Journal of Jilin University(Earth Science Edition) ›› 2019, Vol. 49 ›› Issue (4): 1145-1159.doi: 10.13278/j.cnki.jjuese.20180128

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Review of Microseismic Data Denoising Methods

Dai Liyan1,2, Dong Hongli1,2, Li Xuegui1,3   

  1. 1. Institute of Complex Systems and Advanced Control, Northeast Petroleum University, Daqing 163318, Heilongjiang, China;
    2. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing 163318, Heilongjiang, China;
    3. College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
  • Received:2018-05-24 Online:2019-07-26 Published:2019-07-26
  • Supported by:
    Supported by National Natural Science Foundation of China(61873058), PetroChina Innovation Foundation (2018D-5007-0302), Heilongjiang Natural Science Foundation Key Projects (ZD2019F001),Heilongjiang Postdoctoral Foundation(LBH-Z18045) and Northeast Petroleum University Innovation Foundation for Postgraduate (JYCX_CX06_2018, YJSCX2017-026NEPU)

Abstract: With the continuous depletion of conventional oil and gas resources, the exploration and development of unconventional oil and gas reservoirs have gradually become an inevitable trend, which makes micro-seismic monitoring technology develop rapidly. The duration of microseismic events is relatively short,and the frequency of sound waves is relatively high, which result in a low signal-to-noise ratio of the microseismic data collected by actual acquisition. Firstly, the authors briefly introduce in this paper the possibility of guaranteeing high productivity in unconventional oil and gas development by microseismic monitoring technology, and the importance of microseismic noise suppression in data processing, as it directly affects the accuracy and reliability of subsequent microseismic research. Then, the noise sources and several common noise types in the ground microseismic monitoring data are summarized,such as strong pulse interference, 50 Hz industrial alternating current interference, drilling interference, acoustic interference, regular interference, and their basic characteristics are analyzed. The results of denoising methods in the ground microseismic data are classified according to the characteristics of frequency, direction of propagation,and spatial distribution area,also the noise types and the influences of various denoising methods on effective signals are discussed. Finally, based on the stronger representation abilities of deep learning complex functions, the structure and characteristics of three typical deep learning models are analyzed. In combination with the successful application of data denoising in other related fields,deep learning can solve the problem of noise suppression of current microseismic data, and can be used as a new method for microseismic data denoising. Considering that the current number of microseismic data samples may affect the large-scale application of deep learning in microseismic monitoring, the authors propose to build a microseismic data sample library by generating countermeasure network to solve this problem,and which can be used in model training in the subsequent deep learning processe.

Key words: microseismic, data denoising, deep learning

CLC Number: 

  • TP183
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