Detecting boundary of salt dome in seismic data with edge‐detection technique

Zhou, Jing; Yan-qing, Zhang; Chen, Zhigang; Li, Jianhua

doi:10.1190/1.2792759

Cited by 43 publications

(11 citation statements)

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“…There are extensive research efforts on seismic attributes for extracting kinds of geologic features, such as salts (Lomask, Biondi and Shragge ; Jing et al . ; Aqrawi et al . ; Hegazy and AlRegib ; Amin and Deriche ), channels (Kadlec et al .…”

Section: Proposed Connectivity Constrained Segmentation Methodsmentioning

confidence: 99%

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Connectivity constrained segmentation of geologic features

Liu

Song

She

et al. 2019

Geophysical Prospecting

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A B S T R A C TSegmentation of geologic features plays a significant role in seismic interpretation. Based on the segmentation results, interpreters can readily recognize the shape and distribution of geologic features in three-dimensional space and conduct further quantitative analysis. Usually, there are mainly two steps for the segmentation of geologic features: the first step is to extract seismic attributes that can highlight the occurrence of geologic features, and the second step is to apply the segmentation algorithm on the seismic attribute volumes. However, the occurrence of geologic features is not always corresponding to the anomaly value on the seismic attribute volumes and vice versa because of several factors, such as noise in the seismic data, the limited resolution of seismic images and the limited effectiveness of the seismic attribute. Therefore, the segmentation results, which are generated solely based on seismic attributes, are not sufficient to give an accurate depiction of geologic features. Aiming at this problem, we introduce the connectivity constraint into the process of segmentation based the assumption that for one single geologic feature all of its components should be connected to each other. Benefiting from this global constraint, the segmentation results can precisely exclude the interference by false negatives on seismic attribute volumes. However, directly introducing the connectivity constraint into segmentation would face the risk that the segmentation results would deteriorate significantly because of false positives with relatively large area when the connectivity constraints are enforced. Therefore, based on the seismic attribute that highlights the boundary of geologic feature, we further propose a post-processing technique, called pruning, to refine the segmentation results. By taking the segmentation of the channel as an example, we demonstrate that the proposed method is able to preserve the connectivity in the process of segmentation and generate better segmentation results on the field data.

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Section: Proposed Connectivity Constrained Segmentation Methodsmentioning

confidence: 99%

“…Jing et al . () convolved the seismic image with a two‐dimensional (2D) Sobel filter and considered the extraction of salt boundary as a problem of edge detection. Furthermore, Aqrawi et al .…”

Section: Introductionmentioning

confidence: 99%

Connectivity constrained segmentation of geologic features

Liu

Song

She

et al. 2019

Geophysical Prospecting

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“…Such an attribute image can be a seismic envelope image, in which salt boundaries can be recognized as reflections with relatively high envelope values. Other seismic attributes including discontinuities (Jing et al, 2007;Aqrawi et al, 2011;Asjad and Mohamed, 2015), textures (Berthelot et al, 2013;Hegazy and Al-Regib, 2014;Wang et al, 2015), reflection dip or normal vector fields (Halpert and Clapp, 2008;Haukås et al, 2013), and salt likelihoods (Wu, 2016) are also used to highlight salt boundaries. Different from seismic envelopes, all these attributes are based on the observations that the seismic reflections, inside and outside a salt boundary, are often differently oriented across the salt boundary.…”

Section: Introductionmentioning

confidence: 99%

Fast salt-boundary interpretation with optimal path picking

Fomel

Hudec

2017

SEG Technical Program Expanded Abstracts 2017

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Salt boundary interpretation is a crucial step for velocitymodel building in seismic migration, but it remains a highly labor-intensive task for manual interpretation and a big challenge for automatic methods. We have developed a semiautomatic method to efficiently and accurately extract 2D and 3D complicated salt boundaries from a seismic attribute image that highlights salt boundaries. In 2D salt boundary extraction, we first pick a few points to interpolate an initial curve that is close to the true salt boundary. These points are picked near the salt boundary but are not required to be exactly on the boundary, which makes human interactions convenient and efficient. We then resample the salt boundary attribute image in a band area centered at the initial curve to obtain a new image in which the true salt boundary is an open curve extending laterally. We then extract the salt boundary in the new image using an optimal-path picking algorithm, which is robust enough to track a stable salt boundary from highly discontinuous attribute values by solving a global maximization problem. We finally map the picked path back to the original image to obtain a final salt boundary. In 3D salt boundary extraction, we apply the 2D method to recursively pick 2D salt boundaries in a sequence of inline or crossline slices and then use these 2D boundaries to fit an implicit (level-set) surface of the 3D salt boundary. In our recursive picking, human interactions are greatly reduced by using a salt boundary picked in the previous slice as an initial curve for picking in a followed slice. The effectiveness of our method is confirmed with 2D and 3D real seismic images.

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“…However, with the striking increase in the size of seismic data over the last few years, researchers in academia and industry have utilized semi-automated seismic interpretation software and tools to overcome the time-consuming and laborintensive manual interpretation. Researchers have proposed several methods to delineate salt domes which include edgebased detection methods by Aqrawi et al (2011), Zhou et al (2007), and Amin and Deriche (2015b), texture-based methods by Berthelot et al (2013), Shafiq et al (2017b), and Wang et al (2015), active-contour-based methods by Haukas et al (2013) and Shafiq et al (2015), Saliency-based methods by Drissi et al (2008) and Shafiq et al (2017a), machine-learning-based methods by Guillen et al (2015) and Amin and Deriche (2015a), and different image processing techniques by Halpert et al (2009), Lomask et al (2007), Felzenszwalb and Huttenlocher (2004), Larrazabal et al (2015), Wu (2016), and Qi et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Salt dome detection within migrated seismic volumes using phase congruency

Shafiq

Alaudah

et al. 2017

SEG Technical Program Expanded Abstracts 2017

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In this paper, we propose a method based on phase congruency (PC) for computational seismic interpretation with an application to subsurface structures delineation within migrated seismic volumes. Phase congruency highlights small discontinuities in images with varying illumination and contrast using the congruency of phase in Fourier components. PC can not only detect the subtle variations in the image intensity but can also highlight the anomalous values to develop a deeper understanding of the images content. We show the effectiveness of the proposed method on the SEAM dataset, which models the complex salt structures found in the Gulf of Mexico. Experimental results show that the proposed method effectively delineates salt domes within migrated seismic volumes.

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Detecting boundary of salt dome in seismic data with edge‐detection technique

Cited by 43 publications

References 1 publication

Connectivity constrained segmentation of geologic features

Connectivity constrained segmentation of geologic features

Fast salt-boundary interpretation with optimal path picking

Salt dome detection within migrated seismic volumes using phase congruency

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