A martingale-based temporal analysis of pre-earthquake anomalies at Jiuzhaigou, China, in the period of 2009-2018

Ling, Lin; Kong, Xiangzeng; Li, Nan

doi:10.1051/e3sconf/201913101072

Cited by 2 publications

(1 citation statement)

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“…We focus on probabilistic approaches for anomaly detection, as they do not require predefined thresholds, which is advantageous given the difficulty in defining anomalies within the EMF domain. Building on previous work, we propose a novel method utilizing Martingale probability theory, offering a robust probabilistic model without the need for predefined thresholds [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics

Harrigan,

Bi,

Huang

et al. 2024

Sensors

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The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged, and a growing body of empirical evidence suggests that the Earth’s electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals, allowing for more detailed studies on weather and earthquakes. In this paper, we present an approach designed to detect anomalies in electromagnetic field data from Swarm satellites. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements. We develop an enhanced form of a probabilistic model based on the Martingale theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detection overall. Secondly, we use three case studies of seismic activity (namely, earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data.

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Section: Introductionmentioning

confidence: 99%