{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Demo: Processing and interpreting magnetic data\n", "\n", "This is an outline of the live demo that I'll give at the presentation. \n", "We'll use some aeromagnetic total field anomaly data from Brazil. \n", "\n", "The goal is demostrate what can currently be done in [Fatiando a Terra](http://fatiando.org/).\n", "We'll use a few methods that aren't usually available in comercial software, like the Equivalent Layer method, a method for estimating the direction of magnetization, and reduction to the pole with remanent magnetization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the data from a text file\n", "\n", "I'll use the numpy library for loading the data and matplotlib for visualizing it in a map." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline \n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xp, yp, zp, topo, data = np.loadtxt('mag-data.txt', unpack=True)\n", "inc = -19.5\n", "dec = -18.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quickly plot this in a map to see how the data points are distributed and what height was the aquisition." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2sTyY1gPfUuR54HmK3B946Kwfl4YeSp9nxoZX6RcufBZVPtQuobqDi5gqCjxZ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vjYmSj6t1qm+4qqbvyouLl7GRqLttPNrKYKPBe7gsALSb/oPPIGYvvk8NESDt+oN8FehEk3Qg6/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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title('Height (m)')\n", "plt.tricontourf(yp, xp, -zp, 30)\n", "plt.colorbar()\n", "plt.plot(yp, xp, '.k', markersize=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the topography." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5m5m/RURmrPmZHSpab4H7lNaTcRQqmslZ+IucYo3Hynt/yv46qcsKr8LaDulF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\nn6p+z7w+ANxH9IW6gAgWmL/vNK+X0mZV9RHgIeB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(yp, xp, topo, 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the magnetic total field anomaly." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(yp, xp, data, 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gridding and leveling\n", "\n", "The problem with this dataset is that it's not on a regular grid (so we can't use any processing with the Fourier transform) and the points aren't all on the same height. So changes in the anomaly can be due to a height difference instead of a difference in the underlying source. Before we can go on processing, we must fix this. \n", "\n", "Thankfully, Fatiando implements a method called the Equivalent Layer which lets us calculate the anomaly anywhere we want. The ideia is that we estimate a magnetization distribution on a layer that fits our observed anomaly (an inversion). Then, thanks to potential field theory, we can use this \"equivalent layer\" to forward model the anomaly on any point we want, for example on a regular grid with uniform height." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we load the required modules from Fatiando.\n", "We'll need to use regularization for this inversion. \n", "A simple damping will be enough." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/leo/bin/anaconda/envs/tgif-demo/lib/python2.7/site-packages/fatiando/vis/mpl.py:76: UserWarning: This module will be removed in v0.6. We recommend the use of matplotlib.pyplot module directly. Some of the fatiando specific functions will remain.\n", " \"specific functions will remain.\")\n" ] } ], "source": [ "from fatiando.gravmag.eqlayer import EQLTotalField\n", "from fatiando.inversion import Damping\n", "from fatiando.mesher import PointGrid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create our layer of point masses below the data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "area = [xp.min(), xp.max(), yp.min(), yp.max()]\n", "layer = PointGrid(area, z=-100, shape=(80, 80))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make the data misfit and regularization objects." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "misfit = EQLTotalField(xp, yp, zp, data, inc, dec, layer)\n", "regul = Damping(misfit.nparams)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Combine them with a small regularization parameter (I knew beforehand that this would work)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eql = misfit + 1e-12*regul" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the layer to our data." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 31.3 s, sys: 1.34 s, total: 32.7 s\n", "Wall time: 19 s\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "eql.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we get estimated magnetization intensity on the layer." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ -767336.60865397, 1592588.36522874, 4896565.87784263, ...,\n", " -17092323.07653675, -15505185.856221 , -14534311.75866608])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eql.estimate_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should always look at the residuals (observed - predicted data) to see if the inversion fits the data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Fp1r89S0XL/ADMIqt+ZAiroZZ38SSxD30c98wOctJk8fYdFxy/4hV9vXIcQOb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\neDqKF3iPx+PpKP8b51ykSA65ZM8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(yp, xp, eql[0].residuals(), 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can add our estimated magnetization to the layer. Fatiando requires magnetization to be specified as vectors. It provides a function in the utils module to convert intensity, inclination, and declination into (x, y, z) vectors." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from fatiando import utils" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer.addprop('magnetization', utils.ang2vec(eql.estimate_, inc, dec))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we forward model the anomaly using the layer on a regular grid at a constant height. \n", "The forward modeling is done by the gravmag.sphere module (point sources can be considered unit volume spheres).\n", "Generating point distributions is done by the gridder module." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from fatiando.gravmag import sphere\n", "from fatiando import gridder" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "shape = (85, 85)\n", "x, y, z = gridder.regular(area, shape=shape, z=-700)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tfa_grid = sphere.tf(x, y, z, layer, inc, dec)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3LJK0LbatLeGaNwAvAHuAHwOfMLNniFY+TFsNcSnwOICZTQI/AV6W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\nRK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(y, x, tfa_grid, 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transformations using the FFT\n", "\n", "Now that we have data on a regular grid at uniform height we can use all the transformation functions in the gravmag.transform module." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from fatiando.gravmag import transform" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, the total gradient amplitude (a.k.a., the analytic signal)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tga = transform.tga(x, y, tfa_grid, shape)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(y, x, tga, 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also reduce to the pole. This requires the inclination and declination of the inducing geomagnetic field (inc and dec) **and** the inclination and declination of the sources magnetization (sinc and sdec). \n", "Since we don't know what the later is, we'll just assume that it's the same direction, i.e. there is only induced magnetization." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pole = transform.reduce_to_pole(x, y, tfa_grid, shape, inc, dec, sinc=inc, sdec=dec)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RmDHSjy5ur46OBmmXG6PpHPwQfw5en3A0zjDuH+f9Rz17X/iTvP6Q6FAT49Xx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\n9rNk/iSFf7puAPJQ61SFPf5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(y, x, pole, 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That doesn't look right. The anomaly seems all stretched to the Southeast. This is a clear indication that there **isn't only induced magnetization**. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating the magnetization direction\n", "\n", "Fatiando implements a method for estimating the magnetization direction of approximately spherical bodies from the total field anomaly. This is in the gravmag.magdir module.\n", "The method requires that we know approximately the coordinates of the center of the source.\n", "We can estimate this using the Euler deconvolution method, assuming that our source is spherical (structural index of 3)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from fatiando.gravmag.euler import EulerDeconvEW\n", "from fatiando.gravmag.magdir import DipoleMagDir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Euler deconvolution requires the x, y, z derivatives of the anomaly. We can calculate them using the gravmag.transform module." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dx = transform.derivx(x, y, tfa_grid, shape)\n", "dy = transform.derivy(x, y, tfa_grid, shape)\n", "dz = transform.derivz(x, y, tfa_grid, shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Euler Deconvolution in Fatiando is a bit different from the one you'll find in most commercial software. \n", "We prefer to use an expanding window scheme rather than a moving window scheme (we have that one too if you want). \n", "The main difference is that the expanding window will give you a single solution instead of the many meaningless solutions of the moving window." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "euler = EulerDeconvEW(x, y, z, tfa_grid, dx, dy, dz, structural_index=3, \n", " center=[4000, 5000], sizes=range(500, 8000, 500))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "euler.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The estimated (x, y, z) coorinates of the source are:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3684.09587513, 5253.60075365, 627.67749843])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "euler.estimate_" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OuEOi7X5w9PAkS6ZmO9KMo22IHkrGMi4iTdYAF+54iD+5+XMsOxp12HTavhf4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\nFvH/Ad3L4KnIocOIAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(y, x, tga, 30)\n", "plt.colorbar()\n", "plt.plot(euler.estimate_[1], euler.estimate_[0], 'ro')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use DipoleMagDir to estimate the magnetization direction. Notice that you can chain the creation of the object and the call to fit() to make the code smaller." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mag = DipoleMagDir(x, y, z, tfa_grid, inc, dec, [euler.estimate_]).fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The estimated intensity, inclination, and declination are:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[6908.4450952485677, -63.578688588810586, -39.964109312998175]]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mag.estimate_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a reminder, here are the inclination and declination of the geomagnetic field." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-19.5, -18.5)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inc, dec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test our estimate, we can use it to reduce to the pole and see what the results look like." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sinc, sdec = mag.estimate_[0][1:]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pole = transform.reduce_to_pole(x, y, tfa_grid, shape, inc, dec, sinc, sdec)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.tricontourf(y, x, pole, 30)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Much better! So it seems likely that our estimate is correct. \n", "\n", "Now that we have the inclination and declination we could use it to do forward modeling or an inversion. \n", "Knowing the sources magnetization is crucial! You'll get completely wrong results if you use the wrong one. \n", "And you might not even notice because your wrong model will fit the data perfectly." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }