天然源超低频频谱的曲波分解与分析

由于天然源超低频电磁探测仪器接收的是宽频段多源信号,如何进行信号的分解以将干扰信号滤除,是利用天然源超低频电磁探测技术进行实际探测应用的关键。北京大学自主研发的天然源超低频电磁探测仪器,以山西沁水盆地煤层气探测数据为研究对象,将曲波变换的方法应用在超低频电磁探测频谱的分解上。分解结果表明,曲波变换分解出的高频信息主要是探测仪器直接接收的大气层雷电产生的干扰信号,而低频信息层则主要包含了地下的探测目标信息。基于此,以低频信息为基础重构后的探测频谱曲线相对于原始探测曲线来说,更有利于探测目标的解释。但是对于由于人工工频所引起的干扰信号,该方法并不能有效去除,在实际应用中需要结合其他数据处理方法一同进行。

Decomposition and analysis of the natural source SLF spectrum using curvelet transform method

Because natural source super low frequency (SLF) electromagnetic detection equipment receives wideband multi-source signal, how to decompose the signal to filter out the interference signal was a key factor for the application of natural source SLF electromagnetic detection technology. In the present article, the detection equipment developed by Peking University was used to survey the coal bed methane data in the Qinshui basin, Shanxi province, and the curvelet transform method was employed to decompose those data. The analysis results indicated that the high-frequency information coming from the decomposition is the interference signals mainly generated by lightning in the atmospheric and directly received by the detection equipment, while the low frequency signal mainly contains the target information. So the reconstructed curve based on the low-frequency information was more favorable for the interpretation of the target, compared with the original spectrum curve. But the curvelet transform method could not remove the artificial frequency signal.