Interpreting Potential-Field Data with Horizontal Gradient Vector (HGV) Mapping - Examples from Alberta, Canada

1995 ◽  
Author(s):  
D. J. Edwards ◽  
R. J. Brown ◽  
H. V. Lyatsky
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Horizontal Gradient ◽  
Gradient Vector ◽  
Potential Field Data

Combination of horizontal gradient ratio and Euler methods for the interpretation of potential field data

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. J53-J60 ◽  
Author(s):  
Guoqing Ma
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Magnetic Data ◽  
Horizontal Gradient ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Potential Field Data ◽  
Free Data ◽  
Location Parameters ◽  
Gradient Ratio

The horizontal gradient ratio has been widely used to enhance the linear features of potential field data. I explore a combination of the horizontal gradient ratio and Euler method to interpret gridded potential field data, called HGR-EUL method. A linear equation derived for the Euler equation and expressing the fields as horizontal gradient ratio can be used to estimate the horizontal location and the depth of the source without any priori information about the nature (structural index) of the source. After obtaining the source location parameters, the nature of the source can be determined. The HGR-EUL method is tested on synthetic magnetic anomalies, and the inversion results show that the method can accurately provide the location parameters for noise-free data, and also obtain reasonable results for noise-corrupted data by applying a low pass filter to smooth the data. I also applied the HGR-EUL method to real magnetic data, and the results are compared with results from the standard Euler deconvolution method. The results obtained by the HGR-EUL method show less unjustified variability and are more useful for geologists.

NAV-Edge: Edge detection of potential-field sources using normalized anisotropy variance

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. J43-J53 ◽  
Author(s):  
Heng Lei Zhang ◽  
Dhananjay Ravat ◽  
Yára R. Marangoni ◽  
Xiang Yun Hu
Keyword(s):  
Edge Detection ◽  
Potential Field ◽  
Field Data ◽  
Gaussian Function ◽  
Magnetic Data ◽  
Horizontal Gradient ◽  
Potential Field Data ◽  
Variance Method ◽  
Detection Algorithms ◽  
Total Horizontal Derivative

Most existing edge-detection algorithms are based on the derivatives of potential-field data, and thus, enhance high wavenumber information and are sensitive to noise. The normalized anisotropy variance method (NAV-Edge) was proposed for detecting edges of potential-field anomaly sources based on the idea of normalized standard deviation (NSTD). The main improvement over the balanced, windowed normalized variance method (i.e., NSTD) used for similar purposes was the application of an anisotropic Gaussian function designed to detect directional edges and reduce sensitivity to noise. NAV-Edge did not directly use higher-order derivatives and was less sensitive to noise than the traditional methods that use derivatives in their calculation. The utility of NAV-Edge was demonstrated using synthetic potential-field data and real magnetic data. Compared with several existing methods (i.e., the curvature of horizontal gradient amplitude, tilt angle and its total-horizontal derivative, theta map, and NSTD), NAV-Edge produced superior results by locating edges closer to the true edges, resulting in better interpretive images.

Automated source‐edge location with a new variable pass‐band horizontal‐gradient operator

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 546-554 ◽  
Author(s):  
Jeffrey B. Thurston ◽  
R. James Brown
Keyword(s):  
Edge Detection ◽  
Potential Field ◽  
Field Data ◽  
Transfer Functions ◽  
Numerical Differentiation ◽  
Horizontal Gradient ◽  
Pass Band ◽  
Frequency Content ◽  
Convolution Operators ◽  
Potential Field Data

A new class of space‐domain convolution operators permits computation of the components of the horizontal gradient of gridded potential‐field data. These so‐called gradient‐component operators allow one to vary the passband and thus control the frequency content of the resulting horizontal‐gradient map. This facilitates computation of gradient maps that accommodate data of widely varying frequency content. Examination of the transfer functions of these operators suggests that this method of numerical differentiation is well suited to potential‐field data: in particular, the operators suppress long wavelengths and highfrequency noise bands and amplify signal. Maps of the horizontal gradient of certain potential fields (e.g., gravity, pseudogravity) may be combined with algorithms that locate relative maxima, so‐called thresholding, to automate the procedure of source‐body edge detection, which is a useful tool in mapping, for example, basement grain, fault patterns, and igneous intrusive bodies. We apply this new operator, together with an existing thresholding algorithm, to a field example from western Canada and demonstrate its potential for improved imaging of the horizontal‐gradient magnitude and thus improved edge detection.

Edge enhancement of potential field data using spectral moments

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. G1-G11 ◽  
Author(s):  
Yanyun Sun ◽  
Wencai Yang ◽  
Xiangzhi Zeng ◽  
Zhiyong Zhang
Keyword(s):  
Potential Field ◽  
Moment Method ◽  
Field Data ◽  
Gravity Data ◽  
Magnetic Data ◽  
Horizontal Gradient ◽  
Spectral Moment ◽  
Edge Enhancement ◽  
Potential Field Data ◽  
Geologic Maps

Edge enhancement in potential-field data helps geologic interpretation, where the lineaments on the potential-field frequently indicate subsurface faults, contacts, and other tectonic features. Therefore, a variety of edge-enhancement methods have been proposed for locating edges, most of which are based on the horizontal or vertical derivatives of the field. However, these methods have several limitations, including thick detected boundaries, blurred response to low-amplitude anomalies, and sensitivity to noise. We have developed the spectral-moment method for detecting edges in potential-field anomalies based on the second spectral moment and its statistically invariable quantities. We evaluated the spectral-moment method using synthetic gravity data, EGM-2008 gravity data, and the total magnetic field reduced to the pole. Compared with other edge-enhancing filters, such as the total horizontal derivative (TDX), profile curvature, curvature of the total horizontal gradient amplitude, enhancement of the TDX using the tilt angle, theta map, and normalized standard deviation, this spectral-moment method was more effective in balancing the edges of different-amplitude anomalies, and the detected lineaments were sharper and more continuous. In addition, the method was also less sensitive to noise than were the other filters. Compared with geologic maps, the edges extracted by the spectral-moment method from gravity and the magnetic data corresponded well with the geologic structures.

An improved approach for detecting the locations of the maxima in interpreting potential field data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Luan Thanh Pham ◽  
Ozkan Kafadar ◽  
Erdinc Oksum ◽  
Ahmed M. Eldosouky
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Potential Field Data

Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Potential Field ◽  
Adaptive Sampling ◽  
Field Data ◽  
Computational Cost ◽  
Large Field ◽  
Magnetic Data ◽  
Data Sampling ◽  
Data Types ◽  
Data Set ◽  
Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

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