arXiv:2605.26320v1 Announce Type: new 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 event
The proliferation of generalist multimodal models (GMMs) is now facing the challenge of domain-specific data scarcity, prompting efforts to build specialized datasets to extend AI capabilities into scientific fields.
This development indicates a crucial step towards applying advanced AI techniques to complex scientific problems like seismology, potentially leading to improved earthquake prediction and resource exploration.
The creation of large-scale, structured multimodal datasets integrating diverse data types moves AI beyond generic applications into specialized scientific interpretation.
- · AI/ML researchers
- · Geophysical exploration companies
- · Disaster preparedness organizations
- · Scientific instrument manufacturers
- · Traditional seismic analysis methods
- · Data silos within scientific disciplines
Improved accuracy and speed in seismic event detection and analysis becomes possible through multimodal AI.
Enhanced understanding of geological processes could lead to more efficient energy resource discovery and hazard mitigation.
The methodology for building this dataset could serve as a blueprint for multimodal AI application across other scientific domains, accelerating scientific discovery more broadly.
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Read at arXiv cs.LG