From f8057be7435f99fa99ff32157785c9f68b717eea Mon Sep 17 00:00:00 2001 From: Amir Mardan <46511946+AmirMardan@users.noreply.github.com> Date: Mon, 15 May 2023 16:35:25 -0400 Subject: [PATCH] add documentation for fwi --- docs/index.rst | 22 +- docs/sub_doc/example.ipynb | 432 --------------- docs/sub_doc/example.rst | 242 ++++++++ docs/sub_doc/example_files/example_15_0.png | Bin 0 -> 85167 bytes docs/sub_doc/example_files/example_20_0.png | Bin 0 -> 13143 bytes docs/sub_doc/example_files/example_29_0.png | Bin 0 -> 38582 bytes docs/sub_doc/example_files/example_7_0.png | Bin 0 -> 27633 bytes docs/sub_doc/fwi_example.rst | 341 ++++++++++++ .../fwi_example_files/fwi_example_12_1.png | Bin 0 -> 40241 bytes .../fwi_example_files/fwi_example_15_1.png | Bin 0 -> 14640 bytes .../fwi_example_files/fwi_example_21_0.png | Bin 0 -> 46749 bytes .../fwi_example_files/fwi_example_5_0.png | Bin 0 -> 27633 bytes docs/sub_doc/grad_pytorch.rst | 219 ++++++++ .../grad_pytorch_files/grad_pytorch_17_0.png | Bin 0 -> 16961 bytes .../grad_pytorch_files/grad_pytorch_21_0.png | Bin 0 -> 13143 bytes .../grad_pytorch_files/grad_pytorch_30_0.png | Bin 0 -> 38582 bytes .../grad_pytorch_files/grad_pytorch_7_0.png | Bin 0 -> 14873 bytes example/fwi_example.ipynb | 516 ++++++++++++++++++ {docs/sub_doc => example}/grad_pytorch.ipynb | 20 +- example/gradient_example.ipynb | 456 ++++++++++++++++ requirements.txt | 6 +- 21 files changed, 1807 insertions(+), 447 deletions(-) delete mode 100644 docs/sub_doc/example.ipynb create mode 100644 docs/sub_doc/example.rst create mode 100644 docs/sub_doc/example_files/example_15_0.png create mode 100644 docs/sub_doc/example_files/example_20_0.png create mode 100644 docs/sub_doc/example_files/example_29_0.png create mode 100644 docs/sub_doc/example_files/example_7_0.png create mode 100644 docs/sub_doc/fwi_example.rst create mode 100644 docs/sub_doc/fwi_example_files/fwi_example_12_1.png create mode 100644 docs/sub_doc/fwi_example_files/fwi_example_15_1.png create mode 100644 docs/sub_doc/fwi_example_files/fwi_example_21_0.png create mode 100644 docs/sub_doc/fwi_example_files/fwi_example_5_0.png create mode 100644 docs/sub_doc/grad_pytorch.rst create mode 100644 docs/sub_doc/grad_pytorch_files/grad_pytorch_17_0.png create mode 100644 docs/sub_doc/grad_pytorch_files/grad_pytorch_21_0.png create mode 100644 docs/sub_doc/grad_pytorch_files/grad_pytorch_30_0.png create mode 100644 docs/sub_doc/grad_pytorch_files/grad_pytorch_7_0.png create mode 100644 example/fwi_example.ipynb rename {docs/sub_doc => example}/grad_pytorch.ipynb (99%) create mode 100644 example/gradient_example.ipynb diff --git a/docs/index.rst b/docs/index.rst index d79ca0d..4cfd652 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -14,6 +14,7 @@ This package is implemented in time domain and coded using GPU programming (PyOp :caption: Getting started sub_doc/example + sub_doc/fwi_example .. toctree:: :maxdepth: 1 @@ -52,14 +53,13 @@ Citing PyFWI :: - @software{PyFWI, - author = {Mardan Amir and - Bernard Giroux and - Gabriel Fabien-Ouellet}, - title = {{PyFWI}: A {Python} package for full-waveform inversion and reservoir monitoring}, - month = Jan, - year = 2022, - publisher = {Zenodo}, - doi = {10.5281/zenodo.5813637}, - url = {https://doi.org/10.5281/zenodo.5813637} - } + @article{mardan2023pyfwi, + title = {PyFWI: {A Python} package for full-waveform inversion and reservoir monitoring}, + author = {Mardan, Amir and Giroux, Bernard and Fabien-Ouellet, Gabriel}, + journal = {SoftwareX}, + volume = {22}, + pages = {101384}, + year = {2023}, + publisher = {Elsevier}, + doi = {10.1016/j.softx.2023.101384} +} diff --git a/docs/sub_doc/example.ipynb b/docs/sub_doc/example.ipynb deleted file mode 100644 index d2f707b..0000000 --- a/docs/sub_doc/example.ipynb +++ /dev/null @@ -1,432 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Installation\n", - "============\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PyFWI can be installed using ```pip``` as\n", - "\n", - "```console\n", - "\n", - " (.venv) $ pip install PyFWI\n", - "\n", - "```\n", - "on macOS or\n", - "\n", - "```console\n", - "\n", - " (.venv) $ py -m pip install PyFWI\n", - "\n", - "```\n", - "\n", - "on Windows." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simple Example\n", - "==============\n", - "\n", - "In this section we see some applications of PyFWI.\n", - "First, forward modeling is shown and then we estimate gradient of cost funtion with respect to $V_P$.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " **1. Forward modeling**\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this simple example, we use PyFWI to do forward modeling. So, we need to first import the following packages amd modulus." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import sys\n", - "sys.path.append('../../src/')\n", - "\n", - "import PyFWI.wave_propagation as wave\n", - "import PyFWI.acquisition as acq\n", - "import PyFWI.seiplot as splt\n", - "import PyFWI.model_dataset as md\n", - "import PyFWI.fwi_tools as tools\n", - "import PyFWI.processing as process\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A simple model can be created by using ```model_dataset``` module as" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Model = md.ModelGenerator('louboutin')\n", - "model = Model()\n", - "\n", - "im = splt.earth_model(model, cmap='coolwarm')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we need to create an input dictionary as follow" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model_shape = model[[*model][0]].shape\n", - "\n", - "inpa = {\n", - " 'ns': 1, # Number of sources\n", - " 'sdo': 4, # Order of FD\n", - " 'fdom': 15, # Central frequency of source\n", - " 'dh': 7, # Spatial sampling rate\n", - " 'dt': 0.004, # Temporal sampling rate\n", - " 'acq_type': 1, # Type of acquisition (0: crosswell, 1: surface, 2: both)\n", - " 't': 0.8, # Length of operation\n", - " 'npml': 20, # Number of PML \n", - " 'pmlR': 1e-5, # Coefficient for PML (No need to change)\n", - " 'pml_dir': 2, # type of boundary layer\n", - " 'device': 1, # The device to run the program. Usually 0: CPU 1: GPU\n", - "}\n", - "\n", - "seisout = 0 # Type of output 0: Pressure\n", - "\n", - "inpa['rec_dis'] = 1 * inpa['dh'] # Define the receivers' distance\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we obtain the location of sources and receivers based on specified parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "offsetx = inpa['dh'] * model_shape[1]\n", - "depth = inpa['dh'] * model_shape[0]\n", - "\n", - "src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx,\n", - " inpa['dh'], inpa['sdo']) \n", - "src_loc[:, 1] -= 5 * inpa['dh']\n", - "\n", - "# Create the source\n", - "src = acq.Source(src_loc, inpa['dh'], inpa['dt'])\n", - "src.Ricker(inpa['fdom'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can have the forward modelling as " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the wave object\n", - "W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout, chpr=20)\n", - "\n", - "# Call the forward modelling \n", - "d_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To compute the gradient using the adjoint-state method, we need to save the wavefield during the forward wave propagation. This must be done for the wavefield obtained from estimated model. \n", - "For example, the wavefield at four time steps are presented here in addition to a shot gather. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(8, 4))\n", - "\n", - "count = 1\n", - "\n", - "ax = fig.add_subplot(122)\n", - "ax = splt.seismic_section(ax, d_obs['taux'], t_axis=np.linspace(0, inpa['t'], int(1 + inpa['t'] // inpa['dt'])))\n", - "\n", - "ax_loc = [1, 2, 5, 6]\n", - "snapshots = [40, 80, 130, 180]\n", - "\n", - "for i in range(len(snapshots)):\n", - " ax = fig.add_subplot(2, 4, ax_loc[i])\n", - " ax.imshow(W.W['taux'][:, :, 0, snapshots[i]], cmap='coolwarm')\n", - " \n", - " ax.axis('off')\n", - " count += 1\n", - "fig.suptitle(\"Wave propagation and a shot gather\", fontweight='bold');\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**2. Gradient**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To compute the gradient, we need the observed data and an initial model. So, first we obtain the observed data using more sources.\n", - "\n", - "\n", - "**Note:** For better visualization and avoiding crosstalk, I compute the gradient in acoustic media." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Making medium acoustic\n", - "model['vs'] *= 0.0\n", - "model['rho'] = np.ones_like(model['rho'])\n", - "\n", - "# Increasing number of sources\n", - "inpa['ns'] = 5\n", - "\n", - "src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx,\n", - " inpa['dh'], inpa['sdo']) \n", - "src_loc[:, 1] -= 5 * inpa['dh']\n", - "\n", - "# Create the source\n", - "src = acq.Source(src_loc, inpa['dh'], inpa['dt'])\n", - "src.Ricker(inpa['fdom'])\n", - "\n", - "# Create the wave object\n", - "W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout, chpr=20)\n", - "\n", - "# Call the forward modelling \n", - "db_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave\n", - "\n", - "# preparing data amd applying gain if required\n", - "db_obs = process.prepare_residual(db_obs, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we create the initial model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m0 = Model(smoothing=1)\n", - "m0['vs'] *= 0.0\n", - "m0['rho'] = np.ones_like(model['rho'])\n", - "\n", - "im = splt.earth_model(m0, ['vp'], cmap='coolwarm')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we simulate the wave propagation to obtain estimated data.\n", - "For computing the gradient, we can smooth the gradient and scale it by defining `g_smooth` and `energy_balancing`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "inpa['energy_balancing'] = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We save the wavefield at 20% of the time steps (`chpr = 20`) to be used for gradient calculation. The value of wavefield is accessible using the attribute ```W``` which is a dictionary for $V_x$, $V_z$, $\\tau_x$, $\\tau_z$, and $\\tau_{xz}$ as ```vx```, ```vz```, ```taux```, ```tauz```, and ```tauxz```.\n", - "Each parameter is a 4D tensor. For example, we can have access to the last time step of $\\tau_x$ for the first shot as ```W.W['taux'][:, :, 0, -1]```." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "Lam = wave.WavePropagator(inpa, src, rec_loc, model_shape,\n", - " chpr=20, components=seisout)\n", - "\n", - "d_est = Lam.forward_modeling(m0, False)\n", - "d_est = process.prepare_residual(d_est, 1)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we define the cost function and obtaine the residuals for adjoint-state method." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "CF = tools.CostFunction('l2')\n", - "rms, adj_src = tools.cost_seismic(d_est, db_obs, fun=CF)\n", - "# print(rms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the adjoint source, we can estimate the gradient as " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "grad = Lam.gradient(adj_src, show=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Time to plot the results\n", - "splt.earth_model(grad, ['vp'], cmap='jet');\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "vscode": { - "interpreter": { - "hash": "0f81fc2e4a358d2a0e372de0a65782557c8127804cea09d304df813e671e8a74" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/sub_doc/example.rst b/docs/sub_doc/example.rst new file mode 100644 index 0000000..ffd94ea --- /dev/null +++ b/docs/sub_doc/example.rst @@ -0,0 +1,242 @@ +Installation +============ + +PyFWI can be installed using ``pip`` as + +.. code:: console + + + (.venv) $ pip install PyFWI + +on macOS or + +.. code:: console + + + (.venv) $ py -m pip install PyFWI + +on Windows. + +Simple Gradient Computation +============================ + +In this section we see some applications of PyFWI. First, forward +modeling is shown and then we estimate gradient of cost funtion with +respect to :math:`V_P`. + +**1. Forward modeling** + +In this simple example, we use PyFWI to do forward modeling. So, we need +to first import the following packages amd modulus. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import numpy as np + + import PyFWI.wave_propagation as wave + import PyFWI.acquisition as acq + import PyFWI.seiplot as splt + import PyFWI.model_dataset as md + import PyFWI.fwi_tools as tools + import PyFWI.processing as process + + + +A simple model can be created by using ``model_dataset`` module as + +.. code:: ipython3 + + Model = md.ModelGenerator('louboutin') + model = Model() + + im = splt.earth_model(model, cmap='coolwarm') + + + +.. image:: example_files/example_7_0.png + + +Then we need to create an input dictionary as follow + +.. code:: ipython3 + + model_shape = model[[*model][0]].shape + + inpa = { + 'ns': 1, # Number of sources + 'sdo': 4, # Order of FD + 'fdom': 15, # Central frequency of source + 'dh': 7, # Spatial sampling rate + 'dt': 0.004, # Temporal sampling rate + 'acq_type': 1, # Type of acquisition (0: crosswell, 1: surface, 2: both) + 't': 0.8, # Length of operation + 'npml': 20, # Number of PML + 'pmlR': 1e-5, # Coefficient for PML (No need to change) + 'pml_dir': 2, # type of boundary layer + 'device': 1, # The device to run the program. Usually 0: CPU 1: GPU + } + + seisout = 0 # Type of output 0: Pressure + + inpa['rec_dis'] = 1 * inpa['dh'] # Define the receivers' distance + + +Now, we obtain the location of sources and receivers based on specified +parameters. + +.. code:: ipython3 + + offsetx = inpa['dh'] * model_shape[1] + depth = inpa['dh'] * model_shape[0] + + src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx, + inpa['dh'], inpa['sdo']) + src_loc[:, 1] -= 5 * inpa['dh'] + + # Create the source + src = acq.Source(src_loc, inpa['dh'], inpa['dt']) + src.Ricker(inpa['fdom']) + + +Finally, we can have the forward modelling as + +.. code:: ipython3 + + # Create the wave object + W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout, chpr=20) + + # Call the forward modelling + d_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave + +To compute the gradient using the adjoint-state method, we need to save +the wavefield during the forward wave propagation. This must be done for +the wavefield obtained from estimated model. For example, the wavefield +at four time steps are presented here in addition to a shot gather. + +.. code:: ipython3 + + fig = plt.figure(figsize=(8, 4)) + + count = 1 + + ax = fig.add_subplot(122) + ax = splt.seismic_section(ax, d_obs['taux'], t_axis=np.linspace(0, inpa['t'], int(1 + inpa['t'] // inpa['dt']))) + + ax_loc = [1, 2, 5, 6] + snapshots = [40, 80, 130, 180] + + for i in range(len(snapshots)): + ax = fig.add_subplot(2, 4, ax_loc[i]) + ax.imshow(W.W['taux'][:, :, 0, snapshots[i]], cmap='coolwarm') + + ax.axis('off') + count += 1 + fig.suptitle("Wave propagation and a shot gather", fontweight='bold'); + + + + +.. image:: example_files/example_15_0.png + + +**2. Gradient** + +To compute the gradient, we need the observed data and an initial model. +So, first we obtain the observed data using more sources. + +**Note:** For better visualization and avoiding crosstalk, I compute the +gradient in acoustic media. + +.. code:: ipython3 + + # Making medium acoustic + model['vs'] *= 0.0 + model['rho'] = np.ones_like(model['rho']) + + # Increasing number of sources + inpa['ns'] = 5 + + src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx, + inpa['dh'], inpa['sdo']) + src_loc[:, 1] -= 5 * inpa['dh'] + + # Create the source + src = acq.Source(src_loc, inpa['dh'], inpa['dt']) + src.Ricker(inpa['fdom']) + + # Create the wave object + W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout, chpr=20) + + # Call the forward modelling + db_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave + + # preparing data amd applying gain if required + db_obs = process.prepare_residual(db_obs, 1) + +Then we create the initial model. + +.. code:: ipython3 + + m0 = Model(smoothing=1) + m0['vs'] *= 0.0 + m0['rho'] = np.ones_like(model['rho']) + + im = splt.earth_model(m0, ['vp'], cmap='coolwarm') + + + +.. image:: example_files/example_20_0.png + + +And we simulate the wave propagation to obtain estimated data. For +computing the gradient, we can smooth the gradient and scale it by +defining ``g_smooth`` and ``energy_balancing``. + +.. code:: ipython3 + + inpa['energy_balancing'] = True + +We save the wavefield at 20% of the time steps (``chpr = 20``) to be +used for gradient calculation. The value of wavefield is accessible +using the attribute ``W`` which is a dictionary for :math:`V_x`, +:math:`V_z`, :math:`\tau_x`, :math:`\tau_z`, and :math:`\tau_{xz}` as +``vx``, ``vz``, ``taux``, ``tauz``, and ``tauxz``. Each parameter is a +4D tensor. For example, we can have access to the last time step of +:math:`\tau_x` for the first shot as ``W.W['taux'][:, :, 0, -1]``. + +.. code:: ipython3 + + Lam = wave.WavePropagator(inpa, src, rec_loc, model_shape, + chpr=20, components=seisout) + + d_est = Lam.forward_modeling(m0, False) + d_est = process.prepare_residual(d_est, 1) + + +Now, we define the cost function and obtaine the residuals for +adjoint-state method. + +.. code:: ipython3 + + CF = tools.CostFunction('l2') + rms, adj_src = tools.cost_seismic(d_est, db_obs, fun=CF) + # print(rms) + +Using the adjoint source, we can estimate the gradient as + +.. code:: ipython3 + + grad = Lam.gradient(adj_src, show=False) + +.. code:: ipython3 + + # Time to plot the results + splt.earth_model(grad, ['vp'], cmap='jet'); + + + + +.. image:: example_files/example_29_0.png + + diff --git a/docs/sub_doc/example_files/example_15_0.png b/docs/sub_doc/example_files/example_15_0.png new file mode 100644 index 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Here, a simple example is +provided to show the application of this integration. This notebook can +be compared to the `Simple +Example `__ +for a better understanding of the required changes and the results. + +In this section, we first show the forward modeling, and then we +estimate the gradient of the cost function with respect to :math:`V_P`. + +**1. Forward modeling** + +In this simple example, we use PyFWI to do forward modeling. So, we need +to first import the following packages and modulus. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import numpy as np + + import PyFWI.wave_propagation as wave + import PyFWI.acquisition as acq + import PyFWI.seiplot as splt + import PyFWI.model_dataset as md + import PyFWI.fwi_tools as tools + import PyFWI.processing as process + + import torch + from PyFWI.torchfwi import Fwi + + +A simple model can be created by using ``model_dataset`` module as + +.. code:: ipython3 + + Model = md.ModelGenerator('louboutin') + model = Model() + # Making medium acoustic + model['vs'] *= 0.0 + model['rho'] = np.ones_like(model['rho']) + + im = splt.earth_model(model, ['vp'], cmap='coolwarm') + + + +.. image:: grad_pytorch_files/grad_pytorch_7_0.png + + +Then we need to create an input dictionary as follow + +.. code:: ipython3 + + model_shape = model[[*model][0]].shape + + inpa = { + 'ns': 5, # Number of sources + 'sdo': 4, # Order of FD + 'fdom': 15, # Central frequency of source + 'dh': 7, # Spatial sampling rate + 'dt': 0.004, # Temporal sampling rate + 'acq_type': 1, # Type of acquisition (0: crosswell, 1: surface, 2: both) + 't': 0.8, # Length of operation + 'npml': 20, # Number of PML + 'pmlR': 1e-5, # Coefficient for PML (No need to change) + 'pml_dir': 2, # type of boundary layer + 'device': 1, # The device to run the program. Usually 0: CPU 1: GPU + 'seimogram_shape': '3d', + } + + seisout = 0 # Type of output 0: Pressure + + inpa['rec_dis'] = 1 * inpa['dh'] # Define the receivers' distance + + +Now, we obtain the location of sources and receivers based on specified +parameters. + +.. code:: ipython3 + + offsetx = inpa['dh'] * model_shape[1] + depth = inpa['dh'] * model_shape[0] + + src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx, + inpa['dh'], inpa['sdo']) + src_loc[:, 1] -= 5 * inpa['dh'] + + # Create the source + src = acq.Source(src_loc, inpa['dh'], inpa['dt']) + src.Ricker(inpa['fdom']) + + +Model properties should be with type of ``torch.tensor``. So, we need to +convert these properties. + +.. code:: ipython3 + + vp = torch.tensor(model['vp']) + vs = torch.tensor(model['vs']) + rho = torch.tensor(model['rho']) + +Finally, we can have the forward modelling as + +.. code:: ipython3 + + # Create the wave object + W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout) + + # Call the forward modelling + taux_obs, tauz_obs = Fwi.apply(W, vp, vs, rho) # show=True can show the propagation of the wave + +To compute the gradient using the adjoint-state method, we need to save +the wavefield during the forward wave propagation. This must be done for +the wavefield obtained from estimated model. For example, the wavefield +at four time steps are presented here in addition to a shot gather. + +.. code:: ipython3 + + fig = plt.figure(figsize=(4, 4)) + + ax = fig.add_subplot(111) + ax = splt.seismic_section(ax, taux_obs[..., 2], t_axis=np.linspace(0, inpa['t'], int(1 + inpa['t'] // inpa['dt']))) + + fig.suptitle("Shot gather", fontweight='bold'); + + + + +.. image:: grad_pytorch_files/grad_pytorch_17_0.png + + +**2. Gradient** + +To compute the gradient, we need the observed data and an initial model. + +**Note:** For better visualization and avoiding crosstalk, I compute the +gradient in acoustic media. + +Then we create the initial model. + +.. code:: ipython3 + + m0 = Model(smoothing=1) + m0['vs'] *= 0.0 + m0['rho'] = np.ones_like(model['rho']) + + # Convert to tensor + vp0 = torch.tensor(m0['vp'], requires_grad=True) + vs0 = torch.tensor(m0['vs'], requires_grad=True) + rho0 = torch.tensor(m0['rho'], requires_grad=True) + + im = splt.earth_model(m0, ['vp'], cmap='coolwarm') + + + +.. image:: grad_pytorch_files/grad_pytorch_21_0.png + + +And we simulate the wave propagation to obtain estimated data. For +computing the gradient, we can smooth the gradient and scale it by +defining ``g_smooth`` and ``energy_balancing``. + +.. code:: ipython3 + + inpa['energy_balancing'] = True + +We save the wavefield at 20% of the time steps (``chpr = 20``) to be +used for gradient calculation. The value of wavefield is accessible +using the attribute ``W`` which is a dictionary for :math:`V_x`, +:math:`V_z`, :math:`\tau_x`, :math:`\tau_z`, and :math:`\tau_{xz}` as +``vx``, ``vz``, ``taux``, ``tauz``, and ``tauxz``. Each parameter is a +4D tensor. For example, we can have access to the last time step of +:math:`\tau_x` for the first shot as ``W.W['taux'][:, :, 0, -1]``. + +.. code:: ipython3 + + Lam = wave.WavePropagator(inpa, src, rec_loc, model_shape, + chpr=20, components=seisout) + + taux_est, tauz_est = Fwi.apply(Lam, + vp0, + vs0, + rho0 + ) + + +Now, we define the cost function and obtaine the residuals for +adjoint-state method. + +.. code:: ipython3 + + criteria = torch.nn.MSELoss(reduction='sum') + mse0 = 0.5 * criteria(taux_est, taux_obs) + mse1 = 0.5 * criteria(tauz_est, taux_obs) + mse = mse0 + mse1 + # print(mse.item()) + mse.backward() + +Using the adjoint source, we can estimate the gradient as + +.. code:: ipython3 + + grad = {'vp': vp0.grad, + 'vs': vs0.grad, + 'rho':rho0.grad + } + +.. code:: ipython3 + + # Time to plot the results + splt.earth_model(grad, ['vp'], cmap='jet'); + + + + +.. image:: grad_pytorch_files/grad_pytorch_30_0.png + diff --git a/docs/sub_doc/grad_pytorch_files/grad_pytorch_17_0.png 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'sdo': 4, # Order of FD\n", + " 'fdom': 15, # Central frequency of source\n", + " 'dh': 7, # Spatial sampling rate\n", + " 'dt': 0.004, # Temporal sampling rate\n", + " 'acq_type': 0, # Type of acquisition (0: crosswell, 1: surface, 2: both)\n", + " 't': 0.6, # Length of operation\n", + " 'npml': 20, # Number of PML \n", + " 'pmlR': 1e-5, # Coefficient for PML (No need to change)\n", + " 'pml_dir': 2, # type of boundary layer\n", + " 'device': 1, # The device to run the program. Usually 0: CPU 1: GPU\n", + "}\n", + "\n", + "seisout = 0 # Type of output 0: Pressure\n", + "\n", + "inpa['rec_dis'] = 1 * inpa['dh'] # Define the receivers' distance\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we obtain the location of sources and receivers based on specified parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "offsetx = inpa['dh'] * model_shape[1]\n", + "depth = inpa['dh'] * model_shape[0]\n", + "\n", + "src_loc, rec_loc, n_surface_rec, n_well_rec = acq.acq_parameters(inpa['ns'], \n", + " inpa['rec_dis'], \n", + " offsetx,\n", + " depth,\n", + " inpa['dh'], \n", + " inpa['sdo'], \n", + " acq_type=inpa['acq_type']) \n", + "# src_loc[:, 1] -= 5 * inpa['dh']\n", + "\n", + "# Create the source\n", + "src = acq.Source(src_loc, inpa['dh'], inpa['dt'])\n", + "src.Ricker(inpa['fdom'])\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can have the forward modelling as " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the wave object\n", + "W = wave.WavePropagator(inpa, src, rec_loc, model_shape,\n", + " n_well_rec=n_well_rec,\n", + " components=seisout, chpr=0)\n", + "\n", + "# Call the forward modelling \n", + "d_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(d_obs[\"taux\"], cmap='gray', \n", + " aspect=\"auto\", extent=[0, d_obs[\"taux\"].shape[1], inpa['t'], 0])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. FWI**" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To perform FWI, we need the observed data and an initial model. \n", + "\n", + "\n", + "**Note:** For better visualization and avoiding crosstalk, I compute the gradient in acoustic media.