Comparison of Seismic Traces Clustering Efficiency of Different Unsupervised Machine Learning Algorithms in Forward Seismic Models; 81st EAGE Conference and Exhibition 2019

Comparison of Seismic Traces Clustering Efficiency of Different Unsupervised Machine Learning Algorithms in Forward Seismic Models; 81st EAGE Conference and Exhibition 2019

Bibliographic Details
Parent link:81st EAGE Conference and Exhibition 2019.— 2019.— [4 p.]
Corporate Authors: Национальный исследовательский Томский политехнический университет (ТПУ) Институт природных ресурсов (ИПР) Центр подготовки и переподготовки специалистов нефтегазового дела (ЦППС НД) Лаборатория геологии месторождений нефти и газа (ЛГМНГ), Национальный исследовательский Томский политехнический университет Институт природных ресурсов Центр подготовки и переподготовки специалистов нефтегазового дела
Other Authors: Churochkin I. I. Iljya Igorevich, Volkova A. A. Aleksandra Aleksandrovna, Gavrilova E., Bukhanov N. V. Nikita Vladimirovich, Butorin A. V. Aleksandr Vasiljevich, Rukavishnikov V. S. Valery Sergeevich
Summary:Title screen
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.
Режим доступа: по договору с организацией-держателем ресурса
Language:English
Published: 2019
Series:AI/Digitalization for Interpretation - Various Application
Subjects:
электронный ресурс
труды учёных ТПУ
сейсмические трассы
неконтролируемые процессы
Online Access:https://doi.org/10.3997/2214-4609.201901390
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=660618

Internet

https://doi.org/10.3997/2214-4609.201901390

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