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

Результат исследований: Материалы для книги/типы отчетовМатериалы для конференции

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикации81st EAGE Conference and Exhibition 2019
ИздательEAGE Publishing BV
ISBN (электронное издание)9789462822894
СостояниеОпубликовано - 3 июн 2019
Событие81st EAGE Conference and Exhibition 2019 - London, Великобритания
Продолжительность: 3 июн 20196 июн 2019

Серия публикаций

Название81st EAGE Conference and Exhibition 2019

Конференция

Конференция81st EAGE Conference and Exhibition 2019
СтранаВеликобритания
ГородLondon
Период3.6.196.6.19

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics

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  • Цитировать

    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). EAGE Publishing BV.