阔克沙勒—吉萨尔地震活动时间的多重分形研究

应用多重分形的标度指数谱f(α)～α方法分析阔克沙勒——吉萨尔地区地震活动时间序列,发现一定范围内地震多重分形f(α)～α在大震前有明显的异常显示。说明f(α)谱研究地震的多重分形,简单、有效、能全面地描述地震时间分布这类不均匀分形集。     

塔中Ⅰ号断裂带北段构造裂缝面密度与分形统计

断裂如何控制其附近构造裂缝的发育程度是碳酸盐岩地区构造裂缝定量研究的重要方面,对碳酸盐岩裂缝性油气藏的储层预测具有重要的意义.文中以塔中Ⅰ号断裂带为例,重点研究逆冲断裂控制构造裂缝的分布规律,对其控制的构造裂缝面密度和分形进行了研究.研究结果表明,塔中工号断裂控制的构造裂缝的裂缝面密度与距断裂的距离呈指数关系,其控制的...     

基于Hurst指数的矿化强度识别－以山东大尹格庄金矿为例

探索成矿元素分布的分形特征是揭示矿化富集程度的重要途径之一。运用重标极差分析（R/S）方法,研究了山东大尹格庄金矿-210 m中段多勘探线Au品位序列随采样尺度变化的趋势,探讨元素序列的Hurst指数与矿化强度的关系。结果显示:具有不同矿化强度的勘探线Au的Hurst指数存在明显差异,且均大于0.5;矿化连续分布或间断出现的勘探线元素品位序列的Hurst指数大于0.65,表明元素序列具有标度不变性和长相关性,序列的持续强度与矿化程度基本一致,可为区域矿化强度定量识别提供一种新的有效方法。     

Self-Organized Criticality Applied to Natural Hazards

The concept of self-organizedcriticality evolved from studies of three simplecellular-automata models: the sand-pile, slider-block,and forest-fire models. In each case, there is asteady input and the loss is associated with afractal (power-law) distribution of avalanches. Each of the three models can be associated with animportant natural hazard: the sand-pile model withlandslides, the slider-block model with earthquakes,and the forest-fire model with forest fires. We showthat each of the three natural hazards havefrequency-size statistics that are well approximatedby power-law distributions. The model behaviorsuggests that the recurrence interval for a severeevent can be estimated by extrapolating the observedfrequency-size distribution of small and mediumevents. For example, the recurrence interval for amagnitude seven earthquake can be obtained directlyfrom the observed frequency of occurrence of magnitudefour earthquakes. This concept leads to thedefinition of a seismic intensity factor. Both globaland regional maps of this seismic intensity factor aregiven. In addition, the behavior of the modelssuggests that the risk of occurrence of large eventscan be substantially reduced if small events areencouraged. For example, if small forest fires areallowed to burn, the risk of a large forest fire issubstantially reduced.     

On a geometric method for determining the masses of the Galilean satellites of Jupiter

A geometric method for calculating the relative masses of the Galilean satellites of Jupiter is proposed. Local characteristics coincident with the Hausdorff—Bezikovich dimension have been calculated for each planet—satellite pair. The relative masses calculated by the geometric method differ from those obtained by analyzing the motion of spacecraft by no more than 0.1%.     

Scaling properties and spatial organization of snow depth fields in sub‐alpine forest and alpine tundra

This paper reports on a study analysing the spatial distribution functions, the correlation structures, and the power spectral densities of high‐resolution LIDAR snow depths (～1 m) in two adjacent 500 m × 500 m areas in the Colorado Rocky Mountains, one a sub‐alpine forest the other an alpine tundra. It is shown how and why differences in the controlling physical processes induced by variations in vegetation cover and wind patterns lead to the observed differences in spatial organization between the snow depth fields of these environments. In the sub‐alpine forest area, the mean of snow depth increases with elevation, while its standard deviation remains uniform. In the tundra subarea, the mean of snow depth decreases with elevation, while its standard deviation varies over a wide range. The two‐dimensional correlations of snow depth in the forested area indicate little spatial memory and isotropic conditions, while in the tundra they indicate a marked directional bias that is consistent with the predominant wind directions and the location of topographic ridges and depressions. The power spectral densities exhibit a power law behaviour in two frequency intervals separated by a break located at a scale of around 12 m in the forested subarea, and 65 m in the tundra subarea. The spectral exponents obtained indicate that the snow depth fields are highly variable over scales larger than the scale break, while highly correlated below. Based on the observations and on synthetic snow depth fields generated with one‐ and two‐dimensional spectral techniques, it is shown that the scale at which the break occurs increases with the separation distance between snow depth maxima. In addition, the breaks in the forested area coincide with those of the corresponding vegetation height field, while in the tundra subarea they are displaced towards larger scales than those observed in the corresponding vegetation height field. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing linear models of sea-floor topography

Stochastic models can generate profiles that resemble topography by taking uncorrelated, zero-average noise as input, introducing some correlation in the time series of noise, and integrating the resulting correlated noise. The output profile will depict a nonstationary, randomly rough surface. Two models have been chosen for comparison: a fractal model, in which the noise is correlated even at large distances, and an autoregressive model of order 1, in which the correlation of the noise decays rapidly. Both models have as an end-member a random walk, which is the integration of uncorrelated noise. The models have been fitted to profiles of submarine topography, and the sample autocorrelation, power spectrum and variogram have been compared to the theoretical predictions. The results suggest that a linear system approach is a viable method to model and classify sea-floor topography. The comparison does not show substantial disagreement of the data with either the autoregressive or the fractal model, although a fractal model seems to give a better fit. However, the amplitudes predicted by a nonstationary fractal model for long wavelengths (of the order of 1000 km) are unreasonably large. When viewed through a large window, ocean floor topography is likely to have an expected value determined by isostasy, and to be stationary. Nonstationary models are best applied to wavelengths of the order of 100 km or less.     

Coding random self-similar river networks and calculating geometric distances: 1. General methodology / Codage de réseaux hydrographiques auto-similaires aléatoires et calcul de distances géométriques: 1. Méthodologie générale

Abstract

This study proposes a preliminary method for coding random self-similar river networks as a series of numbers, and investigates the corresponding algorithm that calculates the geometric distances from the code series. The coding method, generating random self-similar patterns with various probabilities of particular pattern links, and transferring the pattern into numerical code series, is proposed, based on the wide range of stochastic characteristics of natural patterns. The heredity of self-similarity and the connecting relationships inside the generated pattern are discussed. Finally, the algorithm to calculate the geometric distances of the generated pattern are developed.