6 papers
Does High Signal-to-Noise Ratio Identify Better Training Data for Seismic Deep Learning?
Ziye Yu, Xin Liu, Yuqi Cai
Hard signal-to-noise ratio (SNR) screening removes weak seismic records, but does retaining clearer waveforms improve learning enough to compensate for the discarded data? We test…
Convergent representations of elastic-wave structure emerge across deliberately distinct seismic training routes
Ziye Yu, Yuqi Cai, Xin Liu
Elastic-wave propagation connects laboratory fracture with earthquakes, raising the prospect of seismic models that reuse waveform structure across physical scales. Here we show th…
Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion
Ziye Yu, Yuqi Cai, Xin Liu
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 underlyin…
Toward Trustworthy Earthquake Catalogs in the Era of Automated Detection: A Probabilistic Framework for Robust Earthquake Location
Ziye Yu, Jinqing Sun, Yuqi Cai +4
The rapid proliferation of deep-learning-based detection and association methods has greatly expanded automatically generated earthquake catalogs, but has also introduced false det…
A Deep-Learning-Based Framework for Focal Mechanism Determination and Its Application to the 2022 Luding Earthquake Sequence
Ziye Yu, Yuqi Cai
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…
A Framework for Uncertainty Estimation in Seismology Data Processing with Application to Extract Rayleigh Wave Dispersion Curves from Noise Cross-correlation Functions
Ziye Yu, Xin Liu
Extracting meaningful information from large seismic datasets often requires estimating the uncertainty associated with the results for quantitative analysis. This uncertainty aris…