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Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation
Caifeng Zou, Yaozhong Shi, Zachary E. Ross +2
Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but are computationally intensive. Data-driv…
SPIDER: Scalable Probabilistic Inference for Differential Earthquake Relocation
Zachary E. Ross, John D. Wilding, Kamyar Azizzadenesheli +1
Seismicity catalogs are larger than ever due to an explosion of techniques for enhanced earthquake detection and an abundance of high-quality datasets. Bayesian inference is an app…
Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multi-Stage Training
Qingkai Kong, Caifeng Zou, Youngsoo Choi +5
The recent development of Neural Operator (NeurOp) learning for solutions to the elastic wave equation shows promising results and provides the basis for fast large-scale simulatio…
Ambient Noise Full Waveform Inversion with Neural Operators
Caifeng Zou, Zachary E. Ross, Robert W. Clayton +2
Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finit…
Deep Neural Helmholtz Operators for 3D Elastic Wave Propagation and Inversion
Caifeng Zou, Kamyar Azizzadenesheli, Zachary E. Ross +1
Numerical simulations of seismic wave propagation in heterogeneous 3D media are central to investigating subsurface structures and understanding earthquake processes, yet are compu…
Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation
Yaozhong Shi, Grigorios Lavrentiadis, Domniki Asimaki +2
We present a data-driven framework for ground-motion synthesis that generates three-component acceleration time histories conditioned on moment magnitude, rupture distance , time-a…
Phase Neural Operator for Multi-Station Picking of Seismic Arrivals
Hongyu Sun, Zachary E. Ross, Weiqiang Zhu +1
Seismic wave arrival time measurements form the basis for numerous downstream applications. State-of-the-art approaches for phase picking use deep neural networks to annotate seism…
Neural mixture model association of seismic phases
Zachary E. Ross, Weiqiang Zhu, Kamyar Azizzadenesheli
Seismic phase association is the task of grouping phase arrival picks across a seismic network into subsets with common origins. Building on recent successes in this area with mach…
Accelerating Time-Reversal Imaging with Neural Operators for Real-time Earthquake Locations
Hongyu Sun, Yan Yang, Kamyar Azizzadenesheli +2
Earthquake hypocenters form the basis for a wide array of seismological analyses. Pick-based earthquake location workflows rely on the accuracy of phase pickers and may be biased w…
Rapid Seismic Waveform Modeling and Inversion with Neural Operators
Yan Yang, Angela F. Gao, Kamyar Azizzadenesheli +2
Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We int…
U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli +2
Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical simulation data can provide a…
Seismic wave propagation and inversion with Neural Operators
Yan Yang, Angela F. Gao, Jorge C. Castellanos +3
Seismic wave propagation forms the basis for most aspects of seismological research, yet solving the wave equation is a major computational burden that inhibits the progress of res…
HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks
Jonathan D. Smith, Zachary E. Ross, Kamyar Azizzadenesheli +1
We introduce a scheme for probabilistic hypocenter inversion with Stein variational inference. Our approach uses a differentiable forward model in the form of a physics informed ne…
Directivity Modes of Earthquake Populations with Unsupervised Learning
Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli +1
We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce rela…