examples/seismic/self_adjoint/sa_02_iso_implementation2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of a Devito self adjoint variable density visco- acoustic isotropic modeling operator <br>-- Linearized Ops --\n",
    "\n",
    "## This operator is contributed by Chevron Energy Technology Company (2020)\n",
    "\n",
    "This operator is based on simplfications of the systems presented in:\n",
    "<br>**Self-adjoint, energy-conserving second-order pseudoacoustic systems for VTI and TTI media for reverse time migration and full-waveform inversion** (2016)\n",
    "<br>Kenneth Bube, John Washbourne, Raymond Ergas, and Tamas Nemeth\n",
    "<br>SEG Technical Program Expanded Abstracts\n",
    "<br>https://library.seg.org/doi/10.1190/segam2016-13878451.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction \n",
    "\n",
    "The goal of this tutorial set is to generate and prove correctness of modeling and inversion capability in Devito for variable density visco- acoustics using an energy conserving form of the wave equation. We describe how the linearization of the energy conserving *self adjoint* system with respect to modeling parameters allows using the same modeling system for all nonlinear and linearized forward and adjoint finite difference evolutions. There are three notebooks in this series:\n",
    "\n",
    "##### 1. Implementation of a Devito self adjoint variable density visco- acoustic isotropic modeling operator -- Nonlinear Ops\n",
    "- Implement the nonlinear modeling operations. \n",
    "- [sa_01_iso_implementation1.ipynb](sa_01_iso_implementation1.ipynb)\n",
    "\n",
    "##### 2. Implementation of a Devito self adjoint variable density visco- acoustic isotropic modeling operator -- Linearized Ops\n",
    "- Implement the linearized (Jacobian) ```forward``` and ```adjoint``` modeling operations.\n",
    "- [sa_02_iso_implementation2.ipynb](sa_02_iso_implementation2.ipynb)\n",
    "\n",
    "##### 3. Implementation of a Devito self adjoint variable density visco- acoustic isotropic modeling operator -- Correctness Testing\n",
    "- Tests the correctness of the implemented operators.\n",
    "- [sa_03_iso_correctness.ipynb](sa_03_iso_correctness.ipynb)\n",
    "\n",
    "There are similar series of notebooks implementing and testing operators for VTI and TTI anisotropy ([README.md](README.md)).\n",
    "\n",
    "Below we continue the implementation of our *self adjoint* wave equation with dissipation only Q attenuation, and linearize the modeling operator with respect to the model parameter velocity. We show how to implement finite difference evolutions to compute the action of the ```forward``` and ```adjoint``` Jacobian. \n",
    "\n",
    "## Outline \n",
    "1. Define symbols \n",
    "2. The nonlinear operator \n",
    "3. The Jacobian opeator \n",
    "4. Create the Devito grid and model fields \n",
    "5. The simulation time range and acquistion geometry \n",
    "6. Implement and run the nonlinear forward operator \n",
    "7. Implement and run the Jacobian forward operator \n",
    "8. Implement and run the Jacobian adjoint operator \n",
    "9. References \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of symbols\n",
    "\n",
    "There are many more symbols for the Jacobian than we had in the previous notebook. We need to introduce terminology for the nonlinear operator, including total, background and perturbation fields for several variables.  \n",
    "\n",
    "| Symbol &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Description  | Dimensionality | \n",
    "|:---|:---|:---|\n",
    "| $\\overleftarrow{\\partial_t}$ | shifted first derivative wrt $t$ | shifted 1/2 sample backward in time |\n",
    "| $\\partial_{tt}$ | centered second derivative wrt $t$ | centered in time |\n",
    "| $\\overrightarrow{\\partial_x},\\ \\overrightarrow{\\partial_y},\\ \\overrightarrow{\\partial_z}$ | + shifted first derivative wrt $x,y,z$ | shifted 1/2 sample forward in space |\n",
    "| $\\overleftarrow{\\partial_x},\\ \\overleftarrow{\\partial_y},\\ \\overleftarrow{\\partial_z}$ | - shifted first derivative wrt $x,y,z$ | shifted 1/2 sample backward in space |\n",
    "| $\\omega_c = 2 \\pi f$ | center angular frequency | constant |\n",
    "| $b(x,y,z)$   | buoyancy $(1 / \\rho)$ | function of space |\n",
    "| $Q(x,y,z)$   | Attenuation at frequency $\\omega_c$ | function of space |\n",
    "| $m(x,y,z)$ | Total P wave velocity ($m_0+\\delta m$) | function of space |\n",
    "| $m_0(x,y,z)$ | Background P wave velocity    | function of space |\n",
    "| $\\delta m(x,y,z)$ | Perturbation to P wave velocity    | function of space |\n",
    "| $u(t,x,y,z)$ | Total pressure wavefield ($u_0+\\delta u$)| function of time and space |\n",
    "| $u_0(t,x,y,z)$ | Background pressure wavefield | function of time and space |\n",
    "| $\\delta u(t,x,y,z)$ | Perturbation to pressure wavefield | function of time and space |\n",
    "| $q(t,x,y,z)$ | Source wavefield | function of time, localized in space to source location |\n",
    "| $r(t,x,y,z)$ | Receiver wavefield | function of time, localized in space to receiver locations |\n",
    "| $F[m; q]$ | Forward nonlinear modeling operator | Nonlinear in $m$, linear in $q$: $\\quad$ maps $m \\rightarrow r$ |\n",
    "| $\\nabla F[m; q]\\ \\delta m$ | Forward Jacobian modeling operator | Linearized at $[m; q]$: $\\quad$ maps $\\delta m \\rightarrow \\delta r$ |\n",
    "| $\\bigl( \\nabla F[m; q] \\bigr)^\\top\\ \\delta r$ | Adjoint Jacobian modeling operator | Linearized at $[m; q]$: $\\quad$ maps $\\delta r \\rightarrow \\delta m$ |\n",
