arXiv subjects

Yuqi Cai

Publications and source records attributed to Yuqi Cai.

4 recordsLinked to original sources

arXiv2026

Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion

Data-driven neural networks are increasingly used as surrogate forward models in geophysics, but it remains unclear whether they recover only the data mapping or also the underlying physical sensitivity structure. Here we test this question using surface-wave dispersion. By comparing automatically differentiated gradients from a neural-network surrogate with theoretical sensitivity kernels, we show that the learned gradients can recover the main depth-dependent structure of physical kernels across a broad range of periods. This indicates that neural surrogate models can learn physically meaningful differential information, rather than acting as purely black-box predictors. At the same time, strong structural priors in the training distribution can introduce systematic artifacts into the inferred sensitivities. Our results show that neural forward surrogates can recover useful physical information for inversion and uncertainty analysis, while clarifying the conditions under which this differential structure remains physically consistent.

cs.LG↗

arXiv2025

Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location

The rapid proliferation of deep-learning-based detection and association methods has greatly expanded automatically generated earthquake catalogs, but has also introduced false detections, mis-associated arrivals, and poorly constrained events, making rigorous uncertainty quantification essential. We present a fully probabilistic earthquake location framework that jointly infers hypocenters, origin times, phase-dependent noise scales, and contamination levels within a unified Bayesian formulation. Robustness is achieved through a two-level hierarchical strategy: arrival-time residuals are modeled using a Student-$t$ scale-mixture to accommodate heavy-tailed noise, while an explicit two-component contamination model probabilistically classifies each phase pick as an inlier or outlier, with phase-specific contamination rates inferred from the data. This formulation avoids heuristic data rejection and manual thresholding. Posterior sampling is accelerated using a neural-network travel-time surrogate, enabling scalable inference for large catalogs. Synthetic tests demonstrate well-calibrated posterior uncertainties, and application to the 2022 Luding $M_s$~6.8 aftershock sequence shows that uncertainty-based screening reduces the catalog from 10{,}590 to 6{,}562 events without loss of recall. This framework provides a principled pathway toward statistically trustworthy earthquake catalogs in the era of automated seismic monitoring.

physics.geo-ph↗

arXiv2025

A Deep-Learning-Based Framework for Focal Mechanism Determination and Its Application to the 2022 Luding Earthquake Sequence

P-wave first-motion polarity plays an important role in resolving focal mechanisms of small to moderate earthquakes (M <= 4.5). High-quality focal mechanism solutions for abundant small events can greatly improve our understanding of regional tectonics, fault geometries, and stress-field characteristics. In this study, we develop an automated focal mechanism determination framework that integrates deep neural networks with P-wave first-motion polarity observations, and apply it to the 2022 Luding earthquake sequence. The model is trained on 12 years (2009-2020) of manually annotated 100 Hz waveform records from the China National Seismic Network, achieving a polarity recall of 97.4 percent and a precision of 98.5 percent. After automatically determining the first-motion polarities, we invert focal mechanisms using the HASH method. The resulting focal mechanism solutions show high consistency with mapped fault structures in the Luding region, demonstrating the reliability and applicability of the proposed workflow.

physics.geo-ph↗

arXiv2024

PRIME-DP: Pre-trained Integrated Model for Earthquake Data Processing

We propose a novel seismic wave representation model, namely PRIME-DP (Pre-trained Integrated Model for Earthquake Data Processing), specifically designed for processing seismic waveforms. Most existing models are designed to solve a singular problem. Unlike these models, PRIME-DP is capable of multi-task single station seismic waveform processing, including Pg/Sg/Pn/Sn phase picking and P polarization classification. Moreover, it can be fine-tunned to various tasks, such as event classification without architecture modifications. PRIME-DP can achieve a recall rate of over 85% for Pg and Sg phases on continuous waveforms and achieves over 80% accuracy in P polarization classification. By fine-tuning classification decoder with NeiMeng dataset, PRIME-DP achieves 95.1% accuracy on event.

physics.geo-ph↗