scholarly journals An edge-assisted smooth method for potential field data

2021 ◽  
Vol 18 (1) ◽  
pp. 113-123
Author(s):  
Shijing Zheng ◽  
Xiaohong Meng ◽  
Jun Wang
Keyword(s):  
Edge Detection ◽  
Potential Field ◽  
Field Data ◽  
Real Data ◽  
Linear Functions ◽  
Detection Methods ◽  
Edge Preserving ◽  
Potential Field Data ◽  
Tectonic Boundaries ◽  
Derivatives Of

Abstract Edge detection is one of the most commonly used methods for the interpretation of potential field data, because it can highlight the horizontal inhomogeneous of underground geological bodies (faults, tectonic boundaries, etc.). A variety of edge detection methods have been reported in the literature, most of which are based on the combined transformation results of horizontal and vertical derivatives of the observations. Consequently, these edge detection methods are sensitive to noise. Therefore, noise reduction is desirable ahead of applying edge detection methods. However, the application of conventional filters smears discontinuities in the data to a certain extent, which would inevitably induce unfavourable influence on subsequent edge detection. To solve this problem, a novel edge-preserving smooth method for potential field data is proposed, which is based on the concept of guided filter developed for image processing. The new method substitutes each data point by a combination of a series of coefficients of linear functions. It was tested on synthetic model and real data, and the results showed that it can effectively smooth potential field data while preserving major structural and stratigraphic discontinuities. The obtained data from the new filter contain more obvious features of existing faults, which brings advantageous to further geological interpretations.

Discrete linear transformations of potential field data

Geophysics ◽  
1989 ◽  
Vol 54 (4) ◽  
pp. 497-507 ◽  
Author(s):  
Jorge W. D. Leão ◽  
João B. C. Silva
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Amazon Basin ◽  
Real Data ◽  
Damping Factor ◽  
Linear Transformations ◽  
Potential Field Data ◽  
Data Window ◽  
Green’S Matrix ◽  
Equivalent Layer

We present a new approach to perform any linear transformation of gridded potential field data using the equivalent‐layer principle. It is particularly efficient for processing areas with a large amount of data. An N × N data window is inverted using an M × M equivalent layer, with M greater than N so that the equivalent sources extend beyond the data window. Only the transformed field at the center of the data window is computed by premultiplying the equivalent source matrix by the row of the Green’s matrix (associated with the desired transformation) corresponding to the center of the data window. Since the inversion and the multiplication by the Green’s matrix are independent of the data, they are performed beforehand and just once for given values of N, M, and the depth of the equivalent layer. As a result, a grid operator for the desired transformation is obtained which is applied to the data by a procedure similar to discrete convolution. The application of this procedure in reducing synthetic anomalies to the pole and computing magnetization intensity maps shows that grid operators with N = 7 and M = 15 are sufficient to process large areas containing several interfering sources. The use of a damping factor allows the computation of meaningful maps even for unstable transformations in the presence of noise. Also, an equivalent layer larger than the data window takes into account part of the interfering sources so that a smaller damping factor is employed as compared with other damped inversion methods. Transformations of real data from Xingú River Basin and Amazon Basin, Brazil, demonstrate the contribution of this procedure for improvement of a preliminary geologic interpretation with minimum a priori information.

A method for inversion of 1D vertical soundings of gravity anomalies

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. G15-G23
Author(s):  
Andrea Vitale ◽  
Domenico Di Massa ◽  
Maurizio Fedi ◽  
Giovanni Florio
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Finite Size ◽  
Gravity Anomalies ◽  
Real Data ◽  
Gravity Inversion ◽  
Chain Process ◽  
Potential Field Data ◽  
Gamma Density

We have developed a method to interpret potential fields, which obtains 1D models by inverting vertical soundings of potential field data. The vertical soundings are built through upward continuation of potential field data, measured on either a profile or a surface. The method assumes a forward problem consisting of a volume partitioned in layers, each of them homogeneous and horizontally finite, but with the density changing versus depth. The continuation errors, increasing with the altitude, are automatically handled by determining the coefficients of a third-order polynomial function of the altitude. Due to the finite size of the source volume, we need a priori information about the total horizontal extent of the volume, which is estimated by boundary analysis and optimized by a Markov chain process. For each sounding, a 1D inverse problem is independently solved by a nonnegative least-squares algorithm. Merging of the several inverted models finally yields approximate 2D or 3D models that are, however, shown to generate a good fit to the measured data. The method is applied to synthetic models, producing good results for either perfect or continued data. Even for real data, i.e., the gravity data of a sedimentary basin in Nevada, the results are interesting, and they are consistent with previous interpretation, based on 3D gravity inversion constrained by two gamma-gamma density logs.

Step-Edge Detection Filters for the Interpretation of Potential Field Data

2015 ◽  
Vol 173 (3) ◽  
pp. 795-803 ◽  
Author(s):  
Guoqing Ma ◽  
Danian Huang ◽  
Cai Liu
Keyword(s):  
Edge Detection ◽  
Potential Field ◽  
Field Data ◽  
Step Edge ◽  
Potential Field Data

EDGE DETECTION OF POTENTIAL FIELD DATA USING AN ENHANCED ANALYTIC SIGNAL TILT ANGLE

2016 ◽  
Vol 59 (4) ◽  
pp. 341-349
Author(s):  
YAN Ting-Jie ◽  
WU Yan-Gang ◽  
YUAN Yuan ◽  
CHEN Ling-Na
Keyword(s):  
Edge Detection ◽  
Tilt Angle ◽  
Potential Field ◽  
Field Data ◽  
Analytic Signal ◽  
Potential Field Data

Edge enhancement of potential-field data using normalized statistics

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. H1-H4 ◽  
Author(s):  
Gordon R. J. Cooper ◽  
Duncan R. Cowan
Keyword(s):  
Standard Deviation ◽  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Edge Enhancement ◽  
Potential Field Data ◽  
Geologic Interpretation ◽  
Derivatives Of ◽  
High Pass ◽  
Detection Filter

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.

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