Statistical pattern recognition technique to enhance anomalies in magnetic surveys

Abstract

To classify and recognize objects or region of interest in an image, several methods in pattern recognition techniques were developed. Texture is one of the main characters used to classify and characterize an image whether it be an aerial photograph, a satellite image, a photomicrograph or, as in this study, a geophysical image. This paper describes the application of pattern recognition techniques to 2D geophysical data. The aim is to determine if they are useful to identify magnetic anomaly, especially with a very low signal/noise ratio. We utilized the method proposed by Haralick et al. (Haralick, R.M., Shanmugam, K. and Dinstein, I., 1973. Textural features for image classification. IEEE Trans. Syst. Man Cybern., 6-3: 610–621.) based on gray tone spatial dependence matrices; 14 features can be calculated to define the spatial relationship of pixels values; we verify that some of these features mark the boundaries of regions having a common statistical character and that match well with magnetic anomalies present in the image. Two synthetic models were utilized to test the discrimination capacity of the PR technique. Synthetic magnetic anomalies were computed by using the method proposed by Talwani (Talwani, M., 1965. Computation with the help of a digital computer of magnetic anomalies caused by bodies of arbitaray shape. Geophysics, 30: 797–817.). The synthetic images were altered with a random noise up to three times higher than anomaly amplitude. Then gray-tone spatial dependence matrix was applied for image classification. The same method has been finally used with experimental data collected during two magnetic surveys carried out in the same area for archaeological purpose. The results indicate that the method can be utilized to interpret geophysical data. Thus it can allow the anomalies detection and consequently facilitates geophysical data interpretation.