Investigation of capability of normalized anisotropy variance in edge detection of grvity anomalies

Summary To determine the location of subsurface objects from the potential field maps, edge detection methods are used. This methods are mainly derivatives of potential field. For edge detection, several methods or filters such as analytic signal, tilt angle, total horizontal derivative of tilt angle and normalized total horizontal derivative are commenly used in geophysical interpretation. In this regard, the method of normalized anisotropy variance (NAV) for edge detection is investigated in this paper. This filter directly do not use high-order derivatives, and also, vertical derivative in calculations, thus, stability of noisy data is preserved in this method. One of the effective parameters in the calculation of this method is the optimal window number that is obtained by maximum of variation of normalized anisotropy variance and window number. In order to assess performance of the NAV-method in the edge detection of gravity anomalies, cubic models used with positive and negative density contrast and different depth, and the NAV-method and also the above-mentioned filters were applied to synthetic models and real gravity data of Safo Manganese mine. Consequently, acceptable results from applying the NAV method for edge detection of gravity anomalies were obtained.
Introduction A variety of techniques based on horizontal and vertical derivatives of potential field data have been developed as effective tools for the edge detection of potential field anomalies. Location of maximum horizontal derivative can be used an indicator of the location of the anomaly’s edges. The first vertical derivative (FVD) is positive over the source, zero-crossing value of FVD over the edge, and outside of a vertical sided source is negative. The peaks of the horizontal derivatives indicate the edges and zero over the body. One of the most important problems in using derivative-based edge detection techniques is that the noise in the potential field data increases during the process. Analytic signal is one of the filters that employs both horizontal and vertical derivatives to determine the boundary of the anomalies. Local phase filters are set of filters based on horizontal and vertical derivatives of potential field data that are used in geophysical interpretation. Tilt angle, total horizontal derivative of tilt angle and normalized total horizontal derivative (TDX) are conventional local phase filters that are sensitive to noise. To achieve the best result for edge detection of gravity anomalies, we introduce normalized anisotropy variance or NAV method that reduces sensitivity to noise.
Methodology and Approaches To determine the edge of subsurface bodies, NAV edge detection filter has been applied to gravity data. The NAV method has been investigated as a tool to detect the edge of anomalies.In this regard, its ability to determine the edge of synthetic models from gravity data, in a noisy and free-noise state has been demonstrated in this paper. Anisotropy scale and window number are two important parameters on the value of the NAV. The anisotropy scale in anomalies separation in the vicinity of each other, is an effective parameter. An empirical approach to select these parameters has been applied. Window number (M) is associated with data quality, for noise-free data M=3 is acceptable. For data that are contaminated with noise, the value of M is determined by variation of the maximum value of NAV with window number. Since the NAV does not use any of higher-order derivatives directly, it is less sensitive to noise than the other methods. In MATLAB platform, a computer program has been prepared and its outputs are extracted.
Results and Conclusions In this research, capability of NAV method in estimating boundaries of synthetic and real potential field anomalies has been investigated. Vertical derivative that creates complexity for the interpretation, has not been used in this method; hence this method or filter for deep resources is more stable. Similar to tilt angle filter, the zero value of NAV corresponds to the edges of anomalies. The value of the NAV filter is positive over the body and negative out of it; therefore, area of the body can be detected from other parts. The results also show that this method is less sensitive to noise and the boundaries that are determined for the model, are in good accordance with reality. For the model that a small prism superposed on a big prism, the NAV could detect the edges of the superposed prism with high accuracy.