Analysis of Seismic Activity Using Deep Learning

This research is a deep learning infrastructure designed for real-time seismic analysis and aims to optimize the precision of earthquake detection while magnitude estimations. The system gathers real-time seismic data from a network of sensors, including accelerometers, geophones, and seismometers. To analyze advanced deep learning models such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and autoencoders. CNNs are employed for feature extraction from raw seismic waveforms, capturing spatial hierarchies in the data, while LSTM networks model temporal dependencies in the seismic signals to enhance event classification over time. Furthermore, autoencoders are used for anomaly detection, identifying unusual seismic patterns that may indicate earthquake precursors. This hybrid approach enables the system to automatically classify seismic events, predict earthquake magnitudes, and differentiate between natural and anthropogenic seismicity with high precision. Integrating these models into an optimized data processing pipeline ensures that the system operates near-real-time, making it highly suitable for earthquake early warning systems. Initial results show that this approach significantly outperforms traditional methods such as signal thresholding and standard machine learning models in terms of both accuracy and computational efficiency.