Laboratory earthquake prediction via multimodal features

With earthquake disasters inflicting immense devastation worldwide, advancing reliable prediction models utilizing diverse data paradigms offers new perspectives to unlock practicable prediction solutions. As reliable earthquake forecasting remains a grand challenge amidst complex fault dynamics, we employ combined finite-discrete element method (FDEM) simulations to generate abundant laboratory earthquake data. We propose a multimodal features fusion model that integrates temporal sensor data and wavelet-transformed visual kinetic energy to predict laboratory earthquakes. Comprehensive experiments under varied stress conditions confirm the superior prediction capability over single modal approaches by accurately capturing stick slip events and patterns. Furthermore, efficient adaptation to new experiments is achieved through fine-tuning of a lightweight adapter module, enabling generalization. We present a novel framework leveraging multimodal features and transfer learning for advancing physics-based, data-driven laboratory earthquake prediction. As increasing multi-source monitoring data becomes available, the established modeling strategies introduced here will facilitate the development of reliable real world earthquake analysis systems.