Texture attributes for detection of salt

Berthelot, Angélique; Solberg, Anne; Gelius, Leiv‐J.

doi:10.1016/j.jappgeo.2012.09.006

Cited by 82 publications

(41 citation statements)

References 25 publications

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“…Figure 16 illustrates one section of this local dataset, and Table 2 shows the performance of various detection methods, in which the proposed GoT-based method still achieves the highest accuracy. (Berthelot et al, 2013) Detection method based on 0.9054 0.0159 20.73 Gradient Maps (Aqrawi et al, 2011) Salt Dome Detection and Tracking (Berthelot et al, 2013) Detection method based on 0.9410 0.00822 8.74 Gradient Maps (Aqrawi et al, 2011)…”

Section: Objective Comparison Of Detected Boundariesmentioning

confidence: 99%

Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

et al. 2015

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The identification of salt-dome boundaries in migrated seismic data volumes is important for locating petroleum reservoirs. The presence of noise in the data makes computer-aided salt-dome interpretation even more challenging. We have developed noise-robust algorithms that could label boundaries of salt domes effectively and efficiently. Our research is twofold. First, we used a texture-based gradient to accomplish salt-dome detection. We found that by using a dissimilarity measure based on the 2D discrete Fourier transform, the algorithm was capable of efficiently detecting salt-dome boundaries with accuracy. At the same time, our analysis determined that the proposed algorithm was robust to noise. Once the detection is performed for an initial 2D seismic section, we track the initial boundaries through the data volume to accomplish an efficient labeling process by avoiding the parameter tuning that would have been necessary if detection had been performed for every seismic section. The tracking process involves a tensor-based subspace learning process, in which we built texture tensors using patches from different seismic sections. To accommodate noise components with various levels in a texture tensor, we used noise-adjusted principal component analysis, so that principal components corresponding to greater signal-to-noise-ratio values might be selected for tracking. We validated our detection and tracking algorithms through experiments using seismic data sets acquired from the Netherlands offshore F3 block in the North Sea with very encouraging results.

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Section: Objective Comparison Of Detected Boundariesmentioning

confidence: 99%

Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

et al. 2015

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“…Berthelot et al [19] proposed a Bayesian classification approach for detecting salt bodies using a combination of seismic attributes such as dip, similarity, frequency-based attributes, and attributes based on the gray-level cooccurrence matrix (GLCM). A workflow based on seismic attributes and a machine-learning algorithm (i.e., extremely random trees ensemble) that automatically detects salt domes from the SEAM dataset is presented in [20].…”

Section: B Salt Dome Detectionmentioning

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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“…McCormack ). For example, integrating multiple attributes through machine learning techniques has proven efficient for improving interpretation accuracy (Berthelot, Solberg and Gelius ; Halpert, Clapp and Biondi ; Zheng, Kavousi and Di ; Amin and Deriche ; Guillen et al . ; Qi et al .…”

Section: Introductionmentioning

confidence: 99%

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

AlRegib

2019

Geophysical Prospecting

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Saltbodies are important subsurface structures that have significant implications for hydrocarbon accumulation and sealing in petroleum reservoirs, and accurate saltbody imaging and delineation is now greatly facilitated with the availability of three‐dimensional seismic surveying. However, with the growing demand for larger survey coverage and higher imaging resolution, the size of seismic data is increasing dramatically. Correspondingly, manual saltbody interpretation fails to offer an efficient solution, particularly in exploration areas of complicated salt intrusion history. Recently, artificial intelligence is attracting great attention from geoscientists who desire to utilize the popular machine learning technologies for evolving the interpretational tools capable of mimicking an experienced interpreter's intelligence. This study first implements two popular machine learning tools, the multi‐layer perceptron and the convolutional neural network, for delineating seismic saltbodies at sample and pattern levels, respectively, then compares their performance through applications to the synthetic SEAM seismic volume, and moreover tentatively investigates what contributes to the better convolutional neural network delineation. Specifically, the multi‐layer perceptron scheme is capable of efficiently utilizing an interpreter's knowledge by selecting, pre‐conditioning and integrating a set of seismic attributes that best highlight the target saltbodies, whereas the convolutional neural network scheme makes it possible for saltbody delineation directly from seismic amplitude and thus significantly reduces the dependency on attribute selection from interpreters. It is concluded that the better performance from the convolutional neural network scheme results from two factors. First, the convolutional neural network builds the mapping relationship between the seismic signals and the saltbodies using the original seismic amplitude instead of manually selected seismic attributes, so that the negative impact of using less representative attributes is virtually eliminated. Second and more importantly, the convolutional neural network defines, learns and identifies the saltbodies by utilizing local seismic reflection patterns, so that the seismic noises and processing artefacts of distinct patterns are effectively identified and excluded.

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Texture attributes for detection of salt

Cited by 82 publications

References 25 publications

Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

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

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

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