INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 5, No 2, 2014

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1 INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 5, No 2, 2014 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN Edge detection process of Qom salt dome gravity anomalies using hyperbolic tilt angle Ahmad Alvandi 1, Rasoul Hoseini Asil 2 1- Young Researchers Club and Elites, Islamic Azad University, Toyserkan Branch, Toyserkan, Iran 2- Young Researchers Club and Elites, Islamic Azad University, Sahneh Branch, Sahneh, Iran a.alvandi@iauh.ac.ir ABSTRACT In recent decade the edge detection procedure has been of great utility in the modeling and interpretation of self-potential, magnetic and gravity anomalies. This paper applies a precise edge detection procedure, called hyperbolic tilt angle (HTA) technique. The sufficiency of the HTA method is indicated using complex synthetic models and a residual gravity data set from Iran. Compared with the formal methods, the HTA filter more detailed outcomes for buried models and is less sensitive to noise. Key Words: Edge Detection, Hyperbolic Tilt Angle, Theoretical and Field Gravity Anomalies, Low-Pass Filtering, Qom Salt Dome, Iran 1. Introduction Gravity and magnetic anomalies are essential to geophysical approaches to geologic mapping (Pilkington and Keating, 2010). Boundaries detection of causative sources is one of the most important stages in the modeling of gravity anomalies (Bournas and Baker, 2001; Ardestani and Motavalli, 2007). Accurate detection of source shape coordinates is becoming the main goal for interpretation and therefore enhanced methods are acquiring an increasing revival in data interpretation (Bournas and Baker, 2001). There are various procedures that have been engaged to attain edge detection, for example, Analytic signal (AS), tilt angle (TI), theta map (TH) and etc. (Arisoy and Dikmen, 2013). Potential field derivatives are largely used to modeling of buried sources (Arisoy and Dikmen, 2013). The analytical signal (AS) is one known filters that is applied to interpretation and modeling gravity and magnetic data (Pilkington and Keating, 2004; Cooper and Cowan, 2008; Cooper, 2009). Miller and Singh (1994) introduced a tilt derivative (TA) filter to detect edge (Hoseini et al., 2013). Verduzco et al (2004) suggested total horizontal derivative of the tilt angle (THDR) to improve edge detection process (Pilkington and Keating, 2004; Cooper and Cowan, 2006). Wijns et al (2005) introduced the usage of theta angle which is supported the AS to magnetic and gravity interpretation (Nejati Kalateh and Roshandel Kahoo, 2012). In this research, we employed a Hyperbolic Tilt Angle filter (Cooper and Cowan, 2006) for detecting gravity source boundaries. In order to illustrate the performance of this technique, we have first given some complex theoretical examples and compared our results with those obtained by edge detection known methods. Then, the approach was applied to one gravity anomaly, extracted from an Iran gravity ground survey data set. All map images applied in our research have been generated using MATLAB 7.11 program. Submitted on September 2014 published on October

2 2. Edge detection filters The five edge detection filters used in this paper to detect the boundaries of buried sources are showed in table 1. Parameter F is the gravity field, F/ x and F/ y are horizontal derivatives of field and F/ z is vertical derivative of field. Main edge detection techniques geometric description is shown in figure 1. Figure 1: Main edge detection techniques of gravimetric anomalies: Analytic Signal (AS), Total Horizontal Derivative (THDR), Tilt Angle (TI) and Theta Map (TH) (Bongiolo and Ferreira, 2012) 3. Theoretical gravity modeling examples 3.1. Model 1 In this part, synthetic examples are applied to test the abilities of the presented techniques. 2- D and 3-D theoretical Model (1), shown in figure 2(a), is generated by using finite prisms located at various depths. The bottom and top depths of prism A were selected as 6 and 3 km, the widths of prism A in the x and y coordinates were selected as 20 and 20 km, respectively. Prism B is deeper than prism A. The top and bottom depths of prism B were selected as 6 and 10 km, and the widths of prism B in the x and y directions were selected as 20 and 20 km, respectively. The synthetic map of residual anomaly is shown in Figure 2(b). The map of THDR, AS, TI, TH and HTI are shown in figures 2(c), 2(d), 2(e), 2(f) and 2(g) respectively. Then, 3 % Gaussian noise was added to the synthetic anomalies. Figures 3(a), 3(b), 3(c), 3(d), 3e and 3(f) show, respectively, the anomaly map with added noise, THDR map, AS map, the TI generated from the noisy anomaly map, TH method and the outputs of the HTI technique. In the noisy model, it is seen that the proposed method produces precise outcomes than the THDR, AS and TI methods. The maps of HTI data filtering and frequency domain filtering are shown in figures 3(g) and 3(h) respectively. 210

3 Table 1: Edge Detection methods for field F, having components X, Y, and Z (Hoseini et al., 2013; Pilkington and Keating, 2004) 211

