Work

Real Missing-Region-Constrained Self-Supervised Inpainting for Borehole Electrical Imaging Logs

Year: 2026

Type: article

Abstract: Borehole electrical imaging logs provide important sensor-derived information for identifying fractures, bedding structures, and reservoir heterogeneity. However, strip-like missing regions commonly arise from uneven tool pad distribution, incomplete borehole coverage, and acquisition limitations, which can distort geological structures and reduce interpretation reliability. Because the true values inside real missing regions are unobservable, complete ground-truth labels are generally unavailable. This study therefore proposes a real missing-region-constrained self-supervised inpainting framework for borehole electrical imaging logs. The key idea is to use real missing masks to define safe intact regions and to generate artificial strip-like training masks only within those reliable regions, thereby avoiding supervision contamination from originally missing or adjacent unstable areas. A UNet-based inpainting model is developed, and two attention-enhanced variants, UNet-SE and UNet-CBAM, are evaluated together with Telea and Navier–Stokes baselines. In addition to the main comparison, mask-level ablation, model-level ablation, repeated-seed robustness analysis, and lightweight expert-assisted geological assessment are used to examine the reliability of the proposed strategy. The results show that learning-based methods consistently outperform conventional approaches for structurally complex strip-like gaps. UNet-CBAM achieves the best overall performance, with mask-MAE, mask-PSNR, and mask-SSIM values of 14.8, 21.2 dB, and 0.8, respectively. The safe-region-constrained strategy further reduces supervision contamination and improves reconstruction quality compared with random or less restrictive mask-generation strategies. These findings indicate that the proposed framework offers a practical self-supervised solution for improving the quality and interpretability of sensor-captured borehole imaging logs when complete labels are unavailable.

Source: Applied Sciences

DOI: 10.3390/app16147320

Authors: Chuanhao Li, Chengwu Xu, Tingting Li, Qi Yao, Mengying Wang

Institution: Northeast Petroleum University

Language: English

Topic: Geophysical and Geoelectrical Methods

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896