Webinar - Machine learning and geophysical inversion – A numerical study

Canadian Society of Exploration Geophysicists (CSEG)

Machine learning and geophysical inversion – A numerical study

<p>Geophysicists are trained to conceptualize geophysical problems in detail.  However, machine learning algorithms are more difficult to understand and are often thought of as simply “black boxes”.  In this talk, a numerical example is used to illustrate the difference between geophysical inversion and machine learning inversion.  In doing so, an attempt is made to demystify machine learning algorithms and show that, like inverse problems, they have a definite mathematical structure that can be written down and understood.  The example used in this study is the extraction of the underlying reflection coefficients from an overlapping wavelet response that was created by convolving a reflection coefficient dipole with a symmetric wavelet.  In discussing the solution to this problem the topics of deconvolution, recursive inversion, linear regression and nonlinear regression using a feedforward neural network are covered.</p>

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Webinar - Machine learning and geophysical inversion – A numerical study

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Webinar - Machine learning and geophysical inversion – A numerical study

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