Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation

Emre Uzundurukan; Ali Kara

doi:10.5281/zenodo.3977620

TR EN

SNR Bağımlı Veri Üretimi kullanılarak Fiber Optik Dağıtılmış Akustik Algılama için Derin Öğrenmeye Dayalı Tehdit Sınıflandırması

Öz

Bu çalışmada, SNR bağımlı veri tabanı oluşturmak ve fiber optik dağıtılmış akustik algılama (DAS) sistemler için sismik olayları sınıflandırmak yöntemi önerilmiştir. DAS sinyallerini almak için optik zaman alanı reflektometrisi (OTDR) kullanılmıştır. Önerilen veri oluşturma yöntemi, gerçek kanal gürültü özelliklerine dayanan farklı SNR değerlerine sahip sinyaller üretir. Bu şekilde, sınırlı veri kümesinden, çekiç vuruşu, kazma ile kazma ve kürekle kazma gibi üç farklı sismik olaydan oluşan büyük veri kümesi elde edilmiştir. Sınıflandırma bölümünde, üç farklı sismik olayı tanımlamak için iki farklı Derin Öğrenme algoritması (Evrişimsel Sinir Ağları ve tam bağlantılı sinir ağları) kullanılır. Sonuçlar, üç farklı SNR aralığı için dikkate değer tanımlama doğruluğunun elde edildiğini göstermektedir

Anahtar Kelimeler

Dağıtık akustik algılama , Derin öğrenme sınıflandırması , Optik zaman-alanı yansıtma ölçüsü , Sismik olay sınıflandırması

Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation

Abstract

In this study, a novel method is proposed to generate SNR dependent database and classify seismic events for fiber optic distributed acoustic sensing (DAS) systems. Optical time-domain reflectometry (OTDR) is used to acquire DAS signals. Proposed data creation method generates signals with different SNR values which is based on real channel noise characteristics. By this way, from the limited dataset, huge dataset consists of three different seismic events such as hammer hit, digging with pickaxe and digging with shovel is generated. In the classification part, two different Deep Learning algorithm (Convolutional Neural Network and fully connected neural networks) are used to identify three different seismic events. Results show that remarkable identification accuracy for the three different SNR ranges is achieved.

Keywords

Deep Learning , Distributed acoustic sensing , Optical time-domain reflectometry , Seismic event classification

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Details

Primary Language

English

Subjects

Electrical Engineering

Journal Section

Research Article

Authors

Emre Uzundurukan ^*
Türkiye

Ali Kara
0000-0002-9739-7619
Türkiye

Publication Date

December 21, 2020

Submission Date

July 16, 2020

Acceptance Date

August 9, 2020

Published in Issue

Year 2020 Volume: 1 Number: 2

DOI

https://doi.org/10.5281/zenodo.3977620

IZ

https://izlik.org/JA58AC95UZ

APA

Uzundurukan, E., & Kara, A. (2020). Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation. Journal of Science, Technology and Engineering Research, 1(2), 4-12. https://doi.org/10.5281/zenodo.3977620

AMA

1.Uzundurukan E, Kara A. Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation. Journal of Science, Technology and Engineering Research. 2020;1(2):4-12. doi:10.5281/zenodo.3977620

Chicago

Uzundurukan, Emre, and Ali Kara. 2020. “Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation”. Journal of Science, Technology and Engineering Research 1 (2): 4-12. https://doi.org/10.5281/zenodo.3977620.

EndNote

Uzundurukan E, Kara A (December 1, 2020) Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation. Journal of Science, Technology and Engineering Research 1 2 4–12.

IEEE

[1]E. Uzundurukan and A. Kara, “Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation”, Journal of Science, Technology and Engineering Research, vol. 1, no. 2, pp. 4–12, Dec. 2020, doi: 10.5281/zenodo.3977620.

ISNAD

Uzundurukan, Emre - Kara, Ali. “Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation”. Journal of Science, Technology and Engineering Research 1/2 (December 1, 2020): 4-12. https://doi.org/10.5281/zenodo.3977620.

JAMA

1.Uzundurukan E, Kara A. Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation. Journal of Science, Technology and Engineering Research. 2020;1:4–12.

MLA

Uzundurukan, Emre, and Ali Kara. “Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation”. Journal of Science, Technology and Engineering Research, vol. 1, no. 2, Dec. 2020, pp. 4-12, doi:10.5281/zenodo.3977620.

Vancouver

1.Emre Uzundurukan, Ali Kara. Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation. Journal of Science, Technology and Engineering Research. 2020 Dec. 1;1(2):4-12. doi:10.5281/zenodo.3977620

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SNR Bağımlı Veri Üretimi kullanılarak Fiber Optik Dağıtılmış Akustik Algılama için Derin Öğrenmeye Dayalı Tehdit Sınıflandırması

Öz

Anahtar Kelimeler

Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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