多尺度CB形态学及其在线划图像噪声清除中的应用

介绍了基于轮廓结构元的形态学（简称CB形态学）的概念及其性质,讨论了CB形态运算的快速实现。由于CB形态学处理的对象不是基于目标的形状,而是基于目标的延伸度,因此它可以用来处理线划图像。在本文中,利用CB形态学的这一特性消除线划图像上的噪声,获得了满意的结果。     

自适应滤波方法扣除规则性噪声

本文采用自适应滤波技术,对山东省莒县地震台网记录到的极微震资料中的规则性干扰进行了去噪声处理。结果表明,这种方法不必对噪声进行模式识别,具有简单、快速等优点,在信号与噪声不相关的情况下,可以得到很好的结果。     

天文CCD片内放大器的噪声特性及其抑制方法

分析了低光度天文应用时CCD器件的主要噪声源的噪声特性和三种典型CCD读出信号处理方式的噪声抑制效果;提出了天文CCD读出信号的相关多次采样(CMS)处理新方法,并分析了四次采样抑制CCD读出噪声的实验结果。     

地震资料叠前去噪技术的现状与未来

地震资料叠前去噪是勘探地震资料处理的关键问题之一，但这个问题长期以来一直没有得到很好的解决，是提高地震资料分辨率的一个主要障碍，其中，如何有效地消除产生与地层间的多次反射波又是地震资料叠前去噪的核心问题。为此，人们进行了长期不懈的努力，以更好地消除多次波。本文着重概述现今实生常中常用的几种比较有效的消除多次波方法，包括拉冬变换，F－K方法和聚束滤波方法；同时也概述了预测反积消除水层混响和用K－L变换方法消除随机噪音提高信噪比等方法；最后，还讨论了近年发展的地震资料叠前去噪新方法及其发展趋势。     

基于站间天然电磁场单位脉冲响应的大地电磁时间序列去噪方法

针对天然大地电磁场信号在人文活动密集地区易受噪声干扰的问题,本文提出利用两个同步测点天然电磁场时间序列之间的单位脉冲响应,合成本地点受干扰时段的数据,从而去除大地电磁噪声.首先,选择高信噪比时段的数据,采用最小二乘法,估算本地点与参考点之间的单位脉冲响应,再根据卷积定律,结合参考磁场合成本地点的磁场和电场.最后用合成数据替换含噪声时段数据,实现时间域去噪.实测高信噪比数据和含噪数据的处理结果表明,该方法可以高精度合成本地点磁场与电场信号,有效去除本地点电场和磁场噪声,包括相关噪声,提高大地电磁数据质量.     

三轴磁通门探头球型反馈线圈的设计方法研究

长周期大地电磁测量要求三轴磁通门传感器具有低的噪声水平及高稳定性,根据实际需求设计一个尺寸合适的球型反馈线圈可以有效提高反馈磁场的均匀性,进而降低磁通门传感器的噪声水平.本文首先分析了非均匀反馈磁场引入磁通门噪声的机理,然后根据毕奥-萨伐尔定律和矢量叠加原理,以等间距多个单匝线圈构成单轴绕组的形式,建立了球型反馈线圈轴线上的轴向磁场分布及磁场均匀度数学模型,在给定磁场均匀度和球型反馈线圈直径的条件下,运用控制变量法确定了球型反馈线圈的单匝线圈数量和间距等关键结构参数.我们把设计的结构参数代入球型空间磁场分布数学模型,验证了本设计方法的正确性.最后,按照设计的结构参数制作了基于球型反馈线圈磁通门探头的三轴磁通门传感器,通过与体积相近的基于亥姆霍兹型反馈线圈磁通门探头的三轴磁通门传感器对比测试噪声水平,结果表明球型反馈线圈磁通门探头能够使得三轴磁通门传感器具有更稳定和较低的噪声水平.     

断裂带震源机制节面聚类确定断裂带产状方法及在2021年漾濞地震序列中的应用

一条断裂带上发生地震的破裂面通常与断裂带形状接近,同一地震破裂带上大量震源机制节面中应该有集中于断层面形状的一个集合,求出此集合的节面中心就可以得到断裂带的几何形状.根据这个原理,本研究给出了震源机制节面之间距离的方法,将带有噪声、基于密度的空间聚类方法(DBSCAN)应用于震源机制的节面聚类分析,并应用于2021年云...     

Attenuation of marine seismic interference noise employing a customized U-Net

Marine seismic interference noise occurs when energy from nearby marine seismic source vessels is recorded during a seismic survey. Such noise tends to be well preserved over large distances and causes coherent artefacts in the recorded data. Over the years, the industry has developed various denoising techniques for seismic interference removal, but although well performing, they are still time-consuming in use. Machine-learning-based processing represents an alternative approach, which may significantly improve the computational efficiency. In the case of conventional images, autoencoders are frequently employed for denoising purposes. However, due to the special characteristics of seismic data as well as the noise, autoencoders failed in the case of marine seismic interference noise. We, therefore, propose the use of a customized U-Net design with element-wise summation as part of the skip-connection blocks to handle the vanishing gradient problem and to ensure information fusion between high- and low-level features. To secure a realistic study, only seismic field data were employed, including 25,000 training examples. The customized U-Net was found to perform well, leaving only minor residuals, except for the case when seismic interference noise comes from the side. We further demonstrate that such noise can be treated by slightly increasing the depth of our network. Although our customized U-Net does not outperform a standard commercial algorithm in quality, it can (after proper training) read and process one single shot gather in approximately 0.02 s. This is significantly faster than any existing industry denoising algorithm. In addition, the proposed network processes shot gathers in a sequential order, which is an advantage compared with industry algorithms that typically require a multi-shot input to break the coherency of the noise.     

Low-rank seismic denoising with optimal rank selection for hankel matrices

Based on the fact that the Hankel matrix representing clean seismic data is low rank, low-rank approximation methods have been widely utilized for removing noise from seismic data. A common strategy for real seismic data is to perform the low-rank approximations for small local windows where the events can be approximately viewed as linear. This raises a fundamental question of selecting an optimal rank that best captures the number of events for each local window. Gavish and Donoho proposed a method to select the rank when the noise is independent and identically distributed. Gaussian matrix by analysing the statistical performance of the singular values of the Gaussian matrices. However, such statistical performance is not available for noisy Hankel matrices. In this paper, we adopt the same strategy and propose a rule that computes the number of singular values exceed the median singular value by a multiplicative factor. We suggest a multiplicative factor of 3 based on simulations which mimic the theories underlying Gavish and Donoho in the independent and identically distributed Gaussian setting. The proposed optimal rank selection rule can be incorporated into the classical low-rank approximation method and many other recently developed methods such as those by shrinking the singular values. The low-rank approximation methods with optimally selected rank rule can automatically suppress most of the noise while preserving the main features of the seismic data in each window. Experiments on both synthetic and field seismic data demonstrate the superior performance of the proposed rank selection rule for seismic data denoising.     

CSAMT单分量数据解释方法

可控源音频大地电磁法(CSAMT)一直沿用大地电磁法(MT)的办法,通过计算电场分量与磁场分量的比值,求取卡尼亚视电阻率.而CSAMT场源已知,电场分量和磁场分量都与地下电阻率存在一定的关系,可以单独采用CSAMT电场分量或者磁场分量提取地下介质的视电阻率.本文通过分析电场分量与磁场分量的数据特性,提出利用CSAMT电场单分量数据进行视电阻率的计算,用改进的广义逆矩阵反演方法,使初始模型中的地电层数等于频道个数,克服了以往反演计算中层数较少的问题;实现全场区电场分量视电阻率曲线的拟合反演.同时对单分量视相位计算方法进行分析,结合山西大同地区积水采空区探测及数据解释结果,论证本文提出的单分量解释方法的有效性.