Next Article in Journal
A Mechanism for the Adsorption of 2-(Hexadecanoylamino)Acetic Acid by Smithsonite: Surface Spectroscopy and Microflotation Experiments
Previous Article in Journal
Carbon and Oxygen Isotopic Composition of Saline Lacustrine Dolomite Cements and Its Palaeoenvironmental Significance: A Case Study of Paleogene Shahejie Formation, Bohai Sea
Previous Article in Special Issue
Applying Electrical Resistivity Tomography in Ornamental Stone Mining: Challenges and Solutions
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Minerals 2019, 9(1), 14; https://doi.org/10.3390/min9010014

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

1,2,3
,
4,*
,
1,2,3
,
1,2,3
and
1,2,3
1
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China
2
Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China
3
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
4
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)
Full-Text   |   PDF [4435 KB, uploaded 26 December 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Minerals EISSN 2075-163X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top