Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering
Gang Yin 1 2 Yingtang Zhang 1 Hongbo Fan 1 Guoquan Ren 1 Zhining Li 1
+ Author Affiliations
- Author Affiliations
1 The Seventh Department, Mechanical Engineering College, No. 97, Hepingxilu Road, Shijiazhuang 050003, China.
2 Corresponding author. Email: gang.gang88@163.com
Exploration Geophysics 48(1) 67-75 https://doi.org/10.1071/EG14126
Submitted: 2 January 2015 Accepted: 3 November 2015 Published: 11 December 2015
Abstract
We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.
Key words: inversion, magnetic gradient tensor, magnetometry, multiple dipole sources.
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