SeisMoLLM is the first foundation model that transfers knowledge from Large Language Models to seismic monitoring, eliminating the need for extensive domain-specific pre-training.

• Employs cross-modal transfer to adapt LLM capabilities to seismic waveform processing
• Uses innovative waveform tokenization techniques to transform seismic signals into LLM-compatible formats
• Demonstrates superior performance across multiple seismic monitoring tasks, especially with degraded signals
• Maintains effectiveness even with limited training data, showing strong few-shot learning capabilities

This innovation represents a significant engineering breakthrough in geophysical monitoring, potentially transforming earthquake detection systems, infrastructure monitoring, and natural disaster response through more accurate and resilient signal processing.

Original Paper: SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model