scholarly journals Fractal and Multifractal Characteristics of Lineaments in the Qianhe Graben and Its Tectonic Significance Using Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 587
Author(s):  
Zhiheng Liu ◽  
Ling Han ◽  
Chengyan Du ◽  
Hongye Cao ◽  
Jianhua Guo ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Tectonic Activity ◽  
Mountainous Area ◽  
Tensor Voting ◽  
Self Similarity ◽  
Plate Movement ◽  
Tectonic Significance ◽  
Regional Tectonic ◽  
Fractal Spectrum ◽  
Tracing Algorithm

The distribution and characteristics of geological lineaments in areas with active faulting are vital for providing a basis for regional tectonic identification and analyzing the tectonic significance. Here, we extracted the lineaments in the Qianhe Graben, an active mountainous area on the southwest margin of Ordos Block, China, by using the tensor voting algorithm after comparing them with the segment tracing algorithm (STA) and LINE algorithm in PCI Geomatica Software. The main results show that (1) the lineaments in this area are mostly induced by the active fault events with the main trending of NW–SE, (2) the box dimensions of all lineaments, NW–SE trending lineaments, and NE–SW trending lineaments are 1.60, 1.48, and 1.44 (R2 > 0.9), respectively, indicating that the faults exhibit statistical self-similarity, and (3) the lineaments have multifractal characteristics according to the mass index τ(q), generalized fractal dimension D(q), fractal width (Δα = 2.25), fractal spectrum shape (f(α) is a unimodal left-hook curve), and spectrum width (Δf = 1.21). These results are related to the tectonic activity in this area, where a higher tectonic activity leads to more lineaments being produced and a higher fractal dimension. All of these results suggest that such insights can be beneficial for providing potential targets in reconstructing the tectonic structure of the area and trends of plate movement.

scholarly journals Supramolecular Fractal Growth of Self-Assembled Fibrillar Networks

Gels ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 46
Author(s):  
Pedram Nasr ◽  
Hannah Leung ◽  
France-Isabelle Auzanneau ◽  
Michael A. Rogers
Keyword(s):  
Fractal Dimension ◽  
Crystallization Temperature ◽  
Cayley Tree ◽  
Fractal Dimensions ◽  
Self Similarity ◽  
Avrami Model ◽  
Cluster Aggregation ◽  
Box Counting ◽  
Self Assembled ◽  
Limited Diffusion

Complex morphologies, as is the case in self-assembled fibrillar networks (SAFiNs) of 1,3:2,4-Dibenzylidene sorbitol (DBS), are often characterized by their Fractal dimension and not Euclidean. Self-similarity presents for DBS-polyethylene glycol (PEG) SAFiNs in the Cayley Tree branching pattern, similar box-counting fractal dimensions across length scales, and fractals derived from the Avrami model. Irrespective of the crystallization temperature, fractal values corresponded to limited diffusion aggregation and not ballistic particle–cluster aggregation. Additionally, the fractal dimension of the SAFiN was affected more by changes in solvent viscosity (e.g., PEG200 compared to PEG600) than crystallization temperature. Most surprising was the evidence of Cayley branching not only for the radial fibers within the spherulitic but also on the fiber surfaces.

scholarly journals A Community-Structure-Based Method for Estimating the Fractal Dimension, and its Application to Water Networks for the Assessment of Vulnerability to Disasters

2021 ◽  
Vol 35 (4) ◽  
pp. 1197-1210
Author(s):  
C. Giudicianni ◽  
A. Di Nardo ◽  
R. Greco ◽  
A. Scala
Keyword(s):  
Fractal Dimension ◽  
Community Structure ◽  
Distribution Systems ◽  
Water Distribution ◽  
Scaling Law ◽  
Vulnerability Index ◽  
The Self ◽  
Node Degree ◽  
Specific Response ◽  
Self Similarity

