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Machine learning delineation of buried igneous features from the offshore Otway Basin in southeast Australia

Yakufu Niyazi, Mark Warne and Daniel Ierodiaconou
Machine learning delineation of buried igneous features from the offshore Otway Basin in southeast Australia
Interpretation (Tulsa) (August 2022) 10 (3): SE101-SE118

Abstract

Magmatic rocks are frequently encountered during hydrocarbon exploration in rift-related sedimentary basins. As magmatic rocks may contribute positively and negatively to the hydrocarbon systems, their spatiotemporal distribution and structural elements are crucial for exploration in frontier basins. With the proliferation and increased density of seismic reflection data, various subsurface magmatic features can be discriminated and illuminated via conventional interpretation approaches, such as attribute extraction, opacity rendering, or geo-body extraction. However, these manual interpretation techniques are labor-intensive, subject to interpreter bias, and often bottleneck with respect to time data delivery. A supervised machine learning approach could efficiently resolve these issues by amalgamating suitable seismic attributes, such as energy, reflection strength, texture, and similarity, and automatically delineating these magmatic features in 3D seismic reflection data. Our machine learning neural network (NN) classified igneous features from nonigneous features in two different seismic surveys within the natural laboratory of the offshore Otway Basin, southeast Australia. This multilayer perception NN designed in this study resulted in an optimized igneous probability meta-attribute cube that could effectively reveal the extension and distribution of igneous features and several structural elements in the study area. We have developed the detailed workflow of this artificial neural network and observed the efficiency of this approach in different seismic surveys. These results illustrate the potential of the NN in imaging other complex igneous features from 3D seismic data in the Otway Basin and worldwide.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 10
Serial Issue: 3
Title: Machine learning delineation of buried igneous features from the offshore Otway Basin in southeast Australia
Affiliation: Deakin University, Centre for Integrative Ecology, Warrnambool, Victoria, Australia
Pages: SE101-SE118
Published: 202208
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 93
Accession Number: 2022-050388
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Annotation: Part of a special issue entitled Machine learning for image-based geologic interpretation, edited by Xu, C.
Illustration Description: illus. incl. sects., 7 tables, chart, sketch map
S40°00'00" - S37°00'00", E141°00'00" - E144°00'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2022, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 202238
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