Machine Learning Methods in Geoscience

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.