{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 13 - Implementation of a Devito acoustic Least-square Reverse time migration\n", "\n", "## This tutorial is contributed by SENAI CIMATEC (2021)\n", "\n", "This tutorial is based on:\n", "\n", "
**LEAST-SQUARES REVERSE TIME MIGRATION (LSRTM) IN THE SHOT DOMAIN** (2016)\n", "
Antonio Edson Lima de Oliveira, Reynam da Cruz Pestana and Adriano Wagner Gomes dos Santos\n", "
Brazilian Journal of Geopyisics\n", "
http://dx.doi.org/10.22564/rbgf.v34i3.831\n", "\n", "
**Plane-wave least-squares reverse-time migration** (2013)\n", "
Wei Dai and Gerard T. Schuster\n", "
GEOPHYSICS Technical Papers \n", "
http://dx.doi.org/10.22564/rbgf.v34i3.831\n", "\n", "
**Two-point step size gradient method** (1988)\n", "
Barzilai, J. and Borwein, J. \n", "
IMA Journal of Numerical Analysis \n", "
https://doi.org/10.1093/imanum/8.1.141" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction \n", "The goal of this tutorial is to implement and validate the Least-squares reverse time migration (LSRTM) using a 2D three-layered velocity model with a square in the middle. The algorithm has been implemented using the Born's appoximation.\n", "\n", "The acoustic wave equation for constant density is:\n", "\n", "\\begin{equation}\n", " m_{0} \\dfrac{\\partial^2 p_0 }{\\partial t^2} - \\nabla^2 p_0 = s (\\mathbf{x},t) \\hspace{0.5cm} (1)\n", "\\end{equation}\n", "\n", "where $s (\\mathbf{x},t) $ is the source, $p_{0}$ is the background wavefield and $m_{0}$ is the smoothed squared slowness.\n", "\n", "A perturbation in the squared slowness $m = m_{0} + \\delta m$ produces a background wavefield ( $p_{0}$ ) and scattered wavefield ( $\\delta p$ ), so the wavefield $p$ is approximated to $ p = p_0 + \\delta p$, that obeys the relation:\n", "\n", "\\begin{equation}\n", " m \\dfrac{\\partial^2 p}{\\partial t^2} - \\nabla^2 p = s (\\mathbf{x},t) \\hspace{0.5cm} (2)\n", "\\end{equation}\n", "\n", "\n", "Using the approximation of $p$ and $m$ into equation (2),\n", "\n", "\\begin{equation}\n", " (m_{0} + \\delta m) \\dfrac{\\partial^2 (p_0 + \\delta p)}{\\partial t^2} - \\nabla^2 (p_0 + \\delta p) = s (\\mathbf{x},t) \\hspace{0.5cm} (3)\n", "\\end{equation}\n", "\n", "Expanding equation (3) we have:\n", "\\begin{equation}\n", "m_{0} \\dfrac{\\partial^2 p_0 }{\\partial t^2} - \\nabla^2 p_0 + m_{0} \\dfrac{\\partial^2 \\delta p }{\\partial t^2} - \\nabla^2\\delta p + \\delta m \\dfrac{\\partial^2 (p_{0} +\\delta p) }{\\partial t^2}= s (\\mathbf{x},t) \\hspace{0.5cm} (4)\n", "\\end{equation}\n", "\n", "Reordering the equation (4), \n", "\\begin{equation}\n", "m_{0} \\dfrac{\\partial^2 p_0 }{\\partial t^2} - \\nabla^2 p_0 + m_{0} \\dfrac{\\partial^2 \\delta p }{\\partial t^2} - \\nabla^2\\delta p = s (\\mathbf{x},t) - \\delta m \\dfrac{\\partial^2 (p_{0} +\\delta p) }{\\partial t^2} \\hspace{0.5cm} (5)\n", "\\end{equation}\n", "\n", "Considering that $ \\delta m \\dfrac{\\partial^2 (p_{0} +\\delta p) }{\\partial t^2} = \\delta m \\dfrac{\\partial^2 p }{\\partial t^2} \\approx \\delta m \\dfrac{\\partial^2 p_{0} }{\\partial t^2} $( Via Born's approximation $p\\approx p_{0}$ ):\n", "\n", "\\begin{equation}\n", "m_{0} \\dfrac{\\partial^2 p_0 }{\\partial t^2} - \\nabla^2 p_0 + m_{0} \\dfrac{\\partial^2 \\delta p }{\\partial t^2} - \\nabla^2\\delta p = s (\\mathbf{x},t) -\\delta m \\dfrac{\\partial^2 p_{0} }{\\partial t^2}\\hspace{0.5cm} (6)\n", "\\end{equation}\n", "\n", "Now we get an equations system:\n", "\n", "\\begin{equation}\n", " \\left\\{\n", "\\begin{array}{lcl}\n", "m_{0} \\dfrac{\\partial^2 p_0 }{\\partial t^2} - \\nabla^2 p_0 = s (\\mathbf{x},t),\\hspace{0.5cm} (7a) \\\\\n", "m_{0} \\dfrac{\\partial^2 \\delta p }{\\partial t^2} - \\nabla^2\\delta p = - \\delta m \\dfrac{\\partial^2 p_{0} }{\\partial t^2} \\hspace{0.5cm} (7b) \\\\\n", "\\end{array}\n", "\\right.\n", "\\end{equation}\n", "\n", "Equations (7a) and (7b) are for Born modelling. The adjoint modelling is defined by the equation:\n", " \n", "\\begin{equation}\n", "m_{0} \\dfrac{\\partial^2 v}{\\partial t^2} - \\nabla^2 v =\\textbf{d} \\hspace{0.5cm} (8)\n", "\\end{equation}\n", "where $\\textbf{d}$ is the shot recorded.\n", "\n", "With all these equations, the migrated image can be constructed\n", "\n", "\\begin{equation}\n", "\\mathbf{m}_{mig}= \\sum_{t} \\dfrac{\\partial^2 p_0}{\\partial t^2}v \\hspace{0.5cm} (9)\n", "\\end{equation}\n", "\n", "In this notebook the migration and gradient has been precoditioned by the source illumination, so equation (9) becomes:\n", "\n", "\\begin{equation}\n", "\\mathbf{m}_{mig}= \\frac{\\sum_{t} \\dfrac{\\partial^2 p_0}{\\partial t^2}v}{\\sum_{t} p_{0}^2} \\hspace{0.5cm} (10)\n", "\\end{equation}\n", "\n", "\n", "LSRTM aims to solve the reflectivity model $\\mathbf{m}$ by minimizing the difference between the forward modeled data $L\\mathbf{m}$ and the recorded data $d$ in a least-squares sense:\n", "\n", "\\begin{equation}\n", " f(\\mathbf{m}) = \\frac{1}{2}||L\\mathbf{m}-\\mathbf{d}||^{2} \\hspace{0.5cm} (11)\n", "\\end{equation}\n", "\n", "To solve equation (11), it has been implemented here the steepest descent method with an appropriate step-length,\n", "\n", "\\begin{equation}\n", "\\textbf{m}_{k+1} = \\textbf{m}_k -\\alpha_k \\textbf{g}_k \\hspace{0.5cm} (12)\n", "\\end{equation}\n", "where $\\textbf{g}_k$ is the gradient and $\\alpha_k$ is the step-length. The gradient computation is simply taking equation (9) and instead of injecting the shot recorded $\\textbf{d}$, injects the residue $\\textbf{d}_{calc}-\\textbf{d}_{obs}$\n", "\n", "For now we are going to import the utilities." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "\n", "from devito import Operator,Eq,solve,Grid,SparseFunction,norm\n", "from devito import TimeFunction,Function\n", "from devito import gaussian_smooth\n", "from devito import mmax\n", "\n", "from devito.logger import info\n", "\n", "from examples.seismic import Model\n", "from examples.seismic import plot_velocity,plot_shotrecord\n", "from examples.seismic import Receiver\n", "from examples.seismic import PointSource\n", "from examples.seismic import plot_image,AcquisitionGeometry\n", "from examples.seismic import TimeAxis\n", "\n", "from examples.seismic.self_adjoint import (setup_w_over_q)\n", "from examples.seismic.acoustic import AcousticWaveSolver\n", "\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import ImageGrid\n", "from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable\n", "import matplotlib.ticker as plticker\n", "\n", "from devito import configuration\n", "configuration['log-level'] = 'WARNING'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Seismic modelling with Devito" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's import all the parameters needed and create the true 2D velocity model and the smoothed model to perform the Born's modelling." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [], "source": [ "shape = (101, 101) # Number of grid point (nx, nz)\n", "spacing = (10., 10.) # Grid spacing in m. The domain size is now 1km by 1km\n", "origin = (0., 0.) # What is the location of the top left corner. This is necessary to define\n", "\n", "fpeak = 0.025# Source peak frequency is 25Hz (0.025 kHz)\n", "t0w = 1.0 / fpeak\n", "omega = 2.0 * np.pi * fpeak\n", "qmin = 0.1\n", "qmax = 100000\n", "npad=50\n", "dtype = np.float32\n", "\n", "nshots = 21\n", "nreceivers = 101\n", "t0 = 0.\n", "tn = 1000. # Simulation last 1 second (1000 ms)\n", "filter_sigma = (5, 5) # Filter's length\n", "\n", "v = np.empty(shape, dtype=dtype)\n", "\n", "# Define a velocity profile. The velocity is in km/s\n", "vp_top = 1.5\n", "\n", "v[:] = vp_top # Top velocity \n", "v[:, 30:65]= vp_top +0.5\n", "v[:, 65:101]= vp_top +1.5\n", "v[40:60, 35:55]= vp_top+1\n", "\n", "init_damp = lambda func, nbl: setup_w_over_q(func, omega, qmin, qmax, npad, sigma=0)\n", "model = Model(vp=v, origin=origin, shape=shape, spacing=spacing,\n", " space_order=8, bcs=init_damp,nbl=npad,dtype=dtype)\n", "model0 = Model(vp=v, origin=origin, shape=shape, spacing=spacing,\n", " space_order=8, bcs=init_damp,nbl=npad,dtype=dtype)\n", "\n", "dt = model.critical_dt \n", "s = model.grid.stepping_dim.spacing\n", "time_range = TimeAxis(start=t0, stop=tn, step=dt)\n", "nt=time_range.num" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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TmZl1Koa0w1AZSU+OiPtbLTe9gudMYP1BV2JIdTr4e3FdO4Mc5NKzs9Z/6InX+dTsems9RN56jL9fO5eqnUv9FC7A7Fzadq1c2jafmi1L0zayupC3zb9fmUvPrsqlbfNp2vR+PFW7Yp3xdQ+ts9746/XHX+fTuQCP/3V8XU1KN58GrzqwQjtzgLa6jXUu93cXgtVDGhUkHQKsHxEnZO+fDVwIPEXSb4BXNUrvFnmQBDMzmw7eC7lvxXAi8FfgA6RrnMe0srMh/Y5hZmYtG+KWJ7A12QRskp4E7AL8Q0T8RNL9wHGt7Gx4T5OZmbUkBKMjnSYkK48z0G8zGK/cS4AAFmXv7wA2bmVn0yt4juBrnoNQdcLodq55rjt+kW3d3HXOddcZf52/rpl/nd4/3HS7qtc888uLKt2qUpC/tln1mudDrFf3df567tprjb9+aK3cNU7g4dzISg/XXP/Mnfh2rnl2ev3Teif3KxgSq2d2GhbK/w4G7GZgH+BS4ADgfyJi7I9hM2BZKzubXsHTzMwaWj0ytN1tPw+cIelAYAPgdbl1u5Hu86zMwdPMzIZeRPyHpKXAi4CrI+Ly3Oq/AJXu7xzTUvCUtCm1A+reGhGTto0+wQwmpv2sO9q5VaVy2rbaCEH5VO0G/PWJ1/kU7Pq55cX3ZWnbtWs66JWPPpQfeagbt6qUjSRUM4pQ7taUYt3yr9cqGQlpwqhI69Sv26O5ge7XFG5vYWbulpb8z7BR2ta3qkweuUucgRpeSphqJH0C+GFEXA8QEb8AflHcLiI+1eq+m/7Lk7QAOJg0qO5WhdWrJF0N/Cfw3Yh4qFjezMymhkA1w0IOgbcAn5Z0C3AeKZD+shs7Lg2eWdD8PPBS4PfAj4DfMD4a/YbANqQm8PHA8ZI+B3whIopdCszMbApYPURX8yJie0nPBP4B2A/4V0n3kAaK/yHws3azp43O0mLgm8C7xpq8ZSTNySr2EVIS4NPtVKbnhNO2/VKlh23DtG39VO3cQtq2Sq/afGq2Udq2SgoXimnbaoPEV+ltW/zGv6pCD9t8j9pifdpJHdfUoSyFW3i/pqbHby6F207aNr98eP6HT265H9mwpW0BIuIGxht4mzIeSH8IrJR0Ufb6x1WmIhvT6NfzqVWHKspammcDZ0vapOrBzczM+iWLaScDJ0taj3Tryn7A14G1JS2KiD2r7Ks0eLYyxl+h3F/aKWdmZoM1jC3PMlkfnbOAsyTNAl5GCqSVtJUYySYPLVak0rASkrYEvgjsQUoYXAJ8ICKWtliHj5GGU7oiIl5SrRBOBXWqnfNXdZCEXKqW3M36s2te116eyPdwzQ/mvnbN69zA8BMGSaif3q2atl27pkdrecq0Stq2UW/bFbnUaKPUbP592b5Xlgy+UHxfkzbOnffR0dp6PlbzPre/xwpzheaV9cqt+vvlnrjdU/gx9Tp4Stof+CdgAWlUn6XAucC/tdLptK0YUCIiHgf+O3tUUulXVdLawKdIN5VuUadcVNmXpLmk0R1WAgdm5Y4FLpP0nIh4pGJ9tgWOAO6psr2ZmTXXp962h5EC5ieAO4HnAUcBu0naqUpDrJ0YkDX6DiXFsS2Z2AMmImLrqvur+j3va8CbSD2UzqL98ZcOAbYFnhERtwBIupY0bNI7SKPcV/F14EzgGbgtaWY2lbw6Iu7NvV8saRlwGrArqYHVTDsx4HPAh0h3jVxNh+MIVj3ovsBhEfGVTg6W7efKscAJEBG3SrqClGtuGjwlvRH4O1Kz/9yWju60bXd1OodnPk0LMHM8Fzcjd1P+zNzr2SMra4rUDlJQ//XckhQulI9h23g83PpzgDZO29bPM+ZvC2iUtq06AEN+H7Vj446/nlszyELtgAe1Y/XmXo/kBmyYObumTP5ntSb3M2RO7gffKIVblXvi9saE3ra9PbmFwDnm6ux582blO4gBbwY+3c6ACPVUPUsrgYa3q1S0A+lG1aIl1I4zWJekDUjXSz8SEcukLvxBmpnZEwbUYWiX7LnZbZGdxICZwOVNt6qo6twzp5JGoe/UhsDyOsuXkQbqbeYE4KasPpVIOlTSNZKuYXm9LzxmZgbjvW07eQAbjf3PzR6HNjqmpM1JE1FfEhHXNKliyzEg5xzSSHldUbXl+Ung65IuBi6iTgCMiG93q1L1SPp74K3A30VENNt+TEQsBBYCaP6CyuXMzKabYOJgHW24LyIWVNlQ0rqkbOQo8LYm27YVA3I+BJwpaSHlcazK9VagevB8Pul65cbAy+usD6BK8FxO/RZmWYs07xvAKcCdktbPls0ERrL3KyJiZUnZxNc8+6fq7Sk168avnY2UvC6O3JN/XzvaT/2Rf9Yq9BGYXXOrS/1B1YvXPNeu2W78emrVuT3L5K9RQrURgooptrKRiNYqOR/Feubflw0gn/95FN+vya8bzf2wiz/3suuXjW5B8d9ubwzo6ld2F8cFpE6ku0TEnU2KdBoDnpIdaz/SeO1jgnQWAqp/c6j663gycD+pt+wNtN9LaQnpumfRfOC6JmW3zx7vrLNuOfBB4Ett1svMzPrQYQggG5TgHNK9nntExO8rFOs0BnwH2Ah4P53FMaB68HwmsH9E/KSTg5HmS/u8pG0j4k8AkuYBOwMfa1J2tzrLvkT6pvBe4JY6683MrKJ+jDCU3W95JrA78KqIuLJi0U5jwALgrRFxTsXjNVQ1eN5I6TDRLfkm8B7gPElHkJrJnwbuIDXJAZC0NfBH4JiIOAYgIhYVdybpr8DMeuusD9qawzN3qWJmbY5uRk2qdjT3Ore8wYg6+ddrlaRqi2nKslRr2WuoTdWWze1ZrOeEeTMz+etLxdtZykclGj+hjdKuZZ9trZJtiscsfT0hbTte79Uzxz9PzW0rE35ZSgaQb/Q75VGF+qIPvW2/Srq74jPAI5J2zK27MyLu7FEMWEqHrc28qr1tPwYckX2gtmUjCO1O6i11Bunbx63A7hHxcG5Tkb5NVK2fmZl1qEu9bZvZO3s+HPhl4TF2LbIXMeBY4KNZJ6WOVW07HEHqLHSTpJuY2LknImKXicUmysawfW2TbW6jwmXsiNi1yjHNzGxyiIh5Fba5je7HgD1Jw8veJumX1I9jB1bdWdXguZp0gXXqc4+9oVKlR2pt+rE8NVqW9p1YZrRumdry1bJD+TIrG/S2rT3m+Ovqadf6da5at0nBf7s916exbQflJcAa4CHgWXXWt3T7S6VfR7fwzMymh370th2EiNimm/urlE+WtEWT9ZVStmZmNnn16ZrnQEh6UpP181vZX9WvGBdJ2jki/lrngH8P/AhYr5UD25Dr8MtrfjD4kZHy3rZ5VVKTxV6vjVK6ZWVmlpSZWZIyrap4nNUl+y7ruVusz0iFMlUHsK85TyOF85H7WbXVlbHqIAlmnfmRpJfXG0hB0vbAz0gDKVRStSfTw8CPJdXMfybpJcBPSPdvmpnZFDbMLU9gU+AsFUaTl/RM0jRoS1rZWdXguQ/wZOD72Q2uSNqJFDh/TJrqxczMprhRRjp6TGJ7AjtSO6bA00mB80bg1a3srGqHofsk7QVcAZySDax7IWlw3Te1OUivmZlNIv2Yz3NQIuJPkl4JLJL0F9Lk25eRBmPYJyJWNNxBQeWzFBG3SdobWAy8iTSg7wERMcn6s5uZWTv6MTzfIEXEbyT9Iylj+m7S/KF7ZQP4tKQ0eEr655JV55NGiLgYOHAsfdzrKcnMzMxaIWn3OouDNB/oP5LmB31RLo51ZUqybzUp+/VCZRw8zcymuCFreV7C+JRjY/Lvf5A9d3VKsq7eUGo2GXSjQ0N+H7XXh1blltcep3yQ9/L65Pfdab0neUcOmySGcIShejOxdEVp8IyI23t1UDMzm3yGrcNQRCzu1b6H5yyZmVnHhixt2zONOgz9Fjga+K8qt6JkQ/h9hDQf2+e6VkOblkZH6/8BV01zrq5JrTYqUz8Fm09drWStmjKzS9Kz+YHdiwPDV/mHVBwYvqxu+foUU2xl56B8v+Xly8oUlf2szAZN0vnApyLiNxW3nwP8C/BoRJzcaNtGLc/TSZNXnyTpe8DPgd8B9wIrgQ2AbYEXkm4u3YU0vNFJVSppZmaTyxDeqnIbcGXWGDwT+AVwbUQ8MRCkpM0Yj2P/CPwf8LZmO250zfNESaeQJid9O/B+Jk7ZIlIgPQ94WS/zy2Zm1lvDFjwj4n2Svgx8ADgKeBIQkh4kxa71gdmkWHZVtt13q4xf0PCaZ0Q8AHwB+IKkrUhDG20GzAHuJ83xeVW9gXbNumX16twfc+HvuuwPfbQkNbmqkILNp0pX1bwe3241tQOP5LcrG0i9HVXr1ujz5LdbWfp5xss36llZmvZdPTz/XG2iIettS0T8EXivpH8FXgy8iIlx7PJWO8m2MsLQUmBpKzs3MzObDCJiFWmEvK5kSN3b1szMgOG7VaWXfJbMzAwYvmueveTgaUOjym0nxX8M+WuBK0uuFz7K2jVlykcLan6bSCPFW2Jqr1+Or8vXp3h7S6vXOYutDP/jNP8OVOPgaWZmwFAOz9czVSfDNjMzs4xbntYdffpNmjgiTv2UbNntHI3SnCuY+8Tr/AhBM0vStMXjjNYcs/Y4VQaGL37jX1WSqs3XM/+6+L4sDV2Wzi2+rzpKU1flf4+6dxeQVTTMHYYkPT0iburW/iqfJUnbAq8HtiLdH5MXEfH2blXKzMwGY4ived4g6TLgZOCH+VGG2lEpeEr6B+B7pDTvPaSRGfKajn1rZmaT25D3tv1n4FDgbOAeSd8GvhkRt7azs6otz08Di4A3RcS97RzIhkQ7GZ2ZnX23Wp0beHx0pPYPu3YA97I0Zf3ULNSmZ/Ovy9KsxWPmU1wjudRqcWD4KiYODD++77K07UOsV1Pm0ZKUbv512ShExfejZT10uzEQfP53YlQl2zQo75Ruzwxr8IyIU4FTJT0HeAdpAPiPSLoE+DpwQUSsqbq/qh2GtgU+78BpZmZTWURcGxHvJg3R9w5gE+BcYKmkoyRtUmU/VYPnDcCT26qpmZlNCWO3qnTymELmAc/JnlcBfwA+BNwi6TXNCldNwn0E+JKkX0XEn9qr5yQg3L+4X8pStTO7m2+rHZhg/HVtD9v6r6E2zTm7Ytq2rCfv7FxXgBUNypdp1Nt2Vc2gDXPrvm60ruwcFAeWLzufHSv+3Edz+66Swp2wv86rZJncKR/m3rYAkmYDryO1OHcGbgeOB06JiPskbQAsBE4EfthoX40mw768sOjJwPWSbgaWFdZFROzS0qcwM7NJp9fXPCXtD/wTsADYmDThyLnAv0XEQw3KLSB1+Hkp6a6P+0jzTB9RpdOPpC8AbyXNRX0RsC/wk4h44ttbRCzPpjArxr8JGn3FWENtL9obm+3MzMysicNIAfMTwJ3A80hzbe4maacGnXYOAHYAvgIsATYHPglcI+m5EXFHk+O+Bfg2cHKTYHsDHU6GvWuzwmZmNjz6dKvKqwudTxdLWgacBuwKXFpS7rPFTquSrgBuBQ4Bjmxy3C2yackaioj7sro0VPU+z7cCP46I++us2xB4VUScXmVfAyU6vm3C2lB2nXNm69cFGw1kXj7CUP5WleIg76N1X5cdA2pv58jfkjJ7wu3P48pGKWrUwaJsZKTakYdqr3k+xLq5dfnbW8Zfl02sXXxfNtB+Zfmfb/H2lvzvRNn1T+uPwjXPXnf6Kblr4+rsefNWykXE7ZLubVQuZ4WkF0fEVcUVkp4PXBURlT981d623wGeWrJum2y9mZlNcauZ2dGjTWN9Zq5vpZCk7UnXTauUa9QbbYQWB/up+kkbHXQdfMuymdmUN4gRhiRtDhwDXBIR17RQbiZpqL17gVMabDeD8Rg2I3uftzawN6kDUmWNets+F/i73KJXS3pWnYMeANzcykEHRtH1WyWsgpL07IzC8pE20rh5+T/6fJpzds3tJLWXPPK3pJSlVouDvK/No0+8zt+SUtx37XHKUsLl319XlYz2Uzb4e3Hdigq3qkwc1aizf5xlP8PKw7ZY/6nrqfKNJOWD4MKIWFj30NK6wHmkBljTTjoFJwE7AftExPKS/X+K8WuhAVzRYH9fa+XgjVqe+wGfyh308JLt7gc8KLyZ2RTXpZbnfRGxoNlGktYGLiCNYLdLRNxZ9QCSjifdtnJgRFzcYNNFY0VIQfQUUg/fvJXAdcCPqh4fGgfPLwGnZgf9E/CPwG/qHPQv+ftkzMxs6upH2lbSLOAc0r2ee0TE71soezjwUeC9EXFGo20jYjGwOCsXwLci4q62K57T6FaVB4AHsoNuA9xdpZvvpKZgxpyp/RGmukap2ZFcSn1mbrt8mbL0ZyNlPW+L+xvJpTlrB39fUbq/fNq37HU79Sy+r01J15/nExr10C3vYVtFzXmaWVt+ZoV0e/HnXjO4fIfpeuvMmlzath+9bbNrjmcCu5Pu1LiyhbLvA44FDo+Ik1o5bkQc3VJFm6jUYSgibgeQtBvwYlK34LuAX0bEZd2skJmZDbWvkobI+wzwiKQdc+vujIg7JW0N/BE4JiKOAZB0ACkj+t/ApYVyD0bEdcUDZdOOfToibs1eN9LSvNRV7/PcEPg+sBvp2v9y0hBHyiYXfX1EFIfsMzOzKaRPY9vunT0fzsS+NEeTRhsS6faRfM/YvbLle2WPvMWkARaKdgO+nL3enca3o/TkVpWvAC8A3gx8PyIez3LWryf1UPoyaeijSU0z1jB7TvmN7NaaKum6RoqpvJr07Ej9VG2xR2yV9Gg+TVlMSdUOJl+b0q2yv9pUbS8Hva/fk7g4sHttena8/GhJCriR0p7II7V/QyPrjK9bvbr+vJ/dmAN0tBvziNoEK2fU9oXu9TXPiJhXYZvbKNwiGREHAQe1eKxtWjluK6oGz1cDH4+I/8hV5HHgzKxVemw3K2VmZv03iPs8p6qqwXM15fdy3pitNzOzKawfHYYGRdLbgK0j4qg6644Cbo2IpmPajqk6PN95wBtK1h0A/FfVA5qZmQ3A+0njEtRzD/CBVnZWteV5AfBFST8mdRz6C7AJ6ZrnDsD7Je0+tnFElI2KP1AjI2tY70kPD7oa007Va4EzK9z2UdzXWiUTWJftq6jseuhIg0HR81f8ao/T3W/sZXUruxY6cbv69Wk0qlLZhOD5895wcP6RXJ1zrwuXZgvlh3fy5algdKR4zXNofx7bkaYyq+d6ysdvr6vqWTone96S8Z5SeT/InkXqsTSc7X4zsyE25Nc8R4GNStb9Tas7qxo8d2t1x2ZmNrUMefC8Cngn8L06697J+LRolVQdJGFxKzudrEZYzfrUHT/YuqxskPVGytKrjdK2+eOUpRzXyiVai8fIz8FZlgKuquwWlnbKV1U8zloNBqcf0yilnT+Hq2oG1x8/T43StlWWNzKsnVUmswcLvwND/DP4DHCJpF8B3yIN9LM5cDBpEpQ9WtlZS8ltSRsBOwJPBi6IiGWS5gCrIsITJ5iZ2aQUEYsl7U8apegbuVW3Aa+NiEWt7K/qCEMCPge8F5hNuq75AmAZqSfuL4BPt3JgMzObXPo0wtDARMR5wHmSnkFqBN4XETe1s6+qZ+njwHtIE5b+FPhVbt0FpNGFKgVPSVsCXyQ1kQVcAnwgIpZWLL99Vo/dSBNxLwW+FhFfblgQmMXjbMI9VQ5jfVA1tVl1hKGy9G7VNGXVMmXHL6tzVY3+adX25B2vc6PetrUp2Ppl8nOTFutQNjB9o1GaygzxdbQp724ef+L1kF/zfEJE3NjpPqoGz4NJA/QeJ6l4Zm+hYhdfSXOBS0m9/Q8ktWCPBS6T9JyIeKRJ+QVZ+UVZnR4AngasW/FzmJlZA8McPCU9mzRP9S6k8dmXA5eRBo+vPC0aVA+emwNl08asIrUAqziENPHpMyLiFgBJ15JGL3oHcGJZwWwam9OBn0XEa3KrPKuLmZk1JOkFpAHkVwDnA38GNiUNP7uPpJdGxK+r7q9q8LwLeBb1A9XfArdW3M++wJVjgRMgmyrmCmA/GgRP0oj525OCbFtm8Tib8X/tFrcuaKcXauO0aWup1uLyKuXbSdt2W9Uere0MrNBq+ap1a2SYWzdTzZLpk7Y9DvgD8LKIeGhsoaT1SJcPjwNeUXVnVYfn+z5wpKSdc8tC0tOBfwXOqrifHUiVL1oCzG9S9iXZ8xxJV0p6XNI9kr4iae2GJc3MrKkgfWnq5DGJ7Qgclw+cANn7z5Lmqq6sasvzKGAn4HLg9mzZ90kjDv0PcHzF/WwIdW+0XEbKPzeyWfZ8NnAS8DFgAanz0JbAa+oVknQocCjA3K2eXLGaZmbT0VD3tm02X2f35/OMiBWSdgXeCOxJ6iR0P6mH7ZkR0d2JDOsbayV/NyKOzF4vyjowHS9p+4i4vlgoIhYCCwE2XbBZzOO2PlTV2lElBdqoF2vZwAzVU7D19111wIdup3CrpM8afdMv+ydY1ou26r6r/nMd4vTfUMkPgDHkadtfAZ+QdEkhbbsO8FHK+/XUVfkrRkSsBs7IHu1aTv0WZlmLNG9sNPyfFpZfTGr5Po80uK+ZmVnRJ0h3atwu6UfA3aQOQ68E5pL61VRWdZCEOaQU6VNITdu7gV9HxGOtHIx0bXOHOsvnA9dVKNuIRzgyM+vQsLY8I+IqSTsCR5IyqBuSLhl2/1YVSWuRRhY6hDSpkMbqATwm6evAJyKi+YCayfnA5yVtGxF/yo4xD9iZdA2zkQtJ94fuSRqYYcxe2fM1FetgZmZ1DPNk2AARcS2wfzf21azl+SNgd9IQfD8hjeYjUgedVwEfJLUaX1nxeN8kjVR0nqQjSEH408Ad5MYalLQ18EfSwAzHAETE/ZKOAz4p6UHSYAkLSN8iTsvf/lJmDivZjqabWZf18naOqrekjC9vfW7Rdo7fbVVbA1X+8bVz20kvWyPD2tKZKuZMuOY5tB2Guqr0LEl6HWkIvP0j4od1NvmWpNcCZ0v6x4g4t9nBIuKRbNLsL5KunQr4GWl4vvws1SLNCVq8leYY4CHgX4DDSOnjE/C4umZmXTFMX2YkfbuFzSMi3l5140ZfMf4J+F5J4Bw70g8kfR94E9A0eGZllgKvbbLNbYyniPPLgzSQQqPBFMzMzCBlTqvegtK1W1WeBxxRYR8/Io1PO+nN4TG2b9ovySabduYGzas+AH2nx+nuHVudps+qtCC60coY5mtk08Ecxvt9DtutKhExr1f7bvTX+Teka5zNLAU27k51zMxsUAKxes3wBM9eahQ850LuSnK5VcCc7lTHzMwGJmB0dHiDZzYgwtuBl5Lm8zw0Im6WdADw24i4oeq+muWFNpe0bZNttqh6sEGbwwrmO207ac1c3fueqyOj3T3GyGh/bi9ePbPqMNRV9tWff46jI8P7T3iYzKnURpr6srmkF5Fi1g2kyU7Wy1bvBrycNNVlJc2C5zlV6kSLF1rNzGzyiRCrR4f2VpUvkLKpTyfNFJYfn2AxaZ7Pyhqdpbe1XDUzM5uyUvAc2ozBHqQ07e3ZmOh5d5Hmra6sNHhGxGltVG5Sm/losOE1rY4oaFNSf8Yu6KNupocb7Kur/zcfb76JDdzMR3NvgmEOnrNJ4wTU8yRorbv80LbPzcysNRFi9PGhDZ7XksYY+O866/YGft3Kzhw8zcxsOjgBOEcSwH9ky+ZL2o/UA3ffVnbWvS58ZmY2xYk1q2d29Gh6BGl/ST+QdLukFZJulHScpPUqlJ0j6QRJd2dlfynppVU+WTaE7L8ArwMuyRafDnwAeE9E1GuRlppeLc9Hgd8OuhLWVUN3bXOSGtpMnlG45knvr3keRhpc5xPAnaTR7I4CdpO0U0Q0usB/CrAP8GHgT8C7gYskvTgiflvcOBvb9tSIuBwgIk6WdAbwYtLgPvcD/5OfHLuq6RU8zcysXKgfwfPVEXFv7v1iScuA00gTUl9ar5CkvwXeCPxzRHwnW7aYNNfzMdRPu74BOFDSUlIr8/SI+CPjLc+2OW1rZmZJAKPq7NHsELWBc8zV2XOj20X2JXXhPju3r1HgLGDPbP7pok1IAx/cRhqr/SZJV0g6RNKTmla2genV8nTa1sys1qPNN+mDXbLn6xtsswNwa0QUa7yEdBvKdtnrJ2RTXX4H+E42wtBbgDeT5o/+sqTzSS3ei5qkiydwy9PMzMaNdviAjSRdk3sc2uhwkjYnpV0viYhrGmy6IbC8zvJlufWlIuKOiPi3iJgP7Ah8mzRl2Y+AuyR9vlH5ounV8jQzs3JBi0MF1HVfRCyosqGkdYHzsqP2bVS7iLgKuErSB4HjgA9mj8Oq7mN6Bc/HaJwUMKtnMvfodS9Y61R+0LXuBM9KJK0NXABsC+wSEXc2KbIc2LrO8rEW57I668qOvR3wVlIKdx7wIPC9quVhugVPMzMbOEmzSBOPLAD2iIjfVyi2BHiNpLmF657zSYO839LkmBsAB5CC5gtJXxV+Srpl5r8ioqWxW33N08zMkiD1Z+3k0YSkGcCZpOuN/xARV1as3QXALNIgB2P7mkm6HeXiiJgwt5qkWZJeI+mHwN3AV0nTkH0M2DIi9o6Is1oNnDDdWp6PATcPuhJmZpNIMW3b+8sUXyUFwM8Aj0jaMbfuzoi4U9LWwB+BYyLiGICI+I2ks4EvZS3XW4F3AdsAbyo51l9Ig74vAxYCp0VES2PYlplewdPMzBrr/TXPvbPnw7NH3tGk0YZEuqJfzI6+jRR0jwXWB34H7BUR/1tyrMWkW1F+HBFdnebHwdPMzJI+dBiKiHkVtrmNFECLy1cAH8oeVY71mharV5mveZqZmbVoWrU8H38c/nLHoGthZjZ51OQy+3irylQ3rYKnmZk14OBZmYOnmZklDp6VTavguYo0tL6ZmSWrigscPCtxhyEzM7MWTauWp5mZNTA2wpA1Na2C5+Ok4SbMzCyZ0Nt2Mk+EMIlMq+BpZmYNuMNQZb7maWZm1qJp1fJcTQsTvpmZTQM1WVq3PCubVsHTzMwacPCszMHTzMzGOXhW4uBpZmaJW56VTavguRp4cNCVMDObRHxnSnumVfA0M7MG3PKszMHTzMwSjzBU2bQKnv5SZWZWK4pvnMetZFoFTzMza8ItjEo8wpCZmVmLplXLcw2wYtCVMDObRNbk3/jaVmXTKniamVkDDp6VOXiamVni3raV+ZqnmZlZi9zyNDOzxLeqVObgaWZm43zNsxIHTzMzS9xhqLJpFTx9LdzMrNaEEYb8T7ISdxgyMzNr0bRqeZqZWQPuMFRZ31uekraUdI6kByQ9KOlcSVtVLLuVpNMkLZW0QtJNko6VtE6v621mNvTGrnl28qhA0haS/l3SLyU9KikkzatY9smSvizpT1kcuFXSSZL+poVP2rG+tjwlzQUuBVYCB5J+VMcCl0l6TkQ80qDsOsAlwCzgk8BS4AXA0cDTgDf0tvZmZtNAfzoMbQe8Hvg18HPgFVUKSRJwPvB04EjgemA+cAywQNKLIyIa7KJr+p22PQTYFnhGRNwCIOla4GbgHcCJDcruTAqSe0bExdmyyyRtCBwmaW5EPNq7qpuZDbn+dRi6PCI2AZB0MBWDJykG7AS8IyIWZssWSVoDfJ0UVG/sdmXr6Xfadl/gyrHACRARtwJXAPs1KTs7e36wsPyvpM+hLtXRzMx6KCLWNN+qrkZxAPoY0/odPHcA/lBn+RJS07uRS0gt1M9Kmi9pXUm7A+8HTm6U8jUzswrGOgx18uitJcDlwCclLcjiwAtJKdwLI+L6ntcg0+/guSGwvM7yZcAGjQpGxGPAS0h1XgI8BPwM+BHwnu5W08xsGupTh6G2q5euZ76SlJq9mhQHfgX8CXhtb49ea8rcqiJpDnA2sDHwFlKHobFvHKPAu0rKHQocCvCkvtTUzGyK6s4IQxtJuib3fmHu+mQ3fBPYEXgnqcPQ9qSOo+dIenUHKeGW9Dt4Lqd+C7OsRZr3dmBXYLuI+GO27HJJDwALJZ0cEb8rFsp+aAsBNpP60gvLzGwauy8iFvRix5L2Af4JeHlE/CxbfLmkPwEXA68GzuvFsYv6nbZdQrruWTQfuK5J2WcDy3OBc8xV2fP2HdbNzGx6G+tt28mjt56dPV9dWN73ONDv4Hk+sKOkbccWZDfG7pyta+TPwAaStissf1H2fFe3KmlmNm1N7g5Df86eX1hY3vc40O/g+U3gNuA8SftJ2pfUxL4D+MbYRpK2ljQq6chc2VNJF4d/IulASbtJ+jDwedKNtlf06TOYmQ2nPnYYkrS/pP2B52eL9s6W7ZLbZlTSKbli5wL/B5wu6V1ZHHgXcDopjvyw1Y/crr5e84yIR7LbS74InEG6N/NnwAci4uHcpgJGyAX3iLhN0o7AUaRRiTYinayFwGf6dZHYzGxo9XdKsu8X3n8te15M6t8CKQ6MjG0QEQ/m4sBHgKcAdwMXAEcV4khP9b23bUQspUmX4oi4jTqDHkTEdaQhnczMbAqLiKYD29TbJiLuIHUgHagpc6uKmZn1mOfzrMzB08zMEk9JVpmDp5mZjevfNc8pre/zeZqZmU11bnmamVnS3962U5qDp5mZJe4wVJmDp5mZJe4wVJmDp5mZJU7bVuYOQ2ZmZi1yy9PMzMa55VmJg6eZmSXuMFSZg6eZmSXuMFSZg6eZmSXuMFSZOwyZmZm1yC1PMzNL3PKszMHTzMwSdxiqzMHTzMzGucNQJb7maWZm1iK3PM3MbFwMugJTg1ueZmZmLXLwNDMza5GDp5mZWYscPM3MzFrkDkNmZpbxjZ5VOXiamVnGQwxV5eBpZmYZtzyrcvA0M7OMW55VucOQmZlZi9zyNDOzjNO2VTl4mplZxsGzKgdPMzPL8TXPKnzN08zM+krSFpL+XdIvJT0qKSTNa6H85pK+LenPklZKulXScT2s8gRueZqZWaZvadvtgNcDvwZ+DryiasEsyF4B3Aq8D/gLMC/bZ984eJqZWaZvt6pcHhGbAEg6mBaCJ3AycBewW0SMRfrFXa5fUw6eZmaW6U/LMyLWtFNO0lOBPYG35gLnQPiap5mZZcZanp08emrn7HmFpJ9m1zuXSzpd0pN7ffA8B08zM+umjSRdk3sc2sV9b5Y9fxu4Cdgb+CiwD3CRpL7FNKdtzcws05W07X0RsaALlalnLDguioh3Z68vlfQAcBYppXthj45dtyJmZjbtTfq07f3Z808Lyy/Onp/X6wqMccvTzMwyk36EoSVN1rfVEakdbnmamVlm0rc8rwT+TErP5u2VPV/d6wqMccvTzMz6TtL+2cvnZ897S7oXuDciFmfbjAKnRcTbASJiVNLHgFMlnQycSxoc4TPAIuDSftXfwdPMzDJ9Tdt+v/D+a9nzYmDX7PVI9nhCRJwmaQ2pl+3bgGXAd4GPR0T0rLYFDp5mZpbTn4HhI0LtbhMRZwBndL1SLXDwNDOzzKTvMDRpuMOQmZlZi9zyNDOzjFueVTl4mplZpm+zqkx5Dp5mZpZxy7MqB08zM8u45VmVOwyZmZm1yC1PMzPLOG1bVd9bnpK2kPTvkn4p6VFJIWlexbIzJH1c0m2SHpP0O0mv7XGVzcymiUk/tu2kMYi07XbA64HlwM9bLPtp4CjgJNIkqFcC35f0ym5W0MxsehpreXbymB4Gkba9PCI2AZB0MPCKKoUkbQwcBhwfEZ/PFl8maTvgeOAnvaismdn04Q5DVfW95RkR7c63ticwmzQAcN53gWdL2qajipmZmVU0lToM7QCsBG4pLB+bHHU+cGtfa2RmNlTcYaiqqRQ8NwT+WmfKmWW59WZm1janbauaSsGzLZIOBQ7N3q48Gv4wyPpMAhsB9w26EgPmc+BzMMbnAZ4x/vLui+CojTrc37Q4n1MpeC4H1pekQutzrMW5rE4ZImIhsBBA0jURsaC31ZzcfA58DsDnYIzPQzoHY68jYq9B1mUqmUojDC0B1gKeWlg+P3u+rr/VMTOz6WoqBc//Jl3JflNh+ZuBP0SEOwuZmVlfDCRtK2n/7OXzs+e9Jd0L3BsRi7NtRoHTIuLtABFxj6QTgY9Legj4X+ANwO7AvhUPvbBbn2EK8znwOQCfgzE+Dz4HbdHEzqt9OKhUdtDFEbFrbpvTIuKgXLkR4OPAIcCmwI3AMRFxTk8rbGZmljOQ4GlmZjaVTaVrnnVJ2lLSOZIekPSgpHMlbVWx7BxJJ0i6W9KKbLD6l/a6zt3W7jmQtEDSQkk3ZIP0L5V05lQcramT34PCfj6WTVbwi17Us9c6PQ+Stpf0fUn3ZX8TN0p6fy/r3G0d/k/YStJp2d/CCkk3STpW0jq9rnc3eQKO3pvSwVPSXOBS4JnAgcBbgKeRxryt8st+CikFfCTwKuBu4CJJz+1JhXugw3NwAGnkpq+QBtr/GPB3wDWStuxZpbusC78HY/vZFjgCuKcX9ey1Ts+DpAXAr0i92g8GXgl8ARjpVZ27rZNzkK2/BHgp8EnS5/8W8K/At3tY7V7wBBy9FhFT9gG8H1gNbJdbtg1piIwPNSn7t6ThNN6WWzaTdB31/EF/tj6dg7+ps2xrYA3pWvLAP1+vz0FhPxcB3wAWAb8Y9Ofq8+/CDNLtXj8c9OcY4Dl4RfY/4RWF5cdn5ecO+vO1cB5m5F4fnH2ueRXKbUwaBvXowvKfAdcO+nNNpseUbnmSetleGRFPjHcb6ZaVK4D9KpR9HDg7V3YUOAvYU9Ja3a9uT7R9DiLi3jrLbgfuBTbvcj17qZPfAwAkvZHU6v54T2rYH52ch12B7YETe1a7/ujkHMzOnh8sLP8r6cuFulTHngtPwNFzUz147kD94faWMD54QqOyt0bEo3XKzialPaaCTs7BBJK2J337vL7DevVTR+dA0gbAF4GPRETdkaqmiE7Ow0uy5zmSrpT0uKR7JH1F0tpdrWVvdXIOLgFuBj4rab6kdSXtTmrNnhwRj3S3qpNSlQk4jKkfPDck5fSLlgEbdFB2bP1U0Mk5qCFpJnAyqeV5SudV65tOz8EJwE3AqV2s0yB0ch42y57PBi4G9gA+R0r5/Ue3KtgHbZ+DiHiM9CViBilYPERKV/4IeE93qzlpeQKOiqbS2LbWeycBOwH7RES9f0BDR9LfA28F/q7OP4zpZOyL9Hcj4sjs9aLs3urjJW0fEVMpG9EySXNIXx42JnU0Wgq8kNShcBR41+BqZ5PNVA+ey6n/bbLs22ex7NYlZaFkoPlJqJNz8ARJx5NmnzkwIi7uUt36pZNz8A1SK/tOSetny2YCI9n7FRGxskv17LVOzsP92fNPC8svJnWYeR5TI5XfyTl4O+na73YR8cds2eWSHgAWSjo5In7XtZpOTm1NwDEdTfW07RJSjr5oPs0Hil8CbJN1bS+WXcXEnP9k1ck5AEDS4cBHgfdFxBldrFu/dHIOtgfeSfqnMfbYGdgxez2VWhud/j000m4HlH7r5Bw8G1ieC5xjrsqet++wblOBJ+CoaKoHz/OBHbP78wDIbgTeOVvXyAXALOB1ubIzSePlXjyFWhudnAMkvQ84Fjg8Ik7qVSV7rJNzsFudx+9InU52A6bS0I+dnIcLSR1F9iwsH5ui6hqmhk7OwZ+BDSQVOwu+KHu+q1uVnMQ8AUdVg75XppMHsA6phfh7Ujf0fUn/+P4ErJvbbmvSNYsjC+XPIrUuDgZeRvpH+Rjp+tfAP1+vzwFpkIQ1pH+cOxYe8wf92fr1e1Bnf4uYmvd5dvr38Kls+b8BLycNmrECOHXQn60f5wCYR7pN5SbSAAu7AR/Oll1D7t7JqfAA9s8eXyfd5/mu7P0uuW1GgVMK5Y7P/g9+iJTG/nr2f+JVg/5Mk+kx8Ap04RdkK+AH2S/4Q8B/UbgZOPujCOCowvK1Sfe1/Tn7ZfkVsOugP1O/zgGpd2mUPBYN+nP16/egzr6mZPDs9DyQ7mP8UBZ8VgG3A8cAswb9ufp4DuYD3wPuIH1xuAn4PLDBoD9XG+eh6d929v7UQrkR0khbt5OyEdcC+w/680y2hweGNzMza9FUv+ZpZmbWdw6eZmZmLXLwNDMza5GDp5mZWYscPM3MzFrk4GlmZtYiB0+bFCSdLWmZpE0Ly0ckXS3p5sk0NZakeZJC0kG5ZQdJ+uc62x6UbTuvn3XMjj1D0m8lHZZbdlRWn56NbS3pA5J+L8n/Y2wo+RfbJov3km7Y/lph+WHA84GDI2JF32tV7m7gxcCPc8sOAiYEz2ybF2dl+u3NwFOYeF577RvA35BG6jEbOg6eNilExD3AB4HXSHodgKSnA0cB34iIxQOs3gQRsTIiroyIeytse2+27SDGSz4MOD0mTvreU9kXndOz45sNHQdPmzQi4nTSwNQnSdqINFXYvcBHmpXNpUZfKum/JD0s6X5JXy2meyU9RdLpku6TtFLStZLeXNhmU0mnSfq/bJu7Jf1I0sbZ+pq0raRFwC7AztnyyJbVTdtKmiXpWEm3SVqVPR8raVZum7FjvEPSMVkd/irpAklbVDgnLyLNFNJ0MmtJe2Xn7KQs1Tt27HdKOk7SnyU9JOm7kuZK2k7SRVmZWyTVa2GeBcyXtFOz45tNNVN9Pk8bPu8gTYv0K2Bb0sTcD7VQ/ruksUm/xvhExuuQUqpIWgdYTJrz8ROkMUzfDJwhaW5ELMz2cwZp8PAPZ9tsQpo8oDiF3Zh/yY49kn0GSGOrljkNeD1pEPZfkCYhPzz7zG8sbPtx4H9IKeGNgS9kx9q1wf4hzYjyEGlg9FKS3gp8CzgmIo7NluWPvYiUfp0PfI40SPjzgG+Sxn19F/AdSddERH5qs99mx98rq7/Z8Bj04Lp++FF8AMeRrn/+oIUyB2VlTi4sPxxYDTw9e/+ebLtdC9tdAtwDjGTvHybNb1p2vHnZfg7KLVtEnQHlc3Wbl71/FvUHJT8iW/6cwjEWFbY7LFu+WZNzciFwRZ3lR2XlZ5Ja9Y+TrinX+3yXFpafmy1/c27ZBqTZOT5V51g/J03xN/DfKz/86ObDaVubVCT9P+AtpH/QL5C0Xou7+F7h/VmkyxMvzN6/FLgrIhYVtvsuqYPL2KS/VwMflvR+Sc9WrinWBS/NHbNYB0jp37yfFN7/PnveqslxNiOlvct8ETiaNGPGt0q2ubDw/obs+aKxBRGxnPTFY8s65e/N6mE2VBw8bbI5gdSS2YeUojyuxfJ/KXm/efa8IfV7vf45tx7SpOjnk1pm1wJ3STqyS7dejB2jWI9iHcYsK7wf63g0p8lx5uS2reefSJN+X9Jgm+WF96saLK9XnxWkqf/MhoqDp00aknYFDgGOiIgLgWOBd7XY4WSTkvd3Zc/LgE2ZaNPceiLinoh4d0RsDjyTNPfp0Yxfz+zEWDAs1mPTwvpO3U/6IlLmZaTW64WS1u3SMYs2BO7r0b7NBsbB0yaFrEfsN0np0i9niz9L6jz0LUmzK+7q9YX3B5A6uPwqe78Y2ELSzoXt3khKPV5X3GFE3BgRnyC1tp7V4NgrqdbKujxXt7w3Zc+LKuyjihtIHZDKLCF1OnoavQug2wA39mC/ZgPl4GmTxTGk3q0HR8QagIh4HDgYeAap408Vr5R0gqQ9JB0OfIp0n+PN2fpTgZuBcyUdnN2icQawB/DJiFgt6UnZqEYfyNa/TNJXSK24ixsc+zrgWZLeIGmBpGfU2ygi/gD8J3CUpE9ldT2S1JHnPyPi9/XKteFy4KmSnly2QURcTwqgTwUuauMacylJ6wNPZ/zLgtnQ8K0qNnCSFpAGSPi3YuCIiKskfRn4mKTvRe2tEPW8GfhX0u0Tq0it2Sdu1I+IRyTtQrrl4nhgPVLL6C0RMdZh5zHgf0kp5K1JLdcbgTdFxHkNjv1ZUqD/FrAuqZW7a8m2BwF/It1+cgTwf1n5o5t8vlacR/osryLdGlNXRNyYnZPLgIsl7dml4+9D+hn8sEv7M5s0FBGDroNZx7LBCr4DPC0ibhlwdSYNSacCW0TEywdw7AuB+yLiLf0+tlmvueVpNtyOBq6XtCAirunXQSU9F9gd2KFfxzTrJ1/zNBtiEXErKUW8cZ8PvSlpAAlnAWwoOW1rZmbWIrc8zczMWuTgaWZm1iIHTzMzsxY5eJqZmbXIwdPMzKxFDp5mZmYt+v+gK6MSUR6RggAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "# Create initial model and smooth the boundaries\n", "gaussian_smooth(model0.vp, sigma=filter_sigma)\n", "# Plot the true and initial model\n", "plot_velocity(model)\n", "plot_velocity(model0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are going to set the source and receiver position." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# First, position source centrally in all dimensions, then set depth\n", "src_coordinates = np.empty((1, 2))\n", "src_coordinates[0, :] = np.array(model.domain_size) * .5\n", "src_coordinates[0, -1] = 30. \n", "\n", "# Define acquisition geometry: receivers\n", "\n", "# Initialize receivers for synthetic and imaging data\n", "rec_coordinates = np.empty((nreceivers, 2))\n", "rec_coordinates[:, 0] = np.linspace(0, model.domain_size[0], num=nreceivers)\n", "rec_coordinates[:, 1] = 30.