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 language | English |
|---|---|
| Title of host publication | 81st EAGE Conference and Exhibition 2019 |
| Publisher | EAGE Publishing BV |
| ISBN (Electronic) | 9789462822894 |
| Publication status | Published - 3 Jun 2019 |
| Event | 81st EAGE Conference and Exhibition 2019 - London, United Kingdom Duration: 3 Jun 2019 → 6 Jun 2019 |
Publication series
| Name | 81st EAGE Conference and Exhibition 2019 |
|---|
Conference
| Conference | 81st EAGE Conference and Exhibition 2019 |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 3.6.19 → 6.6.19 |
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ASJC Scopus subject areas
- Geochemistry and Petrology
- Geophysics
Cite this
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. EAGE Publishing BV, 2019. (81st EAGE Conference and Exhibition 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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=85073632095&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073632095&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85073632095
T3 - 81st EAGE Conference and Exhibition 2019
BT - 81st EAGE Conference and Exhibition 2019
PB - EAGE Publishing BV
ER -