کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4740513 1358589 2011 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فیزیک زمین (ژئو فیزیک)
پیش نمایش صفحه اول مقاله
Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)
چکیده انگلیسی

Artificial neural networks (ANN) have been used in a variety of problems in the fields of science and engineering. Applications of ANN to the geophysical problems have increased within the last decade. In particular, it has been used to solve such inversion problems as seismic, electromagnetic, resistivity. There are also some other applications such as parameter estimation, prediction, and classification. In this study, multilayer perceptron neural networks (MLPNN) and radial basis function neural networks (RBFNN) were applied to synthetic gravity data and Seferihisar gravity data to investigate the applicability and performance of these networks for the method of gravity. Additionally performance of MLPNN and RBFNN were tested by adding random noise to the same synthetic test data. The structure parameters, such as the depths, the density contrasts, and the locations of the structures were obtained closely for different signal-to-noise ratios (S/N). Bouguer data of Seferihisar area were analyzed by MLPNN and RBFNN to estimate depth, density contrast, and location of the structure. The results of MLPNN, RBFNN, and classical inversion method were compared for real data obtained from Seferihisar Geothermal area and similar structure parameters were obtained. The experiments show that in general RBFNN not only increases the speed of the training stage enormously, but also provides slightly better performance.


► The applications of MLPNN and RBF NN on synthetic and real gravity data were analysed.
► The locations, depths and density contrasts of 2D structures were obtained by MLPNN and RBF NN.
► Of course real data contains some noise.
► We want to examine the performance of the ANN in the noisy data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Applied Geophysics - Volume 75, Issue 4, December 2011, Pages 711–718
نویسندگان
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