Data mining for large scale 3D seismic data analysis

Deighton, M.; Petrou, Maria

doi:10.1007/s00138-007-0101-3

Cited by 5 publications

(1 citation statement)

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“…A large amount of literature dealing with automatic procedures for detecting and tracking seismic events and features exists. A recent overview can be found in Deighton and Petrou (2009). Most approaches focus on extracting and visualizing multiple seismic attributes (see, e.g., Tingdahl and de Rooij 2005;Bakhtiari, Riahi and Tingdahl 2009).…”

Section: Introductionmentioning

confidence: 99%

Salt diapirs without well defined boundaries – a feasibility study of semi‐automatic detection

Berthelot

Solberg

Morisbak

et al. 2011

Geophysical Prospecting

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A B S T R A C TPrestack depth imaging of seismic data in complex areas such as salt structures requires extensive velocity model updating. In many cases, salt boundaries can be difficult to identify due to lack of seismic reflectivity. Traditional amplitude based segmentation methods do not properly tackle this problem, resulting in extensive manual editing. This paper presents a selection of seismic attributes that can reveal texture differences between the salt diapirs and the surrounding geology as opposed to amplitude-sensitive attributes that are used in case of well defined boundaries. The approach consists of first extracting selected texture attributes, then using these attributes to train a classifier to estimate the probability that each pixel in the data set belongs to one of the following classes: near-horizontal layering, highly-dipping areas and the inside of the salt that appears more like a low amplitude area with small variations in texture. To find the border between the inside of the salt and the highly-dipping surroundings, the posterior probability of the class salt is input to a graph-cut algorithm that produces a smooth, continuous border. An in-line seismic section and a timeslice from a 3D North Sea data set were employed to test the proposed approach. Comparisons between the automatically segmented salt contours and the corresponding contours as provided by an experienced interpreter showed a high degree of similarity.

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Section: Introductionmentioning

confidence: 99%

Salt diapirs without well defined boundaries – a feasibility study of semi‐automatic detection

Berthelot

Solberg

Morisbak

et al. 2011

Geophysical Prospecting

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Texture in Biomedical Images

Petrou

2010

Biomedical Image Processing

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Data mining applications in hydrocarbon exploration

et al. 2010

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This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term "intelligent" is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.

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Data mining for large scale 3D seismic data analysis

Cited by 5 publications

References 20 publications

Salt diapirs without well defined boundaries – a feasibility study of semi‐automatic detection

Salt diapirs without well defined boundaries – a feasibility study of semi‐automatic detection

Texture in Biomedical Images

Data mining applications in hydrocarbon exploration

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