AbstractMost real-world networks, from the World-Wide-Web to biological systems, are known to have common structural properties. A remarkable point is fractality, which suggests the self-similarity across scales of the network structure of these complex systems. Managing the computational complexity for detecting the self-similarity of big-sized systems represents a crucial problem. In this paper, a novel algorithm for revealing the fractality, that exploits the community structure principle, is proposed and then applied to several water distribution systems (WDSs) of different size, unveiling a self-similar feature of their layouts. A scaling-law relationship, linking the number of clusters necessary for covering the network and their average size is defined, the exponent of which represents the fractal dimension. The self-similarity is then investigated as a proxy of recurrent and specific response to multiple random pipe failures – like during natural disasters – pointing out a specific global vulnerability for each WDS. A novel vulnerability index, called Cut-Vulnerability is introduced as the ratio between the fractal dimension and the average node degree, and its relationships with the number of randomly removed pipes necessary to disconnect the network and with some topological metrics are investigated. The analysis shows the effectiveness of the novel index in describing the global vulnerability of WDSs.

Runoff Fractal Dimension of Songhua River Basin in Harbin Station Based on Db4 Wavelet

2012 ◽  
Vol 550-553 ◽  
pp. 2537-2540
Author(s):  
Hai Yan Gu ◽  
Yong Wang ◽  
Lei Yu
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
River Basin ◽  
Fractal Theory ◽  
Measurement Methods ◽  
Self Similarity ◽  
Songhua River ◽  
Songhua River Basin ◽  
The Songhua River Basin ◽  
The Songhua River

The wavelet analysis and fractal theory into the analysis of hydrological time series, fluctuations in hydrological runoff sequence given the complexity of the measurement methods--- fractal dimension. The real monthly runoffs of 28 years from Songhua River basin in Harbin station are selected as research target. Wavelet transform combined with spectrum method is used to calculate the fractal dimension of runoff. Moreover, the result demonstrates that the runoff in Songhua River basin has the characteristic of self-similarity, and the complexity of runoff in the Songhua River basin in Harbin station is described quantificationally.

scholarly journals Correlation analysis between multifractal characteristics of regional geomorphology and development of geological disasters

2021 ◽  
Vol 25 (1) ◽  
pp. 49-55
Author(s):  
Yiying Xiong
Keyword(s):  
Correlation Analysis ◽  
Spatial Relationship ◽  
Analysis Method ◽  
Self Similarity ◽  
Geological Disasters ◽  
Geological Disaster ◽  
Correlation Analysis Method ◽  
Fractal Spectrum ◽  
Fractal Characteristics ◽  
Relationship Of

In view of the inaccuracy of the traditional correlation analysis method, this paper proposes a correlation analysis method between the multifractal characteristics of regional landforms and the development of geological disasters. Firstly, the multifractal characteristics of regional landforms are described by using the basic fractal characteristics of self-similarity or scale invariance. Then the corresponding relation table is established according to the width of the fractal spectrum and the number of landslides and hidden dangers, and the spatial relationship of geological disaster development is analyzed. Combined with the above-mentioned spatial relationship of geological disaster development and the multifractal characteristic data of regional landforms, the correlation coefficient between the two is calculated to complete the correlation analysis between the multifractal characteristics of regional geomorphology and the development of geological disasters. The experimental results show that compared with the traditional correlation analysis method, the correlation analysis results of the multifractal characteristics of regional geomorphology and the development of geological disasters are more accurate.

scholarly journals Topographic Analysis of Wetlandscapes: Fractal Dimension and Scaling Properties

10.29007/c7r5 ◽  
2018 ◽  
Author(s):  
Leonardo Enrico Bertassello ◽  
P. Suresh Rao ◽  
Gianluca Botter ◽  
Antoine Aubeneau
Keyword(s):  
Fractal Dimension ◽  
The United States ◽  
Self Similarity ◽  
Topographic Analysis ◽  
Scaling Properties ◽  
Dem Analysis ◽  
Water Filling ◽  
Topographic Depressions ◽  
And Storage

Wetlands are ubiquitous topographic depressions on landscapes and form criticalelements of the mosaic of aquatic habitats. The role of wetlands in the global hydrological and biogeochemical cycles is intimately tied to their geometric characteristics. We used DEM analysis and local search algorithms to identify wetland attributes (maximum stage, surface area and storage volume) in four wetlandscapes across the United States. We then derived the exceedance cumulative density functions (cdfs) of these attributes for the identified wetlands, applied the concept of fractal dimension to investigate the variability in wetland’ shapes. Exponentially tempered Pareto distributions were fitted to DEM derived wetland attributes. In particular, the scaling exponents appear to remain constant through the progressive water-filling of the landscapes, suggesting self-similarity of wetland geometrical attributes. This tendency is also reproduced by the fractal dimension (D) of wetland shorelines, which remains constant across different water-filling levels. In addition, the variability in D is constrained within a narrow range (1 <D < 1.33) in all the four wetlandscapes. Finally, the comparison between wetlands identified by the DEM-based model are consistentwith actual data.

