Datensatz

Zusammenfassung

There has been growing recognition of the importance of the accurate seismic locations in quantitative seismological studies, such as seismic hazard analyses, fault zone characterization, and Earth's deformation. Accurate estimation of seismic locations is critical since a wrong estimate of the seismic source location will result in wrong interpretations in the subsequent analyses. We present SCOTER, an open-source Python program package that is designed to relocate multiple seismic events by using P- and S-wave station correction terms. The package implements static and shrinking-box source-specific station terms techniques extended to regional and teleseimic distances and adopted for probabilistic, non-linear, global-search location for large-scale multiple-event location. This program provides robust relocation results for seismic event sequences over a wide range of spatial and temporal scales by applying empirical corrections for the biasing effects of 3-D velocity structure. Written in the Python programming language, SCOTER is run as a stand-alone command-line tool (requiring no knowledge of Python) and also provides a set of sub-commands to develop inputs (dataset, configuration etc) and export results (hypocenter parameters, travel-time residuals etc) { routine but non-trivial tasks that can consume much user time. This package can be used for relocation in local, regional, and teleseimic scales. We describe SCOTER's functionality, design and technical implementation, accompanied by an overview of its use cases. As an illustration, we demonstrate the applicability of this tool through two examples based on (1) a catalogue of several hundred events in the Arctic plate boundary region using regional and teleseismic arrival times and (2) a small dataset of low-magnitude seismic events recorded by dense, local stations at the western Iberia, central Portugal. The relocated datasets highlight the future potential for applying the SCOTER relocation tool to greatly improve the relative location accuracy among nearby events.