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Minerals 2019, 9(1), 14;

Geophysical Field Data Interpolation Using Stochastic Partial Differential Equations for Gold Exploration in Dayaoshan, Guangxi, China

Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China
Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Author to whom correspondence should be addressed.
Received: 25 November 2018 / Revised: 11 December 2018 / Accepted: 21 December 2018 / Published: 26 December 2018
(This article belongs to the Special Issue Mining and Mineral Exploration Geophysics)
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In a geophysical survey, one of the main challenges is to estimate the physical parameter using limited geophysical field data with noise. Geophysical datasets are measured with sparse sampling in a survey. However, the limited data constrain the geophysical interpretation. Traditionally, the field data has been interpolated using mathematical algorithm. In many cases, the estimated field data uncertainties are required to determine which earth models are consistent with the observations. A model-based data-estimation method can provide precise information for imaging and interpretation. The approach used in this paper is based on a stochastic partial differential equation, and it is employed to predict the geophysical data. With this statistical model-based approach, the sparse sample from a survey is used to estimate the underlying spatial surface, and it is assumed that the predicted geophysical data have the same probability density function as the observed data. Furthermore, this method can return the uncertainties of the prediction. Both the synthetic data and the gold mineral exploration field data cases illustrate that this approach leads to better results than traditional methods. View Full-Text
Keywords: statistical model-based; SPDE; data prediction; gold exploration statistical model-based; SPDE; data prediction; gold exploration

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Guo, Z.; Hu, X.; Liu, J.; Liu, C.; Xiao, J. Geophysical Field Data Interpolation Using Stochastic Partial Differential Equations for Gold Exploration in Dayaoshan, Guangxi, China. Minerals 2019, 9, 14.

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