{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 3 - Perfectly Matched Layer (PML)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.1 - Introduction\n", "\n", "In this notebook, we describe the *Perfectly Matched Layer* method, originally proposed by Berenger for electromagnetic waves, a scheme also employing an absorbing layer to reduce the wave reflections coming from the computational boundaries. This method is one of the most efficient schemes for this purpose. The formulation that we present here, designed for the wave equation in the form of a second order equation (instead of its formulation as a first order system), is proposed by Grote and Sim. \n", "\n", "We will use all the previous setups employed in the notebooks Introduction to Acoustic Problem and Damping, detailing only the new features specific to the\n", "PML method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.2 - Acoustic Problem with PML\n", "\n", "We again use an extended spatial domain $\\Omega=\\left[x_{I}-L_{x},x_{F}+L_{x}\\right] \\times\\left[z_{I},z_{F}+L_{z}\\right]$, with an absorption region as depicted in blue in the figure below.\n", "In the PML scheme, two auxiliary functions are included, which will provide adequate damping of the wave reflections. The design of the method is such that it would ideally suppress all the reflections in a continuous setting, but since we employ a finite difference discretization some reflections remain, although strongly attenuated.\n", "\n", "\n", "\n", "The set of equations for the acoustic wave equation with PML, including the auxiliary functions, is given by:\n", "\n", "\\begin{eqnarray}\n", " \\frac{\\partial^2 u(x,z,t)}{\\partial t^2}\n", " + (\\zeta_1(x,z)+\\zeta_2(x,z))\\frac{\\partial u(x,z,t)}{\\partial t}\n", "\t+ \\zeta_1(x,z) \\zeta_2(x,z) u(x,z,t)& = & \\\\\n", "\t c^2(x,z)\\Delta u(x,z,t) \n", "\t+\\frac{\\partial \\phi_1(x,z,t)}{\\partial x}+ \n", "\t\\frac{\\partial \\phi_2(x,z,t)}{\\partial z}+ f(x,z,t) & &\n", "\\end{eqnarray}\n", "\n", "\\begin{equation}\n", " \\frac{\\partial \\phi_1(x,z,t)}{\\partial t} =\n", " - \\zeta_1(x,z)\\phi_1(x,z,t)+c^2(x,z)(\\zeta_2(x,z)-\\zeta_1(x,z))\n", " \\frac{\\partial u(x,z,t)}{\\partial x}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", " \\frac{\\partial \\phi_2(x,z,t)}{\\partial t} =\n", " - \\zeta_2(x,z)\\phi_2(x,z,t)+c^2(x,z)(\\zeta_1(x,z)-\\zeta_2(x,z))\n", " \\frac{\\partial u(x,z,t)}{\\partial y}\n", "\\end{equation}\n", "\n", "where $u(x,z,t):D\\rightarrow \\mathbb{R}$ is the wave displacement, $f(x,z,t)$ is the source term, $c(x,z)$ is the wave speed and $\\phi_{1}(x,z)$ and $\\phi_{2}(x,z)$ are the auxiliary variables, which will be non zero only in the absorption region. The damping functions $\\zeta_1(x,z)$ and $\\zeta_2(x,z)$ will be defined as in the Damping notebook. The initial and outer boundary conditions for the displacement $u$ are the same as in the previous notebooks. The auxiliary functions will also be kept equal to zero in all the outer boundary of\n", "$\\Omega$. The Ricker source term is defined as before.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.3 - Finite Difference Operators and Discretization of Spatial and Temporal Domains\n", "\n", "We employ the same methods as in the previous notebooks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.4 - *Staggered* e *non-staggered* variables\n", "\n", "We consider the PML on the staggered grid. \n", "\n", "The staggered spatial domain is $\\Omega^{x,y}_\\Delta=\\{(x_{i+1/2},z_{j+1/2}, i=0,\\cdots,nx-1,\\ j=0,\\cdots,nz-1\\}, \\mbox{ with } x_{i+1/2}=(i+\\frac{1}{2})\\Delta x \\mbox{ and } z_{j+1/2}=(j+\\frac{1}{2})\\Delta z$. Some variables will also be staggered in time, being defined\n", "at intermediary time instantes $t_{k+1/2}=t_k+\\Delta t$.\n", "\n", "\n", "In the formulation of PML that is described here, we will define the variables as follows:\n", "\n", "- The variable $u(x,z,t)$ is a *non-staggered* variable;\n", "- The variables $\\phi_{1}(x,z,t)$ and $\\phi_{2}(x,z,t)$ are *staggered* variables;\n", "- The functions $\\zeta_{1}(x,z)$, $\\zeta_{2}(x,z)$, $c(x,z)$ and $f(x,z,t)$ will be used as *staggered* or *non-staggered* variables depending on the equation in which they are required.\n", "\n", "The direct implication of *staggered* and *non-staggered* variables appearing in the same equation is that:\n", "\n", "- When updating $u(x,z,t)$ we average the neighboring values of $\\phi_{1}(x,z,t)$ and $\\phi_{2}(x,z,t)$ in order to define them on the non-staggered grid. \n", "\n", "- When updating $\\phi_{1}(x,z,t)$ and $\\phi_{2}(x,z,t)$ we average the neighboring values of $u(x,z,t)$ in order to define them on the staggered grid.\n", "\n", "The calculation of these averages will be explicit when we write the equations in the Devito code." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.5 - Standard Problem\n", "\n", "Recall the Standard Problem definitions discussed on the notebook Introduction to Acoustic Problem we have that:\n", "\n", "- $x_{I}$ = 0.0 Km;\n", "- $x_{F}$ = 1.0 Km = 1000 m;\n", "- $z_{I}$ = 0.0 Km;\n", "- $z_{F}$ = 1.0 Km = 1000 m;\n", "\n", "The spatial discretization parameters are given by:\n", "- $\\Delta x$ = 0.01 km = 10m;\n", "- $\\Delta z$ = 0.01 km = 10m;\n", "\n", "Let's consider a $I$ the time domain with the following limitations:\n", "\n", "- $t_{I}$ = 0 s = 0 ms;\n", "- $t_{F}$ = 1 s = 1000 ms;\n", "\n", "The temporal discretization parameters are given by:\n", "\n", "- $\\Delta t$ $\\approx$ 0.0016 s = 1.6 ms;\n", "- $NT$ = 626.\n", "\n", "The source term, velocity model and positioning of receivers will be as in the previous notebooks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.6 - Damping Functions\n", "\n", "\n", "\n", "We choose the pair of functions $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$ as in the Damping notebook. They will act in the directions $x$ and $z$, respectively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.7 - Numerical Simulations\n", "\n", "For this notebook's numerical simulations, we use several parts the notebook codes Damping. We will highlight only the significant changes and comment on the most relevant operations since the computational structure of the acoustic equation with PML is very similar to the case of the acoustic equation with Damping.\n", "\n", "We import the following Python and Devito packages:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plot\n", "import math as mt\n", "import matplotlib.ticker as mticker \n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from matplotlib import cm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From Devito's library of examples we import the following structures:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "%matplotlib inline\n", "from examples.seismic import TimeAxis\n", "from examples.seismic import RickerSource\n", "from examples.seismic import Receiver\n", "from devito import SubDomain, Grid, NODE, TimeFunction, Function, Eq, solve, Operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mesh parameters that we choose define the domain $\\Omega_{0}$ plus the absorption region. For this, we use the following data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "nptx = 101\n", "nptz = 101\n", "x0 = 0.