Matching of Multi-Run MFL In-Line Inspection Data Based on Dynamic Thresholds and Adaptive Anchor-Based Segmentation

Matching of multi-run magnetic flux leakage (MFL) in-line inspection data for oil and gas pipelines provides an essential basis for defect evolution analysis, corrosion growth assessment, and integrity management. However, in practical engineering applications, inconsistencies in total measured mileage, differences in the number of key points, and cumulative mileage errors across different inspection runs significantly increase the difficulty of data matching. To address these issues, this study proposes a report-level matching framework for multi-run MFL in-line inspection data that combines key-point alignment with defect matching. The proposed method improves the adaptability of defect matching under complex defect-size and spatial-distribution conditions through a dynamic-threshold mechanism and mitigates the influence of cumulative mileage errors on the matching results in later pipeline sections when large total mileage discrepancies exist between inspection runs through an adaptive anchor-based segmentation mechanism. Experiments based on multi-run MFL in-line inspection data from two actual pipelines demonstrate that the proposed method can achieve stable key-point and defect correspondence in scenarios with both small and large total mileage differences, thereby providing a basis for subsequent defect growth analysis.