Probabilistic Conic Mixture Model and its Applications to Mining Spatial Ground Penetrating Radar Data

This paper proposes a probabilistic conic mixture model based on a classification expectation maximization algorithm and applies this algorithm to Ground Penetrating Radar (GPR) spatial data interpretation. Previous work tackling this problem using Hough transform or neural networks for identifying GPR hyperbolae are unsuitable for on-site applications owing to their computational demands and the difficulties of getting sufficient appropriate training data for neural network based approaches. By incorporating a swift conic fitting algorithm into the probabilistic mixture model, the proposed algorithm can identify the hyperbolae in GPR data in real time and further calculate the depth and the size of the buried utility pipes. The number of the hyperbolae can be determined by conducting model selection using a Bayesian information criterion. The experimental results on both the synthetic/simulated and real GPR data show the effectiveness of this algorithm.