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1.
鉴于地震数据不连续性检测的重要性,本文提出了一种保持和检测地震图像不连续性(如:岩层,断层,河道等)的方法.通常在图象不连续的地方,象素值会有较大的差异,文中用4×4的方向模板计算目标点周围六个方向上的差值,当最大差值超过某个门限值时,则可认为该点为不连续点,由此来检测地震图像的不连续性.由于包含重要信息的区域受噪声的影响通常比其他地方严重,而且基于差值的不连续性检测算法对噪声较为敏感,所以在不连续性检测之前降低地震图像的噪声是很重要的.普通的平滑去噪方法会造成边缘模糊,不利于不连续性检测.本文采用旋转模板的非线性平滑方法,用四个六边形和一个八边形模板在目标像素周围旋转,用与目标像素标准差最小(最同类)的模板内那些点的均值代替目标像素的值,从而实现对地震图象的保边缘平滑去噪处理.理论模型和实际数据处理结果表明:与Y、Luo等人提出的保边缘平滑方法相比,本文的保边缘平滑处理方法提高了计算效率和峰值信噪比;将文中提出的保边缘平滑方法和基于方向模板的不连续性检测方法结合使用,得到的不连续性检测结果比直接检测更清晰.地震数据解释人员可根据检测到的不连续性来识别断层、岩层、河道等. 相似文献
2.
空间归一化边界识别方法用于判断地质体的水平位置及深度(英文) 总被引：1，自引：1，他引：0
边界识别是重磁数据解释中的常用方法之一,依据其结果可划分出地质体的水平范围。边界识别结果受地质体埋深及导数计算误差的影响所识别边界与真实边界之间存在一定的差距,且边界识别法无法直观地给出地质体的深度信息。为了获得异常体的水平位置和深度信息,本文提出空间归一化边界识别方法,其对不同深度的边界识别函数进行归一化计算,空间归一化边界识别法的最大值对应于异常体的水平位置和深度。常规边界识别结果的误差随理深的减小而减小,而空间归一化边界识别法是通过最大值来判断地质体的位置,最大值是在地质体处获得,因此归一化边界识别方法所获得的结果是准确的。通过理论模型试验证明归一化边界识别方法能有效地完成异常体的水平位置和深度的计算,所获得的水平位置和深度信息与理论值相一致,为下一步的勘探计划提供了更加可靠的依据。将其应用于实际航磁数据的解释,获得了断裂的具体分布形式。 相似文献
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Edge detection is a useful tool in the interpretation of potential field data, and the existing edge detection filters are almost functions of first-order horizontal and vertical derivatives. We propose step-edge detection filters to improve the resolution of edge detection results, which use the functions of different-order derivatives to accomplish the edge detection task. We demonstrate the proposed filters on synthetic potential field data, and the results show that the new methods can recognize the edges of the sources more precisely and clearly. We also discuss the application effect of different step-edge detection filters. Lastly, we apply the proposed filters to real potential field data, and the recognized edges of the stratigraphic markers are more precise and clear. 相似文献
5.
基于可靠地估计反射方位和边界的构造约束保边滤波(英文) 总被引：2，自引：1，他引：1
本文给出一种既能有效衰减地震噪音又可保护地层及构造的不连续性的新方法。构造约束保边平滑技术需要已知反射局部方位和边界信息,通常这些信息由全频率地震资料估算获得,但在资料信噪比很低的情况下,噪音往往会降低估算的可靠度。对于信噪比极低的地震资料,其主频成分相对非主频成分信噪比高,所以由主频资料获取的方位和边界信息比由其它频率成分获取的更可靠。方位和边界信息通常用倾角和相干值差异来描述。由于不同频率所引起的倾角和相干值差异的变化均比地震记录的变化缓慢,所以由主频资料获取的倾角及边界信息能够近似代表所有频率成分的倾角及边界信息。Ricker子波广泛用于地震勘探,Marr小波与Ricker子波在时间和频率域均具有相同的形态,所以选用Marrl小波变换将地震数据按照倍频程分为几个分频体。扫描主频分频体,用不等权二次曲面拟合并求解极大值来获取视倾角,通过比较9个滑动窗口的相干值来确定反射边界。将这些信息用构造约束保边平滑技术可选择性地（selectively）对主频、低频、高频分频体做平滑处理,最后将平滑后的各频段地震记录合成为滤波去噪后的地震记录。理论模型和实际资料处理效果表明该方法能有效压制噪音,保护边界,保护同相轴的连续性,且灵活地保留地震记录中的有用信息。 相似文献
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Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data 
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下载免费PDF全文 Haleh Karbalaali Abdolrahim Javaherian Stephan Dahlke Rafael Reisenhofer Siyavash Torabi 《Geophysical Prospecting》2018,66(7):1272-1289
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. 相似文献
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利用观测数据确定地质体的边界位置是位场数据解译中的一项重要工作,传统的边界识别滤波器通常不能均衡深、浅部的地质体边界,近些年相关研究开始致力于发展均衡边界滤波器.本文基于Theta图法定义了新的边界识别滤波器,详述了滤波器波数域及空间域的主要计算公式,通过模型验证,该滤波器显著压制了Theta图法对深部地质体边界的放大作用,较好地平衡了深部和浅部边界.通过与传统的边界识别滤波器对比,本文定义的滤波器能够清晰且更加收敛地圈定出地质体的水平边界位置.以长江中下游成矿带庐枞矿集区为例,开展了1∶5万重磁数据的处理分析,并结合物性资料进行了讨论,结果表明:重磁数据的检测结果精确刻画了郯庐断裂带的位置;庐枞盆地的磁力数据检测边界整体与盆地的地质边缘一致,明确了边界断裂在深部倾向盆地内部;识别出庐枞盆地外围一系列环形边界,这些边界封闭区域与最新勘探发现的深部岩体及铁铜矿化体相对应,对于指导区域深部找矿工作有着重要的意义. 相似文献
