arXiv subjects

Chengping Chai

Publications and source records attributed to Chengping Chai.

4 recordsLinked to original sources

arXiv2026

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

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.

cs.LG↗

arXiv2025

Evaluating Multi-station Phase Picking Algorithm Phase Neural Operator (PhaseNO) on Local Seismic Networks

Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these algorithms, the Phase Neural Operator (PhaseNO) shows promising results on regional datasets comparing to existing algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on 4 different local datasets and compared the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. With manually reviewing the newly detected events, we find the PhaseNO model outperformed the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO's behaviors when only using one station, as well as gradually increase the number of stations in the seismic network to understand it better. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.

physics.geo-ph↗

arXiv2024

Toward path-invariant embeddings for local distance source characterization

This work builds on recent advances in foundation models in the language and image domains to explore similar approaches for seismic source characterization. We rely on an architecture called Barlow Twins, borrowed from an understanding of the human visual cortical system and originally envisioned for the image domain and adapt it for learning path invariance in seismic event time series. Our model improves the performance on event characterization tasks such as source discrimination across catalogs by 10-12% and provides more reliable predictive uncertainty estimates. We suggest that dataset scale and diversity more than architecture may determine aspects of the current ceiling on performance. We leverage decision trees, linear models, and visualization to understanding the dependencies in learned representations.

cs.CE↗

arXiv2019

Aftershocks of the 2012 Off-Coast of Sumatra Earthquake Sequence

Aftershocks of the 2012 Off-Coast of Sumatra Earthquake Sequence exhibit a complex and diffuse spatial distribution. The first-order complexity in aftershock distribution is clear and well beyond the influence of typical earthquake location uncertainty. The sequence included rupture of multiple fault segments, spatially separated. We use surface-wave based relative centroid locations to examine whether, at the small scale, the distribution of the aftershocks was influenced by location errors. Surface-wave based relative location has delineated precise oceanic transform fault earthquake locations in multiple regions. However, the relocated aftershocks off the coast of Sumatra seldom align along simple linear trends that are compatible with the corresponding fault strikes as estimated for the GCMT catalog. The relocation of roughly 60 moderate-earthquake epicentroids suggests that the faulting involved in the 2012 earthquake aftershock sequence included strain release along many short fault segments. Statistical analysis and temporal variations of aftershocks show a typical decay of the aftershocks but a relatively low number of aftershocks, as is common for intraplate oceanic earthquakes. Coulomb stress calculations indicate that most of the moderate-magnitude aftershocks are compatible with stress changes predicted by the large-event slip models. The patterns in the aftershocks suggest that the formation of the boundary and eventual localization of deformation between the Indian and Australian plate is a complicated process.

physics.geo-ph↗