4 Figure 2: a) Buried synthetic models in subsurface; b) Theoretical model anomaly map (mgal); c) Total horizontal derivative; d) analytic signal; e) Tilt angle; f) Theta map; g) Hyperbolic tilt angle 212

5 213

6 Figure 3: a) Noisy anomaly map (mgal); b) Total horizontal derivative; c) analytic signal; d) Tilt angle; e) Theta map; f) Hyperbolic tilt angle; g) HTI data filtering; h) Frequency domain filtering 3.2 Model 2 2-D and 3-D theoretical Model (2), shown in Figure 4(a), is produced by using finite prisms located at various depths. The bottom and top depths of prism A were selected as 5.1 and 3.1 km. the widths of prism A in the x and y coordinates were selected as 20 and 20 km, respectively. Prism B is deeper than prism A. The top and bottom depths of prism B were selected as 6 and 8 km, and the widths of prism B in the x and y directions were selected as 20 and 15 km, respectively. Prism C is deeper than prism B. The top and bottom depths of prism C were selected as 9 and 11 km, and the widths of prism C in the x and y directions were selected as 20 and 20 km, respectively. Prism D is deeper than prism C. The top and bottom depths of prism D were selected as 12 and 14 km, and the widths of prism D in the x and y directions were selected as 20 and 16 km, respectively. The synthetic gravity anomaly map is shown in Figure 4(b). The results of using total horizontal derivative, analytic signal, tilt angle, theta map, and hyperbolic tilt angle are shown in figures 4(c), 4(d), 4(e), 4(f) and 4(g) respectively. To demonstrate how this approach performs on contaminated with noise data, random noise with amplitude equal to 5 % of the maximum data amplitude was added to the gravity data set shown in Figure 5(a). Figures 5(b), 5(c), 5(d), 5(e) and 5(f) show, respectively, THDR map, AS map, the TI obtained from the noisy anomaly map, TH map and the outputs of the proposed method. In the case of noisy data, it is seen that the HTI technique produces better results than the THDR, AS and TI methods. The maps of HTI data filtering and frequency domain filtering are shown in figures 5(h) and 5(g) respectively. 3.3 Model 3 The third example shows five prisms with different geometries, inserted in to a surface of 100 km 100 km. Figure 6(a) displays the shapes built with the amounts of table2. Figure 6(b) displays the residual anomalies generated from the prisms of figure 6(a) with the parameters of table (2). The results of using total horizontal derivative, analytic signal, tilt angle, theta map, and hyperbolic tilt angle are shown in figures 6(c), 6(d), 6(e), 6(f) and 6(g) respectively.to demonstrate how this approach performs on noisy data, random noise with amplitude equal to 9 % of the maximum data amplitude was added to the gravity data set shown in picture 7(a). pictures 7(b), 7(c), 7(d), 7(e) and 7(f) show, respectively, the anomaly map with added noise, THDR map, AS map, the TI obtained from the noisy anomaly map, 214

7 TH map and the outputs of the proposed method. In the case of noisy data, it is seen that the proposed method produces better results than the THDR, AS and TI methods. The maps of HTI data filtering and frequency domain filtering are shown in figures 7(g) and 7(h) respectively. 215

8 Figure 4: a) Spatial distribution of the 2-D and 3-D synthetic models in subsurface; b) theoretical model anomaly map (mgal); c) Total horizontal derivative; d) analytic signal; e) Tilt angle; f) Theta map, g) Hyperbolic tilt angle 216

9 Figure 5: a) Noisy anomaly map (mgal); b) Total horizontal derivative; c) Analytic signal; d) tilt angle; e) Theta map; f) Hyperbolic tilt angle; g) Frequency domain filtering; h) HTI data low-pass filtering 217

10 Table 2: Parameters of the shapes in figure 6(a) Density Width Length Thickness Depth of top Anomaly (g/cm 3 ) (Km) (Km) (Km) (Km) A B C D E

11 Figure 6: a) 2D and 3D representation of the synthetic shapes A, B, C, D and E, with parameters listed in Table 2; b) Theoretical model anomaly map (mgal); c) Total horizontal derivative; d) Analytic signal; e) Tilt angle; f) Theta map, g) Hyperbolic tilt angle 219

12 Figure 7: a) Noisy anomaly map (mgal); b) Total horizontal derivative; c) Analytic signal; d) tilt angle; e) Theta map; f) Hyperbolic tilt angle; g) HTI data filtering; h) Frequency domain filtering 220

13 4. Field gravity example This section considers the application and abilities of edge detection methods to field gravity data from the Qom salt dome in the center of Iran (Motasharreie et al., 2010). The gravity anomalies (mgal) and up-ward continuation mapping (0.5 km) are shown in figure 8 (a) and 8(b) respectively. The results of using THDR, AS, TI, and TH are shown in figures 8(c), 8(d), 8(e), and 8(f) respectively. The edge detection by the HTI procedure is more accurate and better than the THDR, AS and TI method (figure 8g). The maps of HTI data filtering and frequency domain filtering are shown in figures 8(h) and 8(I) respectively. 221

