 [2305.11183] Assessing the predicting power of GPS data for aftershocks forecasting




























  








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Physics > Geophysics


arXiv:2305.11183 (physics)
    




  [Submitted on 17 May 2023]
Title:Assessing the predicting power of GPS data for aftershocks forecasting
Authors:Vincenzo Maria Schimmenti, Giuseppe Petrillo, Alberto Rosso, Francois P. Landes View a PDF of the paper titled Assessing the predicting power of GPS data for aftershocks forecasting, by Vincenzo Maria Schimmenti and 3 other authors
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Abstract:We present a machine learning approach for the aftershock forecasting of Japanese earthquake catalogue from 2015 to 2019. Our method takes as sole input the ground surface deformation as measured by Global Positioning System (GPS) stations at the day of the mainshock, and processes it with a Convolutional Neural Network (CNN), thus capturing the input's spatial correlations. Despite the moderate amount of data the performance of this new approach is very promising. The accuracy of the prediction heavily relies on the density of GPS stations: the predictive power is lost when the mainshocks occur far from measurement stations, as in offshore regions.
    


 
Comments:
15 pages main + appendix. 3 figures main, 2 appendix


Subjects:

Geophysics (physics.geo-ph); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)

Cite as:
arXiv:2305.11183 [physics.geo-ph]


 
(or 
arXiv:2305.11183v1 [physics.geo-ph] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2305.11183



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                arXiv-issued DOI via DataCite
              







Submission history From: Vincenzo Maria Schimmenti [view email]       [v1]
        Wed, 17 May 2023 18:49:37 UTC (1,599 KB)



 

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