Seismic image segmentation by fuzzy fusion of attributes

Bert Müller; Philipp Thurner; Felix Beckmann; Timm Weitkamp; Christoph Rau; Ricardo Bernhardt; Erdal Karamuk; Ludwig Eckert; Jörg Brandt; Stefan Buchloh; Erich Wintermantel; Dieter Scharnweber; Hartmut Worch

Seismic image segmentation by fuzzy fusion of attributes

Bert Müller, Philipp Thurner, Felix Beckmann, Timm Weitkamp, Christoph Rau, Ricardo Bernhardt, Erdal Karamuk, Ludwig Eckert, Jörg Brandt, Stefan Buchloh, Erich Wintermantel, Dieter Scharnweber, Hartmut Worch

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

5 Zitate (Scopus)

Abstract

Nowadays, petroleum companies want to find some deposits to be explored in future years. To know the subsoil organization, Geophysicists try to interpret seismic images acquired by sending acoustic waves in the soil. This paper presents a method for the segmentation of these images in geological regions in order to help interpreters in their study. Information fusion of image attributes allows to solve part of this complex problem. To realize aggregation, the fuzzy subset theory is an appropriate tool to code interpreter knowledge by a set of rules. The results obtained are promising and show the necessity to evaluate their quality more thoroughly.

Originalsprache	Englisch
Seiten (von - bis)	360-364
Seitenumfang	5
Fachzeitschrift	Conference Record - IEEE Instrumentation and Measurement Technology Conference
Jahrgang	1
Publikationsstatus	Veröffentlicht - 2000
Extern publiziert	Ja

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title = "Seismic image segmentation by fuzzy fusion of attributes",

abstract = "Nowadays, petroleum companies want to find some deposits to be explored in future years. To know the subsoil organization, Geophysicists try to interpret seismic images acquired by sending acoustic waves in the soil. This paper presents a method for the segmentation of these images in geological regions in order to help interpreters in their study. Information fusion of image attributes allows to solve part of this complex problem. To realize aggregation, the fuzzy subset theory is an appropriate tool to code interpreter knowledge by a set of rules. The results obtained are promising and show the necessity to evaluate their quality more thoroughly.",

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AU - Müller, Bert

AU - Thurner, Philipp

AU - Beckmann, Felix

AU - Weitkamp, Timm

AU - Rau, Christoph

AU - Bernhardt, Ricardo

AU - Karamuk, Erdal

AU - Eckert, Ludwig

AU - Brandt, Jörg

AU - Buchloh, Stefan

AU - Wintermantel, Erich

AU - Scharnweber, Dieter

AU - Worch, Hartmut

PY - 2000

Y1 - 2000

N2 - Nowadays, petroleum companies want to find some deposits to be explored in future years. To know the subsoil organization, Geophysicists try to interpret seismic images acquired by sending acoustic waves in the soil. This paper presents a method for the segmentation of these images in geological regions in order to help interpreters in their study. Information fusion of image attributes allows to solve part of this complex problem. To realize aggregation, the fuzzy subset theory is an appropriate tool to code interpreter knowledge by a set of rules. The results obtained are promising and show the necessity to evaluate their quality more thoroughly.

AB - Nowadays, petroleum companies want to find some deposits to be explored in future years. To know the subsoil organization, Geophysicists try to interpret seismic images acquired by sending acoustic waves in the soil. This paper presents a method for the segmentation of these images in geological regions in order to help interpreters in their study. Information fusion of image attributes allows to solve part of this complex problem. To realize aggregation, the fuzzy subset theory is an appropriate tool to code interpreter knowledge by a set of rules. The results obtained are promising and show the necessity to evaluate their quality more thoroughly.

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EP - 364

JO - Conference Record - IEEE Instrumentation and Measurement Technology Conference

JF - Conference Record - IEEE Instrumentation and Measurement Technology Conference

ER -

Seismic image segmentation by fuzzy fusion of attributes

Abstract

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