    "| $\\Delta_t, \\Delta_x, \\Delta_y, \\Delta_z$ | sampling rates for $t, x, y , z$ | $t, x, y , z$ | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A word about notation \n",
    "\n",
    "We use the arrow symbols over derivatives $\\overrightarrow{\\partial_x}$ as a shorthand notation to indicate that the derivative is taken at a shifted location. For example:\n",
    "\n",
    "- $\\overrightarrow{\\partial_x}\\ u(t,x,y,z)$ indicates that the $x$ derivative of $u(t,x,y,z)$ is taken at $u(t,x+\\frac{\\Delta x}{2},y,z)$.\n",
    "\n",
    "- $\\overleftarrow{\\partial_z}\\ u(t,x,y,z)$ indicates that the $z$ derivative of $u(t,x,y,z)$ is taken at $u(t,x,y,z-\\frac{\\Delta z}{2})$.\n",
    "\n",
    "- $\\overleftarrow{\\partial_t}\\ u(t,x,y,z)$ indicates that the $t$ derivative of $u(t,x,y,z)$ is taken at $u(t-\\frac{\\Delta_t}{2},x,y,z)$.\n",
    "\n",
    "We usually drop the $(t,x,y,z)$ notation from wavefield variables unless required for clarity of exposition, so that $u(t,x,y,z)$ becomes $u$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Nonlinear operator\n",
    "\n",
    "The nonlinear operator is the solution to the self adjoint scalar isotropic variable density visco- acoustic wave equation shown immediately below, and maps the velocity model vector $m$ into the receiver wavefield vector $r$.\n",
    "\n",
    "$$\n",
    "\\frac{b}{m^2} \\left( \\frac{\\omega_c}{Q}\\overleftarrow{\\partial_t}\\ u + \\partial_{tt}\\ u \\right) =\n",
    "    \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x}\\ u \\right) +\n",
    "    \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y}\\ u \\right) +\n",
    "    \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z}\\ u \\right) + q\n",
    "$$\n",
    "\n",
    "In operator notation, where the operator is nonlinear with respect to model $m$ to the left of semicolon inside the square brackets, and linear with respect to the source $q$ to the right of semicolon inside the square brackets.\n",
    "\n",
    "$$\n",
    "F[m; q] = r\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Jacobian operator\n",
    "\n",
    "In this section we linearize about a background model and take the derivative of the nonlinear operator to obtain the Jacobian forward operator. \n",
    "\n",
    "In operator notation, where the derivative of the modeling operator is now linear in the model perturbation vector $\\delta m$, the Jacobian operator maps a perturbation in the velocity model $\\delta m$ into a perturbation in the receiver wavefield $\\delta r$.\n",
    "\n",
    "$$\n",
    "\\nabla F[m; q]\\ \\delta m = \\delta r\n",
    "$$\n",
    "\n",
    "#### 1. We begin by simplifying notation \n",
    "To simplify the treatment below we introduce the operator $L_t[\\cdot]$, accounting for the time derivatives inside the parentheses on the left hand side of the wave equation. \n",
    "\n",
    "$$\n",
    "L_t[\\cdot] \\equiv \\frac{\\omega_c}{Q} \\overleftarrow{\\partial_t}[\\cdot] + \\partial_{tt}[\\cdot]\n",
    "$$\n",
    "\n",
    "Next we re-write the wave equation using this notation.\n",
    "\n",
    "$$\n",
    "\\frac{b}{m^2} L_t[u] =\n",
    "    \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x}\\ u \\right) +\n",
    "    \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y}\\ u \\right) +\n",
    "    \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z}\\ u \\right) + q\n",
    "$$\n",
    "\n",
    "#### 2. Linearize\n",
    "To linearize we treat the total model as the sum of background and perturbation models $\\left(m = m_0 + \\delta m\\right)$, and the total pressure as the sum of background and perturbation pressures $\\left(u = u_0 + \\delta u\\right)$.\n",
    "\n",
    "$$\n",
    "\\frac{b}{(m_0+\\delta m)^2} L_t[u_0+\\delta u] =\n",
    "    \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x} (u_0+\\delta u) \\right) +\n",
    "    \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y} (u_0+\\delta u) \\right) +\n",
    "    \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z} (u_0+\\delta u) \\right) + q\n",
    "$$\n",
    "\n",
    "Note that *model parameters* for this variable density isotropic visco-acoustic physics is only velocity, we do not treat perturbations to density.\n",
    "\n",
    "We also write the PDE for the background model, which we subtract after linearization to simplify the final expression. \n",
    "\n",
    "$$\n",
    "\\frac{b}{m_0^2} L_t[u_0] =\n",
    "    \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x} u_0 \\right) +\n",
    "    \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y} u_0 \\right) +\n",
    "    \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z} u_0 \\right) + q\n",
    "$$\n",
    "\n",
    "#### 3. Take derivative w.r.t. model parameters\n",
    "Next we take the derivative with respect to velocity, keep only terms up to first order in the perturbations, subtract the background model PDE equation, and finally arrive at the following linearized equation:\n",
    "\n",
    "$$\n",
    "\\frac{b}{m_0^2} L_t\\left[\\delta u\\right] =\n",
    "    \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x} \\delta u \\right) +\n",
    "    \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y} \\delta u \\right) +\n",
    "    \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z} \\delta u \\right) + \n",
    "    \\frac{2\\ b\\ \\delta m}{m_0^3} L_t\\left[u_0\\right]\n",
    "$$\n",
    "\n",
