Unsupervised outlier detection techniques for well logs and geophysical data
Autor: | Oghenekaro Osogba, Siddharth Misra, Mark Powers |
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Rok vydání: | 2020 |
Předmět: | |
DOI: | 10.1016/b978-0-12-817736-5.00001-6 |
Popis: | Outliers in well logs and other borehole-based subsurface measurements are often due to poor borehole condition, problems in data acquisition, irregularity in operating procedures, the presence of rare geological formations, or certain rare process/phenomenon in the subsurface. Detection of outliers is an important step prior to building a robust data-driven or machine learning-based model. We perform a comparative study of the performances of four unsupervised outlier detection techniques (ODTs) on various original and synthetic well-log datasets. The four unsupervised ODTs compared in this study are isolation forest (IF), one-class SVM (OCSVM), local outlier factor (LOF), and density-based spatial application with noise (DBSCAN). The unsupervised ODTs are evaluated on four labeled outlier-prone validation datasets using precision-recall curve, F1 score, area under the curve (AUC) score, and receiver operating characteristic (ROC) curve. Isolation forest is the most robust unsupervised ODT for detecting various types of outliers, whereas DBSCAN is particularly effective in detecting noise in a well-log dataset. Efficient feature engineering and feature selection is important to ensure robust detection of outliers in well-log and subsurface measurements using unsupervised outlier detection methods. |
Databáze: | OpenAIRE |
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