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Using machine learning as an aid to seismic geomorphology, which attributes are the best input?

Lennon Infante-Paez and Kurt J. Marfurt
Using machine learning as an aid to seismic geomorphology, which attributes are the best input?
Interpretation (Tulsa) (August 2019) 7 (3): SE1-SE18

Abstract

Volcanic rocks with intermediate magma composition indicate distinctive patterns in seismic amplitude data. Depending on the processes by which they were extruded to the surface, these patterns may be chaotic, moderate-amplitude reflectors (indicative of pyroclastic flows) or continuous high-amplitude reflectors (indicative of lava flows). We have identified appropriate seismic attributes that highlight the characteristics of such patterns and use them as input to self-organizing maps to isolate these volcanic facies from their clastic counterpart. Our analysis indicates that such clustering is possible when the patterns are approximately self-similar, such that the appearance of objects does not change at different scales of observation. We adopt a workflow that can help interpreters to decide what methods and what attributes to use as an input for machine learning algorithms, depending on the nature of the target pattern of interest, and we apply it to the Kora 3D seismic survey acquired offshore in the Taranaki Basin, New Zealand. The resulting clusters are then interpreted using the limited well control and principles of seismic geomorphology.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 7
Serial Issue: 3
Title: Using machine learning as an aid to seismic geomorphology, which attributes are the best input?
Affiliation: University of Oklahoma, School of Geology and Geophysics, Norman, OK, United States
Pages: SE1-SE18
Published: 201908
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 45
Accession Number: 2020-010974
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Annotation: Special section: Machine learning in seismic data analysis
Illustration Description: illus. incl. sects., strat. cols., geol. sketch maps
S41°15'00" - S38°00'00", E172°30'00" - E174°40'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 202008
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