552 citations · 1.9k across the 19 of their papers we have counts for
19 papers
Insights on the dip of fault zones in Southern California from modeling of seismicity with anisotropic point processes
Zachary E. Ross
Accurate models of fault zone geometry are important for scientific and hazard applications. While seismicity can provide high-resolution point measurements of fault geometry, extr…
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…
Seismic Arrival-time Picking on Distributed Acoustic Sensing Data using Semi-supervised Learning
Weiqiang Zhu, Ettore Biondi, Jiaxuan Li +3
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. The recorded seismic signals by DAS have several distinct characteris…
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…
Generative Adversarial Neural Operators
Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar +2
We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences a…
U-NO: U-shaped Neural Operators
Md Ashiqur Rahman, Zachary E. Ross, Kamyar Azizzadenesheli
Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g., function spaces. Prior works on neural operators proposed a series of novel…
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…
Deep Learning-based Damage Mapping with InSAR Coherence Time Series
Oliver L. Stephenson, Tobias Köhne, Eric Zhan +4
Satellite remote sensing is playing an increasing role in the rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth's s…
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…
Data-driven Accelerogram Synthesis using Deep Generative Models
Manuel A. Florez, Michaelangelo Caporale, Pakpoom Buabthong +3
Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks…
EikoNet: Solving the Eikonal equation with Deep Neural Networks
Jonathan D. Smith, Kamyar Azizzadenesheli, Zachary E. Ross
The recent deep learning revolution has created an enormous opportunity for accelerating compute capabilities in the context of physics-based simulations. Here, we propose EikoNet,…
Extracting dispersion curves from ambient noise correlations using deep learning
Xiaotian Zhang, Zhe Jia, Zachary E. Ross +1
We present a machine-learning approach to classifying the phases of surface wave dispersion curves. Standard FTAN analysis of surfaces observed on an array of receivers is converte…
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…
Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
Men-Andrin Meier, Zachary E. Ross, Anshul Ramachandran +7
In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive…
PhaseLink: A Deep Learning Approach to Seismic Phase Association
Zachary E. Ross, Yisong Yue, Men-Andrin Meier +2
Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is…
Generalized Seismic Phase Detection with Deep Learning
Zachary E. Ross, Men-Andrin Meier, Egill Hauksson +1
To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a centu…
P-wave arrival picking and first-motion polarity determination with deep learning
Zachary E. Ross, Men-Andrin Meier, Egill Hauksson
Determining earthquake hypocenters and focal mechanisms requires precisely measured P-wave arrival times and first-motion polarities. Automated algorithms for estimating these quan…