\n", "\n", "# Geometry\n", "geometry = AcquisitionGeometry(model, rec_coordinates, src_coordinates, t0, tn, f0=fpeak, src_type='Ricker')\n", "\n", "solver = AcousticWaveSolver(model, geometry, space_order=8)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "source_locations = np.empty((nshots, 2), dtype=dtype)\n", "source_locations[:, 0] = np.linspace(0., 1000, num=nshots)\n", "source_locations[:, 1] = 30. # Depth is 30m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to compute the LSRTM gradient." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def lsrtm_gradient(dm):\n", " \n", " residual = Receiver(name='residual', grid=model.grid, time_range=geometry.time_axis,\n", " coordinates=geometry.rec_positions)\n", " \n", " d_obs = Receiver(name='d_obs', grid=model.grid,time_range=geometry.time_axis,\n", " coordinates=geometry.rec_positions)\n", "\n", " d_syn = Receiver(name='d_syn', grid=model.grid,time_range=geometry.time_axis,\n", " coordinates=geometry.rec_positions)\n", " \n", " grad_full = Function(name='grad_full', grid=model.grid)\n", " \n", " grad_illum = Function(name='grad_illum', grid=model.grid)\n", " \n", " src_illum = Function (name =\"src_illum\", grid = model.grid)\n", "\n", " # Using devito's reference of virtual source\n", " dm_true = (solver.model.vp.data**(-2) - model0.vp.data**(-2))\n", " \n", " objective = 0.\n", " for i in range(nshots):\n", " \n", " #Observed Data using Born's operator\n", " geometry.src_positions[0, :] = source_locations[i, :]\n", "\n", " _, u0, _ = solver.forward(vp=model0.vp, save=True)\n", " \n", " _, _, _,_ = solver.jacobian(dm_true, vp=model0.vp, rec = d_obs)\n", " \n", " #Calculated Data using Born's operator\n", " solver.jacobian(dm, vp=model0.vp, rec = d_syn)\n", " \n", " residual.data[:] = d_syn.data[:]- d_obs.data[:]\n", " \n", " grad_shot,_ = solver.gradient(rec=residual, u=u0, vp=model0.vp)\n", " \n", " src_illum_upd = Eq(src_illum, src_illum + u0**2)\n", " op_src = Operator([src_illum_upd])\n", " op_src.apply()\n", " \n", " grad_sum = Eq(grad_full, grad_full + grad_shot)\n", " op_grad = Operator([grad_sum])\n", " op_grad.apply()\n", " \n", " objective += .5*norm(residual)**2\n", " \n", " grad_f = Eq(grad_illum, grad_full/(src_illum+10**-9))\n", " op_gradf = Operator([grad_f])\n", " op_gradf.apply()\n", " \n", " return objective,grad_illum,d_obs,d_syn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the first LSRTM iteration, we used a quite simple step-length using the maximum value of the gradient. For the other LSRTM iterations we used the step-length proposed by Barzilai Borwein.\n", "\\begin{equation}\n", "\\alpha_{k}^{BB1} = \\frac{\\mathbf{s}_{k-1}^{T}\\mathbf{s}_{k-1}}{\\mathbf{s}_{k-1}^{T}\\mathbf{y}_{k-1}}\n", "\\end{equation}\n", "where $\\textbf{s}_{k-1} = \\textbf{m}_{k}-\\textbf{m}_{k-1}$ and $\\textbf{y}_{k-1} = \\textbf{g}_{k}-\\textbf{g}_{k-1}$\n", "\n", "A second option is:\n", "\\begin{equation}\n", "\\alpha_{k}^{BB2} = \\frac{\\mathbf{s}_{k-1}^{T}\\mathbf{y}_{k-1}}{\\mathbf{y}_{k-1}^{T}\\mathbf{y}_{k-1}}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", "\\alpha_{k} = \n", "\\begin{cases}\n", "\\alpha_{k}^{BB2},& \\text{if}\\ 0 < \\frac{\\alpha_{k}^{BB2}}{\\alpha_{k}^{BB1}} < 1 \\\\\n", "\\alpha_{k}^{BB1},& \\text{else}\n", "\\end{cases}\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_alfa(grad_iter,image_iter,niter_lsrtm):\n", " \n", " \n", " term1 = np.dot(image_iter.reshape(-1), image_iter.reshape(-1))\n", " \n", " term2 = np.dot(image_iter.reshape(-1), grad_iter.reshape(-1))\n", " \n", " term3 = np.dot(grad_iter.reshape(-1), grad_iter.reshape(-1))\n", " \n", " if niter_lsrtm == 0:\n", " \n", " alfa = .05 / mmax(grad_full)\n", " \n", " else:\n", " abb1 = term1 / term2\n", " \n", " abb2 = term2 / term3\n", " \n", " abb3 = abb2 / abb1\n", " \n", " if abb3 > 0 and abb3 < 1:\n", " alfa = abb2\n", " else:\n", " alfa = abb1\n", " \n", " return alfa " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now is the kernel of the LSRTM. The migration will be updated iteratively, using the step-length and the gradient." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LSRTM Iteration 1\n", "LSRTM Iteration 2\n", "LSRTM Iteration 3\n", "LSRTM Iteration 4\n", "LSRTM Iteration 5\n", "LSRTM Iteration 6\n", "LSRTM Iteration 7\n", "LSRTM Iteration 8\n", "LSRTM Iteration 9\n", "LSRTM Iteration 10\n", "LSRTM Iteration 11\n", "LSRTM Iteration 12\n", "LSRTM Iteration 13\n", "LSRTM Iteration 14\n", "LSRTM Iteration 15\n", "LSRTM Iteration 16\n", "LSRTM Iteration 17\n", "LSRTM Iteration 18\n", "LSRTM Iteration 19\n", "LSRTM Iteration 20\n" ] } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "image_up_dev = np.zeros((model0.vp.shape[0], model0.vp.shape[1]),dtype)\n", "\n", "image = np.zeros((model0.vp.shape[0], model0.vp.shape[1]))\n", "\n", "nrec=101\n", "niter=20 # number of iterations of the LSRTM\n", "history = np.zeros((niter, 1)) #objective function\n", "\n", "image_prev = np.zeros((model0.vp.shape[0],model0.vp.shape[1]))\n", " \n", "grad_prev = np.zeros((model0.vp.shape[0],model0.vp.shape[1]))\n", "\n", "yk = np.zeros((model0.vp.shape[0],model0.vp.shape[1]))\n", "\n", "sk = np.zeros((model0.vp.shape[0],model0.vp.shape[1]))\n", "\n", "for k in range(niter) :\n", " \n", " dm = image_up_dev # Reflectivity for Calculated data via Born\n", "\n", " print('LSRTM Iteration',k+1)\n", " \n", " objective,grad_full,d_obs,d_syn = lsrtm_gradient(dm)\n", " \n", " history[k] = objective\n", " \n", " yk = grad_full.data - grad_prev\n", " \n", " sk = image_up_dev - image_prev\n", "\n", " alfa = get_alfa(yk,sk,k)\n", " \n", " grad_prev = grad_full.data\n", "\n", " image_prev = image_up_dev\n", " \n", " image_up_dev = image_up_dev - alfa*grad_full.data\n", " \n", " if k == 0: # Saving the first migration using Born operator.\n", " \n", " image = image_up_dev" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_SKIP\n", "plt.figure()\n", "plt.plot(history)\n", "plt.xlabel('Iteration number')\n", "plt.ylabel('Misift value Phi')\n", "plt.title('Convergence')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def plot_image(data, vmin=None, vmax=None, colorbar=True, cmap=\"gray\"):\n", " \"\"\"\n", " Plot image data, such as RTM images or FWI gradients.\n", "\n", " Parameters\n", " ----------\n", " data : ndarray\n", " Image data to plot.\n", " cmap : str\n", " Choice of colormap. Defaults to gray scale for images as a\n", " seismic convention.\n", " \"\"\"\n", " domain_size = 1.e-3 * np.array(model.domain_size)\n", " extent = [model.origin[0], model.origin[0] + domain_size[0],\n", " model.origin[1] + domain_size[1], model.origin[1]]\n", " plot = plt.imshow(np.transpose(data),\n", " vmin=-.05,\n", " vmax=.05,\n", " cmap=cmap,extent=extent)\n", " \n", " plt.xlabel('X position (km)')\n", " plt.ylabel('Depth (km)')\n", "\n", " # Create aligned colorbar on the right\n", " if colorbar:\n", " ax = plt.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " plt.colorbar(plot, cax=cax)\n", " plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The image below is our first migration. The reflectors are not well focused and backscaterring noise is very strong." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "slices=tuple(slice(model.nbl,-model.nbl) for _ in range(2))\n", "rtm = image[slices]\n", "plot_image(np.diff(rtm, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So here we have the LSRTM migration after 20 iterations, it is clear that the reflector is well focused and backscaterring noise has been strongly attenuated." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_SKIP\n", "slices=tuple(slice(model.nbl,-model.nbl) for _ in range(2))\n", "lsrtm = image_up_dev[slices]\n", "plot_image(np.diff(lsrtm, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we have the true reflectivity. The idea in showing it is to demonstrate that the amplitude range of the LSRTM is in the same amplitude range of the true reflectivity." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "slices=tuple(slice(model.nbl,-model.nbl) for _ in range(2))\n", "dm_true = (solver.model.vp.data**(-2) - model0.vp.data**(-2))[slices]\n", "plot_image(np.diff(dm_true, axis=1))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_SKIP\n", "plt.figure(figsize=(8,9))\n", "x = np.linspace(0,1,101)\n", "plt.plot(rtm[50,:],x,color=plt.gray(),linewidth=2)\n", "plt.plot(lsrtm[50,:],x,'r',linewidth=2)\n", "plt.plot(dm_true[50,:],x, 'k--',linewidth=2)\n", "\n", "plt.legend(['Initial reflectivity', 'Reflectivity via LSRTM','True Reflectivity'],fontsize=15)\n", "plt.ylabel('Depth (Km)')\n", "plt.xlabel('Amplitude')\n", "plt.gca().invert_yaxis()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_SKIP\n", "time = np.linspace(t0, tn, nt)\n", "plt.figure(figsize=(8,9))\n", "plt.ylabel('Time (ms)')\n", "plt.xlabel('Amplitude')\n", "plt.plot(d_syn.data[:, 20],time, 'y', label='Calculated data (Last Iteration)')\n", "plt.plot(d_obs.data[:, 20],time, 'm', label='Observed data')\n", "plt.legend(loc=\"upper left\",fontsize=12)\n", "ax = plt.gca()\n", "ax.invert_yaxis()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }