Comparison of seismic traces clustering efficiency of different unsupervised machine learning algorithms in forward seismic models

Abstract

In this study, it is proposed to build geological model based on proportions of fluvial deposits outcrop. Then forward seismic model is constructed and clustering of seismic traces by using different unsupervised algorithms (k-means, DBSCAN and Agglomerative clustering) is performed. Results are compared with ground truth, which in our case is NTG map of interval of interest in geological model. Finally the optimal settings of the algorithms and the most accurate clustering method are identified.

Original language	English
Title of host publication	81st EAGE Conference and Exhibition 2019
Publisher	EAGE Publishing BV
ISBN (Electronic)	9789462822894
Publication status	Published - 3 Jun 2019
Event	81st EAGE Conference and Exhibition 2019 - London, United Kingdom Duration: 3 Jun 2019 → 6 Jun 2019

Publication series

Name	81st EAGE Conference and Exhibition 2019

Conference

Conference	81st EAGE Conference and Exhibition 2019
Country	United Kingdom
City	London
Period	3.6.19 → 6.6.19

ASJC Scopus subject areas

Geochemistry and Petrology
Geophysics

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Comparison of seismic traces clustering efficiency of different unsupervised machine learning algorithms in forward seismic models. / Churochkin, I.; Volkova, A.; Gavrilova, E.; Bukhanov, N.; Butorin, A.; Rukavishnikov, V.

81st EAGE Conference and Exhibition 2019. EAGE Publishing BV, 2019. (81st EAGE Conference and Exhibition 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Churochkin, I, Volkova, A, Gavrilova, E, Bukhanov, N, Butorin, A & Rukavishnikov, V 2019, Comparison of seismic traces clustering efficiency of different unsupervised machine learning algorithms in forward seismic models. in 81st EAGE Conference and Exhibition 2019. 81st EAGE Conference and Exhibition 2019, EAGE Publishing BV, 81st EAGE Conference and Exhibition 2019, London, United Kingdom, 3.6.19.

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