Enhancing earthquake detection systems through ai based frameworks for multimodal learning and sensor integration models

This study proposes an AI-driven seismic detection method that uses deep learning, sensor networks, multimodal data fusion, and adaptive seismic modeling. The study addresses a major issue in current seismic detection and forecasting systems. They struggle to integrate multiple seismic data sources, limiting prediction accuracy and system efficiency. This study aims to develop a robust and scalable framework that integrates these diverse seismic data sources into a unified AI-driven system to improve earthquake detection, real-time monitoring, early warning, and disaster risk reduction and preparedness. The databases contain 25 years (1998-2023) of seismic waveforms, tomography images, borehole data, and accelerometer measurements from seismically active areas like California, Nevada, Oregon and New York. This study fuses temporal and spatial features from seismic waveforms, accelerometer data, strain gauges, and borehole readings using a Convolutional Neural Network (CNN) with an attention mechanism. The seismic model optimizes prediction performance by weighting each modality like Seismic Waveforms, Accelerometer Data and Borehole Readings and its adaptive capabilities allow it to continuously refine its predictions based on new data, improving accuracy and responsiveness in dynamic seismic environments. Features were extracted after Fourier Transform was used to remove noise and isolate critical seismic signals. The use of waveform, accelerometer, borehole, and tomography inputs together in the multimodal framework aids in the detection of cross-sensor relationships which are not obtainable by any singlemodality system, thus enhancing the reliability of detection and minimizing the occurrence of false alarms. The AI-driven model captures seismic data modalities' interdependencies and adapts to real-time seismic activity to improve earthquake prediction. This hybrid approach can improve earthquake preparedness and forecasting, revealing how AI and geophysical methods can mitigate disasters. AI-driven seismic detection models with multimodal data fusion, noise filtering, and adaptive forecasting reduce earthquake prediction errors and false positives. These models optimize sensor deployment and seismic data integration for event classification and forecasting. This method improves early warning, disaster preparedness, and seismic hazard assessments, reducing risks and strengthening earthquake-prone areas.