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Scattered ground roll is a type of noise observed in land seismic data that can be particularly difficult to suppress. Typically, this type of noise cannot be removed using conventional velocity‐based filters. In this paper, we discuss a model‐driven form of seismic interferometry that allows suppression of scattered ground‐roll noise in land seismic data. The conventional cross‐correlate and stack interferometry approach results in scattered noise estimates between two receiver locations (i.e. as if one of the receivers had been replaced by a source). For noise suppression, this requires that each source we wish to attenuate the noise from is co‐located with a receiver. The model‐driven form differs, as the use of a simple model in place of one of the inputs for interferometry allows the scattered noise estimate to be made between a source and a receiver. This allows the method to be more flexible, as co‐location of sources and receivers is not required, and the method can be applied to data sets with a variety of different acquisition geometries. A simple plane‐wave model is used, allowing the method to remain relatively data driven, with weighting factors for the plane waves determined using a least‐squares solution. Using a number of both synthetic and real two‐dimensional (2D) and three‐dimensional (3D) land seismic data sets, we show that this model‐driven approach provides effective results, allowing suppression of scattered ground‐roll noise without having an adverse effect on the underlying signal. 相似文献
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边界检测在地球物理位场数据解释中占有重要位置.现有的传统边界识别方法有的不能同时显示不同振幅的异常边界,有的虽然能均衡不同振幅的异常,但识别出来的边界信息中含有一些额外的错误的边界信息,尤其是当测量的异常中同时含有正异常和负异常时.目前已有的去除额外错误边界信息的方法存在着一定的人为主观性.为了解决这些问题,本文定义了加强解析信号倾斜角来进行地质体边界识别.通过模型试验证明了该方法不仅能同时清晰地识别深部和浅部地质体的边界,而且能有效地避免引入一些错误边界信息.最后将该方法应用到四川盆地的重力异常数据中,并取得了良好边界结果. 相似文献
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边界识别是位场数据处理解释中的重要环节,传统边界识别方法通常不能均衡深、浅部地质体边界.基于尺度空间技术和归一化的Facet模型检测算子,本文开发了一种带通空间滤波和边缘检测相结合的边界识别方法,有效地提高位场数据边界识别的精度和可靠性.为了验证本文算法的有效性和稳定性,分析了不同尺度空间函数和检测算子对算法的影响,并且对比了传统边界识别方法的效果.理论模拟和实际数据分析表明,利用位场垂向二阶导数进行的基于尺度空间技术的归一化Facet模型边界识别方法不仅算法的稳定性强,而且可以避免高阶导数对噪声干扰放大作用,同时均衡深部和浅部地质体边界,从而可以更精确地识别地质体的形态. 相似文献
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Virgil Bardan 《Geophysical Prospecting》1997,45(5):819-830
3D seismic data are usually recorded and processed on rectangular grids, for which sampling requirements are generally derived from the usual 1D viewpoint. For a 3D data set, the band region (the region of the Fourier space in which the amplitude spectrum is not zero) can be approximated by a domain bounded by two cones. Considering the particular shape of this band region we can use the 3D sampling viewpoint, which leads to weaker sampling requirements than does the 1D viewpoint; i.e. fewer sample points are needed to represent data with the same degree of accuracy. The 3D sampling viewpoint considers regular nonrectangular sampling grids. The recording and processing of 3D seismic data on a hexagonal sampling grid is explored. The acquisition of 3D seismic data on a hexagonal sampling grid is an advantageous economic alternative because it requires 13.4% fewer sample points than a rectangular sampling grid. The hexagonal sampling offers savings in data storage and processing of 3D seismic data. A fast algorithm for 3D discrete spectrum evaluation and trace interpolation in the case of a 3D seismic data set sampled on a hexagonal grid is presented and illustrated by synthetic examples. It is shown that by using this algorithm the hexagonal sampling offers, approximately, the same advantage of saving 13.4% in data storage and computational time for 3D phase-shift migration. 相似文献
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Marine seismic data are always affected by noise. An effective method to handle a broad range of noise problems is a time‐frequency de‐noising algorithm. In this paper we explain details regarding the implementation of such a method. Special emphasis is given to the choice of threshold values, where several different strategies are investigated. In addition we present a number of processing results where time‐frequency de‐noising has been successfully applied to attenuate noise resulting from swell, cavitation, strumming and seismic interference. Our seismic interference noise removal approach applies time‐frequency de‐noising on slowness gathers (τ?p domain). This processing trick represents a novel approach, which efficiently handles certain types of seismic interference noise that otherwise are difficult to attenuate. We show that time‐frequency de‐noising is an effective, amplitude preserving and robust tool that gives superior results compared to many other conventional de‐noising algorithms (for example frequency filtering, τ?p or fx‐prediction). As a background, some of the physical mechanisms responsible for the different types of noise are also explained. Such physical understanding is important because it can provide guidelines for future survey planning and for the actual processing. 相似文献
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本文以提高地震数据的成像质量为目标,提出一种智能的卷积神经网络降噪框架,从带有噪声的地震数据中自适应地学习地震信号。为了加速网络训练和避免训练时出现梯度消失现象,我们在网络中加入残差学习和批标准化的方法,并采用了ReLU激活函数和Adam优化算法优化网络。此外,Marmousi和F3数据集被用来对网络进行训练和测试,经过充分训练的网络不仅能在学习中保留地震数据特征,而且能去除随机噪声。首先充分地训练网络,从中提取出随机噪声,并保留学习到的地震数据特征,之后通过重建地震数据估算测试集中的波形特征。合成记录和实际数据的处理结果显示了深度卷积神经网络在随机噪声压制任务中的潜力,并通过实验验证表明了深度卷积神经网络框架有很好的去噪效果。 相似文献
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Seismic data processing is a challenging task, especially when dealing with vector-valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components. Mitigating such noise while preserving the signal of interest is a primary goal in the seismic-processing workflow. The frequency-space deconvolution is a well-known linear prediction technique, which is commonly used for random noise suppression. This paper represents vector-field seismic data through quaternion arrays and shows how to mitigate random noise by proposing the extension of the frequency-space deconvolution to its hypercomplex version, the quaternion frequency-space deconvolution. It also shows how a widely linear prediction model exploits the correlation between data components of improper signals. The widely linear scheme, named widely-linear quaternion frequency-space deconvolution, produces longer prediction filters, which have enhanced signal preservation capabilities shown through synthetic and field vector-valued data examples. 相似文献
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Jun Feng Bingxue Zhu Wei Li Hui Chen Bin Zhou Shi-hu Wu Ke Guo 《Geophysical Prospecting》2020,68(9):2665-2677
Preserving the structural and stratigraphic discontinuities or edges is essential in seismic data processing and interpretation. According to several numerical experiments, it is obvious that random noise has a constant spectral density, whereas the structural features vary significantly within different frequency bands, which means that the ratio between the densities of noise and structural features varies significantly in different frequency bands. Therefore, we propose a method called adaptive hybrid diffusion to attenuate random noise, which utilizes a novel adaptive frequency-based parameter. First, the adaptive hybrid diffusion method decomposes the seismic sections into several band-limited portions using variational mode decomposition. These portions are called intrinsic mode functions, in which noise and structural energy have distinct differences. Subsequently, utilizing the adaptive frequency-based parameter, each intrinsic mode function is divided into several monotonous portions that represent the noise or structural area. Afterwards, the total variation and L2 minimization algorithms are utilized separately to suppress the noise in different band-limited monotonous areas. The algorithms are chosen dynamically, as the portion changes with the change in the adaptive parameter. Finally, these denoised portions are combined to obtain the denoised seismic section. Experimental results on synthetic and field seismic data showed that seismic noise is effectively suppressed by the adaptive hybrid diffusion method, with the edge details of seismic events well preserved. 相似文献
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卷积神经网络(Convolutional Neural Network,CNN)是一种基于数据驱动的学习算法,简化了传统从特征提取到分类的两阶段式处理任务,被广泛应用于计算机科学的各个领域.在标注数据不足的地震数据去噪领域,CNN的推广应用受到限制.针对这一问题,本文提出了一种基于数据生成和增广的地震数据CNN去噪框架.对于合成数据,本文对无噪地震数据添加不同方差的高斯噪声,增广后构成训练集,实现基于小样本的CNN训练.对于实际地震数据,由于无法获得真实的干净数据和噪声来生成训练样本集,本文提出一种直接从无标签实际有噪数据生成标签数据集的方法.在所提出的方法中,我们利用目前已有的去噪方法从实际地震数据中分别获得估计干净数据和估计噪声,前者与未知的干净数据具有相似纹理,后者与实际噪声具有相似的概率分布.人工合成数据和实际数据实验结果表明,相较于F-X反褶积,BM3D和自适应频域滤波算法,本文方法能更好地压制随机噪声和保护有效信号.最后,本文采用神经网络可视化方法对去噪CNN的机理进行了探索,一定程度上解释了网络每一层的学习内容. 相似文献
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Random noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio. 相似文献
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本文提出一种保护断层、裂缝等地层边缘特征的结构自适应中值滤波器,用于衰减地震资料中的随机噪声.基于地震反射同相轴局部呈线型结构的假设,采用梯度结构张量估计地层倾向,分析地层结构的规则程度,在此基础上引入地震剖面中线型和横向不连续性两种结构特征的置信度量.结构自适应中值滤波器根据这两种置信度量调整滤波器窗函数的尺度和形状,根据地层倾角调整滤波器窗函数的方向,从而使得滤波操作窗能够最佳匹配信号的局部结构特征.将本文方法用于合成和实际数据的处理,并与两种常用中值滤波方法进行对比,结果表明,该方法能够更好地解决地震剖面的随机噪声衰减和有效信号保真的问题,在增强反射同相轴的横向一致性的同时有效保持了剖面内的地层边缘和细节特征,显著改善了地震资料的品质. 相似文献
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Suppression of near‐surface scattered body‐to‐surface waves: a steerable and nonlinear filtering approach 下载免费PDF全文
下载免费PDF全文 M. Nafi Toksöz 《Geophysical Prospecting》2016,64(2):392-405
We present an approach based on local‐slope estimation for the separation of scattered surface waves from reflected body waves. The direct and scattered surface waves contain a significant amount of seismic energy. They present great challenges in land seismic data acquisition and processing, particularly in arid regions with complex near‐surface heterogeneities (e.g., dry river beds, wadis/large escarpments, and karst features). The near‐surface scattered body‐to‐surface waves, which have comparable amplitudes to reflections, can mask the seismic reflections. These difficulties, added to large amplitude direct and back‐scattered surface (Rayleigh) waves, create a major reduction in signal‐to‐noise ratio and degrade the final sub‐surface image quality. Removal of these waves can be difficult using conventional filtering methods, such as an filter, without distorting the reflected signal. The filtering algorithm we present is based on predicting the spatially varying slope of the noise, using steerable filters, and separating the signal and noise components by applying a directional nonlinear filter oriented toward the noise direction to predict the noise and then subtract it from the data. The slope estimation step using steerable filters is very efficient. It requires only a linear combination of a set of basis filters at fixed orientation to synthesize an image filtered at an arbitrary orientation. We apply our filtering approach to simulated data as well as to seismic data recorded in the field to suppress the scattered surface waves from reflected body waves, and we demonstrate its superiority over conventional techniques in signal preservation and noise suppression. 相似文献
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随机噪声的影响在地震勘探中是不可避免的,常规的随机噪声压制方法在处理中往往会破坏具有时空变化特征的非平稳有效地震信号,影响地震数据的准确成像.当前油气勘探的目标已经转变为“两宽一高”,随着数据量的增大,对去噪方法的处理效率也提出了更高的要求.因此,开发高效的非平稳地震数据随机噪声压制方法具有重要意义.预测滤波技术广泛用于地震随机噪声的衰减,本文基于流式处理框架提出一种新的f-x域流式预测滤波方法,通过在频率域建立预测自回归方程,运用直接复数矩阵逆运算代替迭代算法求解非平稳滤波器系数,实现时空变地震同相轴预测,提高自适应预测滤波的计算效率.通过与工业标准的FXDECON方法和f-x域正则化非平稳自回归(RNA)方法进行对比,理论模型和实际数据的测试结果表明,提出的f-x域流式预测滤波方法能更好地平衡时空变有效信号保护、随机噪声压制和高效计算三者之间的关系,获得合理的处理效果. 相似文献