    "Note that the source $q$ in the original equation above has disappeared due to subtraction of the background PDE, and has been replaced by the *Born source*: \n",
    "\n",
    "$$\n",
    "q = \\frac{\\displaystyle 2\\ b\\ \\delta m}{\\displaystyle m_0^3} L_t\\left[u_0\\right]\n",
    "$$\n",
    "\n",
    "This is the same equation as used for the nonlinear forward, only now in the perturbed wavefield $\\delta u$ and with the Born source."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The adjoint of the Jacobian operator\n",
    "\n",
    "In this section we introduce the adjoint of the Jacobian operator we derived above. The Jacobian adjoint operator maps a perturbation in receiver wavefield $\\delta r$ into a perturbation in velocity model $\\delta m$. In operator notation:\n",
    "\n",
    "$$\n",
    "\\bigl( \\nabla F[m; q] \\bigr)^\\top\\ \\delta r = \\delta m\n",
    "$$\n",
    "\n",
    "#### 1. Solve the time reversed wave equation with the receiver perturbation as source\n",
    "The PDE for the adjoint of the Jacobian is solved for the perturbation to the pressure wavefield $\\delta u$ by using the same wave equation as the nonlinear forward and the Jacobian forward, with the time reversed receiver wavefield perturbation $\\widetilde{\\delta r}$ injected as source. \n",
    "\n",
    "Note that we use $\\widetilde{\\delta u}$ and $\\widetilde{\\delta r}$ to indicate that we solve this finite difference evolution time reversed.\n",
    "\n",
    "$$\n",
    "\\frac{b}{m_0^2} L_t\\left[\\widetilde{\\delta u}\\right] =\n",
    "    \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x}\\ \\widetilde{\\delta u} \\right) +\n",
    "    \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y}\\ \\widetilde{\\delta u} \\right) +\n",
    "    \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z}\\ \\widetilde{\\delta u} \\right) + \n",
    "    \\widetilde{\\delta r}\n",
    "$$\n",
    "\n",
    "#### 2. Compute zero lag correlation  \n",
    "\n",
    "We compute the perturbation to the velocity model by zero lag correlation of the wavefield perturbation $\\widetilde{\\delta u}$ solved in step 1 as shown in the following expression: \n",
    "\n",
    "$$\n",
    "\\delta m(x,y,z) = \\sum_t \n",
    "    \\left\\{ \n",
    "        \\widetilde{\\delta u}(t,x,y,z)\\ \n",
    "        \\frac{\\displaystyle 2\\ b}{\\displaystyle m_0^3} L_t\\bigl[u_0(t,x,y,z)\\bigr]\n",
    "    \\right\\}\n",
    "$$\n",
    "\n",
    "Note that this correlation can be more formally derived by examining the equations for two Green's functions, one for the background model ($m_0$) and wavefield ($u_0$), and one for for the total model $(m_0 + \\delta m)$ and wavefield $(u_0 + \\delta u)$, and subtracting to derive the equation for Born scattering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation\n",
    "\n",
    "Next we assemble the Devito objects needed to implement these linearized operators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports \n",
    "\n",
    "We have grouped all imports used in this notebook here for consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from examples.seismic import RickerSource, Receiver, TimeAxis\n",
    "from devito import (Grid, Function, TimeFunction, SpaceDimension, Constant, \n",
    "                    Eq, Operator, solve, configuration, norm)\n",
    "from devito.finite_differences import Derivative\n",
    "from devito.builtins import gaussian_smooth\n",
    "from examples.seismic.self_adjoint import setup_w_over_q\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "# These lines force images to be displayed in the notebook, and scale up fonts \n",
    "%matplotlib inline\n",
    "mpl.rc('font', size=14)\n",
    "\n",
    "# Make white background for plots, not transparent\n",
    "plt.rcParams['figure.facecolor'] = 'white'\n",
    "\n",
    "# We define 32 bit floating point as the precision type \n",
    "dtype = np.float32\n",
    "\n",
    "# Set logging to debug, captures statistics on the performance of operators\n",
    "# configuration['log-level'] = 'DEBUG'\n",
    "configuration['log-level'] = 'INFO'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate the Devito grid for a two dimensional problem\n",
    "\n",
    "We define the grid the same as in the previous notebook outlining implementation for the nonlinear forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape;            (301, 301)\n",
      "origin;           (0.0, 0.0)\n",
      "spacing;          (10.0, 10.0)\n",
      "extent;           (3000.0, 3000.0)\n",
      "\n",
      "shape_pad;        [401 401]\n",
      "origin_pad;       (-500.0, -500.0)\n",
      "extent_pad;       (4000.0, 4000.0)\n",
      "\n",
      "grid.shape;       (401, 401)\n",
      "grid.extent;      (4000.0, 4000.0)\n",
      "grid.spacing_map; {h_x: 10.0, h_z: 10.0}\n"
     ]
    }
   ],
   "source": [
    "# Define dimensions for the interior of the model\n",
    "nx,nz = 301,301\n",
    "dx,dz = 10.0,10.0  # Grid spacing in m\n",
    "shape = (nx, nz)   # Number of grid points\n",
    "spacing = (dx, dz) # Domain size is now 5 km by 5 km\n",
    "origin = (0., 0.)  # Origin of coordinate system, specified in m.\n",
    "extent = tuple([s*(n-1) for s, n in zip(spacing, shape)])\n",
    "\n",
    "# Define dimensions for the model padded with absorbing boundaries\n",
    "npad = 50          # number of points in absorbing boundary region (all sides)\n",
    "nxpad,nzpad = nx + 2 * npad, nz + 2 * npad\n",
    "shape_pad   = np.array(shape) + 2 * npad\n",
    "origin_pad  = tuple([o - s*npad for o, s in zip(origin, spacing)])\n",
