MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding
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Computer Science > Machine Learning
arXiv:2605.26320 (cs)
[Submitted on 25 May 2026]
Title:MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding
Authors:Sai Munikoti, Ian Stewart, Chengping Chai, Lisa Linville, Scott Vasquez, Sameera Horawalavithana, Karl Pazdernik
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Abstract:The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-specific datasets that integrate multiple data modalities beyond text and images. In seismology, understanding earthquake phenomena requires the synthesis of timeseries waveform data, geographical imagery, and contextual metadata, a multimodal integration absent in existing seismic datasets. We present MultiSeismo, a large scale structured multimodal seismic dataset, comprising over 16K seismic events spanning 13 years (2010 to 2023) across diverse geographical regions. Each event data integrates waveform recordings from global station networks, intensity maps, population exposure visualizations, and a comprehensive textual description within a standardized JSON format. We additionally develop MISCE, a multimodal instruction set on top of raw data to enable supervised training and evaluation of GMMs on seismic reasoning tasks ranging from basic information retrieval to complex cross modal analysis. We leverage MISCE to finetune an existing multimodal model (Unified IO 2) enhanced with a specialized timeseries encoder, which yields SeisModal, the first domain specific multimodal model for comprehensive seismic analysis. Evaluation of state of the art multimodal models on MultiSeismo reveals significant challenges, particularly with time-series data processing for general purpose models, while demonstrating SeisModal's superior performance on seismic multimodal reasoning tasks. These results prove that MultiSeismo provides a rigorous benchmark for future multimodal research in seismology and validate the success of our domain specific architectural adaptations.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.26320 [cs.LG] |
| (or arXiv:2605.26320v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26320
arXiv-issued DOI via DataCite (pending registration)
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