SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer
with Pretrained Large Language Model

Xinghao Wang^1,2, Feng Liu^3,1, Rui Su¹, Zhihui Wang², Lihua Fang⁴, Lianqing Zhou⁴, Lei Bai¹, Wanli Ouyang¹

¹ Shanghai AI Laboratory, ² DUT-RU ISE, Dalian University of Technology,
³ SEIEE, Shanghai Jiao Tong University, ⁴ Institute of Earthquake Forecasting, China Earthquake Administration

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• 🔥 Introduciton
• ✨ Highlights
• ⚡️ Usage
• 👍 Acknowledgement
• 🎓 Citation

🔥 Introduction

Large-scale pretraining for seismic monitoring remains challenging due to the lack of effective pretraining algorithms, the high cost of training, and the significant variations across existing datasets. These obstacles have seriously hindered the development of domain foundation models. SeisMoLLM is the first work to explore the cross-modality transfer strategy for seismic monitoring, unleashing the power of pretraing by adapting a pretrained LLM (GPT-2 here) to build a powerflu and general-purpose model.
With a unified network architecture, SeisMoLLM can handle various seismic monitoring tasks, including back-azimuth estimation, epicentral distance estimation, magnitude estimation, phase picking, and first-motion polarity classification, demonstrating its potential as a foundation model for seismology.

✨ Highlights

1. Surperising performance: With standard supervised training on DiTing-light and STEAD datasets, SeisMoLLM achieves the state-of-the-art performance across the above five tasks, getting 36 best results out of 43 task metrics, with many relative improvements ranging from 10% to 50% compared to advanced baselines.

2. Excerllent generalization: Using only 10% data as training set, SeisMoLLM consistently attains better results than the train-from-scratch baselines, with 12 top scores out of 16 metrics.

3. Modest cost: Despite the introduction of large language models, SeisMoLLM still maintains low training cost and rapid inference speed that is comparable to or even better than lightweight baselines. Training only takes 3-36 hours on 4× RTX-4090 GPUs.

⚡️ Usage

🛠️ Preparation

• Install required environment by running pip install -r requirements.txt
• Download required datasets, you can find STEAD at the STEAD repo, and DiTing dataset requires a request for access, please contact the authors of its paper. Then change the --data to your local data path.
• Prepare pre-trained GPT-2 model files at huggingface gpt2 and place them in the your_dir/GPT2 directory, then change the GPT_file_path in models/SeisMoLLM.py to your_dir. We suggest to download manually because huggingface is blocked in many areas.

🚀 Running

To start training or evaluation, run the scripts in run_scripts. Try python main.py --help to see description of every hyperparameter so you can tune the arguments. For model selection, the task abbreviations used in the model names are listed in the table below:

Task	Abbreviation
Detection & Phase Picking	dpk
First-Motion Polarity Classification	pmp
Back-Azimuth Estimation	baz
Magnitude Estimation	emg
Epicentral Distance Estimation	dis

If you want to use a custom dataset, model, or change the task settings, in addition to implement your codes following the provided examples, please remember to modify config.py.

👍 Acknowledgement

Our code is developed based on SeisT codebase. Thanks for their great work.

🎓 Citation

Hopefully, if our work helps, please give us a star ⭐ or cite this work with:

@misc{wang2025seismollmadvancingseismicmonitoring,
      title={SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model}, 
      author={Xinghao Wang and Feng Liu and Rui Su and Zhihui Wang and Lihua Fang and Lianqing Zhou and Lei Bai and Wanli Ouyang},
      year={2025},
      eprint={2502.19960},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.19960}, 
}