    "extent_pad  = tuple([s*(n-1) for s, n in zip(spacing, shape_pad)])\n",
    "\n",
    "# Define the dimensions \n",
    "# Note if you do not specify dimensions, you get in order x,y,z\n",
    "x = SpaceDimension(name='x', spacing=Constant(name='h_x', \n",
    "                   value=extent_pad[0]/(shape_pad[0]-1)))\n",
    "z = SpaceDimension(name='z', spacing=Constant(name='h_z', \n",
    "                   value=extent_pad[1]/(shape_pad[1]-1)))\n",
    "\n",
    "# Initialize the Devito grid \n",
    "grid = Grid(extent=extent_pad, shape=shape_pad, origin=origin_pad, \n",
    "            dimensions=(x, z), dtype=dtype)\n",
    "\n",
    "print(\"shape;           \", shape)\n",
    "print(\"origin;          \", origin)\n",
    "print(\"spacing;         \", spacing)\n",
    "print(\"extent;          \", extent)\n",
    "print(\"\")\n",
    "print(\"shape_pad;       \", shape_pad)\n",
    "print(\"origin_pad;      \", origin_pad)\n",
    "print(\"extent_pad;      \", extent_pad)\n",
    "\n",
    "print(\"\")\n",
    "print(\"grid.shape;      \", grid.shape)\n",
    "print(\"grid.extent;     \", grid.extent)\n",
    "print(\"grid.spacing_map;\", grid.spacing_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define velocity, buoyancy and $\\frac{\\omega_c}{Q}$ model parameters\n",
    "\n",
    "We have the following constants and fields to define:\n",
    "\n",
    "| &nbsp; Symbol &nbsp; | Description |\n",
    "|:---:|:---|\n",
    "| $$m0(x,z)$$ | Background velocity model |\n",
    "| $$\\delta m(x,z)$$ | Perturbation to velocity model |\n",
    "| $$b(x,z)=\\frac{1}{\\rho(x,z)}$$ | Buoyancy (reciprocal density) |\n",
    "| $$\\omega_c = 2 \\pi f_c$$      | Center angular frequency |\n",
    "| $$\\frac{1}{Q(x,z)}$$ | Inverse Q model used in the modeling system |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `WOverQ_Operator` ran in 0.01 s\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# Create the velocity and buoyancy fields as in the nonlinear notebook \n",
    "space_order = 8\n",
    "\n",
    "# Wholespace velocity\n",
    "m0 = Function(name='m0', grid=grid, space_order=space_order)\n",
    "m0.data[:] = 1.5\n",
    "\n",
    "# Perturbation to velocity: a square offset from the center of the model\n",
    "dm = Function(name='dm', grid=grid, space_order=space_order)\n",
    "size = 10\n",
    "x0 = shape_pad[0]//2\n",
    "z0 = shape_pad[1]//2\n",
    "dm.data[:] = 0.0\n",
    "dm.data[x0-size:x0+size, z0-size:z0+size] = 1.0\n",
    "\n",
    "# Constant density\n",
    "b = Function(name='b', grid=grid, space_order=space_order)\n",
    "b.data[:,:] = 1.0 / 1.0\n",
    "\n",
    "# Initialize the attenuation profile for Q=100 model\n",
    "fpeak = 0.010\n",
    "w = 2.0 * np.pi * fpeak\n",
    "qmin = 0.1\n",
    "qmax = 1000.0\n",
    "wOverQ = Function(name='wOverQ', grid=grid, space_order=space_order)\n",
    "setup_w_over_q(wOverQ, w, qmin, 100.0, npad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the simulation time range and the acquisition geometry \n",
    "\n",
    "#### Simulation time range: \n",
    "\n",
    "In this notebook we run 3 seconds of simulation using the sample rate related to the CFL condition as implemented in ```examples/seismic/self_adjoint/utils.py```. \n",
    "\n",
    "We also use the convenience ```TimeRange``` as defined in ```examples/seismic/source.py```.\n",
    "\n",
    "#### Acquisition geometry: \n",
    "\n",
    "**source**:\n",
    "- X coordinate: left sode of model\n",
    "- Z coordinate: middle of model\n",
    "- We use a 10 Hz center frequency [RickerSource](https://github.com/devitocodes/devito/blob/master/examples/seismic/source.py#L280) wavelet source as defined in ```examples/seismic/source.py```\n",
    "\n",
    "**receivers**:\n",
    "- X coordinate: right side of model\n",
    "- Z coordinate: vertical line in model\n",
    "- We use a vertical line of [Receivers](https://github.com/devitocodes/devito/blob/master/examples/seismic/source.py#L80) as defined with a ```PointSource``` in ```examples/seismic/source.py```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_critical_dt(v):\n",
    "    \"\"\"\n",
    "    Determine the temporal sampling to satisfy CFL stability.\n",
    "    This method replicates the functionality in the Model class.\n",
    "    Note we add a safety factor, reducing dt by a factor 0.75 due to the\n",
    "    w/Q attentuation term.\n",
    "    Parameters\n",
    "    ----------\n",
    "    v : Function\n",
    "        velocity\n",
    "    \"\"\"\n",
    "    coeff = 0.38 if len(v.grid.shape) == 3 else 0.42\n",
    "    dt = 0.75 * v.dtype(coeff * np.min(v.grid.spacing) / (np.max(v.data)))\n",
    "    return v.dtype(\"%.5e\" % dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time min, max, dt, num;   0.000000 1200.000000   2.100000 572\n",
      "time_range;  TimeAxis: start=0, stop=1201.2, step=2.1, num=573\n",
      "src_coordinate  X;            +752.0000\n",
      "src_coordinate  Z;           +1505.0000\n",
      "rec_coordinates X min/max;   +2257.0000   +2257.0000\n",
      "rec_coordinates Z min/max;      +0.0000   +3000.0000\n"
     ]
    }
   ],
   "source": [
    "t0 = 0.0     # Simulation time start\n",
    "tn = 1200.0  # Simulation time end (1 second = 1000 msec)\n",
    "dt = compute_critical_dt(m0)\n",
    "time_range = TimeAxis(start=t0, stop=tn, step=dt)\n",
    "print(\"Time min, max, dt, num; %10.6f %10.6f %10.6f %d\" % (t0, tn, dt, int(tn//dt) + 1))\n",
    "print(\"time_range; \", time_range)\n",
    "\n",
