A machine-learning benchmark for facies classification
| Autor: | Ghassan AlRegib, Patrycja Michałowicz, Yazeed Alaudah, Motaz Alfarraj |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
business.industry Computer science Deep learning Geology 010502 geochemistry & geophysics Machine learning computer.software_genre 01 natural sciences Interpretation (model theory) Geophysics Obstacle Facies Benchmark (computing) Artificial intelligence Seismic interpretation Stratigraphy (archaeology) business computer 0105 earth and related environmental sciences |
| Zdroj: | Interpretation. 7:SE175-SE187 |
| ISSN: | 2324-8866 2324-8858 |
| DOI: | 10.1190/int-2018-0249.1 |
| Popis: | The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely, the absence of large publicly available annotated data sets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have led to subjective results and have greatly hindered our ability to compare different machine-learning models against each other and understand the advantages and disadvantages of each approach. To address these issues, we open source a fully annotated 3D geologic model of the Netherlands F3 block. This model is based on study of the 3D seismic data in addition to 26 well logs, and it is grounded on the careful study of the geology of the region. Furthermore, we have developed two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available. Finally, we have developed a scheme for evaluating different models on this data set, and we have evaluated the results of our baseline models. In addition to making the data set and the code publicly available, our work helps advance research in this area by creating an objective benchmark for comparing the results of different machine-learning approaches for facies classification. |
| Databáze: | OpenAIRE |
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