Interpreter-assisted Tracking of Subsurface Structures within Migrated Seismic Volumes Using Active Contour

Shafiq, M.A.; AlRegib, Ghassan

doi:10.3997/2214-4609.201600882

Cited by 5 publications

(3 citation statements)

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“…Berthelot et al (2012) propose detecting the salt dome boundary in one time section and tracking the boundary through adjacent sections by minimizing a defined energy function that maintains curvature and smoothness of the boundaries. Similarly, Shafiq and AlRegib (2016) take advantage of active contour to track salt dome boundaries in neighboring seismic sections. Wang et al (2016) propose a salt dome tracking method that extracts the features of salt dome boundaries in reference sections using tensor-based subspace learning and delineates tracked boundaries by finding points in predicted sections that are the most similar to reference ones.…”

Section: Fault and Salt Dome Trackingmentioning

confidence: 99%

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

et al. 2018

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As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as visual cues of geologic structures, such as texture and geometry. With the dramatic increase in size of seismic data acquired for hydrocarbon exploration, structural interpretation has become more time consuming and labor intensive. By treating seismic data as images rather than signal traces, researchers have been able to utilize advanced image-processing and machine-learning techniques to assist interpretation directly. In this paper, we mainly focus on the interpretation of two important geologic structures, faults and salt domes, and summarize interpretation workflows based on typical or advanced image-processing and machine-learning algorithms. In recent years, increasing computational power and the massive amount of available data have led to the rise of deep learning. Deep-learning models that simulate the human brain's biological neural networks can achieve state-of-the-art accuracy and even exceed human-level performance on numerous applications. The convolutional neural network — a form of deep-learning model that is effective in analyzing visual imagery — has been applied in fault and salt dome interpretation. At the end of this review, we provide insight and discussion on the future of structural interpretation.

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Section: Fault and Salt Dome Trackingmentioning

confidence: 99%

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

et al. 2018

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“…Berthelot et al [26] detected the salt-dome boundary in one time-section using the method in [19] and tracked the boundary through adjacent sections by minimizing a defined energy function that maintains boundaries' curvature and smoothness. Similarly, [27] takes advantage of active contour to track salt-dome boundaries in neighboring seismic sections. More recently, Wang et al [5] have proposed a salt-dome tracking method that extracts the features of salt-dome boundaries in reference sections using tensor-based subspace learning and delineates tracked boundaries by finding points in predicted sections, which are the most similar to reference ones.…”

Section: Fault and Salt-dome Trackingmentioning

confidence: 99%

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

AlRegib

Deriche

Long

et al. 2018

IEEE Signal Process. Mag.

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Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this paper, first, we summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine learning algorithms.Keywords subsurface imaging and exploration, seismic interpretation, digital signal processing, machine learning, human visual system, visual analytics

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“…Similarly, Halpert, Clapp and Biondi () introduced the interpreter guidance (manual picks) into the pairwise region comparison algorithm to prevent the leakage of the segmentation results. Shafiq and AlRegib () added the regularization term, which measured the length of salt boundary, into the objective function, and this can reduce the interference of false positives to some degree.…”

Section: Introductionmentioning

confidence: 99%

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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Interpreter-assisted Tracking of Subsurface Structures within Migrated Seismic Volumes Using Active Contour

Cited by 5 publications

References 0 publications

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

Connectivity constrained segmentation of geologic features

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