Ziolkowski, Tobias , Koschmider, Agnes and Devey, Colin (2024) An optimized outlier detection function for multibeam echo-sounder data. Computers & Geosciences, 186 . Art.Nr. 105572. DOI 10.1016/j.cageo.2024.105572.

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Abstract

Highlights

• Developed an innovative weighted outlier detection function that adaptively selects the best outlier detection technique, markedly improving precision and robustness in multibeam echosounder data analysis.
• Demonstrated superior performance of the weighted function over traditional methods, achieving higher precision, recall, and F1 scores, pivotal for accurate seafloor mapping.
• Enhanced data quality for geoscientific applications by effectively identifying and removing outliers without introducing data voids, preserving the integrity of multibeam sonar data.
• The function’s significance extends to supporting sustainable environmental and resource management practices through improved accuracy in seabed mapping.
• Discussed the adaptability of the method to various outlier patterns and its limitations, highlighting the need for further research and validation across different marine environments and data types.

Abstract

Multibeam sonar data are a valuable tool for seafloor mapping and geological studies. However, the presence of outliers in multibeam data can distort the results of analyses and reduce the accuracy of seafloor maps. In this paper, we define a weighting function based on the performance of various outlier detection techniques (OTDs) for detecting outliers in multibeam data, which calculates an outlier probability score for each sounding. Our results show that each OTD has its own strengths and weaknesses, and that a combination of outlier detection techniques is promising to improve reproducibility, explainability and the accuracy of the detection process. To address the challenge of detecting outliers in multibeam data, we propose a weighted outlier detection function that outperforms individual outlier detection techniques in terms of precision, recall and F1 scores by considering their strengths and combining them in a way that accounts for variations in the data. The function detects various types of outliers with high precision and recall values, resulting in valuable improvements in outlier detection performance for multibeam data. Overall, our proposed workflow has the potential to significantly improve the way multibeam data cleaning is performed, with the weighted outlier detection function being applied first, detecting most of the outlier automatically, followed by a domain-expert review of a small group of soundings whose automatic outlier labelling is not unequivocal.

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