Machine Learning Methods in Geoscience - Hardcover

Schuster, Gerard

Hardcover

ISBN 10: 1560804033 ISBN 13: 9781560804031

Publisher: Society of Exploration Geophysicists, 2024

Synopsis

This book presents the theory of machine learning (ML) algorithms and their applications to geoscience problems. Geoscience problems include traveltime picking of seismograms by a fuzzy cluster method; migration and inversion of seismic data by neural network (NN) methods; geochemical analysis and dating of rock samples by Gaussian discriminant analysis; convolutional neural network (CNN) picking of faults, cracks, and bird types in images; Bayesian inversion of seismic data; clustering of earthquake data and semblance plots; principal component analysis of seismic data and geochemical records; filtering of seismic sections; seismic interpolation by an NN; transformer analysis of seismic data; and recurrent NN deconvolution of a seismic trace. More than half of the described algorithms fall under the class of neural network methods. Their description is at a level that can be understood by anyone with a modest background in linear algebra, calculus, and probability. An elementary working knowledge of MATLAB is useful and almost every chapter is accompanied by lab exercises to reinforce the ML principles.

Edition details

• Publisher: Society of Exploration Geophysicists
• Publication date: 2024
• Language: English
• ISBN 10: 1560804033
• ISBN 13: 9781560804031
• Binding: Hardcover
• Number of pages: 894