Multiview Kernels for Low-Dimensional Modeling of Seismic Events

The problem of learning from seismic recordings has been studied for years. There is a growing interest of developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability to have a reliable identification of man-made explosions. The availability of multiple high-dimensional observations has increased the use of machine learning techniques in a variety of fields. In this paper, we propose to use a kernel-fusion-based dimensionality reduction framework for generating meaningful seismic representations from raw data. The proposed method is tested on 2023 events that were recorded in Israel and Jordan. The method achieves promising results in the classification of event type as well as the estimation of the event location. The proposed fusion and dimensionality reduction tools may be applied to other types of geophysical data.

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