{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 01 - Introduction to seismic modelling\n", "\n", "This notebook is the first in a series of tutorials highlighting various aspects of seismic inversion based on Devito operators. In this first example we aim to highlight the core ideas behind seismic modelling, where we create a numerical model that captures the processes involved in a seismic survey. This forward model will then form the basis for further tutorials on the implementation of inversion processes using Devito operators.\n", "\n", "## Modelling workflow\n", "\n", "The core process we are aiming to model is a seismic survey, which consists of two main components:\n", "\n", "- **Source** - A source is positioned at a single or a few physical locations where artificial pressure is injected into the domain we want to model. In the case of land survey, it is usually dynamite blowing up at a given location, or a vibroseis (a vibrating engine generating continuous sound waves). For a marine survey, the source is an air gun sending a bubble of compressed air into the water that will expand and generate a seismic wave.\n", "- **Receiver** - A set of microphones or hydrophones are used to measure the resulting wave and create a set of measurements called a *Shot Record*. These measurements are recorded at multiple locations, and usually at the surface of the domain or at the bottom of the ocean in some marine cases.\n", "\n", "In order to create a numerical model of a seismic survey, we need to solve the wave equation and implement source and receiver interpolation to inject the source and record the seismic wave at sparse point locations in the grid.\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The acoustic seismic wave equation\n", "The acoustic wave equation for the square slowness $m$, defined as $m=\\frac{1}{c^2}$, where $c$ is the speed of sound in the given physical media, and a source $q$ is given by:\n", "\n", "\\begin{cases}\n", " &m \\frac{d^2 u(x,t)}{dt^2} - \\nabla^2 u(x,t) = q \\ \\text{in } \\Omega \\\\\n", " &u(.,t=0) = 0 \\\\\n", " &\\frac{d u(x,t)}{dt}|_{t=0} = 0 \n", "\\end{cases}\n", "\n", "with the zero initial conditions to guarantee unicity of the solution.\n", "The boundary conditions are Dirichlet conditions:\n", "\\begin{equation}\n", " u(x,t)|_\\delta\\Omega = 0\n", "\\end{equation}\n", "\n", "where $\\delta\\Omega$ is the surface of the boundary of the model $\\Omega$.\n", "\n", "\n", "# Finite domains\n", "\n", "The last piece of the puzzle is the computational limitation. In the field, the seismic wave propagates in every direction to an \"infinite\" distance. However, solving the wave equation in a mathematically/discrete infinite domain is not feasible. In order to compensate, Absorbing Boundary Conditions (ABC) or Perfectly Matched Layers (PML) are required to mimic an infinite domain. These two methods allow to approximate an infinite media by damping and absorbing the waves at the limit of the domain to avoid reflections.\n", "\n", "The simplest of these methods is the absorbing damping mask. The core idea is to extend the physical domain and to add a Sponge mask in this extension that will absorb the incident waves. The acoustic wave equation with this damping mask can be rewritten as:\n", "\n", "\\begin{cases} \n", " &m \\frac{d^2 u(x,t)}{dt^2} - \\nabla^2 u(x,t) + \\eta \\frac{d u(x,t)}{dt}=q \\ \\text{in } \\Omega \\\\\n", " &u(.,0) = 0 \\\\\n", " &\\frac{d u(x,t)}{dt}|_{t=0} = 0 \n", "\\end{cases}\n", "\n", "where $\\eta$ is the damping mask equal to $0$ inside the physical domain and increasing inside the sponge layer. Multiple choice of profile can be chosen for $\\eta$ from linear to exponential." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Seismic modelling with devito\n", "\n", "We describe here a step by step setup of seismic modelling with Devito in a simple 2D case. We will create a physical model of our domain and define a single source and an according set of receivers to model for the forward model. But first, we initialize some basic utilities." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "# Adding ignore due to (probably an np notebook magic) bug\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the physical problem\n", "\n", "The first step is to define the physical model:\n", "\n", "- What are the physical dimensions of interest\n", "- What is the velocity profile of this physical domain\n", "\n", "We will create a simple velocity model here by hand for demonstration purposes. This model essentially consists of two layers, each with a different velocity: $1.5km/s$ in the top layer and $2.5km/s$ in the bottom layer. We will use this simple model a lot in the following tutorials, so we will rely on a utility function to create it again later." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `initdamp` ran in 0.01 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import Model, plot_velocity\n", "\n", "# Define a physical size\n", "shape = (101, 101) # Number of grid point (nx, nz)\n", "spacing = (10., 10.) # Grid spacing in m. The domain size is now 1km by 1km\n", "origin = (0., 0.) # What is the location of the top left corner. This is necessary to define\n", "# the absolute location of the source and receivers\n", "\n", "# Define a velocity profile. The velocity is in km/s\n", "v = np.empty(shape, dtype=np.float32)\n", "v[:, :51] = 1.5\n", "v[:, 51:] = 2.5\n", "\n", "# With the velocity and model size defined, we can create the seismic model that\n", "# encapsulates this properties. We also define the size of the absorbing layer as 10 grid points\n", "model = Model(vp=v, origin=origin, shape=shape, spacing=spacing,\n", " space_order=2, nbl=10, bcs=\"damp\")\n", "\n", "plot_velocity(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Acquisition geometry\n", "\n", "To fully define our problem setup we also need to define the source that injects the wave to model and the set of receiver locations at which to sample the wavefield. The source time signature will be modelled using a Ricker wavelet defined as\n", "\n", "\\begin{equation}\n", " q(t) = (1-2\\pi^2 f_0^2 (t - \\frac{1}{f_0})^2 )e^{- \\pi^2 f_0^2 (t - \\frac{1}{f_0})}\n", "\\end{equation}\n", "\n", "To fully define the source signature we first need to define the time duration for our model and the timestep size, which is dictated by the CFL condition and our grid spacing. Luckily, our `Model` utility provides us with the critical timestep size, so we can fully discretize our model time axis as an array:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from examples.seismic import TimeAxis\n", "\n", "t0 = 0. # Simulation starts a t=0\n", "tn = 1000. # Simulation last 1 second (1000 ms)\n", "dt = model.critical_dt # Time step from model grid spacing\n", "\n", "time_range = TimeAxis(start=t0, stop=tn, step=dt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The source is positioned at a $20m$ depth and at the middle of the $x$ axis ($x_{src}=500m$), with a peak wavelet frequency of $10Hz$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import RickerSource\n", "\n", "f0 = 0.010 # Source peak frequency is 10Hz (0.010 kHz)\n", "src = RickerSource(name='src', grid=model.grid, f0=f0,\n", " npoint=1, time_range=time_range)\n", "\n", "# First, position source centrally in all dimensions, then set depth\n", "src.coordinates.data[0, :] = np.array(model.domain_size) * .5\n", "src.coordinates.data[0, -1] = 20. # Depth is 20m\n", "\n", "# We can plot the time signature to see the wavelet\n", "src.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly to our source object, we can now define our receiver geometry as a symbol of type `Receiver`. It is worth noting here that both utility classes, `RickerSource` and `Receiver` are thin wrappers around the Devito's `SparseTimeFunction` type, which encapsulates sparse point data and allows us to inject and interpolate values into and out of the computational grid. As we have already seen, both types provide a `.coordinates` property to define the position within the domain of all points encapsulated by that symbol. \n", "\n", "In this example we will position receivers at the same depth as the source, every $10m$ along the x axis. The `rec.data` property will be initialized, but left empty, as we will compute the receiver readings during the simulation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import Receiver\n", "\n", "# Create symbol for 101 receivers\n", "rec = Receiver(name='rec', grid=model.grid, npoint=101, time_range=time_range)\n", "\n", "# Prescribe even spacing for receivers along the x-axis\n", "rec.coordinates.data[:, 0] = np.linspace(0, model.domain_size[0], num=101)\n", "rec.coordinates.data[:, 1] = 20. # Depth is 20m\n", "\n", "# We can now show the source and receivers within our domain:\n", "# Red dot: Source location\n", "# Green dots: Receiver locations (every 4th point)\n", "plot_velocity(model, source=src.coordinates.data,\n", " receiver=rec.coordinates.data[::4, :])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Finite-difference discretization\n", "\n", "Devito is a finite-difference DSL that solves the discretized wave-equation on a Cartesian grid. The finite-difference approximation is derived from Taylor expansions of the continuous field after removing the error term.\n", "\n", "## Time discretization\n", "\n", "We only consider the second order time discretization for now. From the Taylor expansion, the second order discrete approximation of the second order time derivative is:\n", "\\begin{equation}\n", "\\begin{aligned}\n", " \\frac{d^2 u(x,t)}{dt^2} = \\frac{\\mathbf{u}(\\mathbf{x},\\mathbf{t+\\Delta t}) - 2 \\mathbf{u}(\\mathbf{x},\\mathbf{t}) + \\mathbf{u}(\\mathbf{x},\\mathbf{t-\\Delta t})}{\\mathbf{\\Delta t}^2} + O(\\mathbf{\\Delta t}^2).\n", "\\end{aligned}\n", "\\end{equation} \n", "\n", "where $\\mathbf{u}$ is the discrete wavefield, $\\mathbf{\\Delta t}$ is the discrete\n", "time-step (distance between two consecutive discrete time points) and $O(\\mathbf{\\Delta\n", " t}^2)$ is the discretization error term. The discretized approximation of the\n", "second order time derivative is then given by dropping the error term. This derivative is represented in Devito by `u.dt2` where u is a `TimeFunction` object.\n", "\n", "## Spatial discretization \n", "\n", "We define the discrete Laplacian as the sum of the second order spatial\n", "derivatives in the three dimensions:\n", "\\begin{equation}\n", "\\begin{aligned}\n", "\\Delta \\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t})= \\sum_{j=1}^{j=\\frac{k}{2}} \\Bigg[\\alpha_j \\Bigg(&\n", "\\mathbf{u}(\\mathbf{x+jdx},\\mathbf{y},\\mathbf{z},\\mathbf{t})+\\mathbf{u}(\\mathbf{x-jdx},\\mathbf{y},\\mathbf{z},\\mathbf{t}) + \\\\\n", "&\\mathbf{u}(\\mathbf{x},\\mathbf{y+jdy},\\mathbf{z},\\mathbf{t})+\\mathbf{u}(\\mathbf{x},\\mathbf{y-jdy},\\mathbf{z}\\mathbf{t}) + \\\\\n", "&\\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z+jdz},\\mathbf{t})+\\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z-jdz},\\mathbf{t})\\Bigg) \\Bigg] + \\\\\n", "&3\\alpha_0 \\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}).\n", "\\end{aligned}\n", "\\end{equation}\n", "\n", "This derivative is represented in Devito by `u.laplace` where u is a `TimeFunction` object.\n", "\n", "## Wave equation\n", "\n", "With the space and time discretization defined, we can fully discretize the wave-equation with the combination of time and space discretizations and obtain the following second order in time and $k^{th}$ order in space discrete stencil to update one grid point at position $\\mathbf{x}, \\mathbf{y},\\mathbf{z}$ at time $\\mathbf{t}$, i.e.\n", "\\begin{equation}\n", "\\begin{aligned}\n", "\\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t+\\Delta t}) = &2\\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}) - \\mathbf{u}(\\mathbf{x},\\mathbf{y}, \\mathbf{z},\\mathbf{t-\\Delta t}) +\\\\\n", "& \\frac{\\mathbf{\\Delta t}^2}{\\mathbf{m(\\mathbf{x},\\mathbf{y},\\mathbf{z})}} \\Big(\\Delta \\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}) + \\mathbf{q}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}) \\Big). \n", "\\end{aligned}\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle damp(x, y) \\frac{\\partial}{\\partial t} u(t, x, y) - \\frac{\\partial^{2}}{\\partial x^{2}} u(t, x, y) - \\frac{\\partial^{2}}{\\partial y^{2}} u(t, x, y) + \\frac{\\frac{\\partial^{2}}{\\partial t^{2}} u(t, x, y)}{vp(x, y)}$" ], "text/plain": [ "damp(x, y)*Derivative(u(t, x, y), t) - Derivative(u(t, x, y), (x, 2)) - Derivative(u(t, x, y), (y, 2)) + Derivative(u(t, x, y), (t, 2))/vp(x, y)**2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# In order to represent the wavefield u and the square slowness we need symbolic objects\n", "# corresponding to time-space-varying field (u, TimeFunction) and\n", "# space-varying field (m, Function)\n", "from devito import TimeFunction\n", "\n", "# Define the wavefield with the size of the model and the time dimension\n", "u = TimeFunction(name=\"u\", grid=model.grid, time_order=2, space_order=2)\n", "\n", "# We can now write the PDE\n", "pde = model.m * u.dt2 - u.laplace + model.damp * u.dt\n", "\n", "# The PDE representation is as on paper\n", "pde" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle u(t + dt, x, y) = \\frac{- \\frac{- \\frac{2.0 u(t, x, y)}{dt^{2}} + \\frac{u(t - dt, x, y)}{dt^{2}}}{vp(x, y)} + \\frac{\\partial^{2}}{\\partial x^{2}} u(t, x, y) + \\frac{\\partial^{2}}{\\partial y^{2}} u(t, x, y) + \\frac{damp(x, y) u(t, x, y)}{dt}}{\\frac{damp(x, y)}{dt} + \\frac{1}{dt^{2} vp(x, y)}}$" ], "text/plain": [ "Eq(u(t + dt, x, y), (-(-2.0*u(t, x, y)/dt**2 + u(t - dt, x, y)/dt**2)/vp(x, y)**2 + Derivative(u(t, x, y), (x, 2)) + Derivative(u(t, x, y), (y, 2)) + damp(x, y)*u(t, x, y)/dt)/(damp(x, y)/dt + 1/(dt**2*vp(x, y)**2)))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This discrete PDE can be solved in a time-marching way updating u(t+dt) from the previous time step\n", "# Devito as a shortcut for u(t+dt) which is u.forward. We can then rewrite the PDE as\n", "# a time marching updating equation known as a stencil using customized SymPy functions\n", "from devito import Eq, solve\n", "\n", "stencil = Eq(u.forward, solve(pde, u.forward))\n", "stencil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Source injection and receiver interpolation\n", "\n", "With a numerical scheme to solve the homogenous wave equation, we need to add the source to introduce seismic waves and to implement the measurement operator, and interpolation operator. This operation is linked to the discrete scheme and needs to be done at the proper time step. The semi-discretized in time wave equation with a source reads:\n", "\n", "\\begin{equation}\n", "\\begin{aligned}\n", "\\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t+\\Delta t}) = &2\\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}) - \\mathbf{u}(\\mathbf{x},\\mathbf{y}, \\mathbf{z},\\mathbf{t-\\Delta t}) +\\\\\n", "& \\frac{\\mathbf{\\Delta t}^2}{\\mathbf{m(\\mathbf{x},\\mathbf{y},\\mathbf{z})}} \\Big(\\Delta \\mathbf{u}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}) + \\mathbf{q}(\\mathbf{x},\\mathbf{y},\\mathbf{z},\\mathbf{t}) \\Big). \n", "\\end{aligned}\n", "\\end{equation}\n", "\n", "It shows that in order to update $\\mathbf{u}$ at time $\\mathbf{t+\\Delta t}$ we have to inject the value of the source term $\\mathbf{q}$ of time $\\mathbf{t}$. In Devito, it corresponds the update of $u$ at index $t+1$ (t = time implicitly) with the source of time $t$.\n", "On the receiver side, the problem is either as it only requires to record the data at the given time step $t$ for the receiver at time $time=t$.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Finally we define the source injection and receiver read function to generate the corresponding code\n", "src_term = src.inject(field=u.forward, expr=src * dt**2 / model.m)\n", "\n", "# Create interpolation expression for receivers\n", "rec_term = rec.interpolate(expr=u.forward)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Devito operator and solve\n", "After constructing all the necessary expressions for updating the wavefield, injecting the source term and interpolating onto the receiver points, we can now create the Devito operator that will generate the C code at runtime. When creating the operator, Devito's two optimization engines will log which performance optimizations have been performed:\n", "* **DSE:** The Devito Symbolics Engine will attempt to reduce the number of operations required by the kernel.\n", "* **DLE:** The Devito Loop Engine will perform various loop-level optimizations to improve runtime performance.\n", "\n", "**Note**: The argument `subs=model.spacing_map` causes the operator to substitute values for our current grid spacing into the expressions before code generation. This reduces the number of floating point operations executed by the kernel by pre-evaluating certain coefficients." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from devito import Operator\n", "\n", "op = Operator([stencil] + src_term + rec_term, subs=model.spacing_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can execute the create operator for a number of timesteps. We specify the number of timesteps to compute with the keyword `time` and the timestep size with `dt`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `Kernel` ran in 0.02 s\n" ] }, { "data": { "text/plain": [ "PerformanceSummary([(PerfKey(name='section0', rank=None),\n", " PerfEntry(time=0.0063620000000000195, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section1', rank=None),\n", " PerfEntry(time=0.0019160000000000086, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section2', rank=None),\n", " PerfEntry(time=0.0027800000000000025, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "op(time=time_range.num-1, dt=model.critical_dt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After running our operator kernel, the data associated with the receiver symbol `rec.data` has now been populated due to the interpolation expression we inserted into the operator. This allows us the visualize the shot record:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import plot_shotrecord\n", "\n", "plot_shotrecord(rec.data, model, t0, tn)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "assert np.isclose(np.linalg.norm(rec.data), 370, rtol=1)" ] } ], "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": "3.10.12" }, "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 } }, "nbformat": 4, "nbformat_minor": 4 }