- Indiana University, Department of Earth and Atmospheric Sciences, United States of America (gongjian@iu.edu)
Oceanic transform faults (OTFs) are a major type of plate boundary on Earth. They are segmented into seismic and aseismic sections, with large earthquakes clustering on individual seismic segments. Some OTF segments even produce quasi-periodic large earthquakes, a behavior that is rarely observed on continental faults. Despite their remote locations, OTFs therefore provide a unique natural laboratory for understanding earthquake processes and faulting mechanisms.
Because OTFs are located on the seafloor, their internal structure and the ways in which they accommodate plate motion remain poorly understood. Over the past two decades, several ocean-bottom seismometer (OBS) experiments have been conducted along OTFs. Microseismicity recorded by these experiments has revealed important and sometimes surprising properties, including the behavior of aseismic sections and deep-penetrating seismicity that has changed our understanding of OTF rheology. To further resolve fault structure and slip behavior, we need earthquake catalogs with both high-resolution locations and high completeness, so that we can interpret fault geometry and identify transient slip processes.
In recent years, machine-learning phase pickers have been increasingly applied to OBS data, greatly improving the efficiency of earthquake detection. However, major challenges remain in building high-quality catalogs, including uncertainties in phase picking, limitations of location algorithms, and the effects of station spacing and velocity models. In this study, we systematically evaluate key components of the data-processing workflow that control catalog quality, including the performance of different phase pickers, the impact of station coverage and velocity models, and the behavior of different earthquake location methods. We also discuss strategies for building multi-tier catalogs that balance location accuracy and catalog completeness, providing datasets that are suitable for both structural interpretation and studies of fault slip behavior.
How to cite: Gong, J.: Microseismicity Detection and Location along Oceanic Transform Faults, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18632, https://doi.org/10.5194/egusphere-egu26-18632, 2026.