Potensoft MATLAB-based software for potential field data processing ...

Computers & Geosciences 37 (2011) 935–942

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Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo

Potensoft: MATLAB-based software for potential field data processing, modeling and mapping$ ¨ zgu¨ Arısoy n, Unal ¨ M. O Dikmen Ankara University, Faculty of Engineering, Department of Geophysical Engineering, 06100 Ankara, Turkey

a r t i c l e i n f o

abstract

Article history: Received 30 October 2009 Received in revised form 8 February 2011 Accepted 9 February 2011 Available online 4 March 2011

An open-source software including an easy-to-use graphical user interface (GUI) has been developed for processing, modeling and mapping of gravity and magnetic data. The program, called Potensoft, is a set of functions written in MATLAB. The most common application of Potensoft is spatial and frequency domain filtering of gravity and magnetic data. The GUI helps the user easily change all the required parameters. One of the major advantages of the program is to display the input and processed maps in a preview window, thereby allowing the user to track the results during the ongoing process. Source codes can be modified depending on the users’ goals. This paper discusses the main features of the program and its capabilities are demonstrated by means of illustrative examples. The main objective is to introduce and ensure usage of the developed package for academic, teaching and professional purposes. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Gravity and magnetic data GUI (Graphical User Interface) Modeling Mapping Spatial domain filtering Frequency domain filtering

1. Introduction Gravity and magnetic methods are potential field methods and used for a wide range of applications and scales in geosciences. Traditionally, they have been used for large scale investigation of geologic structures. Smaller-scale applications of the gravity and magnetic methods are for mining exploration, environmental and engineering studies, etc. (Hinze, 1990; Reynolds, 1987; Sharma, 1997; Telford et al., 1996). In order to arrive at geologically meaningful anomaly values, the survey gravity or magnetic data are firstly processed to make appropriate corrections. After these steps, the survey data is ready for interpretation or displaying as maps. Interpretation of potential field data is performed on either profile or map data. These data are most often interpreted by the use of inversion or data processing techniques. Spatial and frequency domain filtering, image processing and managing grids are essential tools in gravity and magnetic data interpretation. A potential field or image processing filter highlights different aspects of potential field data (Bhattacharyya, 1972; Clement, 1973; Gunn, 1975; Ku et al., 1971; Jacobsen, 1987; Telford et al., 1996; Vaclac et al., 1992). Filters can emphasize boundaries between geological contacts, highlight deeper or shallower sources, highlight features from different angles or reduce undesirable effects $

Code available from server at: http://www.iamg.org/CGEditor/index.htm Corresponding author. Tel.: þ90 312 2033355; fax: þ90 312 2120071. ¨ zgu¨ Arısoy), E-mail addresses: [email protected] (M. O ¨ Dikmen). [email protected] (U. n

0098-3004/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.cageo.2011.02.008

within the dataset. Filtering procedure can be undertaken in the frequency domain by means of Fourier Transform (FT) or in the spatial domain by convolution. Frequency domain filtering involves converting the dataset into the frequency domain, performing an operation on the data by applying the appropriate filter and then transforming the data back to the spatial domain. The most commonly used frequency domain filters include reduction to pole, pseudogravity transformation, analytical continuations and derivative filters. Convolution methods involve convolving a filter impulse response (filter coefficients) with the dataset (Byerly, 1965). In addition to filtering, the main intention of gravity and magnetic data interpretation is calculating the potential field from the constructed model (forward-modeling). Various methods have been developed for different aims: simple geometries, sequence of isolated 2-D or 3-D bodies or layers (Blakely, 1996; Jessel, 2001; Parker, 1972; Rasmussen and Pedersen, 1979; Telford et al., 1996). On the other hand, potential field data visualization has a key role for the interpretation process. Technological developments have resulted in faster and more sensitive visualization of potential field data. Using different types of maps (i.e. pseudocolor, shaded, and contour maps) retains the maximum amount of information from the gravity and magnetic data. A number of commercial software packages are commonly used for filtering, modeling and mapping of gravity and magnetic anomalies (e.g. Geosoft Oasis Montaj and its extensions, Encom Profile Analyst and ModelVision, Intrepid Software, and Golden Software Surfer). The literature contains a large number of papers, including source code or software, for potential field data interpretation (Bezvoda et al., 1990; Cooper, 1997, 2005, 2006;

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Cooper and Cowan, 2004, 2006; Durrheim and Cooper, 1998; Fedi et al., 2005; Gibert and Galdeano, 1985; Mendonc- a and Meguid, 2008). These papers commonly address a specific problem in the interpretation of potential field data. However, there is no opensource software with a GUI, which embodies all of the procedures described above. The main objective of the current paper is to eliminate this gap for gravity and magnetic data interpretation. Potensoft is written in MATLAB, using Version 7.6 Release 2008b under the Windows operating system. It has not been tested in other operating systems (e.g. UNIX, and LINUX). The Potensoft GUI is designed at 1280  1024 screen resolution. Despite the fact that it is designed using normalized screen resolution and can therefore be used at any screen resolution, the interpreter gets the best GUI view at 1280  1024 screen resolution. MATLAB was chosen because of its powerful data analysis capabilities, visualization features, extensive library of mathematical functions and popularity in academic applications. During the last few years, MATLAB has become an increasingly popular tool in earth sciences (Trauth, 2010). The main objective of Potensoft is to help interpretation of the gravity and magnetic data, many types of enhancements (transformations, filters, edge detection techniques, etc.) can be applied to the maps to emphasize features of interest. Potensoft includes basic processing modules for gridding, modeling, filtering of gravity and magnetic data. It provides some functions such as spatial and frequency domain filtering tools and gridding tools. It also provides a convenient tool for application in other disciplines, where the data are presented as map or image. The most important benefits of Potensoft are its ease of use and that it is open-source. Specific and major components of the program are described in the Section 2.

2. Program overview Working in Potensoft requires opening an existing project or creating a new project. A Potensoft project file (n.prj) includes three items in the working project: grid, model and profile files.

The project controls the working directory. If the user creates a new project, Potensoft automatically saves all project files in the working directory. Similarly, if the user opens a previously created project, Potensoft assumes that all project files are located in the same directory. The user launches Potensoft by running the ‘‘start’’ function; the program then opens a GUI window, where the user can open an existing project or create a new project. Selecting either of these two menus opens the main Potensoft GUI (Fig. 1). The Potensoft GUI window includes standard bars such as; title bar, short-cut bar, project path bar, mapping bar and information bar. The remaining part of the Potensoft GUI window is subdivided into two windows, the project explorer window and working window. The project path bar displays the working project path (see Fig. 1). Project name, the map type and map coordinates from the tracked mouse positions are displayed on the information bar (See Fig. 1). Maps are a key method for interpretation of both gravity and magnetic data and many earth science applications. Potensoft provides a wide range of capabilities for performing both basic and advanced mapping. The mapping bar enables users to create a wide variety of maps. The Potensoft working window is divided into two windows, a mapping window and map-editing window. When the user opens a map using the mapping bar, it is displayed in the Potensoft mapping window. When the user double-clicks on the created map, the map-editing window is opened (Fig. 2). Many features of the maps can be changed using the map-editing window. When the user right-clicks on the map, a popup menu with several items is opened (Fig. 2). Two of the highlighted popup menu items are the distance and azimuth tool and the colormap tool. The distance and azimuth tool displays the distance and azimuth between two selected points in a map. The colormap tool enables the user to alter the color zones interactively, by setting the color limits of the map to specified minimum and maximum values. The project explorer enables the user to browse as well as open any project files. Project explorer has 3 windows: a grid files window that includes (n.grd) files, a model files window that

Fig. 1. GUI of Potensoft main window. Potensoft main menu, short-cut bar and project bar are displayed under the title bar. Mapping bar is displayed on the right panel, project explorer window is displayed on the left panel and information bar is displayed on the lower panel of the Potensoft GUI. Created maps are displayed on the central part of the figure where is called as working window.

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Fig. 2. GUI of Potensoft main window showing voxel view of a model, popup menu and map-editing window.

includes (n.mod) files, and a profile files window that includes (n.xy) files. To access the pseudocolor map of the grid files, the user should click one of the listed grid files. When the user clicks one of the model files, a model bar will be displayed at the bottom of the GUI window. This model bar includes three icons. The first icon displays the plan view, the second icon displays both the plan view and the depth projections and the third icon displays the voxel view of the model. The Potensoft main menu is displayed under the title bar when the user opens or creates a new project. Dropdown menus provide direct access to all of the Potensoft capabilities. Using the file menu, a new project can be created, an existing project can be opened, a working project can be saved, a map can be exported in many image formats, or a map can be printed. The edit menu allows a map to be copied to the clipboard or the working window to be cleared. Files (n.grd; n.mod; n.xy) can be added to an existing Potensoft project using the add files to project menu; added files will be placed in the project explorer window. The program structure includes four basic components. Potential field data mapping component was described above. The first component is 3-D modeling of both gravity and magnetic data. Other component is gridding datasets and the ability to perform grid operations interactively. The most basic component is spatial and frequency domain filtering of potential field data. These three basic components will be described in the following paragraphs. The 3-D modeling menu can be used for both gravity and magnetic modeling. Due to the dynamic and comprehensible design of the 3-D modeling GUI (both gravity and magnetic), model parameters can be adjusted very quickly and effectively. Subroutines ‘‘one_prism’’ and ‘‘multi_prism’’ (converted to MATLAB) from Mendonc- a and Meguid (2008) were used to compute 3-D gravity and magnetic anomalies. The ‘‘one_prism.m’’ and ‘‘multi_prism.m’’ functions calculate gravity and magnetic anomalies which result from a number of prismatic bodies. In addition, magnetic field components and gravity derivatives are also computed and all synthetically produced maps are displayed on the bottom-left panel of the 3-Dimensional modeling GUI window. All of these maps can be saved separately as grid (n.grd) files. One of the major benefits of the 3-D modeling tool is that it provides model files (n.mod). Whenever the user creates a model file, it will be placed

in the model files window. The user can open a model file for viewing by clicking on the model filename. Three different forms of visualization are available: plan view, depth projection view and voxel view. An image of the gravity modeling study using 3-Dimensional modeling GUI is presented in Fig. 3. The grid menu includes data gridding and basic grid operation tools. There are many mathematical methods for creating a grid. Potensoft offers a set of gridding algorithm choices such as linear, cubic and nearest interpolation methods. Basic grid operation tools are divided into seven sections. The math expression tool allows the user to create a new grid file from a single or two grid files. The output grid file is based on a mathematical function. The function command can use any command within the MATLAB function library. The smooth tool can use one of the nearest neighbor, linear, cubic spline or cubic interpolation methods to compute new grid nodes. The number of grid nodes added between the existing grid nodes can be selected using the nodes slider. The transform tool includes three options to modify the horizontal grid coordinates. This tool can be used to rotate and flip the grid nodes. The extract grid tool creates a sub-grid of an existing grid file interactively. The grid2data tool converts the grid file to an ASCII data file. The grid info tool provides the user with some properties (e.g. path, min, max values and size of the grid) of a grid file. The noise generator tool adds different types of noise to a grid. Noise density can be adjusted interactively. The profile menu includes Grid profile and Multi Grid profile tools. Grid profile takes up to five cross-sections from an image of a grid file. The Multi Grid profile tool is capable of taking a crosssection from up to six grids at once. Cross-sections are formed when the user enters a polyline. 2-D datasets are automatically interpolated from the grids for generating profile plots and saved as profile (n.xy) files. The generated profile file is immediately displayed on the profile file window. The Spatial Domain filter menu includes common digital image processing filters, including a broad suite of high-pass, low-pass, band-pass, derivative-based, directional and embossing filters. The Spatial Domain filter tool also includes a user-defined filter tool that allows the user to set the width and height of the filter and the filter coefficients. The user can change the number

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Fig. 3. Example of 3-D gravity modeling GUI showing user selection options and pseudocolor maps of synthetically produced gravity field and its derivatives.

