{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 02 - Reverse Time Migration\n", "\n", "This notebook is the second in a series of tutorial highlighting various aspects of seismic inversion based on Devito operators. In this second example we aim to highlight the core ideas behind seismic inversion, where we create an image of the subsurface from field recorded data. This tutorial follows on the modelling tutorial and will reuse the modelling operator and velocity model.\n", "\n", "## Imaging requirement\n", "\n", "Seismic imaging relies on two known parameters:\n", "\n", "- **Field data** - or also called **recorded data**. This is a shot record corresponding to the true velocity model. In practice this data is acquired as described in the first tutorial. In order to simplify this tutorial we will generate synthetic field data by modelling it with the **true velocity model**.\n", "\n", "- **Background velocity model**. This is a velocity model that has been obtained by processing and inverting the field data. We will look at this methods in the following tutorial as it relies on the method we are describing here. This velocity model is usually a **smooth version** of the true velocity model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imaging computational setup\n", "\n", "In this tutorial, we will introduce the back-propagation operator. This operator simulates the adjoint wave-equation, that is a wave-equation solved in a reversed time order. This time reversal led to the naming of the method we present here, called Reverse Time Migration. The notion of adjoint in exploration geophysics is fundamental as most of the wave-equation based imaging and inversion methods rely on adjoint based optimization methods.\n", "\n", "## Notes on the operators\n", "\n", "As we have already described the creation of a forward modelling operator, we will use a thin wrapper function instead. This wrapper is provided by a utility class called `AcousticWaveSolver`, which provides all the necessary operators for seismic modeling, imaging and inversion. The `AcousticWaveSolver` provides a more concise API for common wave propagation operators and caches the Devito `Operator` objects to avoid unnecessary recompilation. Operators introduced for the first time in this tutorial will be properly described.\n", "\n", "As before we initialize printing and import some utilities. We also raise the Devito log level to avoid excessive logging for repeated operator invocations." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib inline\n", "\n", "from devito import configuration\n", "configuration['log-level'] = 'WARNING'" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Computational considerations\n", "\n", "Seismic inversion algorithms are generally very computationally demanding and require a large amount of memory to store the forward wavefield. In order to keep this tutorial as lightweight as possible we are using a very simple\n", "velocity model that requires low temporal and spatial resolution. For a more realistic model, a second set of preset parameters for a reduced version of the 2D Marmousi data set [1] is provided below in comments. This can be run to create some more realistic subsurface images. However, this second preset is more computationally demanding and requires a slightly more powerful workstation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Configure model presets\n", "from examples.seismic import demo_model\n", "\n", "# Enable model presets here:\n", "preset = 'twolayer-isotropic' # A simple but cheap model (recommended)\n", "# preset = 'marmousi2d-isotropic' # A larger more realistic model\n", "\n", "# Standard preset with a simple two-layer model\n", "if preset == 'twolayer-isotropic':\n", " def create_model(grid=None):\n", " return demo_model('twolayer-isotropic', origin=(0., 0.), shape=(101, 101),\n", " spacing=(10., 10.), nbpml=20, grid=grid, ratio=2)\n", " filter_sigma = (1, 1)\n", " nshots = 21\n", " nreceivers = 101\n", " t0 = 0.\n", " tn = 1000. # Simulation last 1 second (1000 ms)\n", " f0 = 0.010 # Source peak frequency is 10Hz (0.010 kHz)\n", "\n", "\n", "# A more computationally demanding preset based on the 2D Marmousi model\n", "if preset == 'marmousi2d-isotropic':\n", " def create_model(grid=None):\n", " return demo_model('marmousi2d-isotropic', data_path='../../../../data/',\n", " grid=grid, nbpml=20)\n", " filter_sigma = (6, 6)\n", " nshots = 301 # Need good covergae in shots, one every two grid points\n", " nreceivers = 601 # One recevier every grid point\n", " t0 = 0.\n", " tn = 3500. # Simulation last 3.5 second (3500 ms)\n", " f0 = 0.025 # Source peak frequency is 25Hz (0.025 kHz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# True and smooth velocity models\n", "\n", "First, we create the model data for the \"true\" model from a given demonstration preset. This model represents the subsurface topology for the purposes of this example and we will later use it to generate our synthetic data readings. We also generate a second model and apply a smoothing filter to it, which represents our initial model for the imaging algorithm. The perturbation between these two models can be thought of as the image we are trying to recover." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import plot_velocity, plot_perturbation\n", "from scipy import ndimage\n", "\n", "# Create true model from a preset\n", "model = create_model()\n", "\n", "# Create initial model and smooth the boundaries\n", "model0 = create_model(grid=model.grid)\n", "model0.vp = ndimage.gaussian_filter(model0.vp.data, sigma=filter_sigma, order=0)\n", "\n", "# Plot the true and initial model and the perturbation between them\n", "plot_velocity(model)\n", "plot_velocity(model0)\n", "plot_perturbation(model0, model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Acquisition geometry\n", "\n", "Next we define the positioning and the wave signal of our source, as well as the location of our receivers. To generate the wavelet for our source we require the discretized values of time that we are going to use to model a single \"shot\",\n", "which again depends on the grid spacing used in our model. For consistency this initial setup will look exactly as in the previous modelling tutorial, although we will vary the position of our source later on during the actual imaging algorithm." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "# Define acquisition geometry: source\n", "from examples.seismic import AcquisitionGeometry\n", "\n", "# First, position source centrally in all dimensions, then set depth\n", "src_coordinates = np.empty((1, 2))\n", "src_coordinates[0, :] = np.array(model.domain_size) * .5\n", "src_coordinates[0, -1] = 20. # Depth is 20m\n", "\n", "\n", "# Define acquisition geometry: receivers\n", "\n", "# Initialize receivers for synthetic and imaging data\n", "rec_coordinates = np.empty((nreceivers, 2))\n", "rec_coordinates[:, 0] = np.linspace(0, model.domain_size[0], num=nreceivers)\n", "rec_coordinates[:, 1] = 30.\n", "\n", "# Geometry\n", "\n", "geometry = AcquisitionGeometry(model, rec_coordinates, src_coordinates, t0, tn, f0=.010, src_type='Ricker')\n", "# We can plot the time signature to see the wavelet\n", "geometry.src.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# True and smooth data\n", "\n", "We can now generate the shot record (receiver readings) corresponding to our true and initial models. The difference between these two records will be the basis of the imaging procedure.\n", "\n", "For this purpose we will use the same forward modelling operator that was introduced in the previous tutorial, provided by the `AcousticWaveSolver` utility class. This object instantiates a set of pre-defined operators according to an initial definition of the acquisition geometry, consisting of source and receiver symbols. The solver objects caches the individual operators and provides a slightly more high-level API that allows us to invoke the modelling modelling operators from the initial tutorial in a single line. In the following cells we use this to generate shot data by only specifying the respective model symbol `m` to use, and the solver will create and return a new `Receiver` object the represents the readings at the previously defined receiver coordinates. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Compute synthetic data with forward operator \n", "from examples.seismic.acoustic import AcousticWaveSolver\n", "\n", "solver = AcousticWaveSolver(model, geometry, space_order=4)\n", "true_d , _, _ = solver.forward(vp=model.vp)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Compute initial data with forward operator \n", "smooth_d, _, _ = solver.forward(vp=model0.vp)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "# Plot shot record for true and smooth velocity model and the difference\n", "from examples.seismic import plot_shotrecord\n", "\n", "plot_shotrecord(true_d.data, model, t0, tn)\n", "plot_shotrecord(smooth_d.data, model, t0, tn)\n", "plot_shotrecord(smooth_d.data - true_d.data, model, t0, tn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Imaging with back-propagation\n", "\n", "As explained in the introduction of this tutorial, this method is based on back-propagation. \n", "\n", "## Adjoint wave equation\n", "\n", "If we go back to the modelling part, we can rewrite the simulation as a linear system solve:\n", "\n", "\\begin{equation}\n", "\\mathbf{A}(\\mathbf{m}) \\mathbf{u} = \\mathbf{q}\n", "\\end{equation}\n", "\n", "where $\\mathbf{m}$ is the discretized square slowness, $\\mathbf{q}$ is the discretized source and $\\mathbf{A}(\\mathbf{m})$ is the discretized wave-equation. The discretized wave-equation matricial representation is a lower triangular matrix that can be solve with forward substitution. The pointwise writing or the forward substitution leads to the time-stepping stencil.\n", "\n", "On a small problem one could form the matrix explicitly and transpose it to obtain the adjoint discrete wave-equation:\n", "\n", "\\begin{equation}\n", "\\mathbf{A}(\\mathbf{m})^T \\mathbf{v} = \\delta \\mathbf{d}\n", "\\end{equation}\n", "\n", "where $\\mathbf{v}$ is the discrete **adjoint wavefield** and $\\delta \\mathbf{d}$ is the data residual defined as the difference between the field/observed data and the synthetic data $\\mathbf{d}_s = \\mathbf{P}_r \\mathbf{u}$. In our case we derive the discrete adjoint wave-equation from the discrete forward wave-equation to get its stencil. \n", "\n", "## Imaging\n", "\n", "Wave-equation based imaging relies on one simple concept:\n", "\n", "- If the background velocity model is cinematically correct, the forward wavefield $\\mathbf{u}$ and the adjoint wavefield $\\mathbf{v}$ meet at the reflectors position at zero time offset. \n", "\n", "The sum over time of the zero time-offset correlation of these two fields then creates an image of the subsurface. Mathematically this leads to the simple imaging condition:\n", "\n", "\\begin{equation}\n", " \\text{Image} = \\sum_{t=1}^{n_t} \\mathbf{u}[t] \\mathbf{v}[t]\n", "\\end{equation}\n", "\n", "In the following tutorials we will describe a more advanced imaging condition that produces shaper and more accurate results.