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Gravity Map Production Using General Regression Neural Network
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1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 7 Gravity Map Production Using General Regression Neural Network Nagi Ishag Mohammed 1, El Tahir Mohammed Hussein2, Adil Mohammed El Sinnari 3 1 National Ribat University, College of Graduate Studies and Scientific Research, Khartoum, Sudan 2Sudan University of Science and Technology, College of Engineering, Khartoum, Sudan 3Omdurman Islamic University, College of Engineering, Khartoum, Sudan ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Gravity map production has been improved by using very complicated and expensive versions of geophysics methods [1]. The main objective of this study is to evaluate the ability of the Artificial Intelligent Techniques product gravity map. To achieve the objective of this study ArtificialNeural Network architecture has been tested. This is the GRNN. The GRNN model was trained with 301 patterns derived from gravity map and satellite image. The maps were converting to ASCII to generate the input part of the learning patterns. The same rows were used to generate the output part. A performance test session was carried out by applying the trained models to the same training patterns and to 75 new test patterns. The output results have been subjected to statistical analysis. Key Words: AI: Artificial Intelligent, ANN: Artificial Neural Networks, ASCII: American Standard Code for Information Interchange, GRNN: General Regression Neural Networks, GRACE: Gravity Recovery and Climate Experiment. . 1. INTRODUCTION Gravity is a potential field, i.e., it is a force that acts at a distance. The gravity method is a non-destructive geophysical technique that measures differences in the earth’s gravitational field at specific locations. It has found numerous applications in engineering, environmental and geothermal studies including locating voids, faults, buried stream valleys, water table levels and geothermal heat sources. The success of the gravity method depends on the different earth materials having different bulk densities (mass) that producevariationsinthemeasuredgravitational field. These variations can then be interpretedbya varietyof analytical and computers methods to determine the depth, geometry and density that causesthegravityfieldvariations. The conventional gravity map is sufficient very costly and not satisfactory enough satisfy the criteria of the research. While the gravity map presents a very rich indicator in oil exploration, to overcome and solve this problem. The research presents a satellite image for the same area, and by using artificial intelligent techniques[1,2, 3]. The main objective of this study is to investigatethe ability of artificial neural networks to product gravity map. This will be achieved by training and testing an appropriate ANN architecture with learning patterns generated from satellite image and corresponding accurate values derived from a satellite image. To improve the opportunity to find oil, geologists apply earth science to the search for oil. Many techniques have been developed, based on indirect methods to view the subsurface. Among the most important are: (i) Seismology, which is the study of the sound waves that bounce off buried rock layers. It involves seismic surveys that are analyzed by knowledgeable personnel [4]. (ii) Geological Mapping, which is used by geologist to define possible reservoir shapes or traps, due to the deformation in the rock layer that contains hydrocarbons [5]. (iii) Educated guesses, which use physical geology and seismic information as the base material to guess where to drill [6]. To solve this problem: a) High spectral space images will be enhanced to highlight gravity issues. b) An existing gravity map will be georeference to bring the whole test data into a common georeference. c) Training and test patterns will be generated from the satellite image and the existing gravity map.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 8 d) An ANN’s system will be built, specified and trained for predicting gravity indicators (map) based on previous experience. e) The performance of the trained model will be checked by applying it to a new set of test data (patterns), whose actual outputs are already known. f) The results will be analyzed, discussed and assessed. 2. STUDY AREA: The experimental test carried out in this study was based on test data collected and observed from satellite image of Khartoum City (Figure 1). Figure 1: Satellite Image for Khartoum City The satellite image of Khartoum City (Figure 1)was acquired by theAmericanIKONOSimaginingsystemin2005. The spatial resolution of this image is one meter, while the spectral resolution covers the visible (two bands) and the infrared (one band) spectrum regions. Table 1 lists the specifications of this image. One of the most important set of the test data used also in this study is a gravity map of Khartoum City (Figure 2). Earth's gravity measured by NASA's GRACE mission, showing deviations from the theoretical gravity of an idealized smooth Earth, the so-called earth ellipsoid. Red shows the areas where gravity is stronger than the smooth, standard value, and blue reveals areas where gravity is weaker. Some data has been derived from gravity map as shown in figure 2. Figure 2: Gravity map of Khartoum City 3. PROCEDURES: A wide range of procedures were applied to meet the objectives of this study. The test was carried out in three phases. Phase one is concerned with image georeferencing, image slicing and production of ASCII images. Inphasetwoa set of training and test patterns were generate to build an artificial neural network model to predict the gravitymapof the test patterns based on the knowledge that was gained during the training session. In phase three the performance of the trained model was assessed. 4. RESULTS AND ANALYSIS: The GRNN architecture was designed and trained with 5000 patterns. GRNN achieved in less than 4 minutes. The small error value is a quick indication that the shell was able to converge with a minimum average error, and hence reasonable activation state has been achieved by the network. This indicates thattheGRNN looksmoresuitable in resolving the research problem. In order to assess the training results, the trained model was applied to the same training patterns. The predicted values were compared to the actual values (derived from gravity map). Since the implicit relationships between the input and output variables of the training patterns are supposed to be well defined to GRNN model, small residual values are expected to be obtained. Therefore one can assess the model was well trained. To assess the performance of the GRNN model for predicting improved values based on input data obtained by gravity map, the shell was applied to 116 new patterns (test patterns) not included in the training session.
