Machine learning and geophysical inversion: A numerical study

Thursday, July 25, 2019

11:30 am - 1:00 pm

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 feed forward neural network are covered.

Date: Thursday, July 25

Time: 11:30-1p.m.

Location: The University of Houston Classroom and Business Building (CBB) Room 310, 4742 Calhoun Rd.

Video livestream: Members of SHPCP will receive the streaming URL shortly before the event.

Pizza will be served!

Speaker: Brian Russell, Ph.D., Vice President, CGG GeoSoftware

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