Waveform cross correlation applied to earthquakes in the Atlantic Ocean

TL;DR

This study evaluates the effectiveness of waveform cross correlation in detecting and analyzing earthquakes in the Atlantic Ocean using IMS data, demonstrating improved detection sensitivity and automated event classification.

Contribution

It applies waveform cross correlation to Atlantic Ocean earthquakes, constructs correlation matrices, and introduces machine learning for automatic event validation, enhancing detection and resolution.

Findings

Station TORD detected 868 of 931 events.

High-quality signals enabled effective waveform templates.

Machine learning improved event validation accuracy.

Abstract

We assess the level of cross correlation between P-waves generated by earthquakes in the Atlantic Ocean and measured by 22 array stations of the International Monitoring System (IMS). There are 931 events with 6,411 arrivals in 2011 and 2012. Station TORD was the most sensitive and detected 868 from 931 events. We constructed several 931 by 931 matrices of cross correlation coefficients (CCs) for individual stations and also for average and cumulative CCs. These matrices characterize the detection performance of the involved stations and the IMS. Sixty earthquakes located in the northern hemisphere were selected as master events for signal detection and building of events populating a cross correlation Standard Event List (XSEL) for the first halves of 2009 and 2012. High-quality signals (SNR>5.0) recorded by 10 most sensitive stations were used as waveform templates. In order to quantitatively estimate the gain in the completeness and resolution of the XSEL we compared it with the Reviewed Event Bulletin (REB) of the International Data Centre (IDC) for the North Atlantic (NA) and with the ISC Bulletin. Machine learning and classification algorithms were successfully applied to automatically reject invalid events in the XSEL for 2009.