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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2503.17148 |
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| _version_ | 1866916659141279744 |
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| author | Lange, D. Ren, Y. Grevemeyer, I. |
| author_facet | Lange, D. Ren, Y. Grevemeyer, I. |
| contents | The Blanco transform fault system (BTFS) is highly segmented and represents an evolving transform plate boundary in the Northeast Pacific Ocean. Its seismic behavior was captured with a dense network of 54 ocean-bottom-seismometers operated for one year. We created a high-resolution earthquake catalog based on different machine learning onset pickers, 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 within pull-apart basins. Seismicity along simple linear fault strands is localized within 2 km of the seafloor expression of the fault. Repeaters indicate an average of 21 cm slip, exceeding the geological slip rate by ~4 times. Based on the repeater behavior, we suggest that the (overall aseismic) slip is spatially very heterogeneous, consisting of many small seismic patches, each one releasing its seismic slip every 4 years. Along the BTFS, the coupling of the fault is variable and varies between fully locked and fully creeping. Local earthquake tomography shows elevated vp/vs values exceeding 2, suggesting significant serpentinization from seawater entering the transform faults, the oceanic crust and mantle. The study shows how to use modern machine learning pickers on OBS data to provide essential insights into the physics of faulting along major plate boundary faults in time and space, including the partitioning of slip seismic and aseismic faulting with high resolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seismotectonics and Slip Behavior of a Submarine Plate Boundary Fault from Seismicity Repeaters and Tomography using a high-resolution earthquake catalog from machine learning Lange, D. Ren, Y. Grevemeyer, I. Geophysics The Blanco transform fault system (BTFS) is highly segmented and represents an evolving transform plate boundary in the Northeast Pacific Ocean. Its seismic behavior was captured with a dense network of 54 ocean-bottom-seismometers operated for one year. We created a high-resolution earthquake catalog based on different machine learning onset pickers, 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 within pull-apart basins. Seismicity along simple linear fault strands is localized within 2 km of the seafloor expression of the fault. Repeaters indicate an average of 21 cm slip, exceeding the geological slip rate by ~4 times. Based on the repeater behavior, we suggest that the (overall aseismic) slip is spatially very heterogeneous, consisting of many small seismic patches, each one releasing its seismic slip every 4 years. Along the BTFS, the coupling of the fault is variable and varies between fully locked and fully creeping. Local earthquake tomography shows elevated vp/vs values exceeding 2, suggesting significant serpentinization from seawater entering the transform faults, the oceanic crust and mantle. The study shows how to use modern machine learning pickers on OBS data to provide essential insights into the physics of faulting along major plate boundary faults in time and space, including the partitioning of slip seismic and aseismic faulting with high resolution. |
| title | Seismotectonics and Slip Behavior of a Submarine Plate Boundary Fault from Seismicity Repeaters and Tomography using a high-resolution earthquake catalog from machine learning |
| topic | Geophysics |
| url | https://arxiv.org/abs/2503.17148 |