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Deep-Learning-Based Earthquake Detection for Fiber-Optic Distributed Acoustic Sensing

In this paper, deep learning models trained with real seismic data are proposed and proven to detect earthquakes in fiber-optic distributed acoustic sensor (DAS) measurements. The proposed neural network architectures cover the three classical deep learning paradigms: fully connected artificial neural networks (FC-ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Results demonstrate that training these networks with seismic waveforms measured by traditional broadband seismometers can extract and learn relevant features of earthquakes, enabling the reliable detection of seismic waves in DAS measurements. The intrinsic differences between DAS and seismograph waveforms, and eventual errors in the labelling of the DAS data, slightly reduce the performance of the models when tested with the distributed acoustic measurements. Despites of that, trained models can still reach up to 96.94% accuracy in the case of CNN and 93.86% in the case of CNN+RNN. The method and results here reported could represent an important contribution to the development of an early warning earthquake system based on DAS technology.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.8 April 15 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2639-2650

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
OPTICAL FIBER SENSORS
DISTRIBUTED ACOUSTIC SENSING
EARTHQUAKE DETECTION

Info Detail Spesifik

10.1109/JLT.2021.3138724

Pernyataan Tanggungjawab

Pablo D. Hernández, Jaime A. Ramírez, Marcelo A. Soto

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