Unreliable determination of fractal characteristics using the capacity dimension and a new method for computing the information dimension. (August 2018)

• Record Type: Journal Article
• Authors: Liu, Jingshou; Ding, Wenlong; Dai, Junsheng; Zhao, Gang; Sun, Yaxiong; Yang, Haimeng
• Is Part Of: Chaos, solitons and fractals. Volume 113(2018)
• Page Start: 16
• Page End: 24
• Publication Date: 2018-08
• Subjects: Information dimension -- Capacity dimension -- Fractal -- Unreliability -- Jinhu sag -- Fault; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2018.05.008
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: The capacity dimension is unreliable in fractal calculations of geological bodies. Geological implications of the fractal dimension and the fitting coefficient are discussed. A new method for the calculation of the information dimension DI is proposed. A selection rule to determine the side length of the fractal statistical units is proposed. Abstract: Fractal theory has been widely applied in a variety of disciplines to understand the theory behind chaotic phenomena based on internal self-similarity. In this study, three ideal geological models are used to analyze the unreliability of the capacity dimension in the fractal calculation of geological bodies with different scales. Additionally, by varying the side length r of the statistical units, the geological meanings of the fractal dimension D and the correlation coefficient R 2 are discussed. The points of information (POIs) are densely filled by binarizing the geological bodies to black/white. Based on the optimized r of a geological body, an algorithm is derived that divides the grids of the statistical units to determine the probability of the POIs falling into different grids. The information dimension (DI ) and R 2 of a geological body are obtained by fitting the variable data. An example calculation of the information dimension field in the Jinhu sag is presented to demonstrate the methodology and to test its reliability. The results show that determining the appropriate side length of the statistical unit is key to evaluating the fractal calculation. Compared to the capacity dimension, DI is more reliable in the fractal calculation of multi-scale geological bodies; DI is thereby the preferred fractal dimension to use in the analyses of these types of geological bodies.