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Towards real-time geologic feature detection from seismic measurements using a randomized machine-learning algorithm



Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We propose to employ an efficient and accurate machine-learning detection approach to extract useful subsurface geologic features automatically. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce the memory usage. Specifically, we utilize a randomized numerical linear algebra technique to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate detection. We validate the performance of our new subsurface geologic feature detection method using synthetic surface seismic data for a 2D geophysical model. Our numerical examples demonstrate that our new detection method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speed-up ratio on the order of ~102 to ~103 in a multi-core computational environment.

Presentation Date: Wednesday, September 27, 2017

Start Time: 3:30 PM

Location: 350D

Presentation Type: ORAL