International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
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
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].
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.
The main objective of this study is to investigate the 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 achieve the objective of this study Artificial Neural 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.
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:
Key Words: AI: Artificial Intelligent, ANN: Artificial Neural
(i)
Networks, ASCII: American Standard Code for Information Interchange, GRNN: General Regression Neural Networks, GRACE: Gravity Recovery and Climate Experiment. .
(ii)
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 produce variations in the measured gravitational field. These variations can then be interpreted by a variety of analytical and computers methods to determine the depth, geometry and density that causes the gravity field variations.
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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]. 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]. 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.
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