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Conventional seismic techniques for detecting the subsurface geologicfeatures are challenged by limited data coverage, computational inefficiency,and subjective human factors. We developed a novel data-driven geologicalfeature detection approach based on pre-stack seismic measurements. Ourdetection method employs an efficient and accurate machine-learning detectionapproach to extract useful subsurface geologic features automatically.Specifically, our method is based on kernel ridge regression model. Theconventional kernel ridge regression can be computationally prohibited becauseof the large volume of seismic measurements. We employ a data reductiontechnique in combination with the conventional kernel ridge regression methodto improve the computational efficiency and reduce memory usage. In particular,we utilize a randomized numerical linear algebra technique, named Nystr\"ommethod, to effectively reduce the dimensionality of the feature space withoutcompromising the information content required for accurate detection. Weprovide thorough computational cost analysis to show efficiency of our newgeological feature detection methods. We further validate the performance ofour new subsurface geologic feature detection method using synthetic surfaceseismic data for 2D acoustic and elastic velocity models. Our numericalexamples demonstrate that our new detection method significantly improves thecomputational efficiency while maintaining comparable accuracy. Interestingly,we show that our method yields a speed-up ratio on the order of $\sim10^2$ to$\sim 10^3$ in a multi-core computational environment.

Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm