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.

Computer Application in Geosciences: Imaging of the Subsurface by Structural Coupled Inversion of Potential Field Data

2014 ◽  
Vol 644-650 ◽  
pp. 2670-2673
Author(s):  
Jun Wang ◽  
Xiao Hong Meng ◽  
Fang Li ◽  
Jun Jie Zhou
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Computer Application ◽  
Magnetic Data ◽  
Imaging Method ◽  
Inversion Algorithm ◽  
Constant Property ◽  
Near Surface ◽  
Potential Field Data

With the continuing growth in influence of near surface geophysics, the research of the subsurface structure is of great significance. Geophysical imaging is one of the efficient computer tools that can be applied. This paper utilize the inversion of potential field data to do the subsurface imaging. Here, gravity data and magnetic data are inverted together with structural coupled inversion algorithm. The subspace (model space) is divided into a set of rectangular cells by an orthogonal 2D mesh and assume a constant property (density and magnetic susceptibility) value within each cell. The inversion matrix equation is solved as an unconstrained optimization problem with conjugate gradient method (CG). This imaging method is applied to synthetic data for typical models of gravity and magnetic anomalies and is tested on field data.

Three-dimensional depth-to-basement modelling based on seismic and potential field data – basement configuration in the westernmost Polish Outer Carpathians

2020 ◽  
Author(s):  
Mateusz Mikołajczak ◽  
Jan Barmuta ◽  
Małgorzata Ponikowska ◽  
Stanislaw Mazur ◽  
Krzysztof Starzec
Keyword(s):  
Qualitative Analysis ◽  
Cross Sections ◽  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Three Dimensional ◽  
Magnetic Data ◽  
Gravity Inversion ◽  
Potential Field Data ◽  
Outer Carpathians

<p>The Silesian Nappe in the westernmost part of the Polish Outer Carpathians Fold and Thrust Belt exhibits simple, almost homoclinal character. Based on the field observations, a total stratigraphic thickness of this sequence equals to at least 5400 m. On the other hand, the published maps of the sub-Carpathian basement show its top at depths no greater than 3000 m b.s.l. or even 2000 m b.s.l. in the southern part of the Silesian Nappe. Assuming no drastic thickness variations within the sedimentary sequence of the Silesian Nappe, such estimates of the basement depth are inconsistent with the known thickness of the Silesian sedimentary succession. The rationale behind our work was to resolve this inconsistency and verify the actual depth and structure of the sub-Carpathian crystalline basement along two regional cross-sections. In order to achieve this goal, a joint 2D quantitative interpretation of gravity and magnetic data was performed along these regional cross-sections. The interpretation was supported by the qualitative analysis of magnetic and gravity maps and their derivatives to recognize structural features in the sub-Carpathian basement. The study was concluded with the 3D residual gravity inversion for the top of basement. The cross-sections along with the borehole data available from the area were applied to calibrate the inversion.</p><p>In the westernmost part of the Polish Outer Carpathians, the sub-Carpathian basement comprises part of the Brunovistulian Terrane. Because of great depths, the basement structure was investigated mainly by geophysical, usually non-seismic, methods. However, some deep boreholes managed to penetrate the basement that is composed of Neoproterozoic metamorphic and igneous rocks. The study area is located within the Upper Silesian block along the border between Poland and Czechia. There is a basement uplift as known mainly from boreholes, but the boundaries and architecture of this uplift are poorly recognized. Farther to the south, the top of the Neoproterozoic is buried under a thick cover of lower Palaeozoic sediments and Carpathian nappes.</p><p>Our integrative study allowed to construct a three-dimensional map for the top of basement the depth of which increases from about 1000 m to over 7000 m b.s.l. in the north and south of the study area, respectively. Qualitative analysis of magnetic and gravity data revealed the presence of some  basement-rooted faults delimiting the extent of the uplifted basement. The interpreted faults are oriented mainly towards NW-SE and NE-SW. Potential field data also document the correlation between the main basement steps and important thrust faults.</p><p> </p><p>This work has been funded by the Polish National Science Centre grant no UMO-2017/25/B/ST10/01348</p>

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.

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.

Preliminary Study on Fusion Scheme of Multi-dimensional and Multi-scale Potential Field Data

2020 ◽  
Author(s):  
Xiaolin Ji ◽  
Wanyin Wang ◽  
Fuxiang Liu ◽  
Min Yang ◽  
Shengqing Xiong ◽  
...  
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Magnetic Data ◽  
Potential Field Data ◽  
Multi Scale ◽  
Gravity And Magnetic ◽  
Different Dimensions ◽  
Fusion Scheme ◽  
Gravity And Magnetic Data

<p>Gravity and magnetic surveys are widely used in geology exploration because of its advantages, such as efficient and economy, green and environment-friendly, widely coverage and strong horizontal resolution. In order to well study in the geology exploration, it is required to comprehensively combine the different scales (different scales data) and different dimensions (satellite data, aeronautical data, ground data, ocean data, well data, etc.) of gravity and magnetic data that were observed in different periods, however, the comprehensive application of the multi-dimensional and multi-scale gravity and magnetic data still stays in the initial stage. In this paper, we do research on the key point of the fusion of potential field data (gravity and magnetic data): the way to fuse the different scales and different dimensions of potential field data into a benchmark and the same surface. Based on this research, we propose a scheme to fuse the multi-dimensional and multi-scale gravity and magnetic data. The synthetic models show that this fusion scheme is able to fuse the multi-dimensional and multi-scale gravity and magnetic data with great fusion results and small errors, in addition, the most important is that the fusion data conform to the characteristics of the potential field data and can meet the needs of data processing in the following steps. One of case studies in China has been accomplished to fuse aeronautical and ground gravity data that are different scales by using this fusion scheme. The fusion scheme we proposed in this study can be used in the fusion of the multi-dimensional (aeronautical, ground and ocean) and multi-scale gravity and magnetic data, which is good for interpretation and popularization.</p>

Deep crustal structures interpreted from potential field data along deep seismic sounding transects in Australia

2015 ◽  
Vol 55 (2) ◽  
pp. 450
Author(s):  
Irena Kivior ◽  
David Boyd ◽  
David Tucker ◽  
Stephen Markham ◽  
Francis Vaughan ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Sedimentary Basins ◽  
Deep Seismic Sounding ◽  
Magnetic Data ◽  
Potential Field Data ◽  
Seismic Sounding ◽  
Crustal Structures

Energy spectral analysis techniques have been applied to magnetic and gravity data acquired across the Olympic Dam cratonic area in Australia and sedimentary basins along the Equatorial Margin of Brazil. Analysis has been conducted along two Deep Seismic Sounding lines (DSS) acquired by Geoscience Australia. There is a good correlation between interfaces found in this analysis and structures interpreted from the seismic data. Interpretation of gravity data using energy spectral analysis along the DSS survey lines show a number of deep crustal structures are evident, including the Moho which was detected using gravity data, while similar analysis of the magnetic data show indications of the Curie isotherm. In addition, the analysis was extended away from the seismic lines to detect many deep crustal horizons and structures at considerable distances from the DSS lines. The results obtained from energy spectral analysis across this area in Australia encouraged the application of this technique on the Equatorial Margin of Brazil, where the potential field data is of much lower resolution. This suggests that a much wider application of this approach could be highly valuable to investigate the deep structure under other sedimentary basins and to assist heat flow studies.

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.

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.

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