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

I. Churochkin, A. Volkova, E. Gavrilova, N. Bukhanov, A. Butorin, V. Rukavishnikov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publication81st EAGE Conference and Exhibition 2019
PublisherEAGE Publishing BV
ISBN (Electronic)9789462822894
Publication statusPublished - 3 Jun 2019
Event81st EAGE Conference and Exhibition 2019 - London, United Kingdom
Duration: 3 Jun 20196 Jun 2019

Publication series

Name81st EAGE Conference and Exhibition 2019

Conference

Conference81st EAGE Conference and Exhibition 2019
CountryUnited Kingdom
CityLondon
Period3.6.196.6.19

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ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics

Cite this

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.