of filter coefficients by clicking the row and column sliders. The input grid and filtered grid are displayed on the lower panel of the window (Fig. 4). The Frequency Domain filtering tool provides a broad library of 2-dimensional Fast Fourier Transform (FFT) geophysical and digital image processing filters, to allow the application of frequencydomain filters to gridded gravity and magnetic datasets. This menu includes circularly symmetrical, tangent hyperbolic, gaussian, butterworth, laplacian of gaussian, cosine roll-off, cosine directional, directional, butterworth elliptic, strike and wiener filters. Applications of these filters to the interpretation and enhancement of potential field data and their advantages and disadvantages were discussed in various papers (Cook et al., 2003; Naidu and Mathew, 1998; Spector, 1968). These filters are also widely used for processing digital images and other scientific datasets (Gonzales and Woods, 2002). If the user converts any type of dataset format to Golden Software ASCII grid format (n.grd) or Geosoft 2-byte binary grid format (n.grd), the converted dataset can be easily filtered using this tool. The Frequency Domain filtering tool has important and specific properties. This tool enables the interpreters to display radially-averaged power spectrum, 1-D filter spectrum and 1-D filtered radially-averaged power spectrum on one axes in the upper panel of the frequency domain filtering GUI window (Fig. 5). In addition, a filter option window can be seen on the middle panel of the GUI window (Fig. 5). Some of the filter parameters can be modified interactively by using the filter option window. Functions ‘‘calcrad2.m’’, ‘‘fcoef1.m’’ and ‘‘pergram2.m’’ from Ridsdill-Smith (2000) were used to compute the radially-averaged power spectrum. Selection of the high and low cut-off frequencies or transition band zone degrees can be done by moving the green colored slider that can be seen in the upper panel axes. The major benefit of the tool is to display the preview window in the lower panel of the GUI window. The preview window includes frequency and spatial domain tabs; The frequency domain tab displays the spectral map of the input dataset, designed filter and output dataset; The spatial domain tab displays

the spatial domain map of the input dataset, designed filter and output dataset. Interactive slider and other filter options allow users to adjust all filter parameters and preview the results instantly. The tangent hyperbolic sub-menu includes 2-D low-pass, high-pass, band-pass and band-stop tangent hyperbolic filters (tanh filters). The properties of both frequency and time domain low-pass and band-pass tanh filter were presented by Bas- okur (1998). The main advantage of the tanh filter is controlling the slopes at the cut-off regions using a smoothness parameter. If the smoothness parameter takes relatively high values, then the slope of the filter function in the frequency domain decreases. This will significantly reduce the oscillations of the filter response in the spatial domain. A special care is needed in the selection of the smoothness parameter because a high value may lead unsuccessful extraction of unwanted frequency components from the input data. The decision about the value of the smoothness parameter can be made interactively depending on the input data. Implementation of 2-D tanh filters for the purpose of gravity and magnetic data filtering is firstly presented in this paper. The gravity and magnetic tools menu includes most of the frequently-used spatial and frequency domain processes. RTP (Reduction to magnetic pole) and RTE (Reduction to magnetic equator) tools reduce the magnetic anomalies to magnetic pole and equator, respectively. Pseudogravity and Pseudomagnetic tools transform magnetic anomalies to gravity anomalies and transform gravity anomalies to magnetic anomalies, respectively. Upward and downward continuation tools compute analytical continuations of the gravity and magnetic anomalies. The vertical integration menu calculates the vertical integral (inverse of the vertical derivative) of the input dataset. The details of the mentioned transformations can be found in Blakely (1996). The TMI transformations tool converts total or vertical magnetic field components to horizontal, vertical or total components. The sunshading tool can be used for enhancement of the linear features in potential field images. The ‘‘cirlesun.m’’ function from Cooper (2003) was used for sunshading computation. The trend remove tool removes the first, second and

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Fig. 4. Example of user-defined spatial domain filtering GUI showing filter coefficients panel and pseudocolor maps of input and output datasets.

