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Interpreting Subsurface Seismic Data

2022
Interpreting Subsurface Seismic Data presents recent advances in methodologies for seismic imaging and interpretation across multiple applications in geophysics including exploration, marine geology, and hazards. It provides foundational information for context, as well as focussing on recent advances and future challenges.
Rebecca Bell, David Iacopini, Mark Vary
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Seismic graph analysis to aid seismic interpretation

Interpretation, 2019
During the seismic interpretation process, geoscientists rely on their experience and visual analysis to assess the similarity between seismic sections. However, evaluating all of the seismic sections in a 3D survey can be a time-consuming task. When interpreters are working on a data set, a common procedure is to divide the cube in increasingly finer ...
Rodrigo S. Ferreira   +3 more
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Fuzzy seismic interpretation

IEEE Transactions on Geoscience and Remote Sensing, 1984
During the past decade, interest in fuzzy sets has led to the development of a well-organized theory developed by Zadeh [1]. This theory has particular applications in pattern recognition, fuzzy automata, and mapping. This paper concerns the application of fuzzy sets to the problem of seismic Interpretation.
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Quantitative Seismic Interpretation

2005
Quantitative Seismic Interpretation demonstrates how rock physics can be applied to predict reservoir parameters, such as lithologies and pore fluids, from seismically derived attributes. The authors provide an integrated methodology and practical tools for quantitative interpretation, uncertainty assessment, and characterization of subsurface ...
Per Avseth, Tapan Mukerji, Gary Mavko
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Porosity interpretation through seismics

International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1991
Abstract This paper describes a procedure for porosity interpretation from seismic data (POISE) by exploiting merits of several recent advances in seismic data processing and interpretation. Briefly, the procedure is as follows: starting from NMO corrected CDP gathers of P-wave data and applying amplitude versus offset (AVO) analysis, one can ...
C.H Mehta, B.M Verma
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Display enhancement for seismic interpretation

Canadian Electrical Engineering Journal, 1981
Proceedings of the 7th Canadian Man-Computer Communications Conference , Waterloo, Ontario, Canada, 10 - 12 June 1981, 57 ...
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An approach to Seismic Interpretation based on Cognitive Vision/Seismic Interpretation

70th EAGE Conference and Exhibition incorporating SPE EUROPEC 2008, 2008
Advanced seismic interpretation most commonly rests on transforming original data representations by considering more or less numerous seismic attributes, which bear no explicit relation with geology. For this reason, they hardly allow fully solving problems such as reassembling sparse geological surface elements or specifying chronological or ...
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Seismic Interpretation Using Global Image Segmentation

66th EAGE Conference & Exhibition, 2003
Summary A rst step in seismic interpretation is seismic image segmentation. For most seismic images, with incompletely or poorly imaged faults and horizons, global methods for segmentation are more robust than local event tracking or region growing methods commonly used today.
Dave Hale, Jeff Emanuel
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Wavelets and Seismic Interpretation

1989
The concept of the wavelet is not new in the petroleum industry, and particularly in geophysics, but this term is used here with a different meaning. For a geophysicist the source signal used during seismic acquisition is often approximated by a wavelet form. The mathematical approach of Wavelet Transform (W.T) permits to go further in the exploitation
J. L. Larsonneur, J. Morlet
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Seismic Interpretation Methods

2016
Seismic interpretation conveys the geologic meaning of seismic data by extracting subsurface information from it and can be of different kinds, such as structural, stratigraphic and seismic stratigraphy. It depends on the geologic objectives linked to the phase of exploration and on the type of available data, its grid density and its quality.
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