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SeisLM: a Foundation Model for Seismic Waveforms

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arxiv 2410.15765 v1 pith:LO3ATUP7 submitted 2024-10-21 physics.geo-ph cs.LG

Tianlin Liu , Jannes Münchmeyer , Laura Laurenti , Chris Marone , Maarten V. de Hoop , Ivan Dokmanić This is my paper · ORCID
classification physics.geo-phcs.LG
keywords seislmseismicmodellanguagetaskswaveformsakinallows
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Foundation Model-Assisted Full Waveform Inversion

    physics.geo-ph 2026-08 conditional novelty 7.0 of 10

    A frozen pretrained seismic waveform encoder, SeisLM, defines an FWI misfit that broadens the basin of attraction and improves early-stage inversion on Marmousi and Overthrust benchmarks.

  2. IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Self-supervised latent prediction on raw complex ultrasound channel data reduces the labeled data needed for sound-speed estimation by roughly 3-4x in simulation, reaching 15.6 m/s error with 10,000 labels.

  3. Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures

    astro-ph.IM 2026-06 unverdicted novelty 6.0 of 10

    SeismoGPT is a transformer autoregressive model achieving median normalized cross-correlation above 0.93 when forecasting synthetic three-component seismograms up to 240 s ahead from P- and S-wave context.

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