Fig. 5. Example of frequency domain filtering GUI showing spectral plots, filter options panel and spectral maps of input dataset, designed filter and output dataset.

third-order polynomial trend from the dataset. Frequency domain processing requires expanding the gravity and magnetic grid to be square so that grid dimensions will be acceptable for the FFT. The subsequent process is a grid-filling procedure. The grid-filling procedure involves padding the edges of the grid with dummy values and then replacing all dummy values with interpolated values. The ‘‘taper2spline’’ function from Cooper and Cowan (2008) was used for this procedure. The derivatives menu includes horizontal (x and y) and vertical (z) derivative tools. Gravity and magnetic data derivatives help to estimate the physical properties of the sources (Verduzco et al., 2004). Horizontal derivatives of the potential field data are computed in the spatial domain by means of central difference approximation of the finite difference method. Vertical derivatives are computed in the frequency domain and therefore involve the use of a FFT. Horizontal derivatives enhance edges and vertical derivatives narrow the width of anomalies and so enhance the details in the map. Horizontal and vertical derivatives can be computed up to 4th order. The most commonly used gravity and magnetic data derivatives are first and second order, higher orders being used less, due to noise problems (Gunn et al., 1997). Edge detection tools can be used to enhance the edges of anomalies and other features. The horizontal location of the boundaries or edges of gravity and magnetic anomaly sources is requested in potential field interpretation. Edge analysis of the gravity and magnetic anomalies is accomplished using a number of methods involving derivatives. Edge detection tools include all these procedures, so it is easy to see which filter achieves the best result for the interpreters’ specific geological problem. The edge detection tool includes analytical signal, the basic concepts for the 2D case were discussed by Nabighian (1972) and for the 3D case by Roest et al. (1992), horizontal derivative (Cordell and Grauch, 1985), tilt angle (Miller and Singh, 1994), total horizontal derivative of tilt angle (Verduzco et al., 2004) and hyperbolic tilt angle (Cooper and Cowan, 2006) methods.

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3. Application to synthetic and real magnetic data Potensoft was applied to artificially-generated magnetic data and real aeromagnetic data from Turkey. Fig. 6 illustrates the results of a magnetic modeling study that used the synthetically produced magnetic data to check some of Potensoft’s spatial and frequency domain filters. Fig. 6a shows a magnetic model composed of four prismatic bodies. Horizontal locations of the prisms can be followed from Fig. 6a and b shows the voxel view of the created model shown in Fig. 6a and c shows the total magnetic field anomalies calculated from the prism model. The direction of the ambient field is D ¼01 and I¼601. The inclination and declination angles of the magnetization are 01 and 601, respectively. The intensity of magnetization for all prisms is 1 A/m. Fig. 6d shows the data in Fig. 6c after magnetic pole reduction has been applied. Fig. 6e shows the pseudogravity anomaly transformed from the data in Fig. 6c and f shows the analytic signal amplitude of the data in Fig. 6d. The tilt angle of the data in Fig. 6d is shown in Fig. 6g and h shows the total magnetic field anomaly in Fig. 6d, continued upward for 10 km. The result of the cosine roll-off high pass filtering of the data in Fig. 6d is shown in Fig. 6i. The degree of

the cosine function was 2, the low frequency starting point of the cosine roll-off filter was 0.04725 Hz and the high frequency endpoint of the cosine roll-off filter was 0.3225 Hz. Fig. 6j shows the sun-shaded data in Fig. 6d when the elevation of the sun was 101 from the horizontal. Fig. 6k shows the spatial domain filter applied to the data in Fig. 6d. A simple 3  3 horizontal differentiation operator was used for computation. Fig. 6l shows the strike filter applied to the data in Fig. 6h. Features located at 1801 clockwise from North are enhanced. Fig. 7a shows aeromagnetic data from Turkey. The aeromagnetic data is 340  340 km2 in size and has a grid resolution of 2 km in both horizontal directions. The data mostly covers the Eskis- ehir fault zone, which comprises of successive fault segments (Koc- yi˘git, 2000). The Eskis-ehir fault and its segments extend in a Northwest to Southeast direction (see Fig. 7a). The area mainly consists of base¨ u¨ blueschists and peridotites. The conment rocks, marbles, Inon tinental Middle-Late Miocene sequences that overlie these rocks with angular unconformity are made up of the Porsuk formation (conglomerate, sandstone, claystone, marl, and lacustrine limestones). Quaternary alluvium covers the older units unconformably ¨ (Gozler et al., 1984). Fig. 7b shows data after being reduced to

Fig. 6. Application to synthetic magnetic model data. (a) Synthetic magnetic model composed of four prismatic bodies. (b) Voxel view of the model showing relative sizes and positions of four buried prismatic bodies. (c) The total magnetic field produced by prism model shown in (a and b). (d) Reduced to the magnetic pole data in (c). (e) Pseudogravity of the data in (c). (f) Analytic signal of the data in (d). (g) Tilt angle of the data in (d). (h) 10 km upward continued data in (d). (i) Cosine roll-off high pass filtered (cosine function degree: 2; low frequency starting point of the filter: 0.04725 Hz; high frequency ending point of the filter: 0.3225 Hz) of the data in (d). (j) Sunshade of the data in (d). The sun elevation was 101 from the horizontal. The filter center location is marked as ‘‘*’’ in (d). (k) Horizontal spatial domain filtered of the data in (d). (l) Strike filtered of the data in (h). Features lying at the 1801 clockwise from North are enhanced.

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‘‘m_files’’ and synthetic datasets, is available via the internet at: http://eng.ankara.edu.tr/arisoy/potensoft.htm.

Acknowledgments The authors thank the General Directorate of Mineral Research & Exploration (MTA) of Turkey for providing aeromagnetic data. Some of the MATLAB functions which are freely available on the internet were reconsidered and modified in designing Potensoft GUI: gdist.m, getgrd2.m, gridfit.m, motionblur.m, uitabpanel.m and voxel.m. We thank the authors of these free MATLAB functions for their work and for sharing their code. This work constitutes part of ¨ . Arısoy (Ankara University). the Ph. D. thesis undertaken by M.O Constructive comments by the three anonymous reviewers were helpful in improving the software and the initial version of the manuscript. We thank them for their critiques and comments.

Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.cageo.2011.02.008.

References

Fig. 7. Application to data from Turkey. (a) Aeromagnetic data covering a 340  340 km2 area from Turkey. Magnetic field values range from -638 nT to 513 nT. (b) Reduced to pole of the magnetic data shown in (a). (c) Pseudogravity of the magnetic data in (a). (d) First-order derivative with respect to variable x of the magnetic data in (b). (e) First-order derivative with respect to variable y of the magnetic data in (b). (f) Theta map of the magnetic data in (b). (g) Total horizontal derivative of the pseudogravity data in (c). (h) Tilt angle of the pseudogravity data in (c). (i) Tanh band pass filtered (low cut-off: 6.125e 5 Hz; high cut-off: 2.60e-4 Hz; smoothness parameter: 5.5e 5) of the pseudogravity data in (c). (j) 5 km upward continued of the magnetic data in (b). (k) 750 m downward continued of the magnetic data in (b). (l) Cosine directional filtered of the magnetic data in (b). Features lying at the 451 anticlockwise from North are passed while filtering.

magnetic pole. Fig. 7c shows data after pseudogravity transformation. Fig. 7d and e shows x and y derivatives, respectively, of the reduced-to-pole data. A Theta map of the reduced-to-pole data is shown in Fig. 7f–h shows total horizontal derivative and tilt angle of the pseudogravity data. Fig. 7i shows a tanh band-pass filter of the pseudogravity data. The low cut-off frequency of the filter was 6.125e5 Hz, the high cut-off frequency was 2.60e 4 Hz and the smoothness parameter was 5.5e5. Fig. 7j and k shows reduced-to-pole data continued 5 km upward and 750 m downward, respectively. Fig. 7l shows cosine directional filtered output of the reduced-to-pole magnetic data. Magnetic anomalies of the Eskisehir fault zone and its segments lying at almost 451 anticlockwise from North are enhanced and the effects of other features are removed from the data using the cosine directional filter. 4. Conclusions The purpose of this paper is to introduce the Potensoft program, which includes the basic steps of gravity and magnetic data processing, mapping and modeling. The program includes a userfriendly interface, providing a useful tool for earth scientists to implement basic and advanced data processing. The design of the GUI allows users to easily modify or extend the developed package, depending on their research requirements. We believe that Potensoft will be modified and extended by users to become a popular tool for processing gravity and magnetic data. Potensoft, including

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