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 9 The predicted values were compared with original gravity map. The absolute average of averages and the standard deviation of averages for these residuals were also computed. For the purpose of comparison and assessment, the original gravity map was compared with predicted values. The figures below explain pattern 6 (randomly) in training session (gravity and predicted) and the difference between two images. The black area means there are difference between two images in the third figure. And the white color means there is no difference between two images. Figure 3: The Gravity Pattern. Figure 4: The Predicted Pattern. Figure 5: The Difference between Two Patterns. The histogram illustrate that there is no difference between gravity image and predicted image for the same area. Figure 6: Histogram for Gravity Pattern. Figure 7: Histogram for Predicted Pattern. 5. CONCLUSION: In the present research work the research is aiming to product gravity map using artificial intelligence technique. According to the test carried out in this study it can be concluded that:
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 10 1. The GRNN model was able to process the whole set of the training patterns (410 patterns), as well as the test patterns (109 patterns). 2. The Artificial Neural Network is able to produce Gravity Map. ACKNOWLEDGEMENT I am grateful to my supervisors Dr. El Tahir Mohammed Hussein. And Dr. Adil El Sinnari who offered his academic experience, both through his guidance and in discussion. Without his continuous assistance and encouragement I would not have been able to complete this work. I am very thankful to all staff member of the National Ribat University. My thanks are due to several of my colleagues and friends for their spiritual support. Finally special thanks to my family for their patience and cooperation during the preparation period of this work. REFERENCES [1] http://www.minerals.er.usgs.gov, June 2016. [2] http://www.nmariita@KENGEN.co.ke, March 2016. [3] Jadhawar, Prashant Sopanrao, University of Adelaide, Australian School of Petroleum, 2010. [4] Landry, Todd J., Mapping and Two-Dimensional Modeling of Basement and Smackover Topography with Filtered Gravity Data in West-Central Mississippi M.S., University of Louisiana at Lafayette, 2015. [5] Wiese, David N., Optimizing Two Pairs of GRACE-like Satellites for Recovering Temporal Gravity Variations , Ph.D., University of Colorado at Boulder, 2011. [6] Walker, Christopher David, A gravityslideorigin forthe Mormon Peak detachment: Re-examining the evidence for extreme extension in the Mormon Mountains, southeastern Nevada, U.S.A. Ph.D.,Columbia University, 2008. BIOGRAPHIES Nagi Ishag Mohammed received the B.S. degrees in Computer Science from College of science and Technology in 2002 and M.S. in computer science from university of Gezira in 2007 respectively. he stayed in Jordanian Sudanese College and Mashreq University as a teacher of computer science from 2004 until now. El Tahir Mohammed Husein received the Ph.D. in Control EngineeringfromChina University in, 1997. He stayed in Sudan University of Science and Technology as a teacher of Control Engineering from1997 until now. Adil Mohammed Ahmed Elsinnari received the B.S. M.S and Ph.D. in Survey Engineering from University of Khartoum 1985,1990and2004 respectively. he stayed in University of Khartoum and Omdurman Islamic University as a teacher of Survey Engineering from 1993 until now.
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