# 多重分形系统

Example of a multifractal electronic eigenstate at the Anderson localization transition in a system with 1367631 atoms.

A multifractal system is a generalization of a fractal system in which a single exponent (the fractal dimension) is not enough to describe its dynamics; instead, a continuous spectrum of exponents (the so-called singularity spectrum) is needed.[1]

A multifractal system is a generalization of a fractal system in which a single exponent (the fractal dimension) is not enough to describe its dynamics; instead, a continuous spectrum of exponents (the so-called singularity spectrum) is needed.

Multifractal systems are common in nature. They include the length of coastlines, mountain topography,[2] fully developed turbulence, real-world scenes, heartbeat dynamics,[3] human gait[4]模板:Failed verification and activity,[5] human brain activity,[6][7][8][9][10][11][12] and natural luminosity time series.[13] Models have been proposed in various contexts ranging from turbulence in fluid dynamics to internet traffic, finance, image modeling, texture synthesis, meteorology, geophysics and more.[citation needed] The origin of multifractality in sequential (time series) data has been attributed to mathematical convergence effects related to the central limit theorem that have as foci of convergence the family of statistical distributions known as the Tweedie exponential dispersion models,[14] as well as the geometric Tweedie models.[15] The first convergence effect yields monofractal sequences, and the second convergence effect is responsible for variation in the fractal dimension of the monofractal sequences.[16]

Multifractal systems are common in nature. They include the length of coastlines, mountain topography, fully developed turbulence, real-world scenes, heartbeat dynamics, human gait and activity, human brain activity, and natural luminosity time series. Models have been proposed in various contexts ranging from turbulence in fluid dynamics to internet traffic, finance, image modeling, texture synthesis, meteorology, geophysics and more. The origin of multifractality in sequential (time series) data has been attributed to mathematical convergence effects related to the central limit theorem that have as foci of convergence the family of statistical distributions known as the Tweedie exponential dispersion models, as well as the geometric Tweedie models. The first convergence effect yields monofractal sequences, and the second convergence effect is responsible for variation in the fractal dimension of the monofractal sequences.

Multifractal analysis is used to investigate datasets, often in conjunction with other methods of fractal and lacunarity analysis. The technique entails distorting datasets extracted from patterns to generate multifractal spectra that illustrate how scaling varies over the dataset. Multifractal analysis techniques have been applied in a variety of practical situations, such as predicting earthquakes and interpreting medical images.[17][18][19]

Multifractal analysis is used to investigate datasets, often in conjunction with other methods of fractal and lacunarity analysis. The technique entails distorting datasets extracted from patterns to generate multifractal spectra that illustrate how scaling varies over the dataset. Multifractal analysis techniques have been applied in a variety of practical situations, such as predicting earthquakes and interpreting medical images.

## Definition

In a multifractal system $\displaystyle{ s }$, the behavior around any point is described by a local power law:

In a multifractal system s, the behavior around any point is described by a local power law:

= = 定义 = = 在多重分形系统中，任何点周围的行为都用局部幂定律来描述:

$\displaystyle{ s(\vec{x}+\vec{a})-s(\vec{x}) \sim a^{h(\vec{x})}. }$
s(\vec{x}+\vec{a})-s(\vec{x}) \sim a^{h(\vec{x})}.

S (vec { x } + vec { a })-s (vec { x }) sim a ^ { h (vec { x })}.

The exponent $\displaystyle{ h(\vec{x}) }$ is called the singularity exponent, as it describes the local degree of singularity or regularity around the point $\displaystyle{ \vec{x} }$.[citation needed]

The exponent h(\vec{x}) is called the singularity exponent, as it describes the local degree of singularity or regularity around the point \vec{x}.

The ensemble formed by all the points that share the same singularity exponent is called the singularity manifold of exponent h, and is a fractal set of fractal dimension $\displaystyle{ D(h): }$ the singularity spectrum. The curve $\displaystyle{ D(h) }$ versus $\displaystyle{ h }$ is called the singularity spectrum and fully describes the statistical distribution of the variable $\displaystyle{ s }$.[citation needed]

The ensemble formed by all the points that share the same singularity exponent is called the singularity manifold of exponent h, and is a fractal set of fractal dimension D(h): the singularity spectrum. The curve D(h) versus h is called the singularity spectrum and fully describes the statistical distribution of the variable s.

In practice, the multifractal behaviour of a physical system $\displaystyle{ X }$ is not directly characterized by its singularity spectrum $\displaystyle{ D(h) }$. Rather, data analysis gives access to the multiscaling exponents $\displaystyle{ \zeta(q),\ q\in{\mathbb R} }$. Indeed, multifractal signals generally obey a scale invariance property that yields power-law behaviours for multiresolution quantities, depending on their scale $\displaystyle{ a }$. Depending on the object under study, these multiresolution quantities, denoted by $\displaystyle{ T_X(a) }$, can be local averages in boxes of size $\displaystyle{ a }$, gradients over distance $\displaystyle{ a }$, wavelet coefficients at scale $\displaystyle{ a }$, etc. For multifractal objects, one usually observes a global power-law scaling of the form:[citation needed]

In practice, the multifractal behaviour of a physical system X is not directly characterized by its singularity spectrum D(h). Rather, data analysis gives access to the multiscaling exponents \zeta(q),\ q\in{\mathbb R}. Indeed, multifractal signals generally obey a scale invariance property that yields power-law behaviours for multiresolution quantities, depending on their scale a. Depending on the object under study, these multiresolution quantities, denoted by T_X(a), can be local averages in boxes of size a, gradients over distance a, wavelet coefficients at scale a, etc. For multifractal objects, one usually observes a global power-law scaling of the form:

$\displaystyle{ \langle T_X(a)^q \rangle \sim a^{\zeta(q)}\ }$
\langle T_X(a)^q \rangle \sim a^{\zeta(q)}\
langle t _ x (a) ^ q rangle sim a ^ { zeta (q)}

at least in some range of scales and for some range of orders $\displaystyle{ q }$. When such behaviour is observed, one talks of scale invariance, self-similarity, or multiscaling.[20]

at least in some range of scales and for some range of orders q. When such behaviour is observed, one talks of scale invariance, self-similarity, or multiscaling.

