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GIS-based regional gravity and magnetic structure of intelligent extraction method

Author: ZhangJianShi
Tutor: YeShuiSheng
School: Northeast Normal University
Course: Computer Software and Theory
Keywords: Gravity and magnetic structure Gravity and magnetic data processing BP neural network Genetic Algorithms
CLC: P631.2
Type: Master's thesis
Year: 2009
Downloads: 86
Quote: 1
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Abstract


Geophysical exploration , referred to as geophysical , geological structure is to study and resolve the basic problem of prospecting and exploration methods. It variety of rock and ore density , magnetism, electrical resistance, flexibility , and other physical properties of the radioactive research base differences . Among them, gravity surveys , magnetic exploration is currently used in two main geophysical methods . Regional gravity and magnetic data processing is one of the objectives of regional geophysical field through the various methods of processing and analysis , inference structural faults within the study area distribution process by analytic continuation , arbitrary horizontal directional derivative , etc., and other comprehensive interpretation of geophysical data on deep structure to obtain a variety of information about the distribution and fracture . Further according to geological differences between the properties , we can infer the presence of faults interpreted and ring structure . In the regional gravity and magnetic data extraction and handling of the process, in which artificial intelligence algorithms have been widely used to determine the axis of the associated problems such as non-linear problems . At present, the application of artificial intelligence model is more mature BP network model of artificial intelligence . This paper studies the traditional BP network model of artificial intelligence in the gravity and magnetic data processing applications, and summarizes some of the shortcomings of traditional BP algorithm and shortcomings , combined with the new improved genetic algorithm, the traditional BP artificial intelligence model is improved , this new method the experiment proved to be effective to overcome the BP algorithm is easy to fall into local minima resulting in failure of the shortcomings of network training , and BP network convergence is slow, long training time issues have been improved.

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CLC: > Astronomy,Earth Sciences > Geology > Geology, mineral prospecting and exploration > Geophysical exploration > Magnetic prospecting
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