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Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool
| Content Provider | MDPI |
|---|---|
| Author | Celecia, Alimed Figueiredo, Karla Rodriguez, Carlos Vellasco, Marley Maldonado, Edwin Silva, Marco Aurélio Rodrigues, Anderson Nascimento, Renata Ourofino, Carla |
| Copyright Year | 2021 |
| Description | Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data. |
| Starting Page | 6347 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21196347 |
| Journal | Sensors |
| Issue Number | 19 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-23 |
| Access Restriction | Open |
| Subject Keyword | Sensors Unsupervised Machine Learning Seismic Interpretation Image Segmentation Well Logs Clustering |
| Content Type | Text |
| Resource Type | Article |
