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Evaluation of Low-cost MEMS Acceleration Sensors to Detect Earthquakes

  • Lee, Jangsoo (School of Computer Science and Engineering, Kyungpook National University) ;
  • Kwon, Young-Woo (School of Computer Science and Engineering, Kyungpook National University)
  • Received : 2020.04.14
  • Accepted : 2020.05.20
  • Published : 2020.05.29

Abstract

As the number of earthquakes gradually increases on the Korean Peninsula, much research has been actively conducted to detect earthquakes quickly and accurately. Because traditional seismic stations are expensive to install and operate, recent research is currently being conducted to detect earthquakes using low-cost MEMS sensors. In this article, we evaluate how a low-cost MEMS acceleration sensor installed in a smartphone can be used to detect earthquakes. To this end, we installed about 280 smartphones at various locations in Korea to collect acceleration data and then assessed the installed sensors' noise floor through PSD calculation. The noise floor computed from PSD determines the magnitude of the earthquake that the installed MEMS acceleration sensors can detect. For the last few months of real operation, we collected acceleration data from 200 smartphones among 280 installed smartphones and then computed their PSDs. Based on our experiments, the MEMS acceleration sensor installed in the smartphone is capable of observing and detecting earthquakes with a magnitude 3.5 or more occurring within 10km from an epic center. During the last several months of operation, the smartphone acceleration sensor recorded an earthquake of magnitude 3.5 in Miryang on December 30, 2019, and it was confirmed as an earthquake using STA/LTA which is a simple earthquake detection algorithm. The earthquake detection system using MEMS acceleration sensors is expected to be able to detect increasing earthquakes more quickly and accurately.

한반도에서 점차 증가하는 지진으로 지진을 빠르고 정확하게 감지하기 위한 연구가 활발하게 이루어지고 있다. 기상청에서 운영하는 기존 관측소는 설치와 운영에 많은 비용이 요구되어 오늘날 저가의 센서를 사용하여 지진을 감지하기 위한 연구가 이루어지고 있다. 논문에서는 스마트폰에 설치된 저가의 MEMS 가속도 센서를 활용하여 지진 관측자료 생성 및 지진 감지 체계를 구축할 수 있는지에 대해 평가한다. 가속도 센서 분석을 위하여 국내의 여러 위치에 설치하여 가속도 데이터를 수집하였으며, PSD 계산을 통하여 각 센서의 바닥 잡음 수준을 파악한다. 분석 결과를 바탕으로 기존 MEMS 가속도 센서의 바닥 잡음 수준과 지진 감지를 위한 노이즈 모델과 비교하여 MEMS 센서가 감지할 수 있는 지진의 규모를 파악한다. 다양한 종류의 건물에 부착된 280 여 개의 가속도 센서 중 200 개의 센서로부터 데이터를 지난 수 개월 간 수집 하였으며 PSD 계산을 통하여 설치된 스마트폰의 MEMS 가속도 센서는 10Km 이내에서 발생하는 규모 3.5 이상의 지진을 관측 할 수 있음을 파악하였다. 지난 몇 개월간의 운영 기간 동안, 스마트폰 가속도 센서는 2019년, 12월 30일 밀양에서 발생한 규모 3.5의 지진을 기록하였으며 지진 감지 기법 중 하나인 STA/LTA 기법에 의해서 지진이 감지됨을 확인할 수 있었다. 제안하는 MEMS 가속도 센서를 사용한 지진 감지 체계는 점차 증가하는 지진을 더욱 빠르고 정확하게 감지할 수 있을 것으로 기대한다.

Keywords

References

  1. Working Group on Instrumentation, Siting, Installation, and Site Metadata, "Instrument Guidelines for the Advanced National Seismic System", U. S. Geological Survey Open-File Report, 2008-1262, p. 41, 2008. https://pubs.usgs.gov/of/2008/1262/
  2. Q. Kong, R. M. Allen, L. Schreier and Y.-W. Kwon, "MyShake: A smartphone seismic network for earthquake early warning and beyond.", Science Advances, 2.2, p. e. 1501055, Feb. 2016. DOI: 10.1126/sciadv.1501055
  3. Y.-M.. Wu, W.-T. Liang, H. Mittal, W.-A.. Chao, C.-H.. Lin, B.-S. Huang, and C.-M. Lin, "Performance of a low‐cost earthquake early warning system (P‐alert) during the 2016 ML 6.4 Meinong (Taiwan) earthquake." Seismological Research Letters, 87.5, pp. 1050-1059, Oct. 2016. DOI: 10.1785/0220160058
  4. Jon R. Peterson, "Observations and modeling of seismic background noise.", US Geological Survey, 1993. DOI: 10.3133/ofr93322
  5. Daniel E. McNamara, and Raymond P. Buland, "Ambient noise levels in the continental United States.", Bulletin of the seismological society of America, 94.4, pp. 1517-1527, Aug. 2004. DOI: 10.1785/012003001
  6. Carlo Cauzzi, and John Clinton., "A high-and low-noise model for high-quality strong-motion accelerometer stations." Earthquake Spectra, 29.1, pp. 85-102, Feb. 2013 DOI: 10.1193/1.4000107
  7. J. R. Evans, R. M. Allan, A. I. Chung, E. S. Cochran, R. Guy, M. Hellweg, and J. F. Lawrence, "Performance of Several Low‐Cost Accelerometers.", Seismological Research Letters, 85.1, pp. 147-158, Jan./Feb. 2014. DOI: 10.1785/0220130091
  8. T. Perol, M. Gharbi, and M. Denolle, "Convolutional neural network for earthquake detection and location", Science Advances, 4.2 p. e1700578, Feb. 2018. DOI: 10.1126/sciadv.1700578
  9. J. Lee, I. Khan, S. Choi, and Y.-W. Kwon, "A smart iot device for detecting and responding to earthquakes," Electronics, 8.12, p. 1546, Dec. 2019, doi: 10.3390/electronics8121546.
  10. I. Khan, S. Choi, and Y.-W. Kwon, "Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method," Sensors, 20.3, p. 800, Feb. 2020. doi: 10.3390/s20030800.
  11. P. Bormann, and E. Wielandt, "Seismic Signals and Noise", in New Manual of Seismological Observatory Practice 2, pp. 1-62, 2013. DOI:10.2312/GFZ.NMSOP-2_ch4
  12. D. E. McNamara, and Richard I. Boaz, "PQLX: A seismic data quality control system description, applications, and users manual.", US Geological Survey Open-File Report, 2010-1292, pp. 41, 2010. https://pubs.usgs.gov/of/2010/1292
  13. L. Krischer, T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, and J. Wassemann, "ObsPy: a bridge for seismology into the scientific Python ecosystem", Computational Science & Discovery, 8.1, p. 014003, May 2015. DOI: 10.1088/1749-46 99/8/1/014003
  14. F. Mohd-Yasin, D. Nagel, and C. Korman., "Noise in MEMS.", Measurement Science and Technology, 21.1, p. 012001, Nov. 2009. DOI: 10.1088/0957-0233/21/1/012001
  15. A. Quinchia, G. Falco, E. Falletti, F. Dovis, and C. Ferrer, "A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.", Sensors, 13.8, pp. 9549-9588, Jul. 2013. DOI: 10.3390/s130809549
  16. A. Trnkoczy, "Understanding and parameter setting of STA/LTA trigger algorithm," in New Manual of Seismological Observatory Practice 2, pp. 1-20, 2012. DOI: 10.2312/GFZ.NMSOP-2_S_8.1