A shallow slow slip event in 2018 in the Semidi segment of the Alaska subduction zone detected by machine learning

Slow slip events (SSEs) have been discovered at shallow depth near the trench in some subduction zones and have been linked to the triggering of large earthquakes and the absence of tsunami. These shallow SSEs are invariably submarine, making it difficult to observe their temporal and spatial extent. Here, we report a shallow SSE in late 2018 near the west Semidi segment of the Alaska subduction zone, up-dip of and preceding the Mw 8.2 Chignik earthquake on July 29th, 2021. The SSE was detected in data from an offshore array of seafloor pressure gauges by a machine learning method. This detection is supported by the spatial pattern of simulated SSE deformation, the increased seismicity after the SSE in the positive Coulomb stress change area, the lack of shallow slip in Chignik earthquake, and the absence of a sizable tsunami following the Chignik earthquake. Our method has the potential to transform the way offshore SSEs are detected and to improve tsunami hazard assessment in subduction zones. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Slow slip events (SSEs) have been discovered near the trench in some subduction zones, and they are linked to triggering large earthquakes and the absence of tsunamis. These SSEs are usually submarine, making their temporal and spatial extent difficult to observe. In this study, a shallow SSE near the Alaska subduction zone was detected using a machine learning method, and it was found to precede a large earthquake and have implications for tsunami hazard assessment.