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Fractal properties of background noise and target signal enhancement using CSEM data

机译:背景噪声的分形特性和使用CSEM数据增强目标信号

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Controlled-source electromagnetic (CSEM) spatial profiles and 2-D conductivity maps were obtained on the Brazos Valley, TX floodplain to study the fractal statistics of geological signals and effects of man-made conductive targets using Geonics EM34, EM31 and EM63. Using target-free areas, a consistent power-law power spectrum (|A(k)| ~ k~ ~-β ) for the profiles was found with β values typical of fractional Brownian motion (fBm). This means that the spatial variation of conductivity does not correspond to Gaussian statistics, where there are spatial correlations at different scales. The presence of targets tends to flatten the power-law power spectrum (PS) at small wavenumbers. Detection and localization of targets can be achieved using short-time Fourier transform (STFT). The presence of targets is enhanced because the signal energy is spread to higher wavenumbers (small scale numbers) in the positions occupied by the targets. In the case of poor spatial sampling or small amount of data, the information available from the power spectrum is not enough to separate spatial correlations from target signatures. Advantages are gained by using the spatial correlations of the fBm in order to reject the background response, and to enhance the signals from highly conductive targets. This approach was tested for the EM31 using a pre-processing step that combines apparent conductivity readings from two perpendicular transmitter-receiver orientations at each station. The response obtained using time-domain CSEM is influence to a lesser degree by geological noise and the target response can be processed to recover target features. The homotopy method is proposed to solve the inverse problem using a set of possible target models and a dynamic library of responses used to optimize the starting model.
机译:在德克萨斯州布拉索斯河谷平原上获得了受控源电磁(CSEM)空间分布图和二维电导率图,以研究地质信号的分形统计以及使用Geonics EM34,EM31和EM63的人造导电靶的影响。使用无目标区域,发现轮廓具有一致的幂律功率谱(| A(k)|〜k〜〜-β),具有典型的分数布朗运动(fBm)的β值。这意味着电导率的空间变化与高斯统计不对应,因为高斯统计在不同尺度上存在空间相关性。目标的存在往往会在小波数时使功率定律功率谱(PS)趋于平坦。目标的检测和定位可以使用短时傅立叶变换(STFT)实现。由于信号能量在目标占据的位置上传播到更高的波数(小尺度数),因此增强了目标的存在。在空间采样差或数据量少的情况下,功率谱中可用的信息不足以将空间相关性与目标特征区分开。通过使用fBm的空间相关性来获得优势,以便拒绝背景响应并增强来自高导电目标的信号。使用预处理步骤对EM31进行了此方法的测试,该步骤将每个站的两个垂直发射器-接收器方向的视电导率读数合并在一起。使用时域CSEM获得的响应受地质噪声的影响较小,可以对目标响应进行处理以恢复目标特征。提出了同伦方法以使用一组可能的目标模型和用于优化初始模型的响应动态库来解决反问题。

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