Edge enhancement of potential-field data using normalized statistics

Abstract

Edge enhancement in potential-field data helps geologic interpretation. There are many methods for enhancing edges, most of which are high-pass filters based on the horizontal or vertical derivatives of the field. Normalized standard deviation (NSTD), a new edge-detection filter, is based on ratios of the windowed standard deviation of derivatives of the field. NSTD is demonstrated using aeromagnetic data from Australia and gravity data from South Africa. Compared with other filters, the NSTD filter produces more detailed results.

Edge Detection of Potential Filed Data Using Correlation Coefficients

“…It has relatively small values when the data are smooth and relatively large values when they are rough, e.g., over edges (Cooper et al, 2008). The zero value of the vertical derivatives can delineate the edges of the sources.…”

“…Normalized standard deviation (NSTD) is based on ratios of the windowed standard deviation of derivatives of the field. It can make large and small amplitude edges visible simultaneously (Cooper and Cowan, 2008). In this paper, we present a new method which is based on the windowed correlation coefficients of the average and the standard deviation of vertical derivatives to delineate the edges of the sources.…”

Evaluation of the gravity data using Edge detection methods; Case study Eskişehir

“…Thurston ve Smithi yapı sınırlarını belirlemek amacıyla gravite ve manyetik verilerin düşey türevini kullanmıştır [5]. Manyetik veriden yapı sınırlarını belirlemek amacıyla theta açısı yöntemi [8] tarafından geliştirilmiştir Bu yöntemlerin çoğu potansiyel alan verisinin yatay ve düşey türevlerine dayanmaktadır [10]- [12], [4], [13].…”

A Comparative Study on Computing Horizontal Derivatives of Gravity Data for Geological Contact Mapping

“…Numerous derivative-based data-processing techniques are frequently used as edge detecting tools such as total horizontal derivatives Grauch 1982, 1985), boundary analysis (Blakely and Simpson 1986), analytic signal amplitude (Roest et al 1992), tilt angle (Miller and Singh 1994), enhanced horizontal derivative method (Fedi and Florio 2001), total horizontal derivative of the tilt angle (Verduzco et al 2004), theta map (Wijns et al 2005), local phase (Cooper and Cowan 2006), normalized standard deviations (Cooper and Cowan 2008), tilt angle derivatives (Salem et al 2008), terracing potential field data (Cooper and Cowan 2009), profile curvature (Cooper and Cowan 2011;Ekinci et al 2013, Ekinci and Yiğitbaş 2015, optimized detection filters , eigenvalue analysis of gravity gradient tensor (Zuo and Hu 2015), improved curvature gravity gradient tensor with principal component analysis (Wang et al 2015), horizontal directional theta method (Yuan et al 2016). In all these image enhancement techniques mentioned above, first-order horizontal derivatives in both x-(east) and y-directions (north) are used.…”