A Journey to put AI in production for real-time Earthquake Monitoring

Speaker: Yangkang Chen, Research Assistant Professor, Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin

Artificial intelligence (AI) seismology has witnessed enormous success in a variety of fields, especially in earthquake detection and P- and S-wave arrival picking. It has become widely accepted that deep learning techniques greatly help routine seismic monitoring by enabling more accurate picking than traditional methods. However, a completely automatic and in-production AI-driven earthquake monitoring framework has not been reported due to concerns about potential false positives using DL pickers. In this talk, I will introduce a novel AI-facilitated real-time monitoring framework developed from scratch over the past decade. It is based on a third-generation deep-learning phase picker (EQCCT) that has been deployed in the Texas seismological network (TexNet). For the West Texas area, the seismic monitoring of TexNet has been relying on the EQCCT picker for reporting earthquake events. For earthquakes with a magnitude above two, the picks are further validated by analysts to output the final TexNet catalog. Due to the fast-increasing seismicity caused by continuing oil & gas production in West Texas, this AI-facilitated framework significantly relieves the workload of TexNet analysts.

[SHUBHAMTIWARI-ym]

Thanks!! This is very insightful Dr Yangkang Chen

[FergusDavignon]

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