examples/seismic/tutorials/15_tti_qp_pure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3208de8a",
   "metadata": {},
   "source": [
    "# 15 - TTI pure qP-wave equation implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672c4e16",
   "metadata": {},
   "source": [
    "The aim of this notebook is to show how to solve the pure qP-wave equation using the finite-difference (FD) scheme. The 2D TTI pure qP-wave equation can be written as ([Mu et al., 2020](https://library.seg.org/doi/10.1190/geo2019-0320.1))\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{1}{v_{p}^{2}}\\frac{\\partial^{2}p(\\textbf{x},t)}{\\partial t^{2}} = & \\,\\, (1+2\\delta\\sin^{2}\\theta\\cos^{2}\\theta + 2\\epsilon\\cos^{4}\\theta)\\frac{\\partial^{4}q(\\textbf{x},t)}{\\partial x^{4}} \\nonumber \\\\\n",
    "& + (1+2\\delta\\sin^{2}\\theta\\cos^{2}\\theta + 2\\epsilon\\sin^{4}\\theta)\\frac{\\partial^{4}q(\\textbf{x},t)}{\\partial z^{4}} \\nonumber \\\\\n",
    "&  + (2 - \\delta\\sin^{2}2\\theta+3\\epsilon\\sin^{2}2\\theta+2\\delta\\cos^{2}\\theta)\\frac{\\partial^{4}q(\\textbf{x},t)}{\\partial x^{2}\\partial z^{2}} \\nonumber \\\\\n",
    "& +(\\delta\\sin4\\theta-4\\epsilon\\sin2\\theta\\cos^{2}\\theta)\\frac{\\partial^4 q(\\textbf{x},t)}{\\partial x^{3}\\partial z} \\nonumber \\\\\n",
    "& +(-\\delta\\sin4\\theta-4\\epsilon\\sin2\\theta\\cos^{2}\\theta)\\frac{\\partial^4 q(\\textbf{x},t)}{\\partial x\\partial z^{3}} \\nonumber \\\\\n",
    "& + f(\\textbf{x}_{s},t), \\nonumber \\\\\n",
    "\\frac{\\partial^{2}q(\\textbf{x},t)}{\\partial x^{2}} + \\frac{\\partial^{2}q(\\textbf{x},t)}{\\partial z^{2}} = & p(\\textbf{x},t), \\nonumber\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $q(\\textbf{x},t)$ is an auxiliary wavefield, which is introduced for implementing the FD scheme."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2219242",
   "metadata": {},
   "source": [
    "First of all, it is necessary to import some Devito modules and other packages that will be used in the implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d81ef0be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from devito import (Function, TimeFunction, cos, sin, solve,\n",
    "                    Eq, Operator, configuration, norm)\n",
    "from examples.seismic import TimeAxis, RickerSource, Receiver, demo_model\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "917216d5",
   "metadata": {},
   "source": [
    "We will start with the definitions of the grid and the physical parameters $v_{p}, \\theta, \\epsilon, \\delta$. For simplicity, we won't use any absorbing boundary conditions to avoid reflections of outgoing waves at the boundaries of the computational domain, but we will have boundary conditions (zero Dirichlet) at $x=0,nx$ and $z=0,nz$ for the solution of the Poisson equation. We use a homogeneous model. The model is discretized with a grid of $101 \\times 101$ and spacing of 10 m. The $v_{p}, \\epsilon, \\delta$ and $\\theta$ parameters of this model are 3600 m∕s, 0.23, 0.17, and 45°, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f545ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `pad_vp` ran in 0.01 s\n",
      "Operator `pad_epsilon` ran in 0.01 s\n",
      "Operator `pad_delta` ran in 0.01 s\n",
      "Operator `pad_theta` ran in 0.01 s\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT   \n",
    "\n",
    "shape   = (101,101) # 101x101 grid\n",
    "spacing = (10.,10.) # spacing of 10 meters\n",
    "origin  = (0.,0.)  \n",
    "nbl = 0  # number of pad points\n",
    "\n",
    "model = demo_model('layers-tti', spacing=spacing, space_order=8,\n",
    "                   shape=shape, nbl=nbl, nlayers=1)\n",
    "\n",
    "# initialize Thomsem parameters to those used in Mu et al., (2020)\n",
    "model.update('vp', np.ones(shape)*3.6) # km/s\n",
    "model.update('epsilon', np.ones(shape)*0.23)\n",
    "model.update('delta', np.ones(shape)*0.17)\n",
    "model.update('theta', np.ones(shape)*(45.*(np.pi/180.))) # radians"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e33564",
   "metadata": {},
   "source": [
    "In cell below, symbols used in the PDE definition are obtained from the `model` object. Note that trigonometric functions proper of Devito are exploited."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5609da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get symbols from model\n",
    "theta = model.theta\n",
    "delta = model.delta\n",
    "epsilon = model.epsilon\n",
    "m = model.m\n",
    "\n",
    "# Use trigonometric functions from Devito\n",
    "costheta  = cos(theta)\n",
    "sintheta  = sin(theta)\n",
    "cos2theta = cos(2*theta)\n",
    "sin2theta = sin(2*theta)\n",
    "sin4theta = sin(4*theta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c005d37",
   "metadata": {},
   "source": [
    "Accordingly to [Mu et al., (2020)](https://library.seg.org/doi/10.1190/geo2019-0320.1), the time sampling can be chosen as \n",
    "$$\n",
    "\\Delta t < \\frac{\\Delta d}{\\pi \\cdot (v_{p})_{max}}\\sqrt{\\dfrac{1}{(1+\\eta_{max}|\\cos\\theta-\\sin\\theta|_{max}^{2})}}\n",
    "$$,\n",
    "\n",
    "where $\\eta_{max}$ denotes the maximum value between $|\\epsilon|_{max}$ and $|\\delta|_{max}$, $|cos\\theta-sin\\theta|_{max}$ is the maximum value of $|cos\\theta-sin\\theta|$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "550ae9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# Values used to compute the time sampling\n",
    "epsilonmax = np.max(np.abs(epsilon.data[:]))\n",
    "deltamax = np.max(np.abs(delta.data[:]))\n",
    "etamax = max(epsilonmax, deltamax)\n",
    "vmax = model._max_vp\n",
    "max_cos_sin = np.amax(np.abs(np.cos(theta.data[:]) - np.sin(theta.data[:])))\n",
    "dvalue = min(spacing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b54c0b0",
   "metadata": {},
   "source": [
    "The next step is to define the simulation time. It has to be small enough to avoid reflections from borders. Note we will use the `dt` computed below rather than the one provided by the property() function `critical_dt` in the `SeismicModel` class, as the latter only works for the coupled pseudoacoustic equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d25d4e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_range;  TimeAxis: start=0, stop=150.313, step=0.884194, num=171\n"
     ]
    }
   ],
   "source": [
    "# Compute the dt and set time range\n",
    "t0 = 0.   #  Simulation time start\n",
    "tn = 150. #  Simulation time end (0.15 second = 150 msec)\n",
    "dt = (dvalue/(np.pi*vmax))*np.sqrt(1/(1+etamax*(max_cos_sin)**2)) # eq. above (cell 3)\n",
    "time_range = TimeAxis(start=t0,stop=tn,step=dt)\n",
    "print(\"time_range; \", time_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4df594",
   "metadata": {},
   "source": [
    "In exactly the same form as in the [Cavity flow with Navier-Stokes]() tutorial, we will use two operators, one for solving the Poisson equation in pseudotime and one for advancing in time. But unlike what was done in such tutorial, in this case, we write the FD solution of the poisson equation in a manually way, without using the `laplace` shortcut and `solve` functionality (just to break up the routine and try to vary). The internal time loop can be controlled by supplying the number of pseudotime steps (`niter_poisson` iterations) as a `time` argument to the operator. A Ricker wavelet source with peak frequency of 20 Hz is located at center of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72831343",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# time stepping \n",
    "p = TimeFunction(name=\"p\", grid=model.grid, time_order=2, space_order=2)\n",
    "q = Function(name=\"q\", grid=model.grid, space_order=8)\n",
    "\n",
    "# Main equations\n",
    "term1_p = (1 + 2*delta*(sintheta**2)*(costheta**2) + 2*epsilon*costheta**4)*q.dx4\n",
    "term2_p = (1 + 2*delta*(sintheta**2)*(costheta**2) + 2*epsilon*sintheta**4)*q.dy4\n",
    "term3_p = (2-delta*(sin2theta)**2 + 3*epsilon*(sin2theta)**2 + 2*delta*(cos2theta)**2)*((q.dy2).dx2)\n",
    "term4_p = ( delta*sin4theta - 4*epsilon*sin2theta*costheta**2)*((q.dy).dx3)\n",
    "term5_p = (-delta*sin4theta - 4*epsilon*sin2theta*sintheta**2)*((q.dy3).dx)\n",
    "\n",
    "stencil_p = solve(m*p.dt2 - (term1_p + term2_p + term3_p + term4_p + term5_p), p.forward)\n",
    "update_p = Eq(p.forward, stencil_p)\n",
    "\n",
    "# Poisson eq. (following notebook 6 from CFD examples)\n",
    "b = Function(name='b', grid=model.grid, space_order=2)\n",
    "pp = TimeFunction(name='pp', grid=model.grid, space_order=2)\n",
    "\n",
    "# Create stencil and boundary condition expressions\n",
    "x, z = model.grid.dimensions\n",
    "t = model.grid.stepping_dim\n",
    "\n",
    "update_q = Eq( pp[t+1,x,z],((pp[t,x+1,z] + pp[t,x-1,z])*z.spacing**2 + (pp[t,x,z+1] + pp[t,x,z-1])*x.spacing**2 -\n",
    "         b[x,z]*x.spacing**2*z.spacing**2) / (2*(x.spacing**2 + z.spacing**2)))\n",
    "\n",
    "bc = [Eq(pp[t+1,x, 0], 0.)]\n",
    "bc += [Eq(pp[t+1,x, shape[1]+2*nbl-1], 0.)]\n",
    "bc += [Eq(pp[t+1,0, z], 0.)]\n",
    "bc += [Eq(pp[t+1,shape[0]-1+2*nbl, z], 0.)]\n",
    "\n",
    "# set source and receivers\n",
    "src = RickerSource(name='src',grid=model.grid,f0=0.02,npoint=1,time_range=time_range)\n",
    "src.coordinates.data[:,0] = model.domain_size[0]* .5\n",
    "src.coordinates.data[:,1] = model.domain_size[0]* .5\n",
    "# Define the source injection\n",
    "src_term = src.inject(field=p.forward,expr=src * dt**2 / m)\n",
    "\n",
    "rec  = Receiver(name='rec',grid=model.grid,npoint=shape[0],time_range=time_range)\n",
    "rec.coordinates.data[:, 0] = np.linspace(model.origin[0],model.domain_size[0], num=model.shape[0])\n",
    "rec.coordinates.data[:, 1] = 2*spacing[1]\n",
    "# Create interpolation expression for receivers\n",
    "rec_term = rec.interpolate(expr=p.forward)\n",
    "\n",
    "# Operators\n",
    "optime=Operator([update_p] + src_term + rec_term)\n",
    "oppres=Operator([update_q] + bc)\n",
    "\n",
    "# you can print the generated code for both operators by typing print(optime) and print(oppres)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f338fd91",
   "metadata": {},
   "source": [
    "The time steps are advanced through a Python loop where both operators `optime` and `oppres`are called. Note the use of module indices to get proper buffers. We set the number of iteration `niter_poisson` for approximating the solution to Poisson equation as 1200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7ae4857",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n",
      "Operator `Kernel` ran in 0.01 s\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "psave =np.empty ((time_range.num,model.grid.shape[0],model.grid.shape[1]))\n",
    "niter_poisson = 1200\n",
    "\n",
    "# This is the time loop.\n",
    "for step in range(0,time_range.num-2):\n",
    "    q.data[:,:]=pp.data[(niter_poisson+1)%2,:,:]\n",
    "    optime(time_m=step, time_M=step, dt=dt)\n",
    "    pp.data[:,:]=0.\n",
    "    b.data[:,:]=p.data[(step+1)%3,:,:]\n",
    "    oppres(time_M = niter_poisson)\n",
    "    psave[step,:,:]=p.data[(step+1)%3,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33785207",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some useful definitions for plotting if nbl is set to any other value than zero\n",
    "nxpad,nzpad = shape[0] + 2 * nbl, shape[1] + 2 * nbl\n",
    "shape_pad   = np.array(shape) + 2 * nbl\n",
    "origin_pad  = tuple([o - s*nbl for o, s in zip(origin, spacing)])\n",
    "extent_pad  = tuple([s*(n-1) for s, n in zip(spacing, shape_pad)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bad90b",
   "metadata": {},
   "source": [
    "We can plot equally spaced snaps (by `factor`) from the full history saved in `psave` using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d5a4a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x504 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# Note: flip sense of second dimension to make the plot positive downwards\n",
    "plt_extent = [origin_pad[0], origin_pad[0] + extent_pad[0],\n",
    "              origin_pad[1] + extent_pad[1], origin_pad[1]]\n",
    "\n",
    "# Plot the wavefields, each normalized to scaled maximum of last time step\n",
    "kt = (time_range.num - 2) - 1\n",
    "amax = 0.05 * np.max(np.abs(psave[kt,:,:]))\n",
    "\n",
    "nsnaps = 10\n",
    "factor = round(time_range.num/nsnaps)\n",
    "\n",
    "fig, axes = plt.subplots(2, 5, figsize=(18, 7), sharex=True)\n",
    "fig.suptitle(\"Snapshots\", size=14)\n",
    "for count, ax in enumerate(axes.ravel()):\n",
    "    snapshot = factor*count\n",
    "    ax.imshow(np.transpose(psave[snapshot,:,:]), cmap=\"seismic\",\n",
    "               vmin=-amax, vmax=+amax, extent=plt_extent)\n",
    "    ax.plot(model.domain_size[0]* .5, model.domain_size[1]* .5, \\\n",
    "         'red', linestyle='None', marker='*', markersize=8, label=\"Source\")\n",
    "    ax.grid()\n",
    "    ax.tick_params('both', length=2, width=0.5, which='major',labelsize=10)\n",
    "    ax.set_title(\"Wavefield at t=%.2fms\" % (factor*count*dt),fontsize=10)\n",
    "for ax in axes[1, :]:\n",
    "    ax.set_xlabel(\"X Coordinate (m)\",fontsize=10)\n",
    "for ax in axes[:, 0]:\n",
    "    ax.set_ylabel(\"Z Coordinate (m)\",fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23446b78",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- **Least-squares reverse time migration in TTI media using a pure qP-wave equation** (2020)\n",
    "<br> Xinru Mu, Jianping Huang, Jidong Yang, Xu Guo, and Yundong Guo\n",
    "<br> Geophysics, Vol. 85, No. 4\n",
    "<br> https://doi.org/10.1190/geo2019-0320.1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}