Computational intelligence approaches for seismic data analysis: a comprehensive analysis of detection and prediction methods

Abstract

The global imperative for early earthquake prediction is indisputable. As computational capabilities continually evolve alongside technological advancements, a profound opportunity emerges for researchers to confront the intricate challenges inherent in detecting and predicting precursory signals associated with earthquakes by developing innovative models using integrating statistical methods with emerging technologies.

This review paper aims to conduct an exhaustive study to centralize a myriad of computational intelligence studies focused on the analysis of seismic data collected from various sources and sensors, providing a consolidated and invaluable source for both researchers and experts in this dynamic domain.

Along with the study on application of various statistical and computational intelligent techniques in addressing the effectiveness of earthquake prediction domain, this paper also examines a set of diverse precursors and their correlation with seismo-electromagnetic precursory studies for detection and prediction that uncovers critical facets demanding scrutiny, including the scientific underpinnings, problem complexity, methodological uncertainties, signal precision, and multifaceted pattern analysis encompassing factors such as direction, location, and magnitude impact along with causal relationships with seismic events and the broader environmental implications that are of paramount concern.

Despite these advancements, unresolved issues persist, including the complexities of detecting and predicting precursory signals, scientific uncertainties, and the need for precise signal analysis and multifaceted pattern interpretation. The research identifies critical challenges and opportunities in deploying these advanced techniques, emphasizing the necessity for deeper exploration of hyperparameters, validation methods, and cross-regional model comparisons to refine forecasting accuracy.