Earthquake Phase Association with Graph Neural Networks

• URL: http://arxiv.org/abs/2209.07086v1
• Date: Thu, 15 Sep 2022 06:53:17 GMT
• Title: Earthquake Phase Association with Graph Neural Networks
• Authors: Ian W. McBrearty, Gregory C. Beroza
• Abstract summary: We develop a Graph Network associator associator to solve the phase association problem. We train synthetic data, and test our method on real data from the Northern California (NC) seismic network. Our results demonstrate that GENIE can effectively solve the association problem under complex seismic monitoring conditions.
• Score: 0.571097144710995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Seismic phase association connects earthquake arrival time measurements to their causative sources. Effective association must determine the number of discrete events, their location and origin times, and it must differentiate real arrivals from measurement artifacts. The advent of deep learning pickers, which provide high rates of picks from closely overlapping small magnitude earthquakes, motivates revisiting the phase association problem and approaching it using the methods of deep learning. We have developed a Graph Neural Network associator that simultaneously predicts both source space-time localization, and discrete source-arrival association likelihoods. The method is applicable to arbitrary geometry, time-varying seismic networks of hundreds of stations, and is robust to high rates of sources and input picks with variable noise and quality. Our Graph Earthquake Neural Interpretation Engine (GENIE) uses one graph to represent the station set and another to represent the spatial source region. GENIE learns relationships from data in this combined representation that enable it to determine robust source and source-arrival associations. We train on synthetic data, and test our method on real data from the Northern California (NC) seismic network using input generated by the PhaseNet deep learning phase picker. We successfully re-detect ~96% of all events M>1 reported by the USGS during 500 random days between 2000$\unicode{x2013}$2022. Over a 100-day continuous interval of processing in 2017$\unicode{x2013}$2018, we detect ~4.2x the number of events reported by the USGS. Our new events have small magnitude estimates below the magnitude of completeness of the USGS catalog, and are located close to the active faults and quarries in the region. Our results demonstrate that GENIE can effectively solve the association problem under complex seismic monitoring conditions.

Related papers

• Double Difference Earthquake Location with Graph Neural Networks [0.2302001830524133]
  We propose a Graph Neural Network based earthquake double-difference relocation framework, Graph Double Difference (GraphDD) Our architecture uses one graph to represent the stations, a second graph to represent the sources, and creates the product graph between the two. We implement our model on several distinct test cases including seismicity from northern California, Turkiye, and northern Chile.
  arXiv  Detail & Related papers  (2024-10-25T06:15:05Z)
• A convolutional neural network approach to deblending seismic data [1.5488464287814563]
  We present a data-driven deep learning-based method for fast and efficient seismic deblending. A convolutional neural network (CNN) is designed according to the special character of seismic data. After training and validation of the network, seismic deblending can be performed in near real time.
  arXiv  Detail & Related papers  (2024-09-12T10:54:35Z)
• 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)
• Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction [2.659901499016884]
  We design SSE SSEdenois, a graph-based attentive denoiser that learns latent characteristics of noise to reveal SSE-related displacement with sub-millimeter precision. The proposed method is applied to the Cascadia subduction zone, where SSEs occur along with rumbling of tectonic tremors.
  arXiv  Detail & Related papers  (2024-05-06T09:55:11Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN) [19.697978881402143]
  A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Here, we propose a graph neural network that operates directly on multi-station seismic data and achieves simultaneous phase picking, association, and location.
  arXiv  Detail & Related papers  (2023-06-24T09:46:18Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• A hybrid approach to seismic deblending: when physics meets self-supervision [0.0]
  We introduce a new concept that consists of embedding a self-supervised denoising network into the Plug-and-Play framework. A novel network is introduced whose design extends the blind-spot network architecture of [28 ] for partially correlated noise. The network is then trained directly on the noisy input data at each step of the supervised time algorithm.
  arXiv  Detail & Related papers  (2022-05-30T19:24:21Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• SeismiQB -- a novel framework for deep learning with seismic data [62.997667081978825]
  We've developed an open-sourced Python framework with emphasis on working with neural networks. It provides convenient tools for fast loading seismic cubes in multiple data formats. It also generates crops of desired shape and augmenting them with various transformations.
  arXiv  Detail & Related papers  (2020-01-10T10:45:56Z)

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.