Harrigan Shane, Bi Yaxin, Huang Mingjun, O'Neill Christopher, Zhai Wei, Sun Jianbao, Zhang Xuemin
School of Computing, Engineering, and Intelligent Systems, Ulster University, Derry-Londonderry BT48 7JL, UK.
School of Computing, Ulster University, Belfast BT15 1AP, UK.
Sensors (Basel). 2024 Jun 5;24(11):3654. doi: 10.3390/s24113654.
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.
作为警报系统一部分的地震活动前兆检测，将为最大限度减少地震造成的社会和经济影响提供机会。长期以来人们一直设想，而且越来越多的经验证据表明，地球电磁场可能包含地震事件的前兆。随着更多传感器和方法的出现，过去几年捕获和监测电磁场活动的能力有所提高。像“蜂群”这样的任务使研究人员能够以秒级间隔获取近乎连续的电磁活动观测数据，从而能够对天气和地震进行更详细的研究。在本文中，我们提出一种旨在检测来自“蜂群”卫星的电磁场数据异常的方法。这有助于基于“蜂群”测量结果开发一个连续且有效的地震活动监测系统。我们基于鞅理论开发了一种概率模型的增强形式，该模型允许检验原假设以指示电磁场活动的异常变化。我们在两个实验中评估这种增强方法。首先，我们与传统方法一起，对广为人知且常用的基准数据集进行定量比较。我们发现增强版本总体上能产生更准确的异常检测结果。其次，我们使用三个地震活动案例研究（即墨西哥、希腊和克罗地亚的地震）来评估我们的方法，结果表明我们的方法能够检测电磁数据中的异常现象。