14 Figure 8: a) Residual anomalies map (mgal); b) up-ward continuation (0.5 km); c) THDR; d) AS; e) TI; f) TH; g) HTI; h) HTI low-pass filtering; I) Frequency domain filtering 222

15 5. Conclusion Edge detection process of Qom salt dome gravity anomalies using hyperbolic tilt angle In this research, we tested the capabilities of hyperbolic tilt angle (HTA) procedure on synthetic data and Qom salt dome data, center of Iran. The HTA filter show the better efficiency on theoretical models and field model of other edge detection methods. Compared with the analytic signal and tilt angle methods, the HTA filter more detailed outcomes for buried models and is less sensitive to noise. 6. References 1. Pilkington, M., and Keating, P., (2010), geologic applications of magnetic data and using enhancements for contact mapping, EGM international workshop Adding new value to electromagnetic, gravity and Magnetic methods for exploration, Capri, Italy, pp D. Aydogan, (2011), Extraction of lineaments from gravity anomaly maps using the gradient calculation: application to Central Anatolia, Earth Planets Space, 63, pp Bournas, Nasreddine, and Baker, Haydar Aziz., (2001), interpretation of magnetic anomalies using the horizontal gradient analytic signal, Annali di Geofisica, 44 (3), pp Arisoy, Muzaffer Ozgo., and DikmenUnal., (2013), Edge Detection of Magnetic Sources Using Enhanced Total Horizontal Derivative of the Tilt Angle, Bulletin of the Earth Sciences Application and Research Centre of Hacettepe University, 34 (1), pp Pilkington, M., and Keating, P., (2004), contact mapping from gridded magnetic data: a comparison of techniques, exploration geophysics, 35, pp Cooper, G.R.J., (2009), balancing images of potential field data, geophysics, 74(3), pp Cooper, G.R.J., and Cowan, D.R., (2006), Enhancing potential field data using filters based on the local phase. Computers and geosciences, 32(10), pp Cooper, G.R.J., and Cowan, D.R., (2008), Edge enhancement of potential-field data using normalized statistics, geophysics, 73(3), pp Miller, H.G., and Singh, V., (1994), Potential field tilt: a new concept for location of potential filed sources. Journal of Applied Geophysics, 32, pp Verduzco, B., Fairhead, J.D., Green, C.M., and MacKenzie, C., (2004), new insights into magnetic derivatives for structural mapping. The Leading Edge, 23(2), pp Wijns, C., Perez, C., and Kowalczyk, P., (2005), Theta map: edge detection in magnetic data, Geophysics, 70(4), pp Roest, W.R., Verhoef, J., and Pilkington, M., (1992), magnetic interpretation using the 3- D analytic signals and Geophysics, 57(1), pp

16 13. Hadadian, A., (2011), Precise boundary detection of potential field anomalies using local phase filters, M.Sc. Thesis, Shahrood University of Technology, p Hoseini Asil, R., (2013), Depth estimation using a tilt derivative map from gravity gradient data, M.Sc. Thesis, Hamedan Branch, Islamic Azad University, p Hoseini, Ali Akbar., Doulati Ardejani, Faramarz.,Tabatabaie, Seyed Hashem., Hezarkhani, Ardeshir., (2013), edge detection in gravity field of the gheshm sedimentary basin, International journal of Min & Geo-Eng (IJMGE), 47( 1), pp Sertcelik, I., Kafadar, O., (2012), application of edge detection to potential field data using eigenvalue analysis of structure tensor. Journal of Applied Geophysics 84, Kalateh, Ali Nejati., and Kahoo, Amin Roshandel., (2012), edge detection of potential field data using Theta maps, Iranian journal of geophysics, 7(1), pp 24-33, (Persian version). 18. Bongiolo ABS., and Ferreira FJF., (2012), evaluation of enhancement techniques of magnetic anomalies applied to structural interpretation of the Itaituba region, Brazil, Revista Brasileira de Geofisica, 30(3), pp Motasharreie, A., Zomorodian, H., SiahKoohi, H. R., Mirzaei, M., (2010), Inversion of gravity data in wavelet domain using normalized forward models, Journal of Earth & space physics, 36(1), Ardestani V.E. and Motavalli H., (2007), constraints of analytic signal to determine the depth of gravity anomalies. Journal of Earth & space physics, 33(2), Ardestani, V.E. (2005). Gravity interpretation via gravity gradients and analytic signal Journal of Earth sciences, 12(54). 22. Cooper, G.R.J., and Cowan, D.R., (2011), a generalized derivative operator for potential field data. Computers & Geosciences, Geophysical Prospecting, 59, pp Ming, W., Zhi-hong, G., and Luofen, H, (2013), edge detection of field data using inverse hyperbolic tangent, Geophysical & Geochemical Exploration, 37(4), pp