Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning

• URL: http://arxiv.org/abs/2408.06629v1
• Date: Tue, 13 Aug 2024 04:33:23 GMT
• Title: Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning
• Authors: Tianning Zhang, Feng Liu, Yuming Yuan, Rui Su, Wanli Ouyang, Lei Bai,
• Abstract summary: 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.
• Score: 56.45067876391473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing EEW approaches often treat phase picking, location estimation, and magnitude estimation as separate tasks, lacking a unified framework. Additionally, most deep learning models in seismology rely on full three-component waveforms and are not suitable for real-time streaming data. To address these limitations, 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. By integrating these tasks within a single model, FisH simplifies the overall process and leverages the nonlinear relationships between tasks for improved performance. The FisH model utilizes RetNet as its backbone, enabling parallel processing during training and recurrent handling during inference. This capability makes FisH suitable for real-time applications, reducing latency in EEW systems. Extensive experiments conducted on the STEAD benchmark dataset provide strong validation for the effectiveness of our proposed FisH model. The results demonstrate that FisH achieves impressive performance across multiple seismic event detection and characterization tasks. Specifically, it achieves an F1 score of 0.99/0.96. Also, FisH demonstrates precise earthquake location estimation, with location error of only 6.0km, a distance error of 2.6km, and a back-azimuth error of 19{\deg}. The model also exhibits accurate earthquake magnitude estimation, with a magnitude error of just 0.14. Additionally, FisH is capable of generating real-time estimations, providing location and magnitude estimations with a location error of 8.06km and a magnitude error of 0.18 within a mere 3 seconds after the P-wave arrives.

Related papers

• SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio [17.811771707446926]
  We show that learning based methods can, even based on synthetic data, significantly outperform GCC-PHAT on novel real world data. We provide our trained model, SONNET, which is runnable in real-time and works on novel data out of the box for many real data applications.
  arXiv  Detail & Related papers  (2024-11-20T10:23:21Z)
• FM-TS: Flow Matching for Time Series Generation [71.31148785577085]
  We introduce FM-TS, a rectified Flow Matching-based framework for Time Series generation. FM-TS is more efficient in terms of training and inference. We have achieved superior performance in solar forecasting and MuJoCo imputation tasks.
  arXiv  Detail & Related papers  (2024-11-12T03:03:23Z)
• 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)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks [2.3895981099137535]
  Interpolation of aliased seismic data is a key step in a seismic processing workflow. We propose to interpolate seismic data by utilizing a physics informed neural network (PINN)
  arXiv  Detail & Related papers  (2023-05-25T12:29:28Z)
• Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency [19.1862172442857]
  Methods need to accommodate target domains with different temporal dynamics. Time-frequency consistency (TF-C) is desirable for pre-training. Experiments show TF-C outperforms baselines by 15.4% (F1 score) on average in one-to-one settings.
  arXiv  Detail & Related papers  (2022-06-17T00:45:04Z)
• 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)
• Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks [0.0]
  Full-waveform inversion (FWI) velocity modeling is an iterative advanced technique that provides an accurate and detailed velocity field model. In this study, we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial network (cGAN) with multiple inputs.
  arXiv  Detail & Related papers  (2021-04-30T21:41:24Z)
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network [66.26231423824089]
  We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models.
  arXiv  Detail & Related papers  (2020-03-04T12:27:36Z)

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.