Japan Geoscience Union Meeting 2022

Presentation information

[E] Poster

S (Solid Earth Sciences ) » S-CG Complex & General

[S-CG44] Science of slow-to-fast earthquakes

Fri. Jun 3, 2022 11:00 AM - 1:00 PM Online Poster Zoom Room (23) (Ch.23)

convener:Aitaro Kato(Earthquake Research Institute, the University of Tokyo), convener:Yoshiyuki Tanaka(Earth and Planetary Science, The University of Tokyo), Asuka Yamaguchi(Atomosphere and Ocean Research Institute, The University of Tokyo), convener:Takahiro Hatano(Department of Earth and Space Science, Osaka University), Chairperson:Takayoshi Nagaya(Graduate School of Science, The University of Tokyo), Anca Opris(Research and Development Center for Earthquake and Tsunami Forecasting)

11:00 AM - 1:00 PM

[SCG44-P27] NccPy: an open-source earthquake relocation package for higher resolution hypocenter and centroid locations with cross correlation approaches

*Ta-Wei Chang1, Satoshi Ide1 (1.University of Tokyo)

Keywords:Earthquake relocation, Cross correlation, Network correlation coefficient

Cross-correlation-based centroid relocation approaches have been widely implemented and are often shown to be very effective in reducing uncertainties in earthquake centroid locations. In comparison, hypocenter locations of earthquakes are conventionally determined and improved by using phase picking information, which tend to result in higher uncertainty that originates from picking errors. Brutal attempts to implement cross correlations with onset phases to relocate hypocenters are prone to failures if “the correct segments”, i.e., the onset segment of seismic phases, are not used to perform the cross corrections. Here, we have succeeded in cross-correlation-based hypocenter relocation by including an additional grid search to automatically identify the onset segments to perform cross correlations with. Combined this new approach with the existing network correlation coefficient (NCC) method, a centroid relocation method, we then further perform joint inversions to determine hypocenter and centroid locations of all events simultaneously, using smaller events as anchors that links the hypocenters and centroids of larger events. Extensive tests and benchmarking have been carried out to evaluate the performance of the relocation results. The package that realizes the above, named NccPy, can now be easily accessed via Github.