A NEW SPECTRAL–SPATIAL JOINTED HYPERSPECTRAL IMAGE CLASSIFICATION APPROACH BASED ON FRACTAL DIMENSION ANALYSIS

Fractals ◽  
2019 ◽  
Vol 27 (05) ◽  
pp. 1950079
Author(s):  
JUNYING SU ◽  
YINGKUI LI ◽  
QINGWU HU
Keyword(s):  
Fractal Dimension ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Spatial Domain ◽  
Spectral Domain ◽  
Hyperspectral Image Classification ◽  
Self Similarity ◽  
Classification Approach ◽  
Image Set

To maximize the advantages of both spectral and spatial information, we introduce a new spectral–spatial jointed hyperspectral image classification approach based on fractal dimension (FD) analysis of spectral response curve (SRC) in spectral domain and extended morphological processing in spatial domain. This approach first calculates the FD image based on the whole SRC of the hyperspectral image and decomposes the SRC into segments to derive the FD images with each SRC segment. These FD images based on the segmented SRC are composited into a multidimensional FD image set in spectral domain. Then, the extended morphological profiles (EMPs) are derived from the image set through morphological open and close operations in spatial domain. Finally, all these EMPs and FD features are combined into one feature vector for a probabilistic support vector machine (SVM) classification. This approach was demonstrated using three hyperspectral images in urban areas of the university campus and downtown area of Pavia, Italy, and the Washington DC Mall area in the USA, respectively. We assessed the potential and performance of this approach by comparing with PCA-based method in hyperspectral image classification. Our results indicate that the classification accuracy of our proposed method is much higher than the accuracies of the classification methods based on the spectral or spatial domain alone, and similar to or slightly higher than the classification accuracy of PCA-based spectral–spatial jointed classification method. The proposed FD approach also provides a new self-similarity measure of land class in spectral domain, a unique property to represent hyperspectral self-similarity of SRC in hyperspectral imagery.

The Blind Creek Limestone, Keremeos, British Columbia: Structure and Regional Tectonic Significance

1975 ◽  
Vol 12 (11) ◽  
pp. 1929-1933
Author(s):  
W. C. Barnes ◽  
J. V. Ross
Keyword(s):  
British Columbia ◽  
Compressive Stress ◽  
Ductile Deformation ◽  
Large Block ◽  
Stress Direction ◽  
Upper Paleozoic ◽  
Tectonic Significance ◽  
Regional Tectonic ◽  
Similar Body ◽  
Paleozoic Rocks

A large block of Upper Paleozoic limestone at Blind Creek near Keremeos, B.C. was emplaced by dry gravity sliding, probably associated with uplift related to nearby Eocene volcanism. The block is a nearly flat tabular unit, exposed over an area of 650 m by 1300 m, and is separated from underlying chaotic breccias derived from adjacent Paleozoic rocks and from Eocene volcanic flow rocks by a sole fault. The block comprises two lithologically and tectonically distinct units, a lower imbricated unit consisting of several slices repeating the same sequence of strata, separated from an upper unit of massive limestone by a low-angle fault. Within the imbricated unit, early faults emanate from lenticular masses of breccia along the sole, become increasingly steeper upward, and are truncated above by the upper low angle fault. Associated minor folds and fractures have a clockwise sense of rotation. Later fractures and associated minor folds have the opposite dip and sense of rotation. These two subsets comprise a conjugate set whose inferred compressive stress direction coincides with the present overall dip direction of the entire mass.The northwesterly adjacent autochthonous Olalla limestone, or a similar body now buried by younger units, is a likely source for the Blind Creek allochthon.Absence of any structures within the limestone indicative of ductile deformation contrasts markedly with those of the highly deformed rocks of the Old Tom and Shoemaker Formations, the Kobau Group, and the nearby gneisses of the western Shuswap Complex.

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