Accurate Locations of Felt Earthquakes Using Crowdsource Detections

István Bondár,Robert Steed,Joachim Saul,Julien Roch,Rémy Bossu,Angelo Strollo,Andres Heinloo

doi:10.3389/feart.2020.00272

Abstract

We present a methodology that uses crowdsource detections as an initial location to obtain fast and reliable hypocenter parameters of felt earthquakes using arrival-time data from the GEOFON Program. We derive selection criteria using a 3-year-long training set from the trial runs at the European-Mediterranean Seismological Centre (EMSC) to identify accurate event locations at a high confidence level. Since an event may have several crowd-sourced detections, we also develop a methodology dealing with multiple triggers. We validate the selection criteria using real-time processing of recent data and demonstrate that 95% of the selected events are within 50 km distance from the traditional seismic location published by the EMSC. We show that our methodology provides accurate locations much faster than those published by conventional seismic methods. On average, the EMSC CsLoc service can provide rapid and accurate locations within a minute after the occurrence of a felt earthquake.

Highlights

Earthquake crowdsourced detections are based on following eyewitnesses’ immediate reactions to felt earthquakes on various social media platforms, such as Twitter (Earle et al, 2011), traffic on the EuropeanMediterranean Seismological Centre (EMSC) website (Bossu et al, 2014), and the number of launches of the EMSC smartphone app, LastQuake (Bossu et al, 2018)
To determine the new selection criteria, we use a training set of crowdsourced detections between January 2016 and May 2019 including 708 events triggered by the EMSC website traffic, 782 events triggered by the LastQuake app, and 648 events triggered by TED
The validation data set contains 288 events of which 123 events triggered by the EMSC web-site traffic, 97 events triggered by the LastQuake app, and 68 events triggered by TED

Summary

Introduction

Earthquake crowdsourced detections are based on following eyewitnesses’ immediate reactions to felt earthquakes on various social media platforms, such as Twitter (Earle et al, 2011), traffic on the EMSC website (Bossu et al, 2014), and the number of launches of the EMSC smartphone app, LastQuake (Bossu et al, 2018). While other crowdsourced approaches in seismology (e.g., Cochran et al, 2009; Minson et al, 2015; Finazzi, 2016; Kong et al, 2016; Cochran, 2018) have focused on using accelerometers in smartphones or dedicated sensors that are maintained by the public, our approach exploits the public’s search for information and their online reactions (Steed et al, 2019). Offering a very fast earthquake location is a way to answer this desire It is instrumental for rapid engagement of eyewitnesses and to ensure efficient felt report collection from eyewitnesses which are in turn essential for rapid impact assessment (Bossu et al, 2015).

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Discussion

Conclusion