An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors

Jangsoo Lee; Jae Heon Sim; Jae Kwang Ahn; Young Woo Kwon

doi:10.1109/BigData55660.2022.10020481

An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors

Jangsoo Lee, Jae Heon Sim, Jae Kwang Ahn, Young Woo Kwon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Due to the high population density and economic value of contemporary cities, earthquakes inflict greater damage on these cities. Consequently, the importance of quick earthquake early warning (EEW) is expanding, yet it is challenging to create a dense seismic monitoring network due to high installation and management costs. In order to overcome such limitations, MEMS sensors to monitor earthquakes and artificial intelligence (AI) technologies to analyze massive earthquake monitoring data are widely used today. In AI-based earthquake detection, the key to accurate detection is the use of sufficient data that accurately represents the various earthquake patterns. Unfortunately, how-ever, there is no publicly accessible database containing IoT-based seismic data. This is the result of relatively short research efforts. During the last two years of operation of CrowdQuake, a MEMS-based earthquake detection system, we collected earthquake and non-earthquake events, as well as normal noise data, which was greatly useful to improve the accuracy of AI models. As a result, we present an open dataset that is publicly available for MEMS-based earthquake detection research.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6755-6757
Number of pages	3
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020481
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

Accelerometer
Dataset
Deep Learning
Earthquake
Earthquake Early Warning

Access to Document

10.1109/BigData55660.2022.10020481

Cite this

Lee, J., Sim, J. H., Ahn, J. K., & Kwon, Y. W. (2022). An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 6755-6757). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020481

Lee, Jangsoo ; Sim, Jae Heon ; Ahn, Jae Kwang et al. / An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 6755-6757 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inproceedings{491edabca0b1479cbac69454d1d2d23e,

title = "An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors",

abstract = "Due to the high population density and economic value of contemporary cities, earthquakes inflict greater damage on these cities. Consequently, the importance of quick earthquake early warning (EEW) is expanding, yet it is challenging to create a dense seismic monitoring network due to high installation and management costs. In order to overcome such limitations, MEMS sensors to monitor earthquakes and artificial intelligence (AI) technologies to analyze massive earthquake monitoring data are widely used today. In AI-based earthquake detection, the key to accurate detection is the use of sufficient data that accurately represents the various earthquake patterns. Unfortunately, how-ever, there is no publicly accessible database containing IoT-based seismic data. This is the result of relatively short research efforts. During the last two years of operation of CrowdQuake, a MEMS-based earthquake detection system, we collected earthquake and non-earthquake events, as well as normal noise data, which was greatly useful to improve the accuracy of AI models. As a result, we present an open dataset that is publicly available for MEMS-based earthquake detection research.",

keywords = "Accelerometer, Dataset, Deep Learning, Earthquake, Earthquake Early Warning",

author = "Jangsoo Lee and Sim, {Jae Heon} and Ahn, {Jae Kwang} and Kwon, {Young Woo}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10020481",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6755--6757",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

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Lee, J, Sim, JH, Ahn, JK & Kwon, YW 2022, An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 6755-6757, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020481

An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors. / Lee, Jangsoo; Sim, Jae Heon; Ahn, Jae Kwang et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6755-6757 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors

AU - Lee, Jangsoo

AU - Sim, Jae Heon

AU - Ahn, Jae Kwang

AU - Kwon, Young Woo

PY - 2022

Y1 - 2022

N2 - Due to the high population density and economic value of contemporary cities, earthquakes inflict greater damage on these cities. Consequently, the importance of quick earthquake early warning (EEW) is expanding, yet it is challenging to create a dense seismic monitoring network due to high installation and management costs. In order to overcome such limitations, MEMS sensors to monitor earthquakes and artificial intelligence (AI) technologies to analyze massive earthquake monitoring data are widely used today. In AI-based earthquake detection, the key to accurate detection is the use of sufficient data that accurately represents the various earthquake patterns. Unfortunately, how-ever, there is no publicly accessible database containing IoT-based seismic data. This is the result of relatively short research efforts. During the last two years of operation of CrowdQuake, a MEMS-based earthquake detection system, we collected earthquake and non-earthquake events, as well as normal noise data, which was greatly useful to improve the accuracy of AI models. As a result, we present an open dataset that is publicly available for MEMS-based earthquake detection research.

AB - Due to the high population density and economic value of contemporary cities, earthquakes inflict greater damage on these cities. Consequently, the importance of quick earthquake early warning (EEW) is expanding, yet it is challenging to create a dense seismic monitoring network due to high installation and management costs. In order to overcome such limitations, MEMS sensors to monitor earthquakes and artificial intelligence (AI) technologies to analyze massive earthquake monitoring data are widely used today. In AI-based earthquake detection, the key to accurate detection is the use of sufficient data that accurately represents the various earthquake patterns. Unfortunately, how-ever, there is no publicly accessible database containing IoT-based seismic data. This is the result of relatively short research efforts. During the last two years of operation of CrowdQuake, a MEMS-based earthquake detection system, we collected earthquake and non-earthquake events, as well as normal noise data, which was greatly useful to improve the accuracy of AI models. As a result, we present an open dataset that is publicly available for MEMS-based earthquake detection research.

KW - Accelerometer

KW - Dataset

KW - Deep Learning

KW - Earthquake

KW - Earthquake Early Warning

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U2 - 10.1109/BigData55660.2022.10020481

DO - 10.1109/BigData55660.2022.10020481

M3 - Conference contribution

AN - SCOPUS:85147977511

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 6755

EP - 6757

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

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Lee J, Sim JH, Ahn JK, Kwon YW. An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 6755-6757. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020481

An Open Dataset for Deep Learning-based Earthquake Detection using MEMS Sensors

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