Breakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs
Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-symmetry azimuths, low acoustic amplitudes, and increased borehole radius. Accurate breakout characterization is crucial for in-situ stress analysis. In recent years, deep learning has been introduced to automate the time-consuming and labor-intensive breakout picking process. However, existing approaches often suffer from misclassification of non-breakout features, leading to high false positive rates. To address this limitation, this study develops a deep learning framework, termed Breakout-picker, with a specific focus on reducing false positives ...
| Authors | Guangyu Wang, Xiaodong Ma, Xinming Wu |
|---|---|
| Primary category | cs.CV |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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