Attribute expression of channel forms in a hybrid carbonate turbidite formation

Wallet, Bradley C.

doi:10.1190/int-2015-0108.1

Cited by 13 publications

(2 citation statements)

References 11 publications

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“…Picking a consistent horizon slice in this Formation is almost impossible because these incised canyons extended laterally in all the Formation. On the other hand, if we pick a reflector above or below the zone of interest, and create phantom horizons, these will cut different geologic ages (Wallet, 2016). For these reasons, we make our analysis using time slices.…”

Section: Data Descriptionmentioning

confidence: 99%

Delineation of thick incised canyons using spectral-decomposition analysis, curvature and self-organizing maps in the Exmouth Plateau, Australia

Lubo-Robles

Marfurt

2017

SEG Technical Program Expanded Abstracts 2017

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Section: Data Descriptionmentioning

confidence: 99%

Delineation of thick incised canyons using spectral-decomposition analysis, curvature and self-organizing maps in the Exmouth Plateau, Australia

Lubo-Robles

Marfurt

2017

SEG Technical Program Expanded Abstracts 2017

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“…; Liu et al . ; Wallet ; Karbalaali et al . ; Wu ), caves (Kaufmann, Romanov and Nielbock ; Liu et al .…”

Section: Introductionunclassified

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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Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities

Lin,

Zhong,

et al. 2024

Earth-Science Reviews

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Attribute expression of channel forms in a hybrid carbonate turbidite formation

Cited by 13 publications

References 11 publications

Delineation of thick incised canyons using spectral-decomposition analysis, curvature and self-organizing maps in the Exmouth Plateau, Australia

Delineation of thick incised canyons using spectral-decomposition analysis, curvature and self-organizing maps in the Exmouth Plateau, Australia

Connectivity constrained segmentation of geologic features

Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities

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