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Data-informed Polarization Analysis to Improve Seismic Discrimination and Source Characterization

Description: 

We investigate techniques for identifying and isolating polarized seismic signals to enhance source identification and explore shallow earth structure. Short period (≥1 Hz) seismic surface wave signals are commonly observed at local distances (<200 km) from near-surface events like explosions and human activities and pose a challenge in local-distance seismic discrimination. These signals can interfere with or bias common discriminants, such as P-to-S phase amplitude ratios, which are commonly used to distinguish earthquakes from explosions. Here we explore methods to remove polarized surface wave signals from locally recorded seismograms to improve phase amplitude discriminants. Furthermore, surface waves provide valuable insight into shallow subsurface velocity structure due to their sensitivity to shallow geologic heterogeneities. We highlight the dual nature of polarized surface wave signals, emphasizing the need to carefully consider their removal or exploitation based on research objectives. Our results demonstrate the value of polarization analysis of seismic signals to enhance both seismic source identification and our understanding of shallow earth structure.

Session: Data-driven and Computational Characterization of Non-earthquake Seismoacoustic Sources [Poster]

Type: Poster

Date: 4/16/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Jonas

Student Presenter: No

Invited Presentation: 

Poster Number: 76

Authors