\n", "x1 = 1000. \n", "compx = x1-x0\n", "z0 = 0.\n", "z1 = 1000.\n", "compz = z1-z0;\n", "hxv = (x1-x0)/(nptx-1)\n", "hzv = (z1-z0)/(nptz-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of points of the absorption layer in the directions $x$ and $z$ are given, respectively, by:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "npmlx = 20\n", "npmlz = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lengths $L_{x}$ and $L_{z}$ are given, respectively, by:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lx = npmlx*hxv\n", "lz = npmlz*hzv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the *grid*:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "nptx = nptx + 2*npmlx\n", "nptz = nptz + 1*npmlz\n", "x0 = x0 - hxv*npmlx\n", "x1 = x1 + hxv*npmlx\n", "compx = x1-x0\n", "z0 = z0\n", "z1 = z1 + hzv*npmlz\n", "compz = z1-z0\n", "origin = (x0,z0)\n", "extent = (compx,compz)\n", "shape = (nptx,nptz)\n", "spacing = (hxv,hzv)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in the case of the Damping acoustic equation, we can here split the computations in the two subdomains:\n", "\n", "- In the blue region:\n", "\n", "\\begin{eqnarray}\n", " \\frac{\\partial^2 u(x,z,t)}{\\partial t^2}\n", " + (\\zeta_1(x,z)+\\zeta_2(x,z))\\frac{\\partial u(x,z,t)}{\\partial t}\n", "\t+ \\zeta_1(x,z) \\zeta_2(x,z) u(x,z,t)& = & \\\\\n", "\t c^2(x,z)\\Delta u(x,z,t) \n", "\t+\\frac{\\partial \\phi_1(x,z,t)}{\\partial x}+ \n", "\t\\frac{\\partial \\phi_2(x,z,t)}{\\partial z}+ f(x,z,t) & &\n", "\\end{eqnarray}\n", "\n", "\\begin{equation}\n", " \\frac{\\partial \\phi_1(x,z,t)}{\\partial t} =\n", " - \\zeta_1(x,z)\\phi_1(x,z,t)+c^2(x,z)(\\zeta_2(x,z)-\\zeta_1(x,z))\n", " \\frac{\\partial u(x,z,t)}{\\partial x}\n", "\\end{equation}\n", "\n", "\\begin{equation}\n", " \\frac{\\partial \\phi_2(x,z,t)}{\\partial t} =\n", " - \\zeta_2(x,z)\\phi_2(x,z,t)+c^2(x,z)(\\zeta_1(x,z)-\\zeta_2(x,z))\n", " \\frac{\\partial u(x,z,t)}{\\partial y}\n", "\\end{equation}\n", "\n", "- In the white region:\n", "\n", "\\begin{equation}\n", "u(x,z,t)_{tt}-c^2(x,z)\\Delta(u(x,z,t))=c^2(x,z)f(x,z,t).\n", "\\end{equation}\n", "\n", "We use the structure of the *subdomains* to represent the white region and the blue region.\n", "\n", "First, we define the white region, naming it as *d0" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class d0domain(SubDomain):\n", " name = 'd0'\n", " def define(self, dimensions):\n", " x, z = dimensions\n", " return {x: ('middle', npmlx, npmlx), z: ('middle', 0, npmlz)}\n", "d0_domain = d0domain()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The blue region is the union of 3 subdomains:\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class d1domain(SubDomain):\n", " name = 'd1'\n", " def define(self, dimensions):\n", " x, z = dimensions\n", " return {x: ('left',npmlx), z: z}\n", "d1_domain = d1domain()\n", "\n", "class d2domain(SubDomain):\n", " name = 'd2'\n", " def define(self, dimensions):\n", " x, z = dimensions\n", " return {x: ('right',npmlx), z: z}\n", "d2_domain = d2domain()\n", "\n", "class d3domain(SubDomain):\n", " name = 'd3'\n", " def define(self, dimensions):\n", " x, z = dimensions\n", " return {x: ('middle', npmlx, npmlx), z: ('right',npmlz)}\n", "d3_domain = d3domain()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure below represents the division of domains that we did previously:\n", "\n", "\n", "\n", "The *spatial grid* is then defined:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "grid = Grid(origin=origin, extent=extent, shape=shape, subdomains=(d0_domain,d1_domain,d2_domain,d3_domain), dtype=np.float64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The velocity field is needed in both staggered and non-staggered grids. As before we, read the file and interpolate it to the non-staggered grid. From these values, we interpolate to the staggered grid." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "v0 = np.zeros((nptx,nptz))\n", "v1 = np.zeros((nptx-1,nptz-1))\n", "X0 = np.linspace(x0,x1,nptx)\n", "Z0 = np.linspace(z0,z1,nptz)\n", " \n", "x10 = x0+lx\n", "x11 = x1-lx\n", " \n", "z10 = z0\n", "z11 = z1 - lz\n", "\n", "xm = 0.5*(x10+x11)\n", "zm = 0.5*(z10+z11)\n", " \n", "pxm = 0\n", "pzm = 0\n", " \n", "for i in range(0,nptx):\n", " if(X0[i]==xm): pxm = i\n", " \n", "for j in range(0,nptz):\n", " if(Z0[j]==zm): pzm = j\n", " \n", "p0 = 0 \n", "p1 = pzm\n", "p2 = nptz\n", "v0[0:nptx,p0:p1] = 1.5\n", "v0[0:nptx,p1:p2] = 2.5\n", "\n", "p0 = 0 \n", "p1 = pzm\n", "p2 = nptz-1\n", "v1[0:nptx-1,p0:p1] = 1.5\n", "v1[0:nptx-1,p1:p2] = 2.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Previously we introduced the local variables *x10,x11,z10,z11,xm,zm,pxm* and *pzm* that help us to create a specific velocity field, where we consider the whole domain (including the absorpion region). Below we include a routine to plot the velocity field." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def graph2dvel(vel):\n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscale = 1/10**(3)\n", " scale = np.amax(vel[npmlx:-npmlx,0:-npmlz])\n", " extent = [fscale*(x0+lx),fscale*(x1-lx), fscale*(z1-lz), fscale*(z0)]\n", " fig = plot.imshow(np.transpose(vel[npmlx:-npmlx,0:-npmlz]), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.title('Velocity Profile')\n", " plot.grid()\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " cbar.set_label('Velocity [km/s]')\n", " plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include the plot of velocity field." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2dvel(v0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then define the temporal properties:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "t0 = 0.\n", "tn = 1000. \n", "CFL = 0.4\n", "vmax = np.amax(v0) \n", "dtmax = np.float64((min(hxv,hzv)*CFL)/(vmax))\n", "ntmax = int((tn-t0)/dtmax)+1\n", "dt0 = np.float64((tn-t0)/ntmax)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "time_range = TimeAxis(start=t0,stop=tn,num=ntmax+1)\n", "nt = time_range.num - 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The symbolic values associated with the spatial and temporal grids that are used in the composition of the equations are given by:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "(hx,hz) = grid.spacing_map \n", "(x, z) = grid.dimensions \n", "t = grid.stepping_dim\n", "dt = grid.stepping_dim.spacing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the parameters for the Ricker source: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "f0 = 0.01\n", "nsource = 1\n", "xposf = 0.5*(compx-2*npmlx*hxv)\n", "zposf = hzv" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "src = RickerSource(name='src',grid=grid,f0=f0,npoint=nsource,time_range=time_range,staggered=NODE,dtype=np.float64)\n", "src.coordinates.data[:, 0] = xposf\n", "src.coordinates.data[:, 1] = zposf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include the plot of Ricker source." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "src.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the receivers: " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "nrec = nptx\n", "nxpos = np.linspace(x0,x1,nrec)\n", "nzpos = hzv" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "rec = Receiver(name='rec',grid=grid,npoint=nrec,time_range=time_range,staggered=NODE,dtype=np.float64)\n", "rec.coordinates.data[:, 0] = nxpos\n", "rec.coordinates.data[:, 1] = nzpos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The displacement field *u* is allocated " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "u = TimeFunction(name=\"u\",grid=grid,time_order=2,space_order=2,staggered=NODE,dtype=np.float64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The auxiliary functions $\\phi_{1}(x,z,t)$ and $\\phi_{2}(x,z,t)$ will be two fields of second order in time and space, which use points of type *staggered*. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "phi1 = TimeFunction(name=\"phi1\",grid=grid,time_order=2,space_order=2,staggered=(x,z),dtype=np.float64)\n", "phi2 = TimeFunction(name=\"phi2\",grid=grid,time_order=2,space_order=2,staggered=(x,z),dtype=np.float64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the velocity on the non-staggered grid" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "vel0 = Function(name=\"vel0\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n", "vel0.data[:,:] = v0[:,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and on the staggered one. Notice that the field has one less point in each direction." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "vel1 = Function(name=\"vel1\", grid=grid,space_order=2,staggered=(x,z),dtype=np.float64)\n", "vel1.data[0:nptx-1,0:nptz-1] = v1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Taking into account the dimension of the array *v1* and the dimension of the field *vel1* we will complete the line *nptx-1* with information from the line *nptx-2* and the column *nptz-1* with information from the column *nptz-2*, information from the *v1* array. This copy of information does not alter the properties of the *vel1* velocity field in view of its structure of constant profiles on the part. Copying information is done by the following sequence of commands:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "vel1.data[nptx-1,0:nptz-1] = vel1.data[nptx-2,0:nptz-1]\n", "vel1.data[0:nptx,nptz-1] = vel1.data[0:nptx,nptz-2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the source term and receivers" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "src_term = src.inject(field=u.forward,expr=src*dt**2*vel0**2)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "rec_term = rec.interpolate(expr=u)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to create the structures that reproduce the functions $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$ and then assign these functions to fields in *non-staggered* and *staggered* grids.\n", "\n", "We define the region $\\Omega_{0}$ by choosing the values of *x0pml* and *x1pml* in the direction $x$ and *z0pml* and *z1pml* in the direction $z$. These points satisfy the following relations with the lengths $L_{x}$ and $L_{z}$:\n", "\n", "- x0pml = x0 + $L_{x}$;\n", "- x1pml = x1 - $L_{x}$;\n", "- z0pml = z0;\n", "- z1pml = z1 - $L_{z}$;\n", "\n", "In terms of program variables, we have the following definitions:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "x0pml = x0 + npmlx*hxv \n", "x1pml = x1 - npmlx*hxv \n", "z0pml = z0 \n", "z1pml = z1 - npmlz*hzv " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having set the boundaries of $\\Omega$, we create a function *fdamp*, which represents $\\zeta_{1}(x,z)$ (when $i=1$) and $\\zeta_{2}(x,z)$ (when $i=2$). " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def fdamp(x,z,i):\n", " \n", " quibar = 0.05\n", " \n", " if(i==1):\n", " a = np.where(x<=x0pml,(np.abs(x-x0pml)/lx),np.where(x>=x1pml,(np.abs(x-x1pml)/lx),0.))\n", " fdamp = quibar*(a-(1./(2.*np.pi))*np.sin(2.*np.pi*a))\n", " if(i==2):\n", " a = np.where(z<=z0pml,(np.abs(z-z0pml)/lz),np.where(z>=z1pml,(np.abs(z-z1pml)/lz),0.))\n", " fdamp = quibar*(a-(1./(2.*np.pi))*np.sin(2.*np.pi*a))\n", " \n", " return fdamp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We created the damping function that represents $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$. We now define arrays with the damping function values on grid points (staggered and non-staggered): c\n", "\n", "- The arrays *D01* and *D02* are associated with points of type *staggered* and represent the functions $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$, respectively. \n", "\n", "- The arrays *D11* and *D12* are associated with points of type *non-staggered* and represent the functions $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$, respectively. \n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def generatemdamp():\n", " \n", " X0 = np.linspace(x0,x1,nptx) \n", " Z0 = np.linspace(z0,z1,nptz)\n", " X0grid,Z0grid = np.meshgrid(X0,Z0)\n", " X1 = np.linspace((x0+0.5*hxv),(x1-0.5*hxv),nptx-1)\n", " Z1 = np.linspace((z0+0.5*hzv),(z1-0.5*hzv),nptz-1)\n", " X1grid,Z1grid = np.meshgrid(X1,Z1)\n", " \n", " D01 = np.zeros((nptx,nptz))\n", " D02 = np.zeros((nptx,nptz))\n", " D11 = np.zeros((nptx,nptz))\n", " D12 = np.zeros((nptx,nptz))\n", " \n", " D01 = np.transpose(fdamp(X0grid,Z0grid,1))\n", " D02 = np.transpose(fdamp(X0grid,Z0grid,2))\n", " \n", " D11 = np.transpose(fdamp(X1grid,Z1grid,1))\n", " D12 = np.transpose(fdamp(X1grid,Z1grid,2))\n", " \n", " return D01, D02, D11, D12" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "D01, D02, D11, D12 = generatemdamp();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include a routine to plot the damping fields." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def graph2damp(D): \n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscale = 1/10**(-3)\n", " fscale = 10**(-3)\n", " scale = np.amax(D)\n", " extent = [fscale*x0,fscale*x1, fscale*z1, fscale*z0]\n", " fig = plot.imshow(np.transpose(D), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.title('Absorbing Layer Function')\n", " plot.grid()\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " cbar.set_label('Damping')\n", " plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include the plot of damping field in $x$ direction." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2damp(D01)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include the plot of damping field in $z$ direction." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RDXb8OpO2NfFTqmp4/c4H0W66+TjtjuufAicneR/t5pw/pP1PeRfgI33PKWnNqqofVNWLq2rLqtq4qjapqidW1dFVdef0I6zx/r2t27Tkl6vqsDE7QkrSmnJCkjuTXJPkM0m2nlK/b8y1A3AbbbnK0Xpw79jueOAttCVQ9wDeSdsE6oQe/Z8UP249tKHXapJsT1uGdaaNqRZNn1U1NgOuG1N+LW1ThLm2Hbw+VXexnA78AnjqmE8nDwReM5j8nuRK2sL4ewIrB/8DTvJY4PVJ1l8K/1OWJEkacQPwPuAc2ipLT6StXHNukidW1U9naNc35toMuH5MIuBe9ZI8nba06iurarAi0JlJrgWOT7JjVV0w4X1sRlsVaqb+rKDFdffSTS/5KC3jPLpD66LruxzduCxLerTLPNoOrAS+QVve6AVVtdogAzeN3DF6cfd85kiAfDHtPT+cdpPPvTuVHEC7s5ONNtro1x760IfNopvrlg03XJ877vCzxySO0WSOz3SO0WSOz3SO0WRXXHElVbffHZM8OlktXbvQrmoZ11uHio6pqmMGv1TVv9LW+x84J8lXabuTHkjLAI/TN+bqW2932v4AJ3fB7MAZ3fPOtDX41x9pe2cXlM81BvwwbUfZPapq3AeBRdUncL6O8ZnhFYz/ZDPsWtrSIuPaDl6fZmfaTl0HzxA0wz1bywJ3b0TBmP7d3j2PnZvTXbjHAGy11TZ1xRWP69G9ddNRR+3NIYcsiQUPlizHaDLHZzrHaDLHZzrHaJrr7/XbzbT12dakt8GtVbXTbNpU1beS/BD49QnV+sZc1wIrkmQk6zxabwvaFI+ZYq+HdM8/Ah45VL4fbT+Aa5k5foQxMWSSd9ISmK+sqjNGX18K+gTOF9HmqYxaScsCT2v7/CSbjMy5WUkLYkfn14zzMWBT2tcCqxZ6jUFJkiRoqdA+N38tkpkyuAN9Y66LaLuIPop7x2GDuc2D2O4aWmb86TOc78ru+Xnd8QYGay9fRNt9dtRK4PLRZGiSw2lL0R1YVZ+e4ZyLrs/1cSrw5CTbDQqSbEPbBnjaJgCn0tbs22eo7Qa0OTNnVNVtPc5fVfU62k19n+u2tJUkSVondJuE/DfgnyZU6xtzfYkWSL98pP0rgO8ObTryJdo39JtW1fljHlcCVNV3RsqvGerPI5I8Y6g/D6IF2veKH5McCBwJHF5VH+o1KIukT8b5r2nbtZ6S5C20Tztvp60BePfSTN2aez+i7TxzBEC3ve2JwNFJNqR9CnktsC2r/4NNVFUHJbkT+EyS9arqxKmNJEmSZmGxM85JTqDFS9+izSV5InAY8B/Ah7o6c465uu3sPwAcluTn3XleQtvWfniZuLOTfBb4fJL30+ZY3wVsQ1vb/tCq+uGEt3IqbafA45P8EW1qxmG0zPl7ht7vS4GjaYH6V5I8eegYN1bVtNkNa9XUwLmqbkqyK23L7U/T3vCXaVtuD6fZA6zP6tfcfrRdwI6kbf94IbB7VX1rtp2tqoOTrKIt0bJeVX12tseQJElawr4L/A5tt71NaGvbfwH4s6r6WVdnvjHX4bS5y2/gni23X1xVp43Ue0XXj/27NrfRVso4HfivSW+iqu5KsidwFPCXtOz1ucAuVfWToaq7d+9n9+4x7BzaJlRLRq9VNarqctpWiZPqXMaYOyWr6hbgTd1jVqpq3PEOpe2vPvh931m0PY42YV2SJOlelsIc56p6J2295El1LmMeMVe34tiR3WNSvbuAv+ges1ZV19KC7v0n1NkX2Hcux18MfZejkyRJWvYWO3DW0ub1IUmSJPVgxlmSJKljRlGTeH1IkiRJPZhxliRJYmncHKilzcBZkiSpY+CsSbw+JEmSpB7MOEuSJOFUDU3n9SFJkiT1YMZZkiSpY0ZRkxg4S5IkdVbbx1oa4gcrSZIkqQczzpIkSbRs8/qL3QktaWacJUmSpB7MOEuSJHXMKGoSrw9JkiSpBzPOkiRJuAGKpjNwliRJ6hg4axKvD0mSJKkHM86SJEkdM4qaxOtDkiRJ6sGMsyRJEt4cqOkMnCVJkjoGzprE60OSJEnqwYyzJEkSbapGFrsTWtLMOEuSJEk9mHGWJEnqrL/YHdCSZuAsSZKEq2poOq8PSZIkqQczzpIkSR0ziprE60OSJEnqwYyzJElSx4yiJjFwliRJwpsDNZ3XhyRJktSDGWdJkqSOGUVN4vUhSZIk9WDGWZIkCec4azqvD0mSJKkHM86SJEmdLHYHtKQZOEuSJHXWX+wOaElzqoYkSZLUgxlnSZIkvDlQ03l9SJIkST2YcZYkSeqYUdQkBs6SJEk4VUPTeX1IkiRJPZhxliRJ6phR1CReH5IkSVIPZpwlSZI6ZhQ1iYGzJEkS3hyo6bw+JEmSpB7MOEuSJHWy2B3QkmbGWZIkSerBjLMkSRIt27z+YndCS5oZZ0mSJKkHM86SJEkdM4qaxMBZkiSpY+CsSbw+JEmSpB7MOEuSJOEGKJrO60OSJEnqoVfgnGSrJJ9PckOSG5N8IcnWPdrtlOSYJBcnuTnJ5UlOSLJtz/NWkiP71JUkSZqv9dbwQ/dtU6dqJNkE+ApwG/BKoIAjgbOSPL6qbprQ/KXADsAHgYuARwB/CpyfZMeq+sk8+y9JkrQgnKqhafrMcX41sB3wmKq6BCDJt4F/A/4AeP+Etu+uqquHC5J8Hbi0O+5b59JpSZIkaW3r88FqL+C8QdAMUFWXAl8H9p7UcDRo7sp+DFxNyz7PSpJNkpyW5KokT+jKjktyRTct5BtJbknygyR7dK+/Kcll3RSTU5I8dLbnlSRJ6wanamiSPv+GOwDfHVN+EbBytidMsj2wBfD9WbbbDDgTeAzwlKq6cOjlBwGfAj4OPB/4KXBykvcBuwB/CBzU/fyR2fZZkiRJSlVNrpDcDry/qt48Un4k8Oaq6r2kXZINgC8D29Omflw3pX4B7wCOAU4HfgE8dziTneQ42tzrZ1TVV7uyxwMXAj8EVlbVnV35+4HXAxsPykbOdwBwAMDmm2/+a29+8zv7vrV1zpZbPpgrrrh+sbuxpDlGkzk+0zlGkzk+0zlGkx1yyCFUXZ/B7zskdeIaPufj4F+qaqc1fBqtIX2D3nHRdcaUTfNh4CnAHtOC5iErgW8A3wNeUFW/GFPnpkHQ3Lm4ez5zJEC+mPaeHw5cMXqQqjqGFqSz1Vbb1CGHnNKzi+ueo47aG8dnMsdoMsdnOsdoMsdnOsdodgKsv9id0JLWZ6rGdcBmY8pXdK/1kuSdtGzu/lV1Rt92wM60+dDHzhA0A1w//EtV3d79ONq/QfnGszi/JEmS1CvjfBFtnvOolbQs8FRJDgfeDBxYVZ/u3z0APgZsChyfZFVVnTzL9pIkSb14A58m6XN9nAo8Ocl2g4Ik2wBP7V6bKMmBtHWfD6+qD82hj1VVr6Pd1Pe5JPvM4RiSJEnSvPTJOP818DrglCRvoc13fjvwE1o2GIAkjwR+BBxRVUd0ZS8Fjga+BHwlyZOHjntjVfXKWANU1UFJ7gQ+k2S9qjU+f1+SJK1D3ABF00wNnKvqpiS7Ah8APk27rr4MHDQy53gwp374mtu9K9+9eww7B3jmbDpbVQcnWQWc0AXPn51Ne0mSJGmueq2qUVWXAy+cUucyRlbaqKp9gX3n1jWoqtVW7qiqQ4FDR87Rt+1xwHFz7Y8kSVrezDhrkt5rMEuSJC13Bs6axOtDkiRpCUvypSTVbT43re7GSd6b5KoktyQ5N8nOY+qtl+SwJJcluTXJhUkmzi6YY99XJPl4kp8luSnJmUkeN1JnpyTHJLk4yc1JLk9yQpJtF7o/82XgLEmSxD03B67Jx6z7lPwO8IRZNDkWeDXwVmBP4Crg9CQ7jtR7O/A22uZ0zwHOA05K8tw5dHOsJKGtwLY7befmFwIbAmcl2XKo6ktpSx9/sOvLm4EnAecn2Wqh+rMQnKohSZK0BCV5MG1xhjcCn+lR/wnAy2ibzX2iKzuHtifHEcBeXdkWwCHAu6rqqK75WUkeDbwL+OICvYW9gKcBu1bVWd25zwUuBf4YOLCr9+6qunrkvXy9qzf4ELAkmHGWJEnqLLGM83uAi2axithewB3A3Uv2VtUq4HPAbkk26op3A+4HHD/S/njgccNTJJJs0E3puDjJbUmuTPK+JH12Yd4LuHIQNHf9uQE4Ddh7qOzq0YZV9WPgatru0UuGgbMkSVIna/jRux/J04DfA/7XLJrtAFxaVTePlF9EC5QfPVTvNuCSMfWg7Q49cDzwFlrGew/gncCrgBN69ue7Y8ovArZO8oCZGibZHtgC+H6P86w1TtWQJElaQpJsSNtk7qiq+sEsmm4GXDem/Nqh1wfP11dVTaqX5OnAS4BXVtWnutfOTHItcHySHavqgin9uWxCf1YAvxh9MckGwEdpGedjJxx/rTNwliRJ4p6d3NawzZOcP/T7MVV1zEidQ4FfAt4xy2OHtsPzuPK51NsduB04uQtmB87onncGLkiy/kjbO7ugvO95Rn0YeAqwR1WN+yCwaAycJUmS1p6fVdVOM72YZGvgcOD3gY2G5iXT/f5g4OdVdeeY5tcCW48pXzH0+uB5RZKMZJ1H621Bm+KxWla485Du+UfAI4fK96NtOHct92S5x/VntaA4yTuBA2hZ7jNGX19sBs6SJEmdJXDz13bAxqx+4x60lTAOAZ4IXDDm9YuA5yfZZGSe80pa5viSoXobAY/i3vOcB3Obv9c9XwPcCjx9hr5e2T0/rzvewKVD53n2mHYrgcur6l4BeZLDaUvRHVhVn57hnIvKwFmSJIl71nFeZBcAu4wpP4sWTB/L6jf1DZwK/DmwD/BJuHu+8EuAM6rqtq7el2iB9Mu7+gOvAL5bVZcO1TsU2LSqvjxTh6vqOxP6s1+SZ1TVOV1/HkQLtO+1vF6SA4EjgcOr6kMznWuxGThLkiQtEVV1PXD2aHnbS4QfV9XZ3e+PpE2ROKKqjujaXpDkRODo7gbDS4HXAtvSguTBOX6a5APAYUl+DnyLFlzvyr2XiTs7yWeBzyd5P/BN4C5gG+C5wKFV9cMJb+dU4FzajYR/RJuacRjtM8p7ht7bS4GjaYH6V5I8eegYN1bV91giDJwlSZI6SyDj3NfgXsbRLu9Hu6nwSODBwIXA7lX1rZF6h9PmLr8BeBjwA+DFVXXaSL1X0Hb9279rcxttpYzTgf+a1MGquivJnsBRwF/SpqCcC+xSVT8Zqrp793527x7DzgGeOek8a5OBsyRJ0hJXVRn5/TLGrE5RVbcAb+oek453Jy24PnJKvbuAv+ges1ZV19KC7v0n1NkX2Hcux1/bDJwlSZJYMnOctYR5fUiSJEk9mHGWJEnqmFHUJAbOkiRJOFVD03l9SJIkST2YcZYkSeqYUdQkXh+SJElSD2acJUmSOmYUNYmBsyRJEt4cqOm8PiRJkqQezDhLkiR1zChqEq8PSZIkqQczzpIkSTjHWdMZOEuSJHUMnDWJ14ckSZLUgxlnSZKkTpI1e4KqNXt8rVFmnCVJkqQezDhLkiQBJLDBGg6N7rhjzR5fa5QZZ0mSJKkHM86SJEkDZpw1gYGzJEkSrJ2pGrpPc6qGJEmS1IMfqyRJksCMs6Yy4yxJkiT14McqSZIkMOOsqbw6JEmSwMBZUzlVQ5IkSerBj1WSJEkDZpw1gRlnSZIkqQc/VkmSJIFznDWVV4ckSRIYOGsqp2pIkiRJPfixSpIkCcw4ayozzpIkSVIPfqySJEkCM86ayoyzJEmS1IMfqyRJkgbMOGsCrw5JkiRwqoamcqqGJEmS1IMfqyRJksCMs6Yy4yxJkiT14McqSZIkMOOsqbw6JEmSwMBZUzlVQ5IkSerBj1WSJEkDZpw1Qa+Mc5Ktknw+yQ1JbkzyhSRbz/ZkSQ5LUkn+sWf9SnLkbM8jSZIkLbSpH6uSbAJ8BbgNeCVQwJHAWUkeX1U39TlRku2Aw4Gfzr27kiRJa4hznDVFn6vj1cB2wGOq6hKAJN8G/g34A+D9Pc/1V8AJwGN6nleSJGntMXDWFH2mauwFnDcImgGq6lLg68DefU6S5GXAk4DD5tLJoeNskuS0JFcleUJXdlySK5LslOQbSW5J8oMke3SvvynJZd0Uk1OSPHQ+fZAkSdK6qc/Hqh2AU8aUXwTsM61xkhXAB4A/rqprk8yuh/ccZzPg74DNgad0wfvAg4BPAUcBV9KmhJyc5CPAfwP+EPhl4GjgI8CL59QJSZK0fJlx1hR9ro7NgOvGlF8LrOjR/r3AD4Hj+nfr3robEU8HfgE8taquHqnyQOA1VfXVrv6VwIXAnsDKqrqzK38s8Pok6w/KJEmSpD76fqyqMWVTU8dJng78HvCkqhp3jD5WAt8Avge8oKp+MabOTYOguXNx93zmSIB8Me09Pxy4Ykx/DwAOANh888056qheM1HWSVtu+WDHZwrHaDLHZzrHaDLHZzrHaLJDDvnavQvMOGuKPlfHdbSs86gVjM9ED/sYcCxwRZIHD51z/e73W6rqtinH2Bl4CHDwDEEzwPXDv1TV7d2UkNH+3d49bzzuIFV1DHAMwFZbbVOHHDJuhooAjjpqbxyfyRyjyRyf6RyjyRyf6RwjaWH1CZwvos1zHrWSlgWeZPvu8Zoxr10HvJE273iSjwGbAscnWVVVJ0+pL0mSNHtmnDVFn6vjVOCoJNtV1b8DJNkGeCrw5iltdxlTdjSwPvB64JIxr4+qqnpdklXA55K8rKpO6tFOkiRpdgycNUGfq+OvgdcBpyR5C22+89uBn9CywQAkeSTwI+CIqjoCoKrOHj1YkuuBDca9NklVHZTkTuAzSdarqhNn016SJEmaj6mBc1XdlGRX2pJyn6bdFPhl4KCROcehZZJ7beM9F1V1cJd5PqELnj+7ps4lSZLWMU7V0BS9ro6quhx44ZQ6l9FjpY2qemafc3Z1VzteVR0KHDr0+76zaHsc81gWT5IkSesuP1ZJkiSBGWdN5dUhSZIEBs6aao3NR5YkSZKWEz9WSZIkgRlnTWXGWZIkSerBj1WSJEkDZpw1gVeHJEkSOFVDUzlVQ5IkSerBj1WSJElgxllTmXGWJEmSevBjlSRJEphx1lReHZIkSWDgrKmcqiFJkiT14McqSZKkATPOmsCMsyRJktSDH6skSZLAOc6ayoyzJEnSEpFktyRfSfKfSW5LckWSv0myskfbjZO8N8lVSW5Jcm6SncfUWy/JYUkuS3JrkguTvHANvJcVST6e5GdJbkpyZpLHjdTZKckxSS5OcnOSy5OckGTbhe7PQjBwliRJgnsyzmvyMd1mwL8ArwOeDRwG7ACcl+SRU9oeC7waeCuwJ3AVcHqSHUfqvR14G/Bh4DnAecBJSZ7bp4N9JAlwKrA78HrghcCGwFlJthyq+lLa+/tg15c3A08Czk+y1UL1Z6H4fYQkSRIsiakaVfVZ4LPDZUm+CVwMvAh437h2SZ4AvAzYv6o+0ZWdA1wEHAHs1ZVtARwCvKuqjuqan5Xk0cC7gC8u0FvZC3gasGtVndWd+1zgUuCPgQO7eu+uqqtH3svXu3qDDwFLhhlnSZKkpe2a7vmOCXX26l4/cVBQVauAzwG7JdmoK94NuB9w/Ej744HHDU+RSLJBN6Xj4m7ayJVJ3pdk4x593gu4chA0d/25ATgN2Huo7OrRhlX1Y+Bq4BE9zrNWmXGWJEmCJZFxHkiyPrA+8EhaJvg/aUHwTHYALq2qm0fKL6IFyo/uft4BuA24ZEw9gJW0bC+0YPp5wLuBbwDb06Z5bEObejHJDsB3x5RfBPxekgdU1