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

Bibliografiske detaljer
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
Andre forfattere:	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
Sprog:	engelsk
Udgivet:	2019
Serier:	AI/Digitalization for Interpretation - Various Application
Fag:	электронный ресурс труды учёных ТПУ сейсмические трассы неконтролируемые процессы
Online adgang:	https://doi.org/10.3997/2214-4609.201901390
Format:	Electronisk Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=660618

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