Depiction of Subsurface Leak Areas Based on Adaptive Sensitive Frequency Attribute Analysis

Abstract

Ground penetrating radar (GPR) frequency attributes are commonly used to describe subsurface structures and characterize anomalous media. Single-frequency slices face challenges in capturing the broadband characteristics of GPR data, so the fusion of extracted multi-frequency components using a fusion algorithm can be effective. However, selecting appropriate attributes and mapping them to media characterization remain unresolved challenges. In this study, we propose a workflow based on adaptive sensitive frequency attribute analysis (ASFAA) to address these issues. First, the generalized S-transform (GST) is used to calculate the multi-frequency attributes of GPR data. Then, a sensitive feature analysis method combining hierarchical clustering and correlation analysis is employed to reduce redundancy in frequency attributes. Multi-frequency data are fused using the potential of heat-diffusion for affinity-based transition embedding (PHATE), which performs affinity-based diffusion embedding. The workflow is tested with synthetic and field data, yielding characterization results consistent with both the forward model results and actual leak extents. Therefore, the proposed workflow effectively integrates multi-frequency components, demonstrating its capability to delineate leak extents.