The textural analysis of potential field data

Abstract

Textural analysis is a powerful tool, but it is rarely applied to geophysical potential field data because the results are often noisy and ambiguous. New texture filters (based on grey-level co-occurrence matrices (GLCMs)) have been specifically designed for gravity and magnetic data, and are useful for the detection of subtle monopolar and dipolar geophysical anomalies. The method uses two GLCM kernels simultaneously: in the first kernel the GLCM vectors are oriented along the contour lines of the feature to be located, and in the second the vectors are oriented in the `uphill' direction ie orthogonal to those of the first kernel. The ratio of the GLCM texture amplitudes from the two kernels then yields a maximum when the kernel is centred over an example of the feature within the data. The method has general application and is not limited to potential fields. It works with any type of target, as long as an example is available. Applications to both synthetic data and real data are demonstrated.