\n", + "\n", + "Here is a homogeneous initial model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m0 = Model(smoothing=1)\n", + "m0['vs'] *= 0.0\n", + "m0['rho'] = np.ones_like(model['rho'])\n", + "\n", + "fig = splt.earth_model(m0, ['vp'], cmap='coolwarm')\n", + "\n", + "fig.axes[0].plot(src_loc[:,0]//inpa[\"dh\"], \n", + " src_loc[:,1]//inpa[\"dh\"], \"rv\", markersize=5)\n", + "\n", + "fig.axes[0].plot(rec_loc[:,0]//inpa[\"dh\"], \n", + " rec_loc[:,1]//inpa[\"dh\"], \"b*\", markersize=3)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can create a FWI object," + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "fwi = FWI(d_obs, inpa, src, rec_loc, model_shape, \n", + " components=seisout, chpr=20, n_well_rec=n_well_rec)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and call it by providing the initial model `m0`, observed data `d_obs`, optimization method `method`, desired frequencies for inversion, number of iterations for each frequency, number of parameters for inversion `n_params`, index of the first parameter `k_0`, and index of the last parameter `k_end`.\n", + "For example, if we have an elastic model, but we want to only invert for P-wave velocity, these parameters should be defined as\n", + "```python\n", + "n_params = 1\n", + "k_0 = 1\n", + "k_end = 2\n", + "```\n", + "\n", + "If we want to invert for P-wave velocity and then $V_S$, these parameters should be defined as\n", + "```python\n", + "n_params = 1\n", + "k_0 = 1\n", + "k_end = 3\n", + "```\n", + "and for simultaneously inverting for these two parameters, we define these parameters as \n", + "```python\n", + "n_params = 2\n", + "k_0 = 1\n", + "k_end = 3\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameter number 1 to 1\n", + "2500.0 2500.0\n", + " for f= 25: rms is: 0.0003187612455803901 with rms_reg: 0, and rms_data: 0.0003187612455803901, rms_mp: 0.0, rms_model_relation: 0\n", + "Parameter number 1 to 1\n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 10000 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 3.18761D-04 |proj g|= 7.19684D-10\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + "10000 0 1 0 0 0 7.197D-10 3.188D-04\n", + " F = 3.1876124558039010E-004\n", + "\n", + "CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " This problem is unconstrained.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2500.0 2500.0\n", + " for f= 45: rms is: 0.004415073432028294 with rms_reg: 0, and rms_data: 0.004415073432028294, rms_mp: 0.0, rms_model_relation: 0\n", + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 10000 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 4.41507D-03 |proj g|= 2.39370D-08\n", + "\n", + "\n", + "ITERATION 1\n", + "\n", + "---------------- CAUCHY entered-------------------\n", + " There are 0 breakpoints \n", + "\n", + " GCP found in this segment\n", + "Piece 1 --f1, f2 at start point -6.4549D-13 6.4549D-13\n", + "Distance to the stationary point = 1.0000D+00\n", + "\n", + "---------------- exit CAUCHY----------------------\n", + "\n", + " 10000 variables are free at GCP 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " This problem is unconstrained.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2499.9827111341883 2500.0297937900996\n", + " for f= 45: rms is: 0.004414360038936138 with rms_reg: 0, and rms_data: 0.004414360038936138, rms_mp: 0.0, rms_model_relation: 0\n", + "2499.9135556709416 2500.1489689504983\n", + " for f= 45: rms is: 0.004411510191857815 with rms_reg: 0, and rms_data: 0.004411510191857815, rms_mp: 0.0, rms_model_relation: 0\n", + "2499.636933817955 2500.6256695920933\n", + " for f= 45: rms is: 0.004400133620947599 with rms_reg: 0, and rms_data: 0.004400133620947599, rms_mp: 0.0, rms_model_relation: 0\n", + "2498.530446406008 2502.5324721584725\n", + " for f= 45: rms is: 0.00435509392991662 with rms_reg: 0, and rms_data: 0.00435509392991662, rms_mp: 0.0, rms_model_relation: 0\n", + "2494.1044967582216 2510.1596824239887\n", + " for f= 45: rms is: 0.004182371310889721 with rms_reg: 0, and rms_data: 0.004182371310889721, rms_mp: 0.0, rms_model_relation: 0\n", + "2476.400698167074 2540.668523486055\n", + " for f= 45: rms is: 0.003607903141528368 with rms_reg: 0, and rms_data: 0.003607903141528368, rms_mp: 0.0, rms_model_relation: 0\n", + " LINE SEARCH 5 times; norm of step = 1365.0000000000000 \n", + "\n", + "At iterate 1 f= 3.60790D-03 |proj g|= 1.91296D-08\n", + "\n", + "\n", + "ITERATION 2\n", + "\n", + "----------------SUBSM entered-----------------\n", + "\n", + "\n", + "----------------exit SUBSM --------------------\n", + "\n", + "2356.1934399025804 2683.887118020453\n", + " for f= 45: rms is: 0.0026031285524368286 with rms_reg: 0, and rms_data: 0.0026031285524368286, rms_mp: 0.0, rms_model_relation: 0\n", + " LINE SEARCH 0 times; norm of step = 3577.6768283163833 \n", + "\n", + "At iterate 2 f= 2.60313D-03 |proj g|= 1.26216D-08\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + "10000 2 8 1 0 0 1.262D-08 2.603D-03\n", + " F = 2.6031285524368286E-003\n", + "\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT \n" + ] + } + ], + "source": [ + "m_est, _ = fwi(m0, method=\"lbfgs\", \n", + " freqs=[25, 45], iter=[2, 2], \n", + " n_params=1, k_0=1, k_end=2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is the estimated model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Time to plot the results\n", + "fig = splt.earth_model(m_est, ['vp'], cmap='jet')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + }, + "vscode": { + "interpreter": { + "hash": "0f81fc2e4a358d2a0e372de0a65782557c8127804cea09d304df813e671e8a74" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/sub_doc/grad_pytorch.ipynb b/example/grad_pytorch.ipynb similarity index 99% rename from docs/sub_doc/grad_pytorch.ipynb rename to example/grad_pytorch.ipynb index a33ce00..a24ee4d 100644 --- a/docs/sub_doc/grad_pytorch.ipynb +++ b/example/grad_pytorch.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -9,6 +10,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -16,6 +18,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -23,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -31,6 +35,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -47,7 +52,7 @@ "import numpy as np\n", "\n", "import sys\n", - "sys.path.append('../../src/')\n", + "sys.path.append('../src/')\n", "\n", "import PyFWI.wave_propagation as wave\n", "import PyFWI.acquisition as acq\n", @@ -61,6 +66,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -94,6 +100,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -129,6 +136,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -154,6 +162,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -172,6 +181,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -192,6 +202,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -225,6 +236,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -232,6 +244,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -242,6 +255,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -278,6 +292,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -295,6 +310,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -319,6 +335,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -340,6 +357,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/example/gradient_example.ipynb b/example/gradient_example.ipynb new file mode 100644 index 0000000..d9fe22b --- /dev/null +++ b/example/gradient_example.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Installation\n", + "============\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PyFWI can be installed using ```pip``` as\n", + "\n", + "```console\n", + "\n", + " (.venv) $ pip install PyFWI\n", + "\n", + "```\n", + "on macOS or\n", + "\n", + "```console\n", + "\n", + " (.venv) $ py -m pip install PyFWI\n", + "\n", + "```\n", + "\n", + "on Windows." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple Gradient Computation\n", + "============================\n", + "\n", + "In this section we see some applications of PyFWI.\n", + "First, forward modeling is shown and then we estimate gradient of cost funtion with respect to $V_P$.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + " **1. Forward modeling**\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this simple example, we use PyFWI to do forward modeling. So, we need to first import the following packages amd modulus." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import sys\n", + "sys.path.append('../src/')\n", + "\n", + "import PyFWI.wave_propagation as wave\n", + "import PyFWI.acquisition as acq\n", + "import PyFWI.seiplot as splt\n", + "import PyFWI.model_dataset as md\n", + "import PyFWI.fwi_tools as tools\n", + "import PyFWI.processing as process\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A simple model can be created by using ```model_dataset``` module as" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Model = md.ModelGenerator('louboutin')\n", + "model = Model()\n", + "\n", + "im = splt.earth_model(model, cmap='coolwarm')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we need to create an input dictionary as follow" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model_shape = model[[*model][0]].shape\n", + "\n", + "inpa = {\n", + " 'ns': 1, # Number of sources\n", + " 'sdo': 4, # Order of FD\n", + " 'fdom': 15, # Central frequency of source\n", + " 'dh': 7, # Spatial sampling rate\n", + " 'dt': 0.004, # Temporal sampling rate\n", + " 'acq_type': 1, # Type of acquisition (0: crosswell, 1: surface, 2: both)\n", + " 't': 0.8, # Length of operation\n", + " 'npml': 20, # Number of PML \n", + " 'pmlR': 1e-5, # Coefficient for PML (No need to change)\n", + " 'pml_dir': 2, # type of boundary layer\n", + " 'device': 1, # The device to run the program. Usually 0: CPU 1: GPU\n", + "}\n", + "\n", + "seisout = 0 # Type of output 0: Pressure\n", + "\n", + "inpa['rec_dis'] = 1 * inpa['dh'] # Define the receivers' distance\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we obtain the location of sources and receivers based on specified parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "offsetx = inpa['dh'] * model_shape[1]\n", + "depth = inpa['dh'] * model_shape[0]\n", + "\n", + "src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx,\n", + " inpa['dh'], inpa['sdo']) \n", + "src_loc[:, 1] -= 5 * inpa['dh']\n", + "\n", + "# Create the source\n", + "src = acq.Source(src_loc, inpa['dh'], inpa['dt'])\n", + "src.Ricker(inpa['fdom'])\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can have the forward modelling as " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the wave object\n", + "W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout, chpr=20)\n", + "\n", + "# Call the forward modelling \n", + "d_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compute the gradient using the adjoint-state method, we need to save the wavefield during the forward wave propagation. This must be done for the wavefield obtained from estimated model. \n", + "For example, the wavefield at four time steps are presented here in addition to a shot gather. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 4))\n", + "\n", + "count = 1\n", + "\n", + "ax = fig.add_subplot(122)\n", + "ax = splt.seismic_section(ax, d_obs['taux'], t_axis=np.linspace(0, inpa['t'], int(1 + inpa['t'] // inpa['dt'])))\n", + "\n", + "ax_loc = [1, 2, 5, 6]\n", + "snapshots = [40, 80, 130, 180]\n", + "\n", + "for i in range(len(snapshots)):\n", + " ax = fig.add_subplot(2, 4, ax_loc[i])\n", + " ax.imshow(W.W['taux'][:, :, 0, snapshots[i]], cmap='coolwarm')\n", + " \n", + " ax.axis('off')\n", + " count += 1\n", + "fig.suptitle(\"Wave propagation and a shot gather\", fontweight='bold');\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. Gradient**" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compute the gradient, we need the observed data and an initial model. So, first we obtain the observed data using more sources.\n", + "\n", + "\n", + "**Note:** For better visualization and avoiding crosstalk, I compute the gradient in acoustic media." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Making medium acoustic\n", + "model['vs'] *= 0.0\n", + "model['rho'] = np.ones_like(model['rho'])\n", + "\n", + "# Increasing number of sources\n", + "inpa['ns'] = 5\n", + "\n", + "src_loc, rec_loc = acq.surface_seismic(inpa['ns'], inpa['rec_dis'], offsetx,\n", + " inpa['dh'], inpa['sdo']) \n", + "src_loc[:, 1] -= 5 * inpa['dh']\n", + "\n", + "# Create the source\n", + "src = acq.Source(src_loc, inpa['dh'], inpa['dt'])\n", + "src.Ricker(inpa['fdom'])\n", + "\n", + "# Create the wave object\n", + "W = wave.WavePropagator(inpa, src, rec_loc, model_shape, components=seisout, chpr=20)\n", + "\n", + "# Call the forward modelling \n", + "db_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave\n", + "\n", + "# preparing data amd applying gain if required\n", + "db_obs = process.prepare_residual(db_obs, 1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we create the initial model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m0 = Model(smoothing=1)\n", + "m0['vs'] *= 0.0\n", + "m0['rho'] = np.ones_like(model['rho'])\n", + "\n", + "im = splt.earth_model(m0, ['vp'], cmap='coolwarm')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we simulate the wave propagation to obtain estimated data.\n", + "For computing the gradient, we can smooth the gradient and scale it by defining `g_smooth` and `energy_balancing`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "inpa['energy_balancing'] = True" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We save the wavefield at 20% of the time steps (`chpr = 20`) to be used for gradient calculation. The value of wavefield is accessible using the attribute ```W``` which is a dictionary for $V_x$, $V_z$, $\\tau_x$, $\\tau_z$, and $\\tau_{xz}$ as ```vx```, ```vz```, ```taux```, ```tauz```, and ```tauxz```.\n", + "Each parameter is a 4D tensor. For example, we can have access to the last time step of $\\tau_x$ for the first shot as ```W.W['taux'][:, :, 0, -1]```." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "Lam = wave.WavePropagator(inpa, src, rec_loc, model_shape,\n", + " chpr=20, components=seisout)\n", + "\n", + "d_est = Lam.forward_modeling(m0, False)\n", + "d_est = process.prepare_residual(d_est, 1)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we define the cost function and obtaine the residuals for adjoint-state method." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "CF = tools.CostFunction('l2')\n", + "rms, adj_src = tools.cost_seismic(d_est, db_obs, fun=CF)\n", + "# print(rms)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the adjoint source, we can estimate the gradient as " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "grad = Lam.gradient(adj_src, show=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Time to plot the results\n", + "splt.earth_model(grad, ['vp'], cmap='jet');\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + }, + "vscode": { + "interpreter": { + "hash": "0f81fc2e4a358d2a0e372de0a65782557c8127804cea09d304df813e671e8a74" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index 1e7d89b..1a00121 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,12 @@ numpy -pyopencl matplotlib scipy hdf5storage requests -segyio +scikit-build +# segyio datetime -pyopencl==2021.2.10 +pyopencl==2023.1 sphinx>=1.4 ipykernel nbsphinx

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First, +forward modeling is shown and then we estimate a model of subsurface +using FWI. + +**1. Forward modeling** + +In this simple example, we use PyFWI to do forward modeling. So, we need +to first import the following packages amd modulus. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import numpy as np + + import PyFWI.wave_propagation as wave + import PyFWI.acquisition as acq + import PyFWI.seiplot as splt + import PyFWI.model_dataset as md + import PyFWI.fwi_tools as tools + import PyFWI.processing as process + from PyFWI.fwi import FWI + + + +A simple model can be created by using ``model_dataset`` module as + +.. code:: ipython3 + + Model = md.ModelGenerator('louboutin') + model = Model() + + im = splt.earth_model(model, cmap='coolwarm') + + + +.. image:: fwi_example_files/fwi_example_5_0.png + + +Then we need to create an input dictionary as follow + +.. code:: ipython3 + + model_shape = model[[*model][0]].shape + + inpa = { + 'ns': 4, # Number of sources + 'sdo': 4, # Order of FD + 'fdom': 15, # Central frequency of source + 'dh': 7, # Spatial sampling rate + 'dt': 0.004, # Temporal sampling rate + 'acq_type': 0, # Type of acquisition (0: crosswell, 1: surface, 2: both) + 't': 0.6, # Length of operation + 'npml': 20, # Number of PML + 'pmlR': 1e-5, # Coefficient for PML (No need to change) + 'pml_dir': 2, # type of boundary layer + 'device': 1, # The device to run the program. Usually 0: CPU 1: GPU + } + + seisout = 0 # Type of output 0: Pressure + + inpa['rec_dis'] = 1 * inpa['dh'] # Define the receivers' distance + + +Now, we obtain the location of sources and receivers based on specified +parameters. + +.. code:: ipython3 + + offsetx = inpa['dh'] * model_shape[1] + depth = inpa['dh'] * model_shape[0] + + src_loc, rec_loc, n_surface_rec, n_well_rec = acq.acq_parameters(inpa['ns'], + inpa['rec_dis'], + offsetx, + depth, + inpa['dh'], + inpa['sdo'], + acq_type=inpa['acq_type']) + # src_loc[:, 1] -= 5 * inpa['dh'] + + # Create the source + src = acq.Source(src_loc, inpa['dh'], inpa['dt']) + src.Ricker(inpa['fdom']) + + +Finally, we can have the forward modelling as + +.. code:: ipython3 + + # Create the wave object + W = wave.WavePropagator(inpa, src, rec_loc, model_shape, + n_well_rec=n_well_rec, + components=seisout, chpr=0) + + # Call the forward modelling + d_obs = W.forward_modeling(model, show=False) # show=True can show the propagation of the wave + +.. code:: ipython3 + + plt.imshow(d_obs["taux"], cmap='gray', + aspect="auto", extent=[0, d_obs["taux"].shape[1], inpa['t'], 0]) + + + + +.. parsed-literal:: + + + + + + +.. image:: fwi_example_files/fwi_example_12_1.png + + +**2. FWI** + +To perform FWI, we need the observed data and an initial model. + +**Note:** For better visualization and avoiding crosstalk, I compute the +gradient in acoustic media. + +Here is a homogeneous initial model. + +.. code:: ipython3 + + m0 = Model(smoothing=1) + m0['vs'] *= 0.0 + m0['rho'] = np.ones_like(model['rho']) + + fig = splt.earth_model(m0, ['vp'], cmap='coolwarm') + + fig.axes[0].plot(src_loc[:,0]//inpa["dh"], + src_loc[:,1]//inpa["dh"], "rv", markersize=5) + + fig.axes[0].plot(rec_loc[:,0]//inpa["dh"], + rec_loc[:,1]//inpa["dh"], "b*", markersize=3) + + + + + +.. parsed-literal:: + + [] + + + + +.. image:: fwi_example_files/fwi_example_15_1.png + + +Now, we can create a FWI object, + +.. code:: ipython3 + + fwi = FWI(d_obs, inpa, src, rec_loc, model_shape, + components=seisout, chpr=20, n_well_rec=n_well_rec) + + +and call it by providing the initial model ``m0``, observed data +``d_obs``, optimization method ``method``, desired frequencies for +inversion, number of iterations for each frequency, number of parameters +for inversion ``n_params``, index of the first parameter ``k_0``, and +index of the last parameter ``k_end``. For example, if we have an +elastic model, but we want to only invert for P-wave velocity, these +parameters should be defined as + +.. code:: python + + n_params = 1 + k_0 = 1 + k_end = 2 + +If we want to invert for P-wave velocity and then :math:`V_S`, these +parameters should be defined as + +.. code:: python + + n_params = 1 + k_0 = 1 + k_end = 3 + +and for simultaneously inverting for these two parameters, we define +these parameters as + +.. code:: python + + n_params = 2 + k_0 = 1 + k_end = 3 + +.. code:: ipython3 + + m_est, _ = fwi(m0, method="lbfgs", + freqs=[25, 45], iter=[2, 2], + n_params=1, k_0=1, k_end=2) + + +.. parsed-literal:: + + Parameter number 1 to 1 + 2500.0 2500.0 + for f= 25: rms is: 0.0003187612455803901 with rms_reg: 0, and rms_data: 0.0003187612455803901, rms_mp: 0.0, rms_model_relation: 0 + Parameter number 1 to 1 + RUNNING THE L-BFGS-B CODE + + * * * + + Machine precision = 2.220D-16 + N = 10000 M = 10 + + At X0 0 variables are exactly at the bounds + + At iterate 0 f= 3.18761D-04 |proj g|= 7.19684D-10 + + * * * + + Tit = total number of iterations + Tnf = total number of function evaluations + Tnint = total number of segments explored during Cauchy searches + Skip = number of BFGS updates skipped + Nact = number of active bounds at final generalized Cauchy point + Projg = norm of the final projected gradient + F = final function value + + * * * + + N Tit Tnf Tnint Skip Nact Projg F + 10000 0 1 0 0 0 7.197D-10 3.188D-04 + F = 3.1876124558039010E-004 + + CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL + + +.. parsed-literal:: + + This problem is unconstrained. + + +.. parsed-literal:: + + 2500.0 2500.0 + for f= 45: rms is: 0.004415073432028294 with rms_reg: 0, and rms_data: 0.004415073432028294, rms_mp: 0.0, rms_model_relation: 0 + RUNNING THE L-BFGS-B CODE + + * * * + + Machine precision = 2.220D-16 + N = 10000 M = 10 + + At X0 0 variables are exactly at the bounds + + At iterate 0 f= 4.41507D-03 |proj g|= 2.39370D-08 + + + ITERATION 1 + + ---------------- CAUCHY entered------------------- + There are 0 breakpoints + + GCP found in this segment + Piece 1 --f1, f2 at start point -6.4549D-13 6.4549D-13 + Distance to the stationary point = 1.0000D+00 + + ---------------- exit CAUCHY---------------------- + + 10000 variables are free at GCP 1 + + +.. parsed-literal:: + + This problem is unconstrained. + + +.. parsed-literal:: + + 2499.9827111341883 2500.0297937900996 + for f= 45: rms is: 0.004414360038936138 with rms_reg: 0, and rms_data: 0.004414360038936138, rms_mp: 0.0, rms_model_relation: 0 + 2499.9135556709416 2500.1489689504983 + for f= 45: rms is: 0.004411510191857815 with rms_reg: 0, and rms_data: 0.004411510191857815, rms_mp: 0.0, rms_model_relation: 0 + 2499.636933817955 2500.6256695920933 + for f= 45: rms is: 0.004400133620947599 with rms_reg: 0, and rms_data: 0.004400133620947599, rms_mp: 0.0, rms_model_relation: 0 + 2498.530446406008 2502.5324721584725 + for f= 45: rms is: 0.00435509392991662 with rms_reg: 0, and rms_data: 0.00435509392991662, rms_mp: 0.0, rms_model_relation: 0 + 2494.1044967582216 2510.1596824239887 + for f= 45: rms is: 0.004182371310889721 with rms_reg: 0, and rms_data: 0.004182371310889721, rms_mp: 0.0, rms_model_relation: 0 + 2476.400698167074 2540.668523486055 + for f= 45: rms is: 0.003607903141528368 with rms_reg: 0, and rms_data: 0.003607903141528368, rms_mp: 0.0, rms_model_relation: 0 + LINE SEARCH 5 times; norm of step = 1365.0000000000000 + + At iterate 1 f= 3.60790D-03 |proj g|= 1.91296D-08 + + + ITERATION 2 + + ----------------SUBSM entered----------------- + + + ----------------exit SUBSM -------------------- + + 2356.1934399025804 2683.887118020453 + for f= 45: rms is: 0.0026031285524368286 with rms_reg: 0, and rms_data: 0.0026031285524368286, rms_mp: 0.0, rms_model_relation: 0 + LINE SEARCH 0 times; norm of step = 3577.6768283163833 + + At iterate 2 f= 2.60313D-03 |proj g|= 1.26216D-08 + + * * * + + Tit = total number of iterations + Tnf = total number of function evaluations + Tnint = total number of segments explored during Cauchy searches + Skip = number of BFGS updates skipped + Nact = number of active bounds at final generalized Cauchy point + Projg = norm of the final projected gradient + F = final function value + + * * * + + N Tit Tnf Tnint Skip Nact Projg F + 10000 2 8 1 0 0 1.262D-08 2.603D-03 + F = 2.6031285524368286E-003 + + STOP: TOTAL NO. of ITERATIONS REACHED LIMIT + + +Here is the estimated model + +.. code:: ipython3 + + # Time to plot the results + fig = splt.earth_model(m_est, ['vp'], cmap='jet') + + + + +.. image:: fwi_example_files/fwi_example_21_0.png + diff --git a/docs/sub_doc/fwi_example_files/fwi_example_12_1.png b/docs/sub_doc/fwi_example_files/fwi_example_12_1.png new file mode 100644 index 0000000000000000000000000000000000000000..273782cc7e8ddb80ecc98c7dc0adb25d3730b203 GIT binary patch literal 40241 zcmeFZWmuF^xHhVS0+K^XBQt=cq;v=j-3T~@ATV@ygLKExAR#5)2!e!kr*uenxAga- 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