\n", "\n", "## Operator\n", "\n", "We will now define the imaging operator that computes the adjoint wavefield $\\mathbf{v}$ and correlates it with the forward wavefield $\\mathbf{u}$. This operator essentially consists of three components:\n", "* Stencil update of the adjoint wavefield `v`\n", "* Injection of the data residual at the adjoint source (forward receiver) location\n", "* Correlation of `u` and `v` to compute the image contribution at each timestep" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Define gradient operator for imaging\n", "from devito import TimeFunction, Operator, Eq, solve\n", "from examples.seismic import PointSource\n", "\n", "def ImagingOperator(model, image):\n", " # Define the wavefield with the size of the model and the time dimension\n", " v = TimeFunction(name='v', grid=model.grid, time_order=2, space_order=4)\n", "\n", " u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=4,\n", " save=geometry.nt)\n", " \n", " # Define the wave equation, but with a negated damping term\n", " eqn = model.m * v.dt2 - v.laplace - model.damp * v.dt\n", "\n", " # Use `solve` to rearrange the equation into a stencil expression\n", " stencil = Eq(v.backward, solve(eqn, v.backward))\n", " \n", " # Define residual injection at the location of the forward receivers\n", " dt = model.critical_dt\n", " residual = PointSource(name='residual', grid=model.grid,\n", " time_range=geometry.time_axis,\n", " coordinates=geometry.rec_positions) \n", " res_term = residual.inject(field=v, expr=residual * dt**2 / model.m)\n", "\n", " # Correlate u and v for the current time step and add it to the image\n", " image_update = Eq(image, image - u * v)\n", "\n", " return Operator([stencil] + res_term + [image_update],\n", " subs=model.spacing_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation of the imaging loop\n", "\n", "As just explained, the forward wave-equation is solved forward in time while the adjoint wave-equation is solved in a reversed time order. Therefore, the correlation of these two fields over time requires to store one of the two fields. The computational procedure for imaging follows:\n", "\n", "- Simulate the forward wave-equation with the background velocity model to get the synthetic data and save the full wavefield $\\mathbf{u}$\n", "- Compute the data residual\n", "- Back-propagate the data residual and compute on the fly the image contribution at each time step. \n", "\n", "This procedure is applied to multiple source positions (shots) and summed to obtain the full image of the subsurface. We can first visualize the varying locations of the sources that we will use. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "\n", "# Prepare the varying source locations\n", "source_locations = np.empty((nshots, 2), dtype=np.float32)\n", "source_locations[:, 0] = np.linspace(0., 1000, num=nshots)\n", "source_locations[:, 1] = 30.\n", "\n", "plot_velocity(model, source=source_locations)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imaging source 1 out of 21\n", "Imaging source 2 out of 21\n", "Imaging source 3 out of 21\n", "Imaging source 4 out of 21\n", "Imaging source 5 out of 21\n", "Imaging source 6 out of 21\n", "Imaging source 7 out of 21\n", "Imaging source 8 out of 21\n", "Imaging source 9 out of 21\n", "Imaging source 10 out of 21\n", "Imaging source 11 out of 21\n", "Imaging source 12 out of 21\n", "Imaging source 13 out of 21\n", "Imaging source 14 out of 21\n", "Imaging source 15 out of 21\n", "Imaging source 16 out of 21\n", "Imaging source 17 out of 21\n", "Imaging source 18 out of 21\n", "Imaging source 19 out of 21\n", "Imaging source 20 out of 21\n", "Imaging source 21 out of 21\n" ] } ], "source": [ "# Run imaging loop over shots\n", "from devito import Function\n", "\n", "# Create image symbol and instantiate the previously defined imaging operator\n", "image = Function(name='image', grid=model.grid)\n", "op_imaging = ImagingOperator(model, image)\n", "\n", "for i in range(nshots):\n", " print('Imaging source %d out of %d' % (i+1, nshots))\n", " \n", " # Update source location\n", " geometry.src_positions[0, :] = source_locations[i, :]\n", "\n", " # Generate synthetic data from true model\n", " true_d, _, _ = solver.forward(vp=model.vp)\n", " \n", " # Compute smooth data and full forward wavefield u0\n", " smooth_d, u0, _ = solver.forward(vp=model0.vp, save=True)\n", " \n", " # Compute gradient from the data residual \n", " v = TimeFunction(name='v', grid=model.grid, time_order=2, space_order=4)\n", " residual = smooth_d.data - true_d.data\n", " op_imaging(u=u0, v=v, vp=model0.vp, dt=model0.critical_dt, \n", " residual=residual)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import plot_image\n", "\n", "# Plot the inverted image\n", "plot_image(np.diff(image.data, axis=1))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "assert np.isclose(np.linalg.norm(image.data), 1e6, rtol=1e1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we have an image of the subsurface with a strong reflector at the original location." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] _Versteeg, R.J. & Grau, G. (eds.) (1991): The Marmousi experience. Proc. EAGE workshop on Practical Aspects of Seismic Data Inversion (Copenhagen, 1990), Eur. Assoc. Explor. Geophysicists, Zeist._" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "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": "3.6.9" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }