# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
###### Abstract  
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. a 2024 24 3654 12 March 2024 13 May  
This paper focuses on detecting abnormalities in electromagnetic field behavior measured by satellites, with a specific interest in the Swarm constellation operated by the European Space Agency (ESA) since 2013 [7]. Swarm, comprising three identical satellites equipped with high-precision electromagnetic field sensors, was originally designed to study ionosphere behavior for space physics research. However, it has also been utilized in research examining electromagnetic field variations associated with seismic events. The Swarm satellite constellation comprises three satellites: Alpha, Beta, and Charlie. They are identical, differing only in altitude and orbit behavior. Alpha and Charlie orbit roughly 450 km high, with a 100-km separation, while Beta orbits at around 510 km, nearly perpendicular to Alpha and Charlie [7]. Originally, Beta was intended to orbit alongside Alpha, but due to issues with Charlie, their altitudes were swapped, although this did not affect the constellation functionality. Each satellite records electromagnetic activity every second, along with corresponding geospatial coordinates (latitude and longitude). Consequently, each satellite produces 86,400 observations per day, totaling 259,200 for all satellites. This amounts to approximately 756,280,000 available records detailing electromagnetic field (EMF) behavior. From this extensive dataset, we focus on a subset within specific geospatial bounds for our experiments. This paper introduces an improved abnormal change detection algorithm based on Martingale probability theory [8; 9]. Unlike the original approach, which required a predefined threshold, our method does not, reducing errors introduced during data aggregation. We conduct two experiments: a quantitative analysis to evaluate our method performance against benchmark data and a qualitative study using real Swarm data to investigate three seismic events--the 2017 Mexican earthquake, the 2020 Greek earthquake, and the 2020 Croatian earthquake. Our results suggest that our approach can detect abnormal EMF patterns preceding seismic events, sometimes months in advance. We believe these findings provide a foundation for developing abnormal change detection algorithms for space-based EMF time-series data. Large-magnitude earthquakes, while rare, have significant societal impacts in terms of loss of life and economic damage.