Volume 5, Issue 1, March 2019, Page: 29-34
Fluctuation Analysis to Sequence of Ore-forming Element Based on Fractal-Jump Model
Lai Simin, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
Wan Li, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China; Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes, Guangzhou University, Guangzhou, China
Zeng Xiangjian, School of Mathematics and Information Science, Guangzhou University, Guangzhou, China
Received: Apr. 12, 2019;       Published: May 23, 2019
DOI: 10.11648/j.ijdst.20190501.14      View  44      Downloads  17
The self-similarity of ore-forming elements is caused by long-term, multi-period characteristics and the abnormal fluctuation induced by emergency in geological process. In this study, the fractal jump model referred as a combination of fractional Brownian motion and Poisson distributed jumps was built to depict the fluctuation pattern of ore-forming elements and simulate the distribution of Au sequence for three different mineralization intensities in Dayingezhuang gold deposit in the Jiaodong gold province, China. By calculating the fitting error and drawing the comparison diagram between the simulated data and the actual data, the applicability and advantages of the model were verified. The results showed that the fractal jump model can be considered as a reliable and computationally efficient method through the comparison of the statistical characteristics between simulated and real data. In addition, this model can well depict the change of the Au element content sequence, and better simulation was achieved when the intensity of mineralization was higher. The present work provides a new insight on the prediction of mineralized levels in concealed orebody.
Ore-Forming Elements, Fractional Brownian Motion, Poisson Distribution
To cite this article
Lai Simin, Wan Li, Zeng Xiangjian, Fluctuation Analysis to Sequence of Ore-forming Element Based on Fractal-Jump Model, International Journal on Data Science and Technology. Vol. 5, No. 1, 2019, pp. 29-34. doi: 10.11648/j.ijdst.20190501.14
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