Automatic detection of microseismic events is a rapidly evolving field, motivated by insights gained from statistical analysis of ever-more-complete earthquake catalogues. This is particularly relevant to volcanically and hydrothermally induced seismic swarms. Traditionally, earthquake detection consisted of identifying seismic arrivals at individual stations using an STA/LTA, kurtosis or machine learning algorithm and performing phase association across a network. This is vulnerable to false triggers from incoherent noise and struggles where events are closely spaced in time. An extreme example is the > 30,000 earthquakes during the 2014 Bárðarbunga-Holuhraun dike intrusion, with events occurring as often as every ~3 seconds during phases of rapid advancement of the dike tip.More recently, migration-based techniques have been introduced, which make use of the coherency of seismic phase arrivals recorded at multiple stations across a network. This enables detection of earthquakes at close to or below the signal-to-noise ratio at individual stations, and implicitly associates phase arrivals even at very small inter-event times. QuakeMigrate is a new modular, open-source Python package to efficiently, automatically and robustly detect and locate microseismicity using a coalescence-based method. The user inputs continuous seismic data, a velocity model or pre-calculated look-up table and list of station locations. We demonstrate the flexibility of this process with examples of basal icequakes detected at the Rutford Ice Stream, Antarctica, using a homogeneous velocity model, and the aftershock sequence from a M5 earthquake at Mt. Kinabalu, North Borneo, recorded on a sparse network of stations recording at 20Hz. A realistic estimate of the event location uncertainty, phase picks with uncertainties and a range of plots and data allow rigorous selection of real events at a sub-SNR detection threshold. We demonstrate its performance in application to the rapidly occurring earthquakes during the 2014 dike intrusion and highlight the opportunity for community-driven improvements and expansion afforded by its modular architecture. We outline our aim to produce the framework for a complete workflow from continuous data to high precision relocations within an open-source Python package.
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- 7299 General or miscellaneous;