{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 10 - Step-by-step NMO correction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Devito is equally useful as a framework for other stencil computations in general; for example, computations where all array indices are affine functions of loop variables. The Devito compiler is also capable of generating\n", "arbitrarily nested, possibly irregular, loops. This key feature is needed to support many complex algorithms that are used in engineering and scientific practice, including applications from image processing, cellular automata, and machine-learning. This tutorial, a step-by-step NMO correction, is an example of it. \n", "\n", "In reflection seismology, normal moveout (NMO) describes the effect that the distance between a seismic source and a receiver (the offset) has on the arrival time of a reflection in the form of an increase of time with offset. The relationship between arrival time and offset is hyperbolic. \n", "\n", "Based on the field geometry information, each individual trace is assigned to the midpoint between the shot and receiver locations associated with that trace. Those traces with the same midpoint location are grouped together, making up a common midpoint gather (CMP). \n", "\n", "Consider a reflection event on a CMP gather. The difference between the two-way time at a given offset and the two-way zero-offset time is called normal moveout (NMO). Reflection traveltimes must be corrected for NMO prior to summing the traces in the CMP gather along the offset axis. The normal moveout depends on velocity above the reflector, offset, two-way zero-offset time associated with the reflection event, dip of the reflector, the source-receiver azimuth with respect to the true-dip direction, and the degree of complexity of the near-surface and the medium above the reflector.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Seismic modelling with devito" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before the NMO corretion we will describe a setup of seismic modelling with Devito in a simple 2D case. We will create a physical model of our domain and define a multiple 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": [ "import numpy as np\n", "import sympy as sp\n", "\n", "from devito import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will create a simple velocity model here by hand for demonstration purposes. This model essentially consists of three layers, each with a different velocity: 1.5km/s in the top layer, 2.5km/s in the middle layer and 4.5 km/s in the bottom layer." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `initdamp` run in 0.05 s\n", "Operator `padfunc` run in 0.01 s\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import Model, plot_velocity\n", "\n", "shape = (301, 501) # Number of grid point (nx, ny, nz)\n", "spacing = (10., 10) # Grid spacing in m. The domain size is now 3km by 5km\n", "origin = (0., 0) # What is the location of the top left corner.\n", "\n", "# Define a velocity profile. The velocity is in km/s\n", "v = np.empty(shape, dtype=np.float32)\n", "v[:,:100] = 1.5\n", "v[:,100:350] = 2.5\n", "v[:,350:] = 4.5\n", "\n", "# With the velocity and model size defined, we can create the seismic model that\n", "# encapsulates these 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, space_order=4, nbl=40, bcs=\"damp\")\n", "\n", "plot_velocity(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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 sources we require the discretized values of time that we are going to use to model a multiple \"shot\", which depends on the grid spacing used in our model. We will use one source and eleven receivers. The source is located in the position (550, 20). The receivers start at (550, 20) with an even horizontal spacing of 100m at consistent depth." ] }, { "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 = 2400. # Simulation last 2.4 second (2400 ms)\n", "dt = model.critical_dt # Time step from model grid spacing\n", "\n", "time_range = TimeAxis(start=t0, stop=tn, step=dt)\n", "\n", "nrcv = 250 # Number of Receivers" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "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", "# 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=4)\n", "\n", "# We can now write the PDE\n", "pde = model.m * u.dt2 - u.laplace + model.damp * u.dt\n", "stencil = Eq(u.forward, solve(pde, u.forward))\n", "\n", "src.coordinates.data[:, 0] = 400 # Source coordinates\n", "src.coordinates.data[:, -1] = 20. # Depth is 20m" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `Kernel` run in 0.85 s\n" ] }, { "data": { "text/plain": [ "PerformanceSummary([(PerfKey(name='section0', rank=None),\n", " PerfEntry(time=0.7779790000000012, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section1', rank=None),\n", " PerfEntry(time=0.028586000000000156, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section2', rank=None),\n", " PerfEntry(time=0.03306400000000026, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "from examples.seismic import Receiver\n", "\n", "rec = Receiver(name='rec', grid=model.grid, npoint=nrcv, time_range=time_range)\n", "rec.coordinates.data[:,0] = np.linspace(src.coordinates.data[0, 0], model.domain_size[0], num=nrcv)\n", "rec.coordinates.data[:,-1] = 20. # Depth is 20m\n", "\n", "# 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)\n", "\n", "op = Operator([stencil] + src_term + rec_term, subs=model.spacing_map)\n", "op(time=time_range.num-1, dt=model.critical_dt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How we are modelling a horizontal layers, we will group this traces and made a NMO correction using this set traces. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "offset = []\n", "data = []\n", "for i, coord in enumerate(rec.coordinates.data):\n", " off = (src.coordinates.data[0, 0] - coord[0])\n", " offset.append(off)\n", " data.append(rec.data[:,i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Auxiliary function for plotting traces:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "mpl.rc('font', size=16)\n", "mpl.rc('figure', figsize=(8, 6))\n", "\n", "def plot_traces(rec, xb, xe, t0, tn, colorbar=True):\n", " scale = np.max(rec)/100 \n", " extent = [xb, xe, 1e-3*tn, t0]\n", " plot = plt.imshow(rec, cmap=cm.gray, vmin=-scale, vmax=scale, extent=extent)\n", " plt.xlabel('X position (km)')\n", " plt.ylabel('Time (s)')\n", "\n", " # Create aligned colorbar on the right\n", " if colorbar:\n", " ax = plt.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " plt.colorbar(plot, cax=cax)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Common Midpoint Gather" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, we have a dataset composed of the receivers. \"If our model wasn't purely horizontal, we would have to sort these traces by common midpoints prior to NMO correction.\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "plot_traces(np.transpose(data), rec.coordinates.data[0][0]/1000, rec.coordinates.data[nrcv-1][0]/1000, t0, tn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# NMO Correction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can correct the measured traveltime of a reflected wave $t$ at a given offset $x$ to obtain the traveltime at normal incidence $t_0$ by applying the following equation:\n", "\n", "\\begin{equation*}\n", "t = \\sqrt{t_0^2 + \\frac{x^2}{V_{nmo}^2}} \n", "\\end{equation*}\n", "\n", "in which $V_{nmo}$ is the NMO velocity. This equation results from the Pythagorean theorem, and is only valid for horizontal reflectors. There are variants of this equation with different degrees of accuracy, but we'll use this one for simplicity." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the NMO Correction we use a grid of size samples x traces." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "ns = time_range.num # Number of samples in each trace\n", "grid = Grid(shape=(ns, nrcv)) # Construction of grid with samples X traces dimension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we will use a constant velocity guide. The guide will be arranged in a SparseFunction with the number of points equal to number of samples in the traces. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "vnmo = 1500\n", "vguide = SparseFunction(name='v', grid=grid, npoint=ns)\n", "vguide.data[:] = vnmo " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The computed offset for each trace will be arraged in another SparseFunction with number of points equal to number of traces." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "off = SparseFunction(name='off', grid=grid, npoint=nrcv)\n", "off.data[:] = offset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The previous modelled traces will be arranged in a SparseFunction with the same dimensions as the grid." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "amps = SparseFunction(name='amps', grid=grid, npoint=ns*nrcv, dimensions=grid.dimensions, shape=grid.shape)\n", "amps.data[:] = np.transpose(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we define SparseFunctions with the same dimensions as the grid, describing the NMO traveltime equation. The $t_0$ SparseFunction isn't offset dependent, so the number of points is equal to the number of samples. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sample, trace = grid.dimensions\n", "\n", "t_0 = SparseFunction(name='t0', grid=grid, npoint=ns, dimensions=[sample], shape=[grid.shape[0]])\n", "tt = SparseFunction(name='tt', grid=grid, npoint=ns*nrcv, dimensions=grid.dimensions, shape=grid.shape)\n", "snmo = SparseFunction(name='snmo', grid=grid, npoint=ns*nrcv, dimensions=grid.dimensions, shape=grid.shape)\n", "s = SparseFunction(name='s', grid=grid, dtype=np.intc, npoint=ns*nrcv, dimensions=grid.dimensions, \n", " shape=grid.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Equation relates traveltimes: the one we can measure ($t_0$) and the one we want to know (t). But the data in our CMP gather are actually a matrix of amplitudes measured as a function of time ($t_0$) and offset. Our NMO-corrected gather will also be a matrix of amplitudes as a function of time (t) and offset. So what we really have to do is transform one matrix of amplitudes into the other.\n", "\n", "With Equations we describe the NMO traveltime equation, and use the Operator to compute the traveltime and the samples for each trace. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `Kernel` run in 0.01 s\n" ] }, { "data": { "text/plain": [ "PerformanceSummary([(PerfKey(name='section0', rank=None),\n", " PerfEntry(time=0.001971, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "\n", "dtms = model.critical_dt/1000 # Time discretization in ms\n", "E1 = Eq(t_0, sample*dtms)\n", "E2 = Eq(tt, sp.sqrt(t_0**2 + (off[trace]**2)/(vguide[sample]**2) ))\n", "E3 = Eq(s, sp.floor(tt/dtms))\n", "op1 = Operator([E1, E2, E3])\n", "op1()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the computed samples, we remove all that are out of the samples range, and shift the amplitude for the correct sample. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Operator `Kernel` run in 0.01 s\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#NBVAL_IGNORE_OUTPUT\n", "\n", "s.data[s.data >= time_range.num] = 0\n", "E4 = Eq(snmo, amps[s[sample, trace], trace])\n", "\n", "op2 = Operator([E4])\n", "op2()\n", "\n", "stack = snmo.data.sum(axis=1) # We can stack traces and create a ZO section!!! \n", "\n", "plot_traces(snmo.data, rec.coordinates.data[0][0]/1000, rec.coordinates.data[nrcv-1][0]/1000, t0, tn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References:\n", " \n", " https://library.seg.org/doi/full/10.1190/tle36020179.1\n", " https://wiki.seg.org/wiki/Normal_moveout\n", " https://en.wikipedia.org/wiki/Normal_moveout" ] } ], "metadata": { "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }