High-Rate Phase Association with Travel Time Neural Fields

• URL: http://arxiv.org/abs/2307.07572v3
• Date: Tue, 26 Mar 2024 20:50:44 GMT
• Title: High-Rate Phase Association with Travel Time Neural Fields
• Authors: Cheng Shi, Maarten V. de Hoop, Ivan Dokmanić,
• Abstract summary: We introduce Harpa, a high-rate association framework built on deep generative modeling and neural fields. Harpa incorporates wave physics by using optimal transport to compare arrival sequences. It is thus robust to unknown wave speeds and estimates the wave speed model as a by-product of association.
• Score: 13.98860980081838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our understanding of regional seismicity from multi-station seismograms relies on the ability to associate arrival phases with their originating earthquakes. Deep-learning-based phase detection now detects small, high-rate arrivals from seismicity clouds, even at negative magnitudes. This new data could give important insight into earthquake dynamics, but it is presents a challenging association task. Existing techniques relying on coarsely approximated, fixed wave speed models fail in this unexplored dense regime where the complexity of unknown wave speed cannot be ignored. We introduce Harpa, a high-rate association framework built on deep generative modeling and neural fields. Harpa incorporates wave physics by using optimal transport to compare arrival sequences. It is thus robust to unknown wave speeds and estimates the wave speed model as a by-product of association. Experiments with realistic, complex synthetic models show that Harpa is the first seismic phase association framework which is accurate in the high-rate regime, paving the way for new avenues in exploratory Earth science and improved understanding of seismicity.

Related papers

• High Resolution Seismic Waveform Generation using Denoising Diffusion [3.5046784866523932]
  This study introduces a novel, efficient, and scalable generative model for high-frequency seismic waveform generation. A spectrogram representation of seismic waveform data is reduced to a lower-dimensional submanifold via an autoencoder. A state-of-the-art diffusion model is trained to generate this latent representation, conditioned on key input parameters.
  arXiv  Detail & Related papers  (2024-10-25T07:01:48Z)
• Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning [56.45067876391473]
  Existing EEW approaches treat phase picking, location estimation, and magnitude estimation as separate tasks, lacking a unified framework. We propose a novel unified seismic neural network called Fast Information Streaming Handler (FisH) FisH is designed to process real-time streaming seismic data and generate simultaneous results for phase picking, location estimation, and magnitude estimation in an end-to-end fashion.
  arXiv  Detail & Related papers  (2024-08-13T04:33:23Z)
• WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning [39.353484781486245]
  We propose a novel AI-enabled framework, WaveCastNet, for forecasting ground motions from large earthquakes. WaveCastNet integrates a novel convolutional Long Expressive Memory (ConvLEM) model into a sequence to sequence (seq2seq) forecasting framework. Our results using simulated data from the San Francisco Bay Area demonstrate the capability to rapidly predict the intensity and timing of destructive ground motions.
  arXiv  Detail & Related papers  (2024-05-30T22:18:16Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model [0.0]
  We present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components.
  arXiv  Detail & Related papers  (2024-02-04T00:07:05Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Real-time gravitational-wave science with neural posterior estimation [64.67121167063696]
  We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. We analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog. We find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.
  arXiv  Detail & Related papers  (2021-06-23T18:00:05Z)
• Memory-Critical Dynamical Buildup of Phonon-Dressed Majorana Fermions [72.46695228124449]
  We study a one-dimensional polaronic topological superconductor with phonon-dressed $p$-wave pairing. We show that when the memory depth increases, the Majorana edge dynamics transits from relaxing monotonically to a plateau of substantial value into a collapse-and-buildup behavior.
  arXiv  Detail & Related papers  (2020-06-24T07:32:51Z)
• Applications of shapelet transform to time series classification of earthquake, wind and wave data [0.0]
  "Shapelet transform" is based on local similarity in the shape of the time series subsequences. "White-box" machine learning model is proposed with understandable features and a transparent algorithm. Model automates event detection without the intervention of domain practitioners.
  arXiv  Detail & Related papers  (2020-04-22T10:17:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.