An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding

Noise suppression or signal‐to‐noise ratio enhancement is often desired for better processing results from a microseismic dataset. In this paper, a polarization–linearity and time–frequency‐thresholding‐based approach is used for denoising waveforms. A polarization–linearity filter is initially applied to preserve the signal intervals and suppress the noise amplitudes. This is followed by time–frequency thresholding for further signal‐to‐noise ratio enhancement in the S transform domain. The parameterisation for both polarization filter and time–frequency thresholding is also discussed. Finally, real microseismic data examples are shown to demonstrate the improvements in processing results when denoised waveforms are considered in the workflow. The results indicate that current denoising approach effectively suppresses the background noise and preserves the vector fidelity of signal waveform. Consequently, the quality of event detection, arrival‐time picking, and hypocenter location improves.     

Independent two-step thresholding of binary images in inter-annual land cover change/no-change identification

Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true. Considering the asymmetrical nature of distribution histogram for the two sides, this study proposes a relatively simple and easy ‘Independent Two-Step’ thresholding approach for optimal threshold value determination for spectrally increased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image. Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or seasonal land cover change/no-change identification. The relative performances of the proposed and two other methods towards the sensitivity of distributions were tested and an improvement of ∼3% in overall accuracy and of ∼0.04 in Kappa was attained with the Proposed Method. This study demonstrated the importance of consideration of normality of data distributions in land-cover change/no-change analysis.     

A fuzzy topology-based maximum likelihood classification

Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, {0, 1}, to the values of a continuous interval, [0, 1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is, a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over-classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classification accuracy.     

Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data

Automatic feature detection from seismic data is a demanding task in today's interpretation workstations. Channels are among important stratigraphic features in seismic data both due to their reservoir capability or drilling hazard potential. Shearlet transform as a multi‐scale and multi‐directional transformation is capable of detecting anisotropic singularities in two and higher dimensional data. Channels occur as edges in seismic data, which can be detected based on maximizing the shearlet coefficients through all sub‐volumes at the finest scale of decomposition. The detected edges may require further refinement through the application of a thinning methodology. In this study, a three‐dimensional, pyramid‐adapted, compactly supported shearlet transform was applied to synthetic and real channelised, three‐dimensional post‐stack seismic data in order to decompose the data into different scales and directions for the purpose of channel boundary detection. In order to be able to compare the edge detection results based on three‐dimensional shearlet transform with some famous gradient‐based edge detectors, such as Sobel and Canny, a thresholding scheme is necessary. In both synthetic and real data examples, the three‐dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise.     

图纸扫描图像二值化阈值选取方法

提出了基于小波变换零交叉的图纸扫描图像自动阈值化方法，用小波变换零交叉描述不同尺度下的图像直方图细节信息，从而聚焦到直方图的任意细节，达到对阈值的精确选取，除了考虑像素点的灰度信息外，还顾及了像素点与其邻域的边缘信息。文中从理论上给出了该方法所得阈值的物理意义。实验表明，该方法同最大墙法相比具有更强的稳健性和自适应能力。     

Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding

Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered to threshold forecast covariance and gain matrices. These include hard, soft, lasso and Smoothly Clipped Absolute Deviation (SCAD) functions. Three benchmarks are used to evaluate the performances of these methods. These benchmarks include a small 1D linear model and two 2D water flooding (in petroleum reservoirs) cases whose levels of heterogeneity/nonlinearity are different. It should be noted that beside the adaptive thresholding, the standard distance dependant localization and bootstrap Kalman gain are also implemented for comparison purposes. We assessed each setup with different ensemble sets to investigate the sensitivity of each method on ensemble size. The results indicate that thresholding of forecast covariance yields more reliable performance than Kalman gain. Among thresholding function, SCAD is more robust for both covariance and gain estimation. Our analyses emphasize that not all assimilation cycles do require thresholding and it should be performed wisely during the early assimilation cycles. The proposed scheme of adaptive thresholding outperforms other methods for subsurface characterization of underlying benchmarks.     

局部SVT算法的遥感反演场数据恢复实验分析

 遥感反演场数据会由于云雾、地物的遮挡,传感器性能等原因造成部分区域数据的缺失而影响遥感反演场数据的应用。矩阵填充理论针对低秩矩阵,利用矩阵的低秩性,即数据的高相关性,可以高精度地对低秩矩阵中的缺值数值进行恢复,其中矩阵填充理论中的SVT(Singular Value Thresholding)算法可以对矩阵中缺失数值进行快速、高精度的估计,应用广泛。本文应用矩阵填充理论的SVT算法,以缺值点为中心,方差最小作为窗口尺度选择的标准,这样可以保证区域数据的高相关性,建立局部窗口,对窗口进行SVT算法填充。本文也针对相同缺值区域进行了距离反比加权插值、Kriging插值法插值和整体SVT算法插值,整体SVT算法插值即并未对缺值点进行相关性窗口判断,而是直接对整个区域进行SVT填充。并对这几种方法的精度进行比较,得到局部SVT算法的精度相比整体SVT算法和距离反比加权插值算法的精度要高,与Kriging算法相比,其精度变化趋势相似,在锋面区域局部SVT算法精度比Kriging方法要高。