Abstract
Magma migrations frequently trigger seismic swarms, resulting in seismic events that overlap in time and hinder real-time phase picking commonly used for hypocenter location. Addressing this challenge, seismic amplitude ratio analysis (SARA) allows identification of seismic migrations in real-time by simply tracking the relative seismic amplitude between a pair of seismic stations.
This paper aims to identify key statistical features of the seismic network array locations that improve their ability to detect seismic migrations using SARA. We evaluated the capability to detect the most frequently oriented magma migrations in over 100 volcanoes, using a criterion previously proposed to study vertical magma migrations in Piton de la Fournaise. Additionally, we investigate the influence of vent-station proximity on magma conduit coverage and identify the distance ratio that yields improved detection.
Furthermore, we estimate the seismic network efficiency by calculating the detection capability volume per station. We then use the random forest regression algorithm to identify which statistical features of the seismic network location contribute more to the efficiency disparity among different volcanoes. Notably, our findings reveal that optimizing seismic network coverage entails maximizing the standard deviation of relative pair station distances, while maintaining a prescribed minimum separation distance between station pairs. Our results reveal important criteria that can be used to optimize seismic network location design.