# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
## 6 Conclusions  
This paper presents a novel probabilistic model based on Martingale theory, which represents an advancement over previous methodologies by leveraging the \(z\)-space to eliminate the need for a predetermined threshold. Our primary objective is to detect anomalies in EMF activity, potentially serving as precursors to seismic events. To comprehensively assess our framework, we conducted two distinct experiments: a quantitative study facilitating rigorous statistical performance analysis and a qualitative investigation centered around three prominent earthquake case studies. The results of our experiments reveal a discernible improvement in performance compared to traditional approaches, underscoring our framework's effectiveness in detecting early signs of abnormal EMF behavior preceding seismic events.  
Throughout our inquiry, we identified several noteworthy concerns and promising avenues for future research. These include delving into the intricacies of electromagnetic patterns to enhance the accuracy of seismic anomaly detection; scrutinizing the reliability of the hypothesis test method; delving deeper into the nature of false positive results to refine anomaly detection algorithms; investigating the disparate sensitivities of different approaches to anomalies across various segments of time-series data; grid size analysis; and integrating anomaly graphs with ground truth data to facilitate transparent analysis, waveform analysis, shape encoding, and anomaly pattern recognition. Furthermore, we propose extending our research endeavors by integrating advanced deep learning techniques and further refining probabilistic models to scrutinize seismic event data for nuanced abnormalities, thereby enhancing the robustness of our framework in seismic precursor detection scenarios.  
Conceptualization, Y.B.; Methodology, S.H. and Y.B.; Software, S.H.; Validation, M.H., W.Z., J.S. and X.Z.; Formal analysis, S.H.; Investigation, S.H., Y.B., W.Z., J.S. and X.Z.; Resources, M.H.; Data curation, S.H.; Writing--original draft, S.H.; Writing--review & editing, Y.B., M.H. and C.O.; Supervision, Y.B. All authors have read and agreed to the published version of the manuscript.  
This research receives vital support from the Dragon 4&5 program, a prestigious collaboration between the European Space Agency (ESA) and the Ministry of Science and Technology of China. The program, operating under grant agreement numbers 38577 and 59308, exemplifies international cooperation in advancing scientific endeavors, particularly in the realm of space exploration and Earth observation. Additionally, we gratefully acknowledge the backing provided by the National Natural Science Foundation of China through grants numbered 42371404 and 41601479, which play a crucial role in facilitating our research efforts. Furthermore, we extend our gratitude to the European Space Agency for their invaluable contribution in providing access to Swarm satellite data, a cornerstone of our investigation. This collaborative support network underscores the significance of international partnerships in driving scientific progress and underscores the importance of collective efforts in advancing our understanding of complex phenomena such as electromagnetic field activity and seismic event precursors.