Classification of Transport Vehicle Noise Events in Magnetotelluric Time Series Data in an Urban area Using Random Forest Techniques

Random Forest 기법을 이용한 도심지 MT 시계열 자료의 차량 잡음 분류

  • Kwon, Hyoung-Seok (Research Research Institute for Earth Resources, Kangwon National University) ;
  • Ryu, Kyeongho (Department of Energy and Resources Engineering, Kangwon National University) ;
  • Sim, Ickhyeon (AAT Co. Ltd.) ;
  • Lee, Choon-Ki (Division of Glacial Environment Research, Korea Polar Research Institute) ;
  • Oh, Seokhoon (Department of Energy and Resources Engineering, Kangwon National University)
  • 권형석 (강원대학교 지구자원연구소) ;
  • 류경호 (강원대학교 에너지자원공학과) ;
  • 심익현 ((주)에이에이티) ;
  • 이춘기 (극지연구소 빙하환경연구본부) ;
  • 오석훈 (강원대학교 에너지자원공학과)
  • Received : 2020.06.12
  • Accepted : 2020.11.10
  • Published : 2020.11.30


We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.