    "# Source at 1/4 X, 1/2 Z, Ricker with 10 Hz center frequency\n",
    "src_nl = RickerSource(name='src_nl', grid=grid, f0=fpeak, npoint=1, time_range=time_range)\n",
    "src_nl.coordinates.data[0,0] = dx * 1 * nx//4\n",
    "src_nl.coordinates.data[0,1] = dz * shape[1]//2\n",
    "\n",
    "# Receivers at 3/4 X, line in Z\n",
    "rec_nl = Receiver(name='rec_nl', grid=grid, npoint=nz, time_range=time_range)\n",
    "rec_nl.coordinates.data[:,0] = dx * 3 * nx//4\n",
    "rec_nl.coordinates.data[:,1] = np.linspace(0.0, dz*(nz-1), nz)\n",
    "\n",
    "print(\"src_coordinate  X;         %+12.4f\" % (src_nl.coordinates.data[0,0]))\n",
    "print(\"src_coordinate  Z;         %+12.4f\" % (src_nl.coordinates.data[0,1]))\n",
    "print(\"rec_coordinates X min/max; %+12.4f %+12.4f\" % \\\n",
    "      (np.min(rec_nl.coordinates.data[:,0]), np.max(rec_nl.coordinates.data[:,0])))\n",
    "print(\"rec_coordinates Z min/max; %+12.4f %+12.4f\" % \\\n",
    "      (np.min(rec_nl.coordinates.data[:,1]), np.max(rec_nl.coordinates.data[:,1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the model \n",
    "\n",
    "We plot the following ```Functions```:\n",
    "- Background Velocity\n",
    "- Background Density\n",
    "- Velocity perturbation\n",
    "- Q Model\n",
    "\n",
    "Each subplot also shows:\n",
    "- The location of the absorbing boundary as a dotted line\n",
    "- The source location as a red star\n",
    "- The line of receivers as a black vertical line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "vmin, vmax = 1.5, 2.0\n",
    "pmin, pmax = -1, +1\n",
    "bmin, bmax = 0.9, 1.1\n",
    "\n",
    "q = w / wOverQ.data[:]\n",
    "\n",
    "x1 = 0.0\n",
    "x2 = dx * nx\n",
    "z1 = 0.0\n",
    "z2 = dz * nz\n",
    "abcX = [x1,x1,x2,x2,x1]\n",
    "abcZ = [z1,z2,z2,z1,z1]\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.imshow(np.transpose(m0.data), cmap=cm.jet, \n",
    "           vmin=vmin, vmax=vmax, extent=plt_extent)\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.colorbar(orientation='horizontal', label='Velocity (m/msec)')\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Background Velocity\")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.imshow(np.transpose(1 / b.data), cmap=cm.jet,\n",
    "           vmin=bmin, vmax=bmax, extent=plt_extent)\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.colorbar(orientation='horizontal', label='Density (kg/m^3)')\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Background Density\")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.imshow(np.transpose(dm.data), cmap=\"seismic\", \n",
    "           vmin=pmin, vmax=pmax, extent=plt_extent)\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.colorbar(orientation='horizontal', label='Velocity (m/msec)')\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Velocity Perturbation\")\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.imshow(np.transpose(np.log10(q.data)), cmap=cm.jet,\n",
    "           vmin=np.log10(qmin), vmax=np.log10(qmax), extent=plt_extent)\n",
    "plt.plot(abcX, abcZ, 'white', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.colorbar(orientation='horizontal', label='log10 $Q_p$')\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"log10 of $Q_p$ Profile\")\n",
    "\n",
    "plt.tight_layout()\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define pressure wavefields\n",
    "\n",
    "We need two wavefields for Jacobian operations, one computed during the finite difference evolution of the nonlinear forward operator $u_0(t,x,z)$, and one computed during the finite difference evolution of the Jacobian operator $\\delta u(t,x,z)$. \n",
    "\n",
    "For this example workflow we will require saving all time steps from the nonlinear forward operator for use in the Jacobian operators. There are other ways to implement this requirement, including checkpointing, but that is way outside the scope of this illustrative workflow.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the TimeFunctions for nonlinear and Jacobian operations\n",
    "nt = time_range.num\n",
    "u0 = TimeFunction(name=\"u0\", grid=grid, time_order=2, space_order=space_order, save=nt)\n",
    "duFwd = TimeFunction(name=\"duFwd\", grid=grid, time_order=2, space_order=space_order, save=None)\n",
    "duAdj = TimeFunction(name=\"duAdj\", grid=grid, time_order=2, space_order=space_order, save=None)\n",
    "\n",
    "# Get the dimensions for t, x, z \n",
    "t,x,z = u0.dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement and run the nonlinear operator\n",
    "\n",
    "We next transcribe the time update expression for the nonlinear operator above into a Devito ```Eq```. Then we add the source injection and receiver extraction and build an ```Operator``` that will generate the c code for performing the modeling.\n",
    "\n",
    "We copy the time update expression from the first implementation notebook, but omit the source term $q$ because for the nonlinear operator we explicitly inject the source using ```src_term```. \n",
    "\n",
    "We think of this as solving for the *background wavefield* $u_0$ not the total wavefield $u$, and hence we use $v_0$ for velocity instead of $v$.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    u_0(t+\\Delta_t) &=\n",
    "        \\frac{\\Delta_t^2 v_0^2}{b} \\left[ \n",
    "            \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x}\\ u_0 \\right) +\n",
    "            \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y}\\ u_0 \\right) +\n",
    "            \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z}\\ u_0 \\right) + q\n",
    "        \\right] \\\\[5pt]\n",
    "        &\\quad +\\ u_0(t) \\left(2 - \\frac{\\Delta_t^2 \\omega_c}{Q} \\right)\n",
    "        - u_0(t-\\Delta_t) \\left(1 - \\frac{\\Delta_t\\ \\omega_c}{Q} \\right)\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "## Self adjoint means support for nonlinear and linearized ops\n",
    "Note that this stencil can be used for all of the operations we need, modulo the different source terms for the nonlinear and linearized forward evolutions:\n",
    "1. the nonlinear forward (solved forward in time, $q$ is the usual source )\n",
    "2. the Jacobian forward (solved forward in time, $q$ is the Born source )\n",
    "3. the Jacobian adjoint (solved backward in time, $q$ is the time reversed receiver wavefield)\n",
    "\n",
    "## Source injection and receiver extraction for nonlinear forward operator\n",
    "\n",
    "Source injection and receiver extraction follow the implementation shown in the first notebook, please refer there for more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spacing_map;  {h_x: 10.0, h_z: 10.0, dt: 2.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` ran in 0.23 s\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# The nonlinear forward time update equation\n",
    "eq_time_update_nl_fwd = (t.spacing**2 * m0**2 / b) * \\\n",
    "    ((b * u0.dx(x0=x+x.spacing/2)).dx(x0=x-x.spacing/2) +\n",
    "     (b * u0.dz(x0=z+z.spacing/2)).dz(x0=z-z.spacing/2)) + \\\n",
    "    (2 - t.spacing * wOverQ) * u0 + \\\n",
    "    (t.spacing * wOverQ - 1) * u0.backward\n",
    "\n",
    "stencil_nl = Eq(u0.forward, eq_time_update_nl_fwd)\n",
    "\n",
    "# Update the dimension spacing_map to include the time dimension\n",
    "# Please refer to the first implementation notebook for more information\n",
    "spacing_map = grid.spacing_map\n",
    "spacing_map.update({t.spacing : dt})\n",
    "print(\"spacing_map; \", spacing_map)\n",
    "\n",
    "# Source injection and Receiver extraction\n",
    "src_term_nl = src_nl.inject(field=u0.forward, expr=src_nl * t.spacing**2 * m0**2 / b)\n",
    "rec_term_nl = rec_nl.interpolate(expr=u0.forward)\n",
    "\n",
    "# Instantiate and run the operator for the nonlinear forward\n",
    "op_nl = Operator([stencil_nl] + src_term_nl + rec_term_nl, subs=spacing_map)\n",
    "u0.data[:] = 0\n",
    "op_nl.apply()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continuous integration hooks \n",
    "# We ensure the norm of these computed wavefields is repeatable\n",
    "# print(norm(u0))\n",
    "assert np.isclose(norm(u0), 3098.012, atol=0, rtol=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement and run the Jacobian forward operator\n",
    "\n",
    "We next transcribe the time update expression for the linearized operator into a Devito ```Eq```. Note that the source injection for the linearized operator is very different, and involves the Born source derived above *everywhere in space*. \n",
    "\n",
    "Please refer to the first notebook for the derivation of the time update equation if you don't follow this step.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "    \\delta u(t+\\Delta_t) &=\n",
    "        \\frac{\\Delta_t^2 v_0^2}{b} \\left[ \n",
    "            \\overleftarrow{\\partial_x}\\left(b\\ \\overrightarrow{\\partial_x}\\ \\delta u \\right) +\n",
    "            \\overleftarrow{\\partial_y}\\left(b\\ \\overrightarrow{\\partial_y}\\ \\delta u \\right) +\n",
    "            \\overleftarrow{\\partial_z}\\left(b\\ \\overrightarrow{\\partial_z}\\ \\delta u \\right) + q\n",
    "        \\right] \\\\[5pt]\n",
    "        &\\quad +\\ \\delta u(t) \\left(2 - \\frac{\\Delta_t^2 \\omega_c}{Q} \\right)\n",
    "        - \\delta u(t-\\Delta_t) \\left(1 - \\frac{\\Delta_t\\ \\omega_c}{Q} \\right) \\\\[10pt]\n",
    "    q &= \\frac{2\\ b\\ \\delta m}{m_0^3} L_t\\left[u_0\\right]\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "## Source injection and receiver extraction for linearized forward operator\n",
    "\n",
    "Note the source for the linearized forward operator is the Born source $q$, so we do not require a source injection term as with the nonlinear operator. \n",
    "\n",
    "As this is a forward operator, we are mapping into receiver gathers and therefore need to define both a container and an extraction term for receiver data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` ran in 0.30 s\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# The linearized forward time update equation\n",
    "eq_time_update_ln_fwd = (t.spacing**2 * m0**2 / b) * \\\n",
    "    ((b * duFwd.dx(x0=x+x.spacing/2)).dx(x0=x-x.spacing/2) +\n",
    "     (b * duFwd.dz(x0=z+z.spacing/2)).dz(x0=z-z.spacing/2) + \n",
    "     2 * b * dm * m0**-3 * (wOverQ * u0.dt(x0=t-t.spacing/2) + u0.dt2)) +\\\n",
    "    (2 - t.spacing * wOverQ) * duFwd + \\\n",
    "    (t.spacing * wOverQ - 1) * duFwd.backward\n",
    "\n",
    "stencil_ln_fwd = Eq(duFwd.forward, eq_time_update_ln_fwd)\n",
    "\n",
    "# Receiver container and receiver extraction for the linearized operator\n",
    "rec_ln = Receiver(name='rec_ln', grid=grid, npoint=nz, time_range=time_range)\n",
    "rec_ln.coordinates.data[:,:] = rec_nl.coordinates.data[:,:]\n",
    "rec_term_ln_fwd = rec_ln.interpolate(expr=duFwd.forward)\n",
    "\n",
    "# Instantiate and run the operator for the linearized forward\n",
    "op_ln_fwd = Operator([stencil_ln_fwd] + rec_term_ln_fwd, subs=spacing_map)\n",
    "duFwd.data[:] = 0\n",
