Weak signal characteristics and its identificationin high-density single sensor data
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摘要: 弱信号的检测和识别是当今地球物理学界非常关注的一个技术问题. 对于高密度单点资料究竟多弱的信号才是弱信号，如何检测和识别，以往学术界很少有这样的文献报道．本文以理论研究为主，结合胜利油田某高密度实际资料，对此做了分析和讨论，得出以下初步结论：①就视觉分辨率而言，当弱信号的信噪比S/N2时，较易识别；S/N=1时，有可能识别错；S/N0.5时，通过肉眼识别和解释已基本不可能．②对于薄储集层来说，S/N=2为计算薄层厚度的信噪比分界点．③单点资料中背景噪声会较大程度上影响深层弱信号，高密度资料弱信号的死亡值就是环境噪声的幅度．④单个弱信号，它所占的频谱成分很少,随机噪声主要影响频谱的高频端和低频端，即使S/N达到5，噪声对信号的频谱改造仍十分严重．⑤研究区高密度资料频带很宽，为5——210 Hz；目标层高频衰减比较快，高频的死亡线在170 Hz；深层20 Hz以上信息基本与噪声变化规律非常相像，弱信号已很难检测．⑥ 对于混杂在噪声中的水平同相轴弱信号（S/N1），经奇异值分解（SVD）法处理后，仍能有效地检测出.经研究确定，在动校正（NMO）后的共中心点道集（CMP）资料中S/N=0.5是能否用SVD方法进行处理的临界点；即使N/S达到3，仍能用曲波变换恢复出弱信号.这给我们一个启示：对于高密度单点资料，只要处理方法得当，仍有很大的潜力识别出更多的弱信号．
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关键词:
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奇异值分解 曲波变换 信噪比 高频死亡线
Abstract: The detection and identification of weak signal is a wellknown technical issue in todayrsquo;s geophysical industry. For high-density single sensor data, there is little information on how weak the signal will be called weak signal and how to detect and identify it in existing academic literatures. Based on theoretical study and combined with analyzing LJ high density data from Shengli Oilfield these questions were touched with and discussed in this paper.We draw the following conclusions: ①In terms of visual resolution, the weak signal is more easily identified when signal to noise ratio S/N2, it may be wrongly identified when S/N=1,and it is basically impossible by visual recognition and interpretation when S/N0.5. 20= 170= 210= for= thin= n= is= the= lower= limit= estimating= its= background= noise= will= significantly= affect= weak= signals= in= deep= part= and= death= value= of= high-density= data= signal= just= amplitude= environmental= noise.= a= single= shares= less= frequency= spectrum.= random= mainly= affects= high= low= spectrum= response= remarkably= altered= even= if= comes= up= to= 5.= has= wide= band= hz.= target= layer= faster= high-frequency= attenuation= at= above= hz= shows= similar= variation= with= difficult= be= horizontal= co-phase= mixed=1) can still be effectively detected after processed with singular value decomposition (SVD), and the S/N=0.5 is the cut-off point determining whether SVD can be used to process the common midpoint (CMP) data after normal moveout (NMO) or not. Even if N/S reaches to 3, it can still be restored by curvelet transform. This gives us an enlightenment that, for high-density single-point data, there is still large potential of identifying more weak signals as long as we use a proper processing technique. -
奇异值分解 曲波变换 信噪比 高频死亡线
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期刊类型引用(6)
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