Supervised learning to detect salt body

Guillén, Pablo; Larrazábal, G.; González, Gladys; Boumber, Dainis; Vilalta, Ricardo

doi:10.1190/segam2015-5931401.1

Cited by 44 publications

(20 citation statements)

References 6 publications

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“…In recent years, there has been a significant interest in machine learning-based techniques for various seismic interpretation applications such as salt body delineation, fault and fracture detection, horizon extraction, and facies classification (e.g., Coléou et al, 2003;Barnes and Laughlin, 2005;Wang et al, 2015a;Guillen et al, 2015;Zhao et al, 2015;Wang et al, 2015b;Figueiredo et al, 2015;Qi et al, 2016;Ramirez et al, 2016;Lin et al, 2017). Supervised machine learning has proven to be one of the most successful machine learning paradigms.…”

Section: Introductionmentioning

confidence: 99%

Structure label prediction using similarity-based retrieval and weakly supervised label mapping

2019

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Structure label prediction using similaritybased retrieval and weakly supervised label mapping", GEOPHYSICS 2019 84:1, V67-V79. ABSTRACTRecently, there has been significant interest in various supervised machine learning techniques that can help reduce the time and effort consumed by manual interpretation workflows. However, most successful supervised machine learning algorithms require huge amounts of annotated training data. Obtaining these labels for large seismic volumes is a very timeconsuming and laborious task. We address this problem by presenting a weakly-supervised approach for predicting the labels of various seismic structures. By having an interpreter select a very small number of exemplar images for every class of subsurface structures, we use a novel similarity-based retrieval technique to extract thousands of images that contain similar subsurface structures from the seismic volume. By assuming that similar images belong to the same class, we obtain thousands of image-level labels for these images; we validate this assumption in our results section. We then introduce a novel weakly-supervised algorithm for mapping these rough image-level labels into more accurate pixel-level labels that localize the different subsurface structures within the image. This approach dramatically simplifies the process of obtaining labeled data for training supervised machine learning algorithms on seismic interpretation tasks. Using our method we generate thousands of automaticallylabeled images from the Netherlands Offshore F3 block with reasonably accurate pixel-level labels. We believe this work will allow for more advances in machine learning-enabled seismic interpretation. 0

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Section: Introductionmentioning

confidence: 99%

Structure label prediction using similarity-based retrieval and weakly supervised label mapping

2019

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“…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 . ; Di and AlRegib ; Di, Shafiq and AlRegib ).…”

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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“…Meanwhile, considering the insufficiency of a single attribute to reliable salt detection, researchers (e.g. Berthelot et al 2013;Halpert et al 2014;Amin & Deriche 2015;Guillen et al 2015;Qi et al 2015;Di & AlRegib 2017; have suggested integrating multiple attributes through machine learning techniques for improved detection accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data

Shafiq

AlRegib

2018

Geophysical Journal International

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Salt bodies are important subsurface structures with significant implications for hydrocarbon accumulation and sealing in offshore petroleum reservoirs. This study presents an unsupervised workflow for delineating the surface of salt bodies from 3-D seismic surveying based on a multi-attribute k-means cluster analysis. The workflow consists of four steps. First, a suite of seismic attributes are selected and computed from the volume of original seismic amplitude, each of which separates the target salt boundaries from the surrounding non-boundary seismic features in its unique way. Second, two sets of representative samples are manually picked in an interpreter-specified vertical section to help initialize the centres of the boundary and nonboundary clusters. Third, the k-means cluster analysis is performed on the seismic attributes to generate a clustering model for volumetric processing. Finally, applying the built k-means model to every sample in the seismic volume provides us with a probability volume in which the high values indicate the presence of salt-dome boundaries. The effectiveness of the proposed method is verified for interpreting the multiple salt bodies in the F3 seismic data set over the Netherlands North Sea, and the generated salt-boundary volume can serve as input for more advanced salt interpretation, such as salt surface/body extraction, to assist structural framework modelling in the subsalt zones of high geologic complexities and petroleum potentials. We conclude that the proposed workflow paves the way for computer-aided seismic interpretation in a more comprehensive manner by incorporating more advanced machine learning algorithms (e.g. artificial neural network) and targeting other important seismic structures (e.g. fault).

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Supervised learning to detect salt body

Cited by 44 publications

References 6 publications

Structure label prediction using similarity-based retrieval and weakly supervised label mapping

Structure label prediction using similarity-based retrieval and weakly supervised label mapping

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

Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data

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