S/GNUyyPbAF8P0p51jrlsbVIUmSpGH/BPxa9/MltCkPP51QfzPgujHl1w69Pni+vqpqUr0kTwdeAryyqj7VvXZmkmuB45PsWFUXTOnPZRP6swJYLXBOsgHwUVrG+dgJx18UBs6SJEkDaz7jvHmS84d+P6aqjhlT73eBBwHb0eYk/0OSp1XVZTMcN8BoMDwon0u93YHbgZO7YHbgjO55Z+CCLjM+3PbOLijve55RHwaeAuxRVeM+CCwqA2dJkiRYW1M1flZVO02rVFWDaQr/lOT/0bK3bwZeM0OTa4Gtx5SvGHp98LwiSUayzqP1tqBN8Rg7nQJ4SPf8I9p0koH9gOO642zG6gbnWS0oTvJO4ABalvuM0deXAgNnSZKkJayqrk9yCW2e8kwuAp6fZJORec4raZnjS4bqbQQ8invPcx6sE/297vka4Fbg6TOc78ru+Xnd8QYG86Mvoi2nN2olcPno/OYkh9M+GBxYVZ+e4ZyLzlU1JEmSYKms4zymW/ll4L/TsrszOZW2TvI+Q+02oM1TPqOqbuuKv0QLpF8+0v4VwHer6tKhehsDm1bV+WMeVwJU1XdGygcrgJwKPCLJM4b68yBaoH3qyPs7EDgSOLyqPtRrUBaJGWdJkqQlIsnfAt8Cvg3cCPw34I3AKro1nLuNUH4EHFFVRwBU1QVJTgSOTrIhLfP7WmBbhoLkqvppkg8AhyX5eXeulwC7cu9l4s5O8lng80neD3wTuIu2osZzgUOr6ocT3sqpwLm0Gwn/iDY14zDaHOf3DL3flwJH0wL1ryR58tAxbqyq77GEGDhLkiTBUlmO7jzgxcDBtDnGPwHOBt45dGNgaEvVjc4c2A94By17+2DgQmD3qvrWSL3DaXOX3wA8DPgB8OKqOm2k3itou/7t37W5jTbX+nTgvya9iaq6K8mewFHAX9Ky1+cCu1TVT4aq7t69n927x7BzgGdOOs/atuhXhyRJ0pKwBALnqno3bd3kSXUuY8zqFFV1C/Cm7jGp/Z204PrIKfXuAv6ie8xaVV1LC7r3n1BnX2DfuRx/MTjHWZIkSerBjLMkSRIsiYyzljYzzpIkSVIPfqySJEkaMOOsCbw6JEmStKwl+T8TXr4LuAH4F+ALVXXrTBUNnCVJksA5zsvbLsCmtGX6VgE/AzanxcLXd3XeCPwoyS5VdcW4gzjHWZIkCZbszoFaEC+jZZVfCGxcVb9CW1t6H9pGM3sCv9mVvXOmg/gvKEmSpOXuA8C7q+pvBwXdOtUnJ9kCOLqqfiPJO4G3znQQA2dJkiRwqsby9gTaNuXj/Ah4bPfz94AVMx3EqRqSJEla7v4TeNEMr+3DPVuIPwi4bqaD+LFKkiQJzDgvb0cDH0jyK8DngZ8CW9CC5ucCB3X1ng7860wH8eqQJEkaMHBelqrqL5LcRJu/vMfQS1cAr66qY7vfPwLcMtNxvDokSZK07FXVx5McC2wJPBy4CriiqmqozmWTjmHgLEmSBE7VWAd0QfJPuseseXVIkiRp2UvyINp85q1p6zUPq6p6+7RjGDhLkiSBGedlLMlTgdNoOweOU4CBsyRJUi8GzsvZ0cBlwKuB71TV7XM5iFeHJEmSlrvtgRdX1b/M5yAGzpIkSWDGeXm7HNhovgdx50BJkiQtd38OvLm7QXDO/FglSZI0YMZ5udoT+GXg0iTnAteOvF5V9cppB/HqkCRJ0nL3NNrKGTcCO4x5vcaUrcbAWZIkCZzjvIxV1bYLcRyvDkmSJDBw1lReHZIkSVp2kmwNXFVVd3Q/T1RVl0+rY+AsSZIEZpyXn0uB/wF8k7b5ybR5zOtPO6BXhyRJkpaj/YEfDf3c6wbASQycJUmSwIzzMlNVnxz6+biFOKZXhyRJ0oCB87KX5FeARwD/UVVXzqatOwdKkiRp2Uvye0kuBX4CnAf8JMmlSV7R9xh+rJIkSQKnaixjSV4HfBA4E3g78F+0nQR/B/hkkk2r6iPTjtMr45xkqySfT3JDkhuTfKHPsh5D7bdPclKSnyW5JckPkryhR7tKcmTf80iSJEljHAwcV1XPrqr/U1V/3z3/T+DTwCF9DjL1Y1WSTYCvALcBr6TdkXgkcFaSx1fVTVPa79S1Pxv4feAG4FeBB/TpoCRJ0lphxnk5exjwuRle+wzw4j4H6XN1vBrYDnhMVV0CkOTbwL8BfwC8f6aGSdYDPgl8uaqeP/TSWX06J0mStNYYOC9n3wEeNcNrvwp8t89B+kzV2As4bxA0A1TVpcDXgb2ntH0msJIJwfVsJNkkyWlJrkryhK7suCRXJNkpyTeGpoLs0b3+piSXdVNMTkny0IXoiyRJku4z3gC8Ock+SdYHSLJ+khcDfwQc2OcgfT5W7QCcMqb8ImCfKW2f1j1vnOQ84NeA62ip8kOr6pY+nQRIshnwd8DmwFO64H3gQcCngKOAK4HDgZOTfAT4b8Af0iaAHw18hJ7peEmStA4x47yc/Q0tXvwccGeS64AVtN0CfwH8TZJB3aqqR447SJ+rYzNasDvq2u6Ek/xK93wi8GHgzcBOwBHAVsDzZ2h3L92NiKfT3thTq+rqkSoPBF5TVV/t6l8JXAjsCaysqju78scCr0+y/qBs5DwHAAcAbL755hx11LSE+rpryy0f7PhM4RhN5vhM5xhN5vhM5xhNdsghX1vsLmjt+TJrcefAcSfKmLJRg6kgx1fVW7ufz+5S5O9KsrKqvjflGCuBbwDfA15QVb8YU+emQdDcubh7PnMkQL6Y9p4fDlwxepCqOgY4BmCrrbapQw4Zl2gXwFFH7Y3jM5ljNJnjM51jNJnjM51jNHt3ucXFslRV+y7EcfpcHdfRss6jVjA+Ez3smu75H0bKz+ied+xx/p1pu7scO0PQDHD98C9VdXv342j/BuUb9zivJEmSdLc+GeeLaPOcR62kZYGntYXVM9aDbPVdPc7/MWBT4Pgkq6rq5B5tJEmSZqUKVq1a7F5oTenul9uDNl14NIlaVfVn047RJ3A+FTgqyXZV9e/dibcBnkqbszzJ/6Ot/7w77ca+gd265/N7nL+q6nVJVgGfS/KyqjqpRztJkqTeDJyXryTPBk4G7j9DlQKmBs59pmr8NXAZcEqSvZPsRVtl4ye0bPCgQ49MsirJYC4zVXUN8E7gNUn+d5JnJXkz8Fbgk8NL3E1TVQfRtkr8TJKX9G0nSZKkdd77gX8FngBsVFXrjTzW73OQqRnnqropya7AB2hbEoZ2Z+JBI3OOQ1vSYzQYPwL4OfC/aNsZXgW8l7ZP+KxU1cFd5vmEJOtV1WdnewxJkqRxzDgva9sAb6yq78znIL1W1aiqy4EXTqlzGWNW2qiqokX5s94EparGHe9Q4NCh3/edRdvjgONm2w9JkiTdp/0r9yyTPGeu8i1JkoQZ52XuTcBxSX5YVefO9SAGzpIkSR0D52XrX2hTjf8xyU2MLGXMhN0Chxk4S5Ikabk7CngdbcrGxdyzt8esGDhLkiThVI1lbl/g7X3Wap7EfSUlSZK03BXw1fkexIyzJEkSZpyXuZOA59DmOc+ZgbMkSRIGzsvc/wM+kGRT4EvAdaMVquor0w5i4CxJkqTl7m+751d1j4Gi7UNStI38JjJwliRJwozzMrfLQhzEwFmSJEnLWlWdsxDHMXCWJEnqmHHWJAbOkiRJWvaSPJY2v/kxwMYjL1dV/da0Yxg4S5Ik4Rzn5SzJbwLnAJcBvwp8G1gBbA1cAVzS5zgGzpIkSRg4L3P/G/gC8LvAHcCrqupbSXYFPg0c2ecg7hwoSZKk5e7xwPG0ZeegW3quW7v5SOCdfQ5ixlmSJAkzzsvchsBNVXVXkmuBhw+99gPgsX0OYsZZkiRJy92PgEd0P38b2D/JeknWA/YD/rPPQcw4S5IkYcZ5mTsNeCbwGdp8578HbgTuBB4AHNjnIAbOkiRJHQPn5amq3jb085lJngy8CPgl4EtVdUaf4xg4S5IkaVlLsjGwE21ucwFXAW+vqltncxwDZ0mSJJyqsRwl2Qh4D/BqYCMg3UsF3Jrkr4A/qarb+xzPwFmSJEnLTpIAfwfsCpwCfBG4nBY8bwXsCbwRWAk8t88xDZwlSZIw47wMvQjYBXhRVf3tmNc/nuQFwN8keUFVfWHaAQ2cJUmSMHBehn4H+JsZgmYAquoLSU4CXk7bWXAi13GWJEnScvRE2rJz0/wd8KQ+BzTjLEmShBnnZeihtDnN01wObNHngGacJUmStBxtAtzWo97twMZ9DmjGWZIkqWPGedl5RJLtptTZsu/BDJwlSZK0XH2+R53Q1nWeysBZkiQJ5zgvQ/st9AENnCVJklgagXOSF9GWUduJdsPa5bRl0v53Vf18StuNgbcDrwAeDFwAHFpVXx2ptx5wKPAHwMOAHwBHVNXJC/xeVgDvBX4b+CXgXOCNVfWdoTo7AQcAOwNbAz8Dvga8paounc/5q+qT82k/jjcHSpIkLR2HAHcCfwLsDvwV8FrgH7qAd5JjaVtLv5W2K95VwOlJdhyp93bgbcCHgecA5wEnJem1e14f3a59p9Lew+uBFwIbAmclGZ5T/FJgB+CDXV/eTFsa7vwkWy1UfxaKGWdJkiSWRsYZeF5VXT30+zlJrgU+CTwT+Mq4RkmeALwM2L+qPtGVnQNcBBwB7NWVbUELzt9VVUd1zc9K8mjgXbRtqRfCXsDTgF2r6qzu3OcClwJ/DBzY1Xv3yPslyde7eoMPAUuGGWdJkqQlYjSI7Pxz9/yICU33Au4AThw61irgc8BuSTbqincD7gccP9L+eOBxSbYdFCTZIMlhSS5OcluSK5O8r5sSMs1ewJWDoLnrzw3AacDeQ2Wrvd+q+jFw9ZT3uyjMOEuSJLFkMs7jPKN7/v6EOjsAl1bVzSPlF9EC5Ud3P+9AW9v4kjH1AFbSsr3QgunnAe8GvgFsT5vmsQ1t6sUkOwDfHVN+EfB7SR5QVb8Y1zDJ9rT53ZPe76IwcJYkSWJpBs5JHkGbanFmVZ0/oepmwHVjyq8den3wfH1VjS6/dq96SZ4OvAR4ZVV9qnvtzG7ayPFJdqyqC6b057IJ/VkBrBY4J9kA+Cgt43zshOMvCgNnSZKktWfzJMMB8DFVdcy4ikkeAJwCrGL60mozrUWcOdbbnbaj3sldMDtwRve8M3BBkvVH2t7ZBeV9zzPqw8BTgD2qatwHgUVl4CxJktRZCxnnn1XVTtMqdfOITwW2A55RVVdMaXItbTm3USuGXh88r0iSkazzaL0taFM8xk6nAB7SPf8IeORQ+X7Acd1xNmN1g/OsFhQneSdtabpXVtUZo68vBQbOkiRJS0iSDYGTgd8AnjW87vEEFwHPT7LJyDznlbTM8SVD9TYCHsW95zmv7J6/1z1fA9wKPH2G813ZPT+vO97AYH70RcCzx7RbCVw+Or85yeG0pegOrKpPz3DORWfgLEmSxNKY49yt1XwC8Fu06Qrn9Wx6KvDnwD60pesG84VfApxRVbd19b5EC6Rf3tUfeAXw3aFNR75E2yRl06r68kwnnRDUnwrsl+QZVXVO158H0QLtzwxXTHIgcCRweFV9qN/bXRwGzpIkSSyNwBn4CC34fQdwU5InD712RVVdkeSRtCkSR1TVEQBVdUGSE4Gju4z1pbSNU7alBcl09X6a5APAYUl+DnyLFlzvyr2XiTs7yWeBzyd5P/BN4C7aihrPpe1I+MMJ7+NU2k6Bxyf5I9rUjMNoc5zfM6iU5KXA0bRA/Ssj7/fGqvoeS4iBsyRJ0tLxnO758O4x7M9pO/4FWJ/V9+PYjxZwH0nbcvtCYPeq+tZIvcNpc5ffwD1bbr+4qk4bqfcK2q5/+3dtbqOtlHE68F+T3kRV3ZVkT+Ao4C+BjWmB9C5V9ZOhqrt372f37jHsHNqmL0uGgbMkSRJLI+NcVdv0qHMZY1anqKpbgDd1j0nt76QF10dOqXcX8BfdY9aq6lpa0L3/hDr7AvvO5fiLwZ0DJUmSpB7MOEuSJLE0Ms5a2sw4S5IkST2YcZYkSeqYcdYkBs6SJEk4VUPTOVVDkiRJ6sGMsyRJEmacNZ0ZZ0mSJKkHM86SJEmYcdZ0Bs6SJEkYOGs6p2pIkiRJPZhxliRJ6phx1iRmnCVJkqQezDhLkiThHGdNZ+AsSZKEgbOmc6qGJEmS1EOvwDnJVkk+n+SGJDcm+UKSrXu23TrJJ5NcnuTmJD9McmSS+/doW0mO7HMeSZKk+RhknNfkQ/dtU6dqJNkE+ApwG/BKoIAjgbOSPL6qbprQ9v7AmcCGwJ8ClwO/Dvw58KvAS+b7BiRJkqS1oc8c51cD2wGPqapLAJJ8G/g34A+A909o+1RagLxbVZ3RlZ2VZDPgkCSbVNXNc+69JEnSAnGOs6bpM1VjL+C8QdAMUFWXAl8H9p7S9n7d840j5dd3506/bjZJNklyWpKrkjyhKzsuyRVJdkryjSS3JPlBkj2619+U5LJuiskpSR46m3NKkiRJ0C9w3gH47pjyi4CVU9qeSctMvzvJyiQPSLIr8Abgo5OmeYzqstRnAo8BnlJVFw69/CDgU8DHgecDPwVOTvI+YBfgD4GDup8/0veckiRp3eIcZ03SZ6rGZsB1Y8qvBVZMalhVtyZ5GnAyLdAe+Djwur6d7G5EPB34BfDUqrp6pMoDgddU1Ve7+lcCFwJ7Aiur6s6u/LHA65OsPyiTJEkCp2pour7rONeYsqnTLJJsDJwIbAH8Lu3mwN8A3gqsAl7b49wrgW8A3wNeUFW/GFPnpkHQ3Lm4ez5zJEC+mPaeHw5cMaa/BwAHAGy++eYcddS0mSjrri23fLDjM4VjNJnjM51jNJnjM51jNNkhh3xtsbug+5g+gfN1tKzzqBWMz0QPexXwTODRVfWjruyrSW4Ajkny0ZEpF+PsDDwEOHiGoBnanOm7VdXtSQZ9H3Z797zxuINU1THAMQBbbbVNHXLIKVO6tu466qi9cXwmc4wmc3ymc4wmc3ymc4xmx4yzpukTOF9Em+c8aiUtCzzJ44DrhoLmgW92z9vTplRM8jFgU+D4JKuq6uQp9SVJkqQF1ydwPhU4Ksl2VfXvAEm2oS019+Ypbf8TWJHk0cOrcgC/2T3/R4/zV1W9Lskq4HNJXlZVJ/VoJ0mS1JsZZ03TJ3D+a9qNfKckeQttvvPbgZ/QssEAJHkk8CPgiKo6ois+DngT8MUk76DNcd6JthnKv9CWtOulqg5KcifwmSTrVdWJfdtKkiRNY+CsaaYGzlV1U7eE3AeAT9NuCvwycNDInOMA6zO0xF1VXZbkycDbaLsNbk4LuI8B3lFVd82ms1V1cJd5PqELnj87m/aSJEnSXPVaVaOqLgdeOKXOZYxZaaOqvge8eC6dq6pxxzsUOHTo931n0fY4WhZckiTpXsw4a5o+G6BIkiRJ67y+6zhLkiQte2acNYmBsyRJEk7V0HRO1ZAkSZJ6MOMsSZKEGWdNZ8ZZkiRJ6sGMsyRJEmacNZ2BsyRJEgbOms6pGpIkSVIPZpwlSZI6Zpw1iRlnSZIkqQczzpIkSTjHWdOZcZYkSZJ6MOMsSZKEGWdNZ+AsSZKEgbOmc6qGJEmS1IMZZ0mSJMw4azozzpIkSVIPZpwlSZI6Zpw1iYGzJEkSTtXQdE7VkCRJknow4yxJkoQZZ01nxlmSJEnqwYyzJEkSZpw1nYGzJEkSBs6azqkakiRJUg9mnCVJkjpmnDWJGWdJkiSpBzPOkiRJOMdZ05lxliRJknow4yxJktSpumuxu6AlzMBZkiQJgALuXOxOaAlzqoYkSZLUgxlnSZKku5lx1szMOEuSJEk9mHGWJEkCnOOsaQycJUmS7uaqGpqZUzUkSZKkHsw4S5IkAU7V0DRmnCVJkqQezDhLkiQBZpw1jYGzJEnS3QycNTOnakiSJEk9mHGWJEkCnKqhacw4S5IkST0YOEuSJN3trjX8mC7Jlkk+lOTcJDcnqSTb9Gy7cZL3JrkqyS3dMXYeU2+9JIcluSzJrUkuTPLCXh2chSQrknw8yc+S3JTkzCSPG6mzU5Jjklzcvd/Lk5yQZNuF7s98GThLkiQtLY8GXgxcB3xtlm2PBV4NvBXYE7gKOD3JjiP13g68Dfgw8BzgPOCkJM+dc69HJAlwKrA78HrghcCGwFlJthyq+lJgB+CDXV/eDDwJOD/JVgvVn4XgHGdJkiRgCc1x/mpV/TJAkt8Hnt2nUZInAC8D9q+qT3Rl5wAXAUcAe3VlWwCHAO+qqqO65mcleTTwLuCLC/Q+9gKeBuxaVWd15z4XuBT4Y+DArt67q+rqkffy9a7e4EPAkmDGWZIkCbgncF6Tjx69qOo3p2N1ewF3ACcOHWsV8DlgtyQbdcW7AfcDjh9pfzzwuOEpEkk26KZ0XJzktiRXJnlfko179ufKQdDc9ecG4DRg76Gyq0cbVtWPgauBR/Q4z1pj4CxJkrQ87ABcWlU3j5RfRAuUHz1U7zbgkjH1AFYOlR0PvAX4DLAH8E7gVcAJPfvz3THlFwFbJ3nATA2TbA9sAXy/x3nWGqdqSJIk3W1JTNWYq81o86JHXTv0+uD5+qqqSfWSPB14CfDKqvpU99qZSa4Fjk+yY1VdMKU/l03ozwrgF6MvJtkA+Cgt43zshOOvdQbOkiRJa8/mSc4f+v2YqjpmgY4d2nyTceVzqbc7cDtwchfMDpzRPe8MXJBk/ZG2d3ZBed/zjPow8BRgj6oa90Fg0Rg4S5IkAWvp5sCfVdVOa+jY1wJbjylfMfT64HlFkoxknUfrbUGb4rFaVrjzkO75R8Ajh8r3A47rjrMZqxucZ7WgOMk7gQNoWe4zRl9fbAbOkiRJd5vrfXlLwkXA85NsMjLPeSUtc3zJUL2NgEdx73nOg7nN3+uerwFuBZ4+w/mu7J6f1x1v4NKh84xbEWQlcHlV3SsgT3I4bSm6A6vq0zOcc1F5c6AkSdLycCptneR9BgXdFIuXAGdU1W1d8ZdogfTLR9q/AvhuVV06VG9jYNOqOn/M40qAqvrOSPk1Q/15RJJnDPXnQbRA+9ThEyc5EDgSOLyqPjTfgVhTzDhLkiQBS2gdZ5K8qPvx17rn5yS5Gri6qs5J8kjaFIkjquoIgKq6IMmJwNFJNqRlfl8LbMtQkFxVP03yAeCwJD8HvkULrnfl3svEnZ3ks8Dnk7wf+CYtJb8N8Fzg0Kr64YS3cSpwLu1Gwj+iTc04jDbH+T1D7/WlwNG0QP0rSZ48dIwbq+p7LBEGzpIkSUvPSSO//2X3fA7wTFrwuT6rzx7YD3gHLXv7YOBCYPeq+tZIvcNpc5ffADwM+AHw4qo6baTeK2i7/u3ftbmNtlLG6cB/TXoDVXVXkj2Bo7r+b0wLpHepqp8MVd29ez+7d49hg/e7JBg4S5Ik3W1pZJyrauLKE1V1GWNWp6iqW4A3dY9J7e+kBddHTql3F/AX3WPWqupaWtC9/4Q6+wL7zuX4a5uBsyRJErCUpmpoafLmQEmSJKmHqYFzki2TfCjJuUluTlJJtul7giQbJ3lvkquS3NIdZ+eebSvJxK8QJEmSFsYg47wmH7ov65NxfjTwYtqdkF+bwzmOBV4NvBXYE7gKOD3JjnM4liRJkrQo+sxx/mpV/TJAkt9n/ELWYyV5AvAyYP+q+kRXdg5tQewjgL1m3WNJkqQ15j69AYrWsKkZ5+5uyrnaC7gDOHHoeKuAzwG7JdlopobjJNkkyWndtI8ndGXHJbkiyU5JvtFNB/lBkj2619+U5LIkNyY5JclD5/F+JEmStI5a0zcH7gBcOrLtI7SM8/1o00B6SbIZcCbwGOApVXXh0MsPAj4FfBx4PvBT4OQk7wN2Af4QOKj7+SNzeieSJGmZc46zJlvTy9FtRpsbPeraodenSrI1baHtXwBPraqrR6o8EHhNVX21q38lbcHvPYGV3VqFJHks8Pok6w/KRs5zAHAAwOabb85RR+09WkWdLbd8sOMzhWM0meMznWM0meMznWM02SGHjLt1y+BWM1vTgXNoH9/Glfe1EvgG8D3gBVX1izF1bhoEzZ2Lu+czRwLki2nv+eHAFaMHqapjgGMAttpqmzrkkFNm0c11y1FH7Y3jM5ljNJnjM51jNJnjM51jJC2sNR04XwtsPaZ8xdDr0+wMPAQ4eIagGeD64V+q6vYksHq2+/bueeMe55UkSesUN0DRZGs6cL4IeH6STUbmOa+kBbGX9DjGx4BNgeOTrKqqk9dAPyVJkqSJ1vTNgacCGwL7DAqSbAC8BDijqm7rcYyqqtfRbur7XJJ9pjWQJEmaG28O1Mx6ZZyTvKj78de65+ckuRq4uqrO6eo8EvgRcERVHQFQVRckORE4OsmGwKXAa4FtgZfPpqNVdVCSO4HPJFmvqk6c2mje1l/zp7jPCo7PNI7RZI7PdI7RZI7PdI7R7BSu46xJ+k7VOGnk97/sns8Bntn9PPivczSLvR/wDuBI4MG01S52r6pvzbKvVNXBSVYBJ3TB82dnewxJkiRpLnoFzlU1dRWMqrqMMatlVNUtwJu6x6yMO29VHQocOvT7vrNoexxw3Gz7IUmS1hVOp9DM1vTNgfdx91/sDixh6+H4TOMYTeb4TOcYTeb4TOcYTeY0Fs2OgbMkSRLgcnSaxsBZkiQJMHDWNGt6OTpJkiRpWTDjPKMAmy92J5awDXB8pnGMJnN8pnOMJnN8pnOMJhsXBrkcnWZmxlmSJEnqwYyzJEkS4BxnTWPgPKP1gK0XuxNL2P1wfKZxjCZzfKZzjCZzfKZzjCa732J3QPcxBs6SJEl3M+OsmRk4S5IkAU7V0DTeHChJkiT1YMZ5RgFWLnYnlrA7cHymcYwmc3ymc4wmc3ymc4wm23hMmRlnzcyMsyRJktSDGWdJkiSgzXF2AxTNzMBZkiQJ8OZATWPgPKP1gF9b7E4sYd8GHr/YnVjiHKPJHJ/pHKPJHJ/pHKPJ7r/YHdB9jIGzJEnS3cw4a2beHChJkiT1YMZ5BuutF/7H/9hisbuxZN3//hs4PlM4RpM5PtM5RpM5PtM5RpN9+9ujYZBznDWZgbMkSdLdDJw1M6dqSJIkST2YcZYkSQJcx1nTGDjPYIMNYN99F7sXS9dmmzk+0zhGkzk+0zlGkzk+0zlGk73jHYvdA93XGDhLkiTdzTnOmplznCVJkqQezDjPYIMN4IDfd57TTM7+KrzoBY7PJI7RZI7PdI7RZI7PdI7RZMccM1ricnSazMBZkiQJMHDWNE7VkCRJknow4yxJknQ3M86amYHzTFatgo9/fLF7sXRttpnjM41jNJnjM51jNJnjM51jNNnVVy92D3QfY+AsSZIEOMdZ0xg4S5Ik3c1VSDQzbw6UJEmSejDjPJNVq+ATn1jsXixdr3wlfPKTi92Lpc0xmszxmc4xmszxmc4xmuyaa0YKnKqhycw4S5IkST2YcZYkSbqbGWfNzMB5JnfdxfXnnbfYvViy7nzRixyfKRyjyRyf6RyjyRyf6RyjyVYPkZ2qocmcqiFJkiT1YMZZkiTpbi5Hp5mZcZYkSZJ6MOM8g7uASxa7E0vYbTg+0zhGkzk+0zlGkzk+0zlGk922WolznDWZGWdJkiSpBzPOkiRJgBlnTWPgPIMCLl/sTixh6+H4TOMYTeb4TOcYTeb4TOcYTXb72FIDZ83MqRqSJElSD2acJUmSAKdqaBozzpIkSVIPZpxnUMBPF7sTS9hDgGsWuxNLnGM0meMznWM0meMznWM02R1jS90ARTMzcJYkSQKcqqFpnKohSZIk9WDGWZIk6W5mnDUzA+cJblzsDixhK3B8pnGMJnN8pnOMJnN8pnOMJjNE1mwZOEuSJAHOcdY0Bs6SJEmAgbOmMXCeQQG3LnYnlrC7cHymcYwmc3ymc4wmc3ymc4wmq8XugO5zDJwlSZLuZsZZM3M5OkmSJKkHM86SJElAm7zhzoGamYHzBP6nM5njM51jNJnjM51jNJnjM51jNDPnOGu2pk7VSLJlkg8lOTfJzUkqyTZ9Dp5kpyTHJLm4a3t5khOSbNuzfSU5sk9dSZKk+btzDT+mS7JVks8nuSHJjUm+kGTrnm03TvLeJFcluaWL33YeU2+9JIcluSzJrUkuTPLCXh1cQEl2SHJGkl8kuSbJJ5JsNqbenMdkIfWZ4/xo4MXAdcDXZnn8lwI7AB8EngO8GXgScH6SrWZ5LEmSpDVosBzd4gXOSTYBvgL8d+CVwO8CvwqcleT+Pd7EscCrgbcCewJXAacn2XGk3tuBtwEfpsVo5wEnJXluj3MsiCS/ApwN/BLwIuAPgWcBf5dkvaF68x2TBdNnqsZXq+qXAZL8PvDsWRz/3VV19XBBkq8Dl3LPP6okSZKaVwPbAY+pqksAknwb+DfgD4D3z9QwyROAlwH7V9UnurJzgIuAI4C9urItgEOAd1XVUV3zs5I8GngX8MX5vokkbwP2raptJlT7I2BD4HlVdX3X7krgHOC3gS909eY8Jgttasa5quY8PWo0aO7KfgxcDTxitsdLskmS07qvH57QlR2X5IpuWsg3uq8lfpBkj+71N3VfQ9yY5JQkD53r+5EkScvZ4mecacHteYMAEaCqLgW+Duzdo+0dwIlDbVcBnwN2S7JRV7wbcD/g+JH2xwOPG55Sm2SDbkrHxUluS3Jlkvcl2bjPm+nR378fBM1df78KXM693+t8xmRBrfXl6JJsD2wBfH+W7TYDzgQeAzylqi4cevlBwKeAjwPPB34KnJzkfcAutNT/Qd3PH5nnW5AkSVpTdgC+O6b8ImBlj7aXVtXNY9rejzb9dlDvNuCSMfUYOc/xwFuAzwB7AO8EXgWcMKUvEyX5JWBb+r3X+YzJglqrq2ok2QD4KC3jfOws2m0NnA78AnjqmEz2A4HXdJ9SBmn+C2lze1ZW1Z1d+WOB1ydZf1AmSZJ0j0Vfh2Qz2n1lo64FVsyj7eD1wfP1VTW6sMi96iV5OvAS4JVV9anutTOTXAscn2THqrqgq7s+kKFjrdeV3yvW7DLgdO8lE/r7mJ7va9qYLKi1vRzdh4GnAHtU1bgBGGcl8A3ge8ALquoXY+rcNAiaOxd3z2eOBMgX097zw4ErRg+S5ADggO7X2/58/KcbARxyyObAzxa7G0uaYzSZ4zOdYzSZ4zOdYzTNY+796w2nw2mbr+Fzbpzk/KHfj6mqY0bqjFspL2PKxtXp07Zvvd2B22nf4g/HjGd0zzsDF3Q//wh45Jhj3nGvEyTbVtVlQ+fq+17nOiYLaq0FzkneSQtKX1lVZ0yrP2Rn4CHAwTMEzQDXD/9SVbcngdU/ndzePY+dl9NduMd0/T2/qnaaRT/XKY7PdI7RZI7PdI7RZI7PdI7RZCMBLFW1+2L1Zch13JMZHraC8VnXYdcC45ZoWzH0+uB5RZKMZJ1H621Bm+IxU/z1kKGfnwdsNPT7AbRv/vcaaXNl93wdLRie6b1eO/T7fMZkQa2VwDnJ4bSl6A6sqk/PsvnHgE1pXwmsqqqTF7yDkiRJS8NFtDm9o1bSvn2f1vb5STYZmee8kpY8vGSo3kbAo7j3POfBfOHBea4BbgWePsP5BkEwVfWd4ReS7AncXlXnr9aq1b85yWXM/F7PGXlfcx2TBbXGbw5MciBwJHB4VX1oDoeoqnod7aa+zyXZZ0E7KEmStHScCjw5yXaDgm7juad2r01ruyFwd6zUTbF4CXBGVd3WFX+JFki/fKT9K4DvditWDOptDGxaVeePeVzJ/JwK7JFk06H+Po025ePUkXpzHZMF1SvjnORF3Y+/1j0/J8nVwNVVdU5X55G0+S1HVNURXdlLgaNpA/+VJE8eOuyNVdX7U0JVHZTkTuAzSdarqhOnNpqf0flGujfHZzrHaDLHZzrHaDLHZzrHaLKlOD5/DbwOOCXJW2jTGd4O/IT2LTwwPu6qqguSnAgcnWRD2r4Zr6WtXnF3kFxVP03yAeCwJD8HvkULrndlaHm3qjo7yWeBzyd5P/BN2t2T2wDPBQ6tqh/O472+lxasn9pN6d0UeE93nr+d7ZisDX2napw08vtfds/nAM/sfg6wPvfOYu/ele/ePYYNt+2lqg5Osgo4oQuePzub9rM811L8j2nJcHymc4wmc3ymc4wmc3ymc4wmW4rjU1U3JdkV+ADwaVoc9WXgoJF7vcbFXQD7Ae+gfdv/YNoqY7tX1bdG6h1Om7v8BuBhwA+AF1fVaSP1XgG8Hti/a3MbcBlttbP/muv7BKiq/0iyC20Dk5NpWfBTaPe13TVUr++YrHFZfSUSSZIkSaPW+gYoCynJ04Z2C/zPJO/vFtSe1u5FSU5O8uOhnQbfmeSBPdo+M0kledbCvIuFlWSrJJ9PckO3W+IXunWw+7TdOMl7u50Zb0lybpKde7atJEfOr/drx1zHKG13ymO63ZNuTnJ5khOGd1ia0v4+MUbzuYZGjnNY957/sWf9+8T4wPzHKMn2SU5K8rOhv0Fv6NHuPjFG8/w7tHWST3b/fd2c5IdJjkxy/x5t7yvjs2WSD3V/Y2/u+r3NLNov67/V8xmfdeXvtBbPfTZwTvJ44B9ouwTuSdvVZj/guB7ND6Hte/kntCkkf0WbA/QPSe7LY7IJ8BXgvwOvBH4X+FXa/vNT/6dD25Tm1cBbaWN6FXB6kh3XSIcXwTzH6KW0u3o/CDyHtlLMk4Dzk2y1xjq9Fi3ANTQ4zna0r/R+uib6uZjmO0ZJdgL+iXZH++/T5gm+j/aV633efMane/1M2jKkf0rbpezjwMHA/1mD3V7bHg28mLaM1tfm0H65/62ez/gs+7/TWmRVdZ980CaN/xuw4VDZ79EmjD9pStuHjikbtN11SttndvWetdhjMKZvb6B9IHj0UNm2wCrgTVPaPqF7X/sNlW1Am/N0ao9zF3DkYo/BGh6jcdfNI2k3ShyxHMZoPuMzcpzTaTdsnA38Y882S358FuAaWo+2rNLfzvHcS36M5jk+z+7e47NHyt/Vtd/kvj4+g+tg6Off7/q9Tc+2y/5v9TzHZ9n/nfaxuI/7ZHY17U7R3YG/qarhHWn+hjaxfO+xDTu1+pbdAP/cPT9iDv3ZLsm/Jfl6khVd2WVJjk/yu93XsLck+VqSX01y/yQfS3JNkv9K8r6MbEk5R3sB51XV3WsyVltS5utMGZOu7R3A3auVVNsW83PAbkk2mqnhOEk2SXJa91XiE7qy45Jc0X2VNphi84Mke3Svv6kbtxuTnJLkobM5Z09zHqNx101V/Zi2hfxcrpulOEbzuYYG7+tltAzPYfPpyBIdH5jfGD2Ttu7o+xeiI0t0jOYzPvfrnm8cKb+e9qFjVruELdHxoYZuepqDZf+3ej7js478ndYiuk8GzrQFuzdmZEvsqrqVtjTLynGNpnhG9/z92TRK8kTaluDfp2Whh3ew2Rn4X8ChtK8sH0W7a/QE4Oe0r5SOAd7EPVt9z8cOjN8m/CKmj8kOwKV17wXTB23vR/vqrJckm9G+bn0M8JSqunDo5QcBn6J9/fp82lf5Jyd5H7AL8IfAQd3PH+l7zlmYzxitJsn2tJ2VZnvdLNUxmtf4pH1w/ADwx1V17bT6E46zVMcH5jdGT+ueN05yXpI7kvw0yQfT4/6MYUt4jOYzPmfSvkl8d5KVSR6Qdif9G4CPVtVNfTuxhMdnvtaVv9ULZhn+ndYiWmtbbi+wwbaL47ZZvJbx2zLOKMkjgCOAM2uGHW5maPdbtCkjJwEHVNWdI1UeQFsC5oau/sOAvwC+WVWHdHX+ofuUug/3LPM3V5sx85isGFPet+3g9anSbgA6nbbEzVPHfPp/IPCaqvpqV/9K2lI5ewIrB2OY5LHA65OsP2Zc52M+Y3Qvad8SfJSWyTh2Fu2W8hjNd3zeC/yQfvcajLXExwfmN0a/0j2fCHyYNv9yJ9rfn61o/wOeaomP0ZzHp6puTdv84GRaIDjwcdoarr0s8fGZr3Xlb/WCWKZ/p7WIlnTGOc0Gw4/BS93zuLX0ZvtV3gNoawauot1c2Nc+wBeBj1TVq2b4D+LcQdDcubh7Pn2k3sW0/2kuhLmOSebRdmAlLfv+E2CXGabE3DT4Q9MZjMmZI2N4Me2D3cNncf6+5n3ddD4MPAV4xcg3DZPcF8ZoTuOT5Om0ewVeW1VzXefyvjA+MPdraPA39/iqemtVnV1VRwF/Dvx2kj7fetwXxmiu19DGtA8VW9BuKnwG8Ee0jRn6Zu3uC+MzH+vS3+qFsFz/TmuRLOnAmfZH846RB0z+ZL1i6PWJuj/SpwLbAbtV1RWz6NsLgVuAT0yoM/of6e0TyjeexbknnW+mMZn2B2OmTP2Koden2Zk2h+zYmnlB8uuHf6mqSWMCCzMuw+YzRndL2+HoAGD/qjpjFudf6mM0n/H5GC2jc0WSByd5MO1/GOt3v/eZe7nUx2dwnrmO0TXd8z+MlA+uoR17nH+pj9F8xudVtHngz62q46vqq90Hi4OB1wzmmE6x1MdnvtaVv9Xztoz/TmsRLfWpGv8C/PqY8h/Rdq7ZYbiwC4S3Y/WdDleTdoPhycBv0OYmf2eWfTuAtqzd2Ul2raqLpzVYCy5iZEw6K4Fp25tfBDw/ySYjc+dW0v7Dv2R8s3v5GG27zOOTrKqqk3u0WdvmM0YAJDmc9hX7gVX16Vmef6mP0XzGZ/vu8Zoxr10HvBE4esoxlvr4wPz/O4PVM4aDbGGfm6KW+hjNZ3weB1xXVT8aKf9m97w97evwSZb6+MzXuvK3el6W+d9pLaIlnXGuqp9X1fnDj678duBLwItz79UoXkRbG/XUScdNW6v5BOC3gL2r6rw5dO9GYDfg32nrk24/h2MstFOBJ6etoQtA2qLxT2XKmHSvb0ibgjJouwHtK9Izquq2Huevqnod7SvVzyXZZ1qDRTCfMSLJgbRtTA+vqg/N4fxLfYzmMz67jHlcSLtRbBfg8z3Ov9THB+Y3Rv+P9qF/95Hy3brnPvdYLPUxms/4/CewIsnoDW6/2T3/R4/zL/Xxma915W/1nK0Df6e1iJZ6xnmStwHnAn+T5CPANrQbkz5fVf8yqJTk92gL5/9WVZ3TFX+E9kfnHcBNSZ48dNwr+k7ZqKqfJ9kd+Hta8PxbVXXRtHZr0F/TbqA5JclbaFmtt9PmaX1sUCnJI2lZ+yOq6giAqrogyYnA0V02/lLapjDbAi+fTSeq6qAkdwKfSbJeVZ04tdHaM+cxSvJSWsb0S8BXRq6bG6uqV8YalvQYzecaOnv0YEmuBzYY99okS3h8YH5jdE339fGfJrmRtlHITrSNLD5ZQ0u4TbOEx2jO40O7qfRNwBeTvAO4nDY+f0r7BvLrfTuxhMcHgCQv6n78te75OUmuBq4e/L9qXf5bPdfxWUf+TmsR3WcD5+6Px27Au2mB6w20pWH+ZKTqerQduYZvnHhO93x49xj257SgvG8/fpHkucBptP9If6uqxi3FtMZV1U1pSzd9APg07T1/GThoZJ5WaGMy+o3DfrQPE0cCD6ZlC3evqm/NoS8HJ1kFnND9wfnsbI+xJsxzjHbvyndn9YzhObS5mbPpy5IbowW4hhayL0tufGBBxugI2nKU/4s23esq2of+t8+hL0tujOYzPlV1WRfovI32d2hzWsB9DPCOmuX6vktxfIaMTikcrKo0/Ldknf1bzdzHZ9n/ndbiSs355ndJkiRp3bGk5zhLkiRJS4WBsyRJktSDgbMkSZLUg4GzJEmS1IOBsyRJktSDgbMkSZLUg4GzJEmS1IOBsyRJktSDgbMkSZLUw/8H+2R8as65IskAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2damp(D02)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As pointed out previously, the functions $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$ define damping in the directions $x$ and $z$ respectively. They will be identified with the symbolic names of *dampx* and *dampz*, respectively.\n", "\n", "As damping acts on non-staggered and staggered grids, we will identify *dampx0* and *dampz0* as being damping on the non-staggered points grid. Similarly, we will identify *dampx1* and *dampz1* as being the damping on the staggered points grid." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "dampx0 = Function(name=\"dampx0\", grid=grid,space_order=2,staggered=NODE ,dtype=np.float64)\n", "dampz0 = Function(name=\"dampz0\", grid=grid,space_order=2,staggered=NODE ,dtype=np.float64)\n", "dampx0.data[:,:] = D01\n", "dampz0.data[:,:] = D02" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "dampx1 = Function(name=\"dampx1\", grid=grid,space_order=2,staggered=(x,z),dtype=np.float64)\n", "dampz1 = Function(name=\"dampz1\", grid=grid,space_order=2,staggered=(x,z),dtype=np.float64)\n", "dampx1.data[0:nptx-1,0:nptz-1] = D11\n", "dampz1.data[0:nptx-1,0:nptz-1] = D12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In terms of dimensions, the arrays *D11* and *D12* have dimension $(nptx-1)\\times (nptz-1)$. As our grid has $nptx\\times nptz$ points, so we complete the line *nptx-1* with information from the line *nptx-2* and the column *nptz-1* with information from the column *nptz-2*, in fields *dampx1* and *dampz1* using the arrays *D11* and *D12*, respectively." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "dampx1.data[nptx-1,0:nptz-1] = dampx1.data[nptx-2,0:nptz-1]\n", "dampx1.data[0:nptx,nptz-1] = dampx1.data[0:nptx,nptz-2]\n", "dampz1.data[nptx-1,0:nptz-1] = dampz1.data[nptx-2,0:nptz-1]\n", "dampz1.data[0:nptx,nptz-1] = dampz1.data[0:nptx,nptz-2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we saw previously, the acoustic equation with PML has the formulations\n", "\n", "\n", " In the white (interior) region:\n", "\n", "- eq1 = u.dt2 - vel0 * vel0 * u.laplace;\n", "\n", " And in the blue (absorption) region:\n", "\n", "- eq2 = u.dt2 + (dampx0+dampz0) * u.dtc + (dampx0 * dampz0) * u - u.laplace * vel0 * vel0 + $\\bar{phi1}$[t,x,z] + $\\bar{phi2}$[t,x,z];\n", "\n", "- eq3 = phi1.dt + dampx1 * 0.5 * (phi1.forward+phi1) -(dampz1-dampx1) * $\\bar{u}$[t,x,z] * vel1 * vel1\n", "\n", "- eq4 = phi2.dt + dampz1 * 0.5 * (phi2.forward+phi2) -(dampx1-dampz1) * $\\bar{u}$[t,x,z] * vel1 * vel1\n", "\n", "In the equation *eq2* the term $\\bar{phi1}$[t,x,z] is given by following expression:\n", "\n", "- -(0.5/hx) * (phi1[t,x,z-1]+phi1[t,x,z]-phi1[t,x-1,z-1]-phi1[t,x-1,z]);\n", "\n", "And the term $\\bar{phi2}$[t,x,z] in the equation *eq2* is given by:\n", "\n", "- -(0.5/hz) * (phi2[t,x-1,z]+phi2[t,x,z]-phi2[t,x-1,z-1]-phi2[t,x,z-1]);\n", "\n", "In the equation *eq3* the term $\\bar{u}$[t,x,z] is given by:\n", "\n", "- a1 = u[t+1,x+1,z] + u[t+1,x+1,z+1] - u[t+1,x,z] - u[t+1,x,z+1]; \n", "- a2 = u[t,x+1,z] + u[t,x+1,z+1] - u[t,x,z] - u[t,x,z+1]; \n", "- $\\bar{u}$[t,x,z] = 0.5 * (0.5/hx) * (a1+a2);\n", "\n", "In the equation *eq4* the term $\\bar{u}$[t,x,z] is given by:\n", "\n", "- b1 = u[t+1,x,z+1] + u[t+1,x+1,z+1] - u[t+1,x,z] - u[t+1,x+1,z]; \n", "- b2 = u[t,x,z+1] + u[t,x+1,z+1] - u[t,x,z] - u[t,x+1,z]; \n", "- $\\bar{u}$[t,x,z] = 0.5 * (0.5/hz) * (b1+b2)\n", "\n", "Then, using the operator *Eq(eq)* and the equation in the format associated with Devito we create the *pdes* that represent the acoustic equations with PML without the external force term in the white and blue regions, respectively by:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# White Region\n", "pde01 = Eq(u.dt2-u.laplace*vel0**2) \n", "\n", "# Blue Region\n", "pde02a = u.dt2 + (dampx0+dampz0)*u.dtc + (dampx0*dampz0)*u - u.laplace*vel0*vel0 \n", "pde02b = - (0.5/hx)*(phi1[t,x,z-1]+phi1[t,x,z]-phi1[t,x-1,z-1]-phi1[t,x-1,z])\n", "pde02c = - (0.5/hz)*(phi2[t,x-1,z]+phi2[t,x,z]-phi2[t,x-1,z-1]-phi2[t,x,z-1])\n", "pde02 = Eq(pde02a + pde02b + pde02c)\n", "\n", "pde10 = phi1.dt + dampx1*0.5*(phi1.forward+phi1)\n", "a1 = u[t+1,x+1,z] + u[t+1,x+1,z+1] - u[t+1,x,z] - u[t+1,x,z+1] \n", "a2 = u[t,x+1,z] + u[t,x+1,z+1] - u[t,x,z] - u[t,x,z+1] \n", "pde11 = -(dampz1-dampx1)*0.5*(0.5/hx)*(a1+a2)*vel1**2\n", "pde1 = Eq(pde10+pde11)\n", " \n", "pde20 = phi2.dt + dampz1*0.5*(phi2.forward+phi2) \n", "b1 = u[t+1,x,z+1] + u[t+1,x+1,z+1] - u[t+1,x,z] - u[t+1,x+1,z] \n", "b2 = u[t,x,z+1] + u[t,x+1,z+1] - u[t,x,z] - u[t,x+1,z] \n", "pde21 = -(dampx1-dampz1)*0.5*(0.5/hz)*(b1+b2)*vel1**2\n", "pde2 = Eq(pde20+pde21)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define the *stencils* for each of the *pdes* that we created previously. The *pde01* is defined on *subdomain* *d0*. " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "stencil01 = Eq(u.forward,solve(pde01,u.forward) ,subdomain = grid.subdomains['d0'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *pdes*: *pde02*, *pde1* and *pde2* are defined on the union of the subdomains *d1*, *d2* and *d3*. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "subds = ['d1','d2','d3']" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "stencil02 = [Eq(u.forward,solve(pde02, u.forward),subdomain = grid.subdomains[subds[i]]) for i in range(0,len(subds))]\n", "stencil1 = [Eq(phi1.forward, solve(pde1,phi1.forward),subdomain = grid.subdomains[subds[i]]) for i in range(0,len(subds))]\n", "stencil2 = [Eq(phi2.forward, solve(pde2,phi2.forward),subdomain = grid.subdomains[subds[i]]) for i in range(0,len(subds))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The boundary conditions are set" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "bc = [Eq(u[t+1,0,z],0.),Eq(u[t+1,nptx-1,z],0.),Eq(u[t+1,x,nptz-1],0.),Eq(u[t+1,x,0],u[t+1,x,1])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then define the operator (*op*) that will join the acoustic equation, source term, boundary conditions and receivers.\n", "\n", "- 1. The acoustic wave equation in the *d0* region: *[stencil01];*\n", "- 2. The acoustic wave equation in the *d1*, *d2* and *d3* regions: *[stencil02];*\n", "- 3. Source term: *src_term;*\n", "- 4. Boundary Condition: *bc;*\n", "- 5. Auxiliary function $\\phi_{1}(x,z,t)$ in the *d1*, *d2* and *d3* regions: *[stencil1];*\n", "- 6. Auxiliary function $\\phi_{2}(x,z,t)$ in the *d1*, *d2* and *d3* regions: *[stencil2];*\n", "- 7. Receivers: *rec_term;*\n", "\n", "We then define the following *op*:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "op = Operator([stencil01,stencil02] + src_term + bc + [stencil1,stencil2] + rec_term,subs=grid.spacing_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So that there are no residuals in the variables of interest, we reset the fields *u*, *phi1* and *phi2* as follows:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "u.data[:] = 0.\n", "phi1.data[:] = 0.\n", "phi2.data[:] = 0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We assign to *op* the number of time steps it must execute and the size of the time step in the local variables *time* and *dt*, respectively. This assignment is done as in Introduction to Acoustic Problem, where we have the following attribution structure:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `Kernel` run in 0.06 s\n" ] }, { "data": { "text/plain": [ "PerformanceSummary([(PerfKey(name='section0', rank=None),\n", " PerfEntry(time=0.024502999999999806, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section1', rank=None),\n", " PerfEntry(time=0.00010099999999999995, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section2', rank=None),\n", " PerfEntry(time=4.399999999999997e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section3', rank=None),\n", " PerfEntry(time=0.025268000000000068, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section4', rank=None),\n", " PerfEntry(time=0.0011940000000000047, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "op(time=nt,dt=dt0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We view the result of the displacement field at the end time using the *graph2d* routine given by:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "def graph2d(U): \n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscale = 1/10**(3)\n", " scale = np.amax(U[npmlx:-npmlx,0:-npmlz])/10.\n", " extent = [fscale*x0pml,fscale*x1pml,fscale*z1pml,fscale*z0pml]\n", " fig = plot.imshow(np.transpose(U[npmlx:-npmlx,0:-npmlz]),vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.axis('equal')\n", " plot.title('Map - Acoustic Problem PML Devito')\n", " plot.grid()\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " cbar.set_label('Displacement [km]')\n", " plot.draw()\n", " plot.show()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2d(u.data[0,:,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The solution obtained here has a reduction in the noise when compared with the results displayed in the notebook Introduction to Acoustic Problem. We plot the result of the Receivers using the *graph2drec* routine." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "def graph2drec(rec): \n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscaled = 1/10**(3)\n", " fscalet = 1/10**(3)\n", " scale = np.amax(rec[:,npmlx:-npmlx])/10.\n", " extent = [fscaled*x0pml,fscaled*x1pml, fscalet*tn, fscalet*t0]\n", " fig = plot.imshow(rec[:,npmlx:-npmlx], vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f s'))\n", " plot.axis('equal')\n", " plot.title('Receivers Signal Profile with PML - Devito')\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " plot.show()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2drec(rec.data)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "assert np.isclose(np.linalg.norm(rec.data), 990, rtol=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.8 - Conclusions\n", "\n", "We conclude our small approach to the PML method applied to the Acoustic Problem, including simulations in the Devito environment for this problem. In this study, we can see that:\n", "\n", "- When we use the PML strategy to reduce reflections at the end of the simulation, we observe a reduction in the amount of noise in our image when compared to the results of the notebook Introduction to Acoustic Problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3.9 - References\n", "\n", "- Berenger, J.-P. (1994). \"A perfectly matched layer for the absorption of electromagnetic waves\", Journal of Computational Physics, 114(2), 185-200. DOI: 10.1006/jcph.1994.1159. Reference Link.\n", "\n", "- Grote, M. J. and Sim, I. (2010). \"Efficient PML for the wave equation\", arXiv preprint arXiv:1001.0319. Reference Link." ] } ], "metadata": { "hide_input": false, "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": "3.8.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }