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

Standard

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. European Association of Geoscientists and Engineers, 2019. (81st EAGE Conference and Exhibition 2019).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование

Harvard

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. в 81st EAGE Conference and Exhibition 2019. 81st EAGE Conference and Exhibition 2019, European Association of Geoscientists and Engineers, 81st EAGE Conference and Exhibition 2019, London, Великобритания, 3/06/19. https://doi.org/10.3997/2214-4609.201901390

APA

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. в 81st EAGE Conference and Exhibition 2019 (81st EAGE Conference and Exhibition 2019). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201901390

Vancouver

Churochkin I, Volkova A, Gavrilova E, Bukhanov N, Butorin A, Rukavishnikov V. Comparison of seismic traces clustering efficiency of different unsupervised machine learning algorithms in forward seismic models. в 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists and Engineers. 2019. (81st EAGE Conference and Exhibition 2019). https://doi.org/10.3997/2214-4609.201901390

Author

Churochkin, I. ; Volkova, A. ; Gavrilova, E. ; Bukhanov, N. ; Butorin, A. ; Rukavishnikov, V. / Comparison of seismic traces clustering efficiency of different unsupervised machine learning algorithms in forward seismic models. 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists and Engineers, 2019. (81st EAGE Conference and Exhibition 2019).

BibTeX

@inproceedings{d757f4228af64763bff262602fa62669,

title = "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.",

author = "I. Churochkin and A. Volkova and E. Gavrilova and N. Bukhanov and A. Butorin and V. Rukavishnikov",

note = "Publisher Copyright: {\textcopyright} 81st EAGE Conference and Exhibition 2019. All rights reserved.; 81st EAGE Conference and Exhibition 2019 ; Conference date: 03-06-2019 Through 06-06-2019",

year = "2019",

month = jun,

day = "3",

doi = "10.3997/2214-4609.201901390",

language = "English",

series = "81st EAGE Conference and Exhibition 2019",

publisher = "European Association of Geoscientists and Engineers",

booktitle = "81st EAGE Conference and Exhibition 2019",

address = "Netherlands",

}

RIS

TY - GEN

T1 - Comparison of seismic traces clustering efficiency of different unsupervised machine learning algorithms in forward seismic models

AU - Churochkin, I.

AU - Volkova, A.

AU - Gavrilova, E.

AU - Bukhanov, N.

AU - Butorin, A.

AU - Rukavishnikov, V.

PY - 2019/6/3

Y1 - 2019/6/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85087228239&partnerID=8YFLogxK

U2 - 10.3997/2214-4609.201901390

DO - 10.3997/2214-4609.201901390

M3 - Conference contribution

AN - SCOPUS:85087228239

T3 - 81st EAGE Conference and Exhibition 2019

BT - 81st EAGE Conference and Exhibition 2019

PB - European Association of Geoscientists and Engineers

T2 - 81st EAGE Conference and Exhibition 2019

Y2 - 3 June 2019 through 6 June 2019

ER -

ID: 88695105