mlsanityData to 14 Oct 2026

SeisLM: a Foundation Model for Seismic Waveforms

arXiv 2410.15765 ↗First posted 21 Oct 2024physics.geo-ph cs.LGcited by 8

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Abstract (arXiv)

We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes. SeisLM is pretrained on a large collection of open-source seismic datasets using a self-supervised contrastive loss, akin to BERT in language modeling. This approach allows the model to learn general seismic waveform patterns from unlabeled data without being tied to specific downstream tasks. When fine-tuned, SeisLM excels in seismological tasks like event detection, phase-picking, onset time regression, and foreshock-aftershock classification. The code has been made publicly available on https://github.com/liutianlin0121/seisLM.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 26 of its 73 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

26 of 73 references.

References · 26

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14 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Evaluation of Seismic Artificial Intelligence with Uncertaintycited by 3
  2. 2025SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Modelcited by 3
  3. 2025AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Modelscited by 1
  4. 2026Data-Driven Forecasting of three-Component Seismograms Using Transformer Architecturescited by 1
  5. 2026Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretationcited by 0
  6. 2026GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Seriescited by 0
  7. 2026IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Datacited by 0
  8. 2026TomoTransformer: Towards a Foundation Model for CT Reconstructioncited by 0