Machine Learning for Geophysical Applications

Date(s): Oct 19, 2016 – 2:00pm Location: Booker Conference Room Speaker(s): Peter Gerstoft Abstract: Machine learning has emerged as a promising tool to analyze massive amount of data. ML has demonstrated impressive results in non-physical sciences. ML has solid footing in statistics and signal processing. In this talk I will highlight three machine learning applications in geophysics: Graph theory: A model-free technique is used to identify weak sources within dense sensor arrays using graph clustering. No knowledge about the propagation medium is needed. We use the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with sensors as vertices. The support of the covariance matrix is estimated from limited-time data using a hypothesis test with a robust phase-only coherence test-statistic.…


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