SeisMoLLM: Advancing Seismic Monitoring via Cross‐Modal Transfer With Pretrained Large Language Model

Abstract Recent advances in deep learning have transformed seismic monitoring, yet most existing methods remain task‐specific and data‐limited, restricting performance on challenging scenarios and generalization to unseen data. Large‐scale pretraining has addressed similar limitations in other fields, but its application to seismic data faces challenges, including the absence of effective pretraining algorithms, fragmented data sets, and prohibitive computational costs. Here, we propose SeisMoLLM, a novel approach that cross‐modally transfers the sequence modeling knowledge of pretrained large language models into a unified framework adaptable to various tasks, unlocking pretraining benefits for seismic monitoring. Evaluations on STEAD and DiTing data sets demonstrates SeisMoLLM outperforms leading methods and generalizes strongly across multiple tasks, with notable improvements of 10%–50%, while maintaining training costs comparable to small baselines and faster inference than the smallest baseline. These results establish SeisMoLLM as a promising framework for foundation models and highlight cross‐modal transfer as a compelling direction for advancing seismic monitoring.