US 12,234,142 B2
Device, method and computer-readable recording medium for detecting earthquake in mems-based auxiliary seismic observation network
Jae-Kwang Ahn, Seoul (KR); Young-Woo Kwon, Daegu (KR); Jangsoo Lee, Daegu (KR); Euna Park, Seoul (KR); and Eui-hong Hwang, Daejeon (KR)
Assigned to KOREA METEOROLOGICAL ADMINISTRATION, Seoul (KR); and KYUNGPOOK NATIONAL UNIVERSITY INDUSTRY-ACADEMIC COOPERATION FOUNDATION, Daegu (KR)
Filed by KOREA METEOROLOGICAL ADMINISTRATION, Seoul (KR); and KYUNGPOOK NATIONAL UNIVERSITY INDUSTRY-ACADEMIC COOPERATION FOUNDATION, Daegu (KR)
Filed on Aug. 9, 2022, as Appl. No. 17/884,033.
Application 17/884,033 is a continuation in part of application No. PCT/KR2022/006701, filed on May 11, 2022.
Claims priority of application No. 10-2021-0062761 (KR), filed on May 14, 2021.
Prior Publication US 2022/0380204 A1, Dec. 1, 2022
Int. Cl. B81B 7/04 (2006.01); G01V 1/01 (2024.01); G01V 1/28 (2006.01); G01V 1/30 (2006.01); G01V 1/38 (2006.01)
CPC B81B 7/04 (2013.01) [G01V 1/01 (2024.01); G01V 1/282 (2013.01); G01V 1/288 (2013.01); G01V 1/307 (2013.01); G01V 1/3808 (2013.01); B81B 2201/0285 (2013.01); B81B 2207/05 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method of detecting an earthquake using a microelectromechanical system (MEMS)-based auxiliary seismic observation network, the method comprising:
preprocessing acceleration data by performing detrending of removing a moving average from original acceleration data received from single sensors of the MEMS-based auxiliary seismic observation network,
calculating a short-term average/long-term average (STA/LTA) value using a filter parameter value specified based on the preprocessed acceleration data,
generating an event occurrence message or an event end message based on the calculated STA/LTA value and transmitting the event occurrence message or the event end message;
when the event occurrence message is generated, calculating an earthquake probability through an earthquake detection deep learning model using the preprocessed acceleration data as an input;
analyzing noise by calculating a power spectral density (PSD) from the original acceleration data which is merged at certain intervals;
determining whether the earthquake occurs in the MEMS-based auxiliary seismic observation network using a network earthquake detection algorithm based on the earthquake probability and a noise analysis result calculated by the single sensors,
wherein the determining of whether the earthquake occurs in the MEMS-based auxiliary seismic observation network comprises:
merging events occurring in a preset sensor array;
when the events occur at more than half of the single sensors in the MEMS-based auxiliary seismic observation network, detecting the earthquake;
when an event occurs at a specific sensor and a set number of sensors or more are within a certain distance from the specific sensor, grouping the sensors in odd numbers and setting groups as a temporary sensor array;
inserting the sensors to be included in the sensor array in order of reliability of the sensors based on the noise analysis results; and
specifying a sensor having lowest noise in the sensor array as a reference sensor of the sensor array.