AI-Based Framework for Identification and Classification of Earthquake Precursors Using Seismic Data Analysis

Abstract

Earthquakes are among the most destructive natural disasters, causing significant loss of life, infrastructure damage, and economic disruption. Despite advancements in geophysical research, accurate prediction of earthquakes remains a major challenge due to the complex and nonlinear nature of seismic processes. One promising approach involves the identification and analysis of earthquake precursors—observable physical or chemical changes that occur prior to seismic events. This research proposes an artificial intelligence-based framework for the identification and classification of earthquake precursors using seismic data analysis.The proposed system focuses on detecting patterns in precursor signals and classifying them automatically using machine learning techniques. Earthquake precursors may include variations in seismic activity, ground deformation, electromagnetic anomalies, gas emissions, and other geophysical indicators. These signals often exhibit subtle and complex patterns, making manual analysis difficult and time-consuming. The integration of artificial intelligence enables efficient processing of large datasets and extraction of meaningful insights from noisy data.The system is implemented using Python and incorporates a backend framework to manage data processing and model execution. The provided code initializes the application environment and supports administrative operations, ensuring a structured and scalable system architecture. The framework processes seismic data, applies preprocessing techniques to remove noise, and extracts relevant features for analysis.Machine learning algorithms are employed to classify precursor signals based on their characteristics. These algorithms learn from historical data and identify patterns associated with seismic events. The classification process enables the system to distinguish between normal and anomalous signals, providing early indications of potential earthquakes. The system can be extended to incorporate deep learning models for improved accuracy and performance.The proposed framework aims to enhance earthquake prediction capabilities by automating the analysis of precursor signals. Experimental results indicate that machine learning models can effectively classify seismic patterns and detect anomalies associated with earthquake precursors. The system offers a scalable and efficient solution for real-time monitoring and analysis.This research contributes to the field of disaster prediction by integrating artificial intelligence with geophysical data analysis. The proposed approach has the potential to improve early warning systems and support decision-making in disaster management. Future work can focus on integrating multiple data sources and advanced algorithms to further enhance prediction accuracy.