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Machine learning-based surface wave tomography of Long Beach, CA, USA

Michael J. Bianco, Kim B. Olsen, Peter Gerstoft, & Fan-Chi Lin

Published August 15, 2018, SCEC Contribution #8754, 2018 SCEC Annual Meeting Poster #094

We use a machine learning-based tomography method to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise processing on a "large-N" array. This locally sparse travel time tomography (LST) method exploits the dense sampling obtained from large arrays by learning a dictionary of local, or small-scale, geophysical features directly from the data. These local features are represented as small rectangular groups of pixels, called patches, from the overall phase-speed image. This local model is combined with the overall phase speed map, called the global model, via an averaging procedure. The global model constrains larger scale features using least squares regularization. Using data recorded from the Long Beach array in 2011, we perform high-resolution surface wave tomography of Long Beach region in the 1 Hz Rayleigh wave band. Among the geophysical features visible in the phase speed map, there is a prominent high-speed anomaly  corresponding to the important aquifer Silverado aquifer, which has not been isolated in previous surface wave tomography studies. This anomaly is likely caused by the higher density of the Silverado relative to other geological units. Our results show promise for the use of LST in resolving high resolution geophysical structure in travel time tomography studies.

Bianco, M. J., Olsen, K. B., Gerstoft, P., & Lin, F. (2018, 08). Machine learning-based surface wave tomography of Long Beach, CA, USA. Poster Presentation at 2018 SCEC Annual Meeting.

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