11:15 〜 11:30
[SSS04-09] Seismicity and Repeating Events of the Blanco Transform Fault System in the Northeast Pacific from Machine Learning
キーワード：Local Seismicity , Blanco transform fault system, Automated Phase Picking , Ocean Bottom Seismometer (OBS), Repeaters, Local Earhtuquake Topmogaphy
The Blanco transform fault system (BTFS) is highly segmented and represents a newly evolving transform plate boundary in the Northeast Pacific Ocean. Its seismic behavior was captured during the deployment of a dense network of 53 ocean-bottom-seismometers operated for one year. We created a high-resolution earthquake catalog based on different machine learning onset pickers (trained with ocean bottom seismometer and land-station data), resulting in a high-resolution seismicity catalog with 12.708 events outlining the current deformation and stress release along a major transform fault. Seismicity reveals lateral changes of seismic behavior, indicating seismic and aseismic fault patches or segments, complex along-strike and off-axis deformation, step-overs, and internal faulting of marine pull-apart basins. Seismicity along simple linear fault strands is localized within 2 km of the seafloor expression of the fault. Repeaters in the Eastern transform segment indicate mostly ~35 cm slip, exceeding the geological slip rate by 5-10 times. Based on the repeater behavior, we suggest that the slip of the Western BTFS is spatially very heterogeneous, consisting of many small seismic patches. Local earthquake tomography shows vp/vs values exceeding 2, suggesting significant serpentinization resulting from seawater descending the transform faults into the oceanic crust and mantle. The study shows how to use modern machine learning pickers on ocean bottom seismometer data to provide essential insights into the physics of faulting of transform faults in time and space, including the seismic and aseismic behavior and the structure of a transform fault system with high resolution.