Interpretation of magnetic anomalies due to dipping dikes using neural network inversion
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ORIGINAL PAPER
Interpretation of magnetic anomalies due to dipping dikes using neural network inversion Mansour A. Al-Garni
Received: 8 September 2014 / Accepted: 19 December 2014 # Saudi Society for Geosciences 2015
Abstract A new approach is proposed for the interpretation of magnetic anomalies caused by dipping dikes. This approach is mainly based on modular neural network inversion for estimating the parameters of dipping dike model. Suitable network training examples and test data have been generated using forward models based on known true parameters. The training procedures adopt supervised learning routine using modular neural networks. The effect of random noise has been examined where the proposed technique showed stability and satisfactory results. The applicability of this technique has been tested on synthetic and field examples data. This technique is particularly applied to two field examples, namely magnetic anomaly over an outcropping quartz dike-like body in Karimnagar area, Andhra Pradesh, India, and Marcona magnetic anomaly, Marcona district, Peru. The results of using this technique showed good agreement with the measured field data compared with most conventional ones. Furthermore, neural networks proved to be efficient and flexible in the interpretation of magnetic anomaly of dipping dike. Keywords Magnetic . Dipping dike . Modular module . Neural networks
Introduction Magnetic survey is well established for detecting geological structures. The dike model is one of the most frequently used models in the interpretation of magnetic anomalies (Dondurur and Pamukçu 2003). Generally, an interpretation of geophysical M. A. Al-Garni (*) Department of Geophysics, Faculty of Earth Sciences, King Abdulaziz University, P.O. Box 80206, Jeddah 21589, Saudi Arabia e-mail: [email protected]
anomaly is mainly fallen into three methods: mathematical solution, nomograms, and inversion. Mathematical solutions such as transforms can be sometimes difficult due to the nature of the causative bodies. On the other hand, inverse theory is an elegant tool for estimating causative target parameters, which is definitely easier than the mathematical solution. There are numerous graphical methods and techniques that have been developed for interpreting magnetic anomalies due to dipping dike models among them (Peters (1949); Cook (1950); Werner (1953); Hutchison (1958); Gay (1963); Bruckshaw and Kunaratnam (1963); Grant and West (1965); Koulomzine et al. (1970); Rao and Murthy (1978); and Kara et al. (1996)). Most of these methods involve characteristic points and distances, nomograms, and standardized curves to estimate the model parameters (Abdelrahman et al. 2007). The main drawback of these methods is that they are highly subjective where they can lead to substantial errors in parameter estimations such as depth and width of the buried dike. In inverse theory, a geometrical model is chosen with initial estimates of the causative target parameters, and then the process is iteratively progressed until a satisfact
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