Automated matching and visualisation of magnetic flux leakage data in shale gas pipeline based on ICP and DBSCAN algorithm

Magnetic flux leakage (MFL) is a non-destructive testing method for shale gas pipeline safety monitoring. Despite many pipelines have completed two or more rounds of internal inspection and have accumulated substantial data, effectively analyzing and utilizing this MFL data remains a significant challenge. To that end, this study proposes a novel combined model that integrates Iterative Closest Point (ICP) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) fusion to match multiple MFL data. The model outputs the matching result and visualisation images of MFL data acquired in different years. Furthermore, RMSE (root mean square error) and overlap rate have been used to evaluate the model. The results indicate that the average distance error of matched defects between two datasets decreased by 72.5%, and the overlap rate increased by 20%. Additionally, the DBSCAN Fusion shows better computational efficiency with an increase in defect quantity within the pipe segments. Finally, the impact of different datasets on the model’s matching accuracy is discussed in this study. The findings show that small-scale datasets or higher false detection rates lead to decreased accuracy. The proposed model fully leverages MFL data to achieve rapid matching of defects, offering a dependable and effective technical solution for safety monitoring and corrosion prediction in shale gas pipelines.