    "op_ln_fwd.apply()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continuous integration hooks \n",
    "# We ensure the norm of these computed wavefields is repeatable\n",
    "# print(norm(duFwd))\n",
    "assert np.isclose(norm(duFwd), 227.063, atol=0, rtol=1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the computed nonlinear and linearized forward wavefields\n",
    "\n",
    "Below we show the nonlinear and Born scattered wavefields at the end of the finite difference evolution. You can clearly see both forward and backward scattered energy from the velocity perturbation in the linearized forward (Born) wavefield, with appropriate polatiry reversals in the events. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amax nl;     1.607966\n",
      "amax ln t=1.20s;     0.704292\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# Plot the two wavefields, each normalized to own maximum\n",
    "kt  = nt - 2\n",
    "\n",
    "amax_nl = 1.0 * np.max(np.abs(u0.data[kt,:,:]))\n",
    "amax_ln = 0.1 * np.max(np.abs(duFwd.data[kt,:,:]))\n",
    "\n",
    "print(\"amax nl; %12.6f\" % (amax_nl))\n",
    "print(\"amax ln t=%.2fs; %12.6f\" % (dt * kt / 1000, amax_ln))\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(np.transpose(u0.data[kt,:,:]), cmap=\"seismic\", \n",
    "           vmin=-amax_nl, vmax=+amax_nl, extent=plt_extent)\n",
    "plt.colorbar(orientation='horizontal', label='Amplitude')\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Nonlinear wavefield at t=%.2fs\" % (dt * kt / 1000))\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(np.transpose(duFwd.data[kt,:,:]), cmap=\"seismic\",\n",
    "           vmin=-amax_ln, vmax=+amax_ln, extent=plt_extent)\n",
    "plt.colorbar(orientation='horizontal', label='Amplitude')\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Born wavefield at t=%.2fs\" % (dt * kt / 1000))\n",
    "\n",
    "plt.tight_layout()\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An alternative implementation for the linearized forward\n",
    "\n",
    "We would like to acknowledge Mathias Louboutin for an alternative method of implementing the linearized forward operator that is very efficient and perhaps novel. The driver code that implements the Jacobian forward operator (```examples/seismic/self_adjoint/operators.py```) can solve for both the nonlinear and linearized finite difference evolutions simultaneously. This implies significant performance gains with respect to cache pressure.\n",
    "\n",
    "We outline below this code for your enjoyment, with line numbers added:\n",
    "\n",
    "```\n",
    "01 # Time update equations for nonlinear and linearized operators\n",
    "02 eqn1 = iso_stencil(u0, model, forward=True)\n",
    "03 eqn2 = iso_stencil(du, model, forward=True,\n",
    "04                   q=2 * b * dm * v**-3 * (wOverQ * u0.dt(x0=t-t.spacing/2) + u0.dt2))\n",
    "05\n",
    "06 # Inject the source into the nonlinear wavefield at u0(t+dt)\n",
    "07 src_term = src.inject(field=u0.forward, expr=src * t.spacing**2 * v**2 / b)\n",
    "08 \n",
    "09 # Extract receiver wavefield from the linearized wavefield, at du(t)\n",
    "10 rec_term = rec.interpolate(expr=du)\n",
    "11 \n",
    "12 # Create the operator\n",
    "13 Operator(eqn1 + src_term + eqn2 + rec_term, subs=spacing_map,\n",
    "14          name='ISO_JacobianFwdOperator', **kwargs)\n",
    "```\n",
    "\n",
    "One important thing to note about this code is the precedence of operations specified on the construction of the operator at line 13. It is guaranteed by Devito that ```eqn1``` will 'run' before ```eqn2```. This means that this specific order will occurr in the generated code: \n",
    "1. The nonlinear wavefield is advanced in time\n",
    "2. The nonlinear source is injected in the nonlinear wavefield\n",
    "3. The linearized wavefield is advanced in time\n",
    "4. The linearixzed wavefield is interpolated at the receiever locations\n",
    "\n",
    "As an exercise, you might implement this operator and print the generated c code to confirm this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement and run the Jacobian adjoint operator\n",
    "\n",
    "The linearized Jacobian adjoint uses the same time update equation as written above so we do not reproduce it here. Note that the finite difference evolution for the Jacobian adjoint runs *time-reversed* and a receiver wavefield is injected as source term. For this example we will inject the recorded linearized wavefield, which will provide an \"image\" of the Born scatterer. \n",
    "\n",
    "## Zero lag temporal correlation to build image\n",
    "\n",
    "We rewrite the zero lag temporal correlation that builds up the image from above. The sum is achieved in Devito via ```Eq(dm, dm + <...>)```, where ```<...>``` is the operand of the zero lag correlation shown immediately below.\n",
    "\n",
    "$$\n",
    "\\delta m(x,y,z) = \\sum_t \n",
    "    \\left\\{ \n",
    "        \\widetilde{\\delta u}(t,x,y,z)\\ \n",
    "        \\frac{\\displaystyle 2\\ b}{\\displaystyle m_0^3} L_t\\left[u_0(t,x,y,z)\\right]\n",
    "    \\right\\}\n",
    "$$\n",
    "\n",
    "Note we instantiate a new ```Function``` $dm_a$ to hold the output from the linearized adjoint operator. \n",
    "\n",
    "## Source injection and receiver extraction for linearized adjoint operator\n",
    "\n",
    "Note the source for the linearized adjoint operator is the receiver wavefield, injected time-reversed.\n",
    "\n",
