Detecting Urban Earthquakes with the San Fernando Valley Nodal Array and Machine Learning

Abstract

The San Fernando Valley, part of the Los Angeles metropolitan area, is a seismically active urban environment. Large-magnitude earthquakes, such as the 1994 Mw 6.7 Northridge event that occurred on a blind fault beneath the valley, caused significant infrastructure damage in the region, underscoring the need for enhanced seismic monitoring to improve the identification of buried faults and hazard evaluation. Currently, the Southern California Earthquake Data Center operates four broadband instruments within the valley; however, the networks ability to capture small earthquakes beneath the region may be limited. To demonstrate how this data gap can be filled, we use recordings from the San Fernando Valley array comprised of 140 nodal instruments with interstation distances ranging from 0.3 to 2.5 km that recorded for one month. High anthropogenic noise levels in urbanized areas tend to conceal earthquake signals, therefore we applied a previously developed machine learning model finetuned on similar waveforms to detect events and pick seismic phases. In a two-step event association workflow, isolated phase picks were first culled, which eliminated false positive detections and reduced computational runtime. We located 62 events within a 209 km radius of our array, including 36 new events that were undetected by the regional network, with magnitudes ranging from ML 0.13 to 4. One event cluster reveals a previously unidentified (5.3 km by 4 km) blind fault zone located ~5 km beneath the southern part of the valley. Seismicity from this zone is rare in the regional catalog (<3 events per year), despite producing a Mb 4.4 event in 2014. Our results highlight the benefits of detecting small-magnitude seismicity for hazard estimation. Temporary nodal arrays can identify critical gaps in regional monitoring and guide site selection for permanent stations.