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	ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION
	Zhao, S. S. ; Wang, N. ; Hui, J. ; Ye, X. ; Qin, Q.
	2016
关键词	Electromagnetic Super Low Frequency Coalbed Methane non-Gaussian Class B model Least Square Gradient Adaptive filter INTERFERENCE MODELS
英文摘要	Natural source Super Low Frequency(SLF) electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM). The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.; National Science and Technology Major Project of China [2011ZX05034]; EI; CPCI-S(ISTP); zhao_ss@pku.edu.cn; wangnan8848@126.com; huijian@mail.bnu.edu.cn; lanlang524@126.com; qmqin@pku.edu.cn; B8; 449-452; 41
语种	英语
出处	23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)
DOI标识	10.5194/isprsarchives-XLI-B8-449-2016
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449600]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Zhao, S. S.,Wang, N.,Hui, J.,et al. ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION. 2016-01-01.

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