    "As this is an adjoint operator, we are mapping into the model domain and therefore do not need to define receivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operator `Kernel` ran in 0.31 s\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "# New Function to hold the output from the adjoint\n",
    "dmAdj = Function(name='dmAdj', grid=grid, space_order=space_order)\n",
    "\n",
    "# The linearized adjoint time update equation\n",
    "# Note the small differencess from the linearized forward above\n",
    "eq_time_update_ln_adj = (t.spacing**2 * m0**2 / b) * \\\n",
    "    ((b * duAdj.dx(x0=x+x.spacing/2)).dx(x0=x-x.spacing/2) +\n",
    "     (b * duAdj.dz(x0=z+z.spacing/2)).dz(x0=z-z.spacing/2)) +\\\n",
    "    (2 - t.spacing * wOverQ) * duAdj + \\\n",
    "    (t.spacing * wOverQ - 1) * duAdj.forward\n",
    "\n",
    "stencil_ln_adj = Eq(duAdj.backward, eq_time_update_ln_adj)\n",
    "\n",
    "# Equation to sum the zero lag correlation\n",
    "dm_update = Eq(dmAdj, dmAdj +\n",
    "               duAdj * (2 * b * m0**-3 * (wOverQ * u0.dt(x0=t-t.spacing/2) + u0.dt2)))\n",
    "\n",
    "# Receiver injection, time reversed\n",
    "rec_term_ln_adj = rec_ln.inject(field=duAdj.backward, expr=rec_ln * t.spacing**2 * m0**2 / b)\n",
    "\n",
    "# Instantiate and run the operator for the linearized forward\n",
    "op_ln_adj = Operator([dm_update] + [stencil_ln_adj] + rec_term_ln_adj, subs=spacing_map)\n",
    "op_ln_adj.apply()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Continuous integration hooks \n",
    "# We ensure the norm of these computed wavefields is repeatable\n",
    "# print(norm(duAdj))\n",
    "assert np.isclose(norm(duAdj), 19218.924, atol=0, rtol=1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the image\n",
    "\n",
    "Below we plot the velocity perturbation and the \"image\" recovered from the linearized Jacobian adjoint. We normalize both fields to their maximum absolute value.\n",
    "\n",
    "Note that with a single source and this transmission geometry, we should expect to see significant horizontal smearing in the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amax dm;   5.000000e-01\n",
      "amax dmAdj 4.128168e+02\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "\n",
    "amax1 = 0.5 * np.max(np.abs(dm.data[:]))\n",
    "amax2 = 0.5 * np.max(np.abs(dmAdj.data[:]))\n",
    "\n",
    "print(\"amax dm;   %12.6e\" % (amax1))\n",
    "print(\"amax dmAdj %12.6e\" % (amax2))\n",
    "\n",
    "dm.data[:] = dm.data / amax1\n",
    "dmAdj.data[:] = dmAdj.data / amax2\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(np.transpose(dm.data), cmap=\"seismic\", \n",
    "           vmin=-1, vmax=+1, extent=plt_extent, aspect=\"auto\")\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.colorbar(orientation='horizontal', label='Velocity (m/msec)')\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Velocity Perturbation\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(np.transpose(dmAdj.data), cmap=\"seismic\", \n",
    "           vmin=-1, vmax=+1, extent=plt_extent, aspect=\"auto\")\n",
    "plt.plot(abcX, abcZ, 'gray', linewidth=4, linestyle=':', label=\"Absorbing Boundary\")\n",
    "plt.plot(src_nl.coordinates.data[:, 0], src_nl.coordinates.data[:, 1], \\\n",
    "         'red', linestyle='None', marker='*', markersize=15, label=\"Source\")\n",
    "plt.plot(rec_nl.coordinates.data[:, 0], rec_nl.coordinates.data[:, 1], \\\n",
    "         'black', linestyle='None', marker='^', markersize=2, label=\"Receivers\")\n",
    "plt.colorbar(orientation='horizontal', label='Velocity (m/msec)')\n",
    "plt.xlabel(\"X Coordinate (m)\")\n",
    "plt.ylabel(\"Z Coordinate (m)\")\n",
    "plt.title(\"Output from Jacobian adjoint\")\n",
    "\n",
    "plt.tight_layout()\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "This concludes the implementation of the linearized Jacobian forward and adjoint operators. \n",
    "\n",
    "Please note there are details critical for practical use of these tools missing from this demonstration. In particular, for practical application to industry scale problems there would need to be a mechanism to save or checkpoint the wavefield from the nonlinear forward for use in the linearized finite difference evolutions.  \n",
    "\n",
    "Please continue with the final notebook in this series concerning the correctness testing of these operators. \n",
    "\n",
    "[sa_03_iso_correctness.ipynb](sa_03_iso_correctness.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "- **A nonreflecting boundary condition for discrete acoustic and elastic wave equations** (1985)\n",
    "<br>Charles Cerjan, Dan Kosloff, Ronnie Kosloff, and Moshe Resheq\n",
    "<br> Geophysics, Vol. 50, No. 4\n",
    "<br>https://library.seg.org/doi/pdfplus/10.1190/segam2016-13878451.1\n",
    "\n",
    "- **Generation of Finite Difference Formulas on Arbitrarily Spaced Grids** (1988)\n",
    "<br>Bengt Fornberg\n",
    "<br>Mathematics of Computation, Vol. 51, No. 184\n",
    "<br>http://dx.doi.org/10.1090/S0025-5718-1988-0935077-0\n",
    "<br>https://web.njit.edu/~jiang/math712/fornberg.pdf\n",
    "\n",
    "- **Self-adjoint, energy-conserving second-order pseudoacoustic systems for VTI and TTI media for reverse time migration and full-waveform inversion** (2016)\n",
    "<br>Kenneth Bube, John Washbourne, Raymond Ergas, and Tamas Nemeth\n",
    "<br>SEG Technical Program Expanded Abstracts\n",
    "<br>https://library.seg.org/doi/10.1190/segam2016-13878451.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
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