2],) # basic widget formatting \n", "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],) # basic widget formatting \n", "ui = widgets.VBox([l,ui1],)\n", "\n", "def convert_type(it):\n", " if it == 'Spherical': \n", " return 1\n", " elif it == 'Exponential':\n", " return 2\n", " else: \n", " return 3\n", "\n", "def f_make(nug,it1,c1, hmaj1, hmin1, it2, c2, hmaj2, hmin2): # function to take parameters, make sample and plot\n", " text_trap = io.StringIO()\n", " sys.stdout = text_trap\n", " \n", " it1 = convert_type(it1); it2 = convert_type(it2)\n", " if c2 > 0.0:\n", " nst = 2\n", " else:\n", " nst = 1\n", " \n", " vario = GSLIB.make_variogram(nug,nst,it1,c1,0.0,hmaj1,hmin1,it2,c2,0.0,hmaj2,hmin2) # make model object\n", " nlag = 100; xlag = 10; \n", " index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,0.0,vario) # project the model in the major azimuth # project the model in the 135 azimuth\n", " index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,90.0,vario) \n", "\n", " plt.subplot(111) # plot experimental variogram\n", " plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi.value), alpha = 0.8)\n", " plt.plot(h_maj,gam_maj,color = 'black')\n", " plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi.value+90.0), alpha = 0.8)\n", " plt.plot(h_min,gam_min,color = 'red')\n", " plt.plot([0,2000],[1.0,1.0],color = 'black')\n", " plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n", " plt.ylabel(r'$\\gamma \\bf(h)$')\n", " if azi_tol.value < 90.0:\n", " plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi.value) + ', Azi. Tol.' + str(azi_tol.value))\n", " else: \n", " plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n", " plt.xlim([0,1000]); plt.ylim([0,1.8])\n", " plt.legend(loc=\"lower right\")\n", " plt.grid(True)\n", " \n", " plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.5, wspace=0.3, hspace=0.3)\n", " plt.show()\n", " \n", "# connect the function to make the samples and plot to the widgets \n", "interactive_plot = widgets.interactive_output(f_make, {'nug':nug, 'it1':it1,'c1':c1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2})\n", "interactive_plot.clear_output(wait = True) # reduce flickering by delaying plot updating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interactive Nested Variogram Modeling Demostration\n", "\n", "* select the nested structures and their types, contributions and major and minor ranges \n", "\n", "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n", "\n", "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n", "\n", "### The Problem\n", "\n", "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n", "\n", "* **nug**: nugget effect\n", "\n", "* **c1 / c2**: contributions of the sill\n", "\n", "* **hmaj1 / hmaj2**: range in the major direction\n", "\n", "* **hmin1 / hmin2**: range in the minor direction" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "91ed7c97647f41e39aea22f7e005e303", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Text(value=' Variogram Modeling, Michael Pyrcz, Associate Professo…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e7a9d12d55c4f588d7d8548d65efdb3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '
', 'i…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(ui, interactive_plot) # display the interactive plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Comments\n", "\n", "This was a basic demonstration / exercise of variogram calculation and modeling for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n", " \n", "#### The Author:\n", "\n", "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n", "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n", "\n", "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n", "\n", "For more about Michael check out these links:\n", "\n", "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n", "\n", "#### Want to Work Together?\n", "\n", "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n", "\n", "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n", "\n", "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n", "\n", "* I can be reached at mpyrcz@austin.utexas.edu.\n", "\n", "I'm always happy to discuss,\n", "\n", "*Michael*\n", "\n", "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n", "\n", "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 2 }