## Estimation

Using so-called multifractal formalism, it can be shown that, under some well-suited assumptions, there exists a correspondence between the singularity spectrum $\displaystyle{ D(h) }$ and the multi-scaling exponents $\displaystyle{ \zeta(q) }$ through a Legendre transform. While the determination of $\displaystyle{ D(h) }$ calls for some exhaustive local analysis of the data, which would result in difficult and numerically unstable calculations, the estimation of the $\displaystyle{ \zeta(q) }$ relies on the use of statistical averages and linear regressions in log-log diagrams. Once the $\displaystyle{ \zeta(q) }$ are known, one can deduce an estimate of $\displaystyle{ D(h), }$ thanks to a simple Legendre transform.[citation needed]

Using so-called multifractal formalism, it can be shown that, under some well-suited assumptions, there exists a correspondence between the singularity spectrum D(h) and the multi-scaling exponents \zeta(q) through a Legendre transform. While the determination of D(h) calls for some exhaustive local analysis of the data, which would result in difficult and numerically unstable calculations, the estimation of the \zeta(q) relies on the use of statistical averages and linear regressions in log-log diagrams. Once the \zeta(q) are known, one can deduce an estimate of D(h), thanks to a simple Legendre transform.

Multifractal systems are often modeled by stochastic processes such as multiplicative cascades. The $\displaystyle{ \zeta(q) }$ are statistically interpreted, as they characterize the evolution of the distributions of the $\displaystyle{ T_X(a) }$ as $\displaystyle{ a }$ goes from larger to smaller scales. This evolution is often called statistical intermittency and betrays a departure from Gaussian models.[citation needed]

Multifractal systems are often modeled by stochastic processes such as multiplicative cascades. The \zeta(q) are statistically interpreted, as they characterize the evolution of the distributions of the T_X(a) as a goes from larger to smaller scales. This evolution is often called statistical intermittency and betrays a departure from Gaussian models.

Modelling as a multiplicative cascade also leads to estimation of multifractal properties.脚本错误：没有“Footnotes”这个模块。 This methods works reasonably well, even for relatively small datasets. A maximum likely fit of a multiplicative cascade to the dataset not only estimates the complete spectrum but also gives reasonable estimates of the errors.[21]

Modelling as a multiplicative cascade also leads to estimation of multifractal properties. This methods works reasonably well, even for relatively small datasets. A maximum likely fit of a multiplicative cascade to the dataset not only estimates the complete spectrum but also gives reasonable estimates of the errors.

## Estimating multifractal scaling from box counting

Multifractal spectra can be determined from box counting on digital images. First, a box counting scan is done to determine how the pixels are distributed; then, this "mass distribution" becomes the basis for a series of calculations.[22][23][24] The chief idea is that for multifractals, the probability $\displaystyle{ P }$ of a number of pixels $\displaystyle{ m }$, appearing in a box $\displaystyle{ i }$, varies as box size $\displaystyle{ \epsilon }$, to some exponent $\displaystyle{ \alpha }$, which changes over the image, as in Eq.0.0 (NB: For monofractals, in contrast, the exponent does not change meaningfully over the set). $\displaystyle{ P }$ is calculated from the box-counting pixel distribution as in Eq.2.0.

$\displaystyle{ P_{[i,\epsilon]} \varpropto \epsilon^{-\alpha_i} \therefore\alpha_i \varpropto \frac{\log{P_{[i,\epsilon]}}}{\log{\epsilon^{-1}}} }$

(Eq.0.0)

Multifractal spectra can be determined from box counting on digital images. First, a box counting scan is done to determine how the pixels are distributed; then, this "mass distribution" becomes the basis for a series of calculations. The chief idea is that for multifractals, the probability P of a number of pixels m, appearing in a box i, varies as box size \epsilon, to some exponent \alpha, which changes over the image, as in (NB: For monofractals, in contrast, the exponent does not change meaningfully over the set). P is calculated from the box-counting pixel distribution as in .

$\displaystyle{ \epsilon }$ = an arbitrary scale (box size in box counting) at which the set is examined
\epsilon = an arbitrary scale (box size in box counting) at which the set is examined

$\displaystyle{ i }$ = the index for each box laid over the set for an $\displaystyle{ \epsilon }$
i = the index for each box laid over the set for an \epsilon
i = 在集合上放置的每个盒子的索引为 ε
$\displaystyle{ m_{[i,\epsilon]} }$ = the number of pixels or mass in any box, $\displaystyle{ i }$, at size $\displaystyle{ \epsilon }$
m_{[i,\epsilon]} = the number of pixels or mass in any box, i, at size \epsilon
m {[ i，epsilon ]} = 任何方框中像素或质量的数量，i，大小为 epsilon
$\displaystyle{ N_\epsilon }$ = the total boxes that contained more than 0 pixels, for each $\displaystyle{ \epsilon }$
N_\epsilon = the total boxes that contained more than 0 pixels, for each \epsilon
n epsilon = 每个 epsilon 包含超过0像素的总盒子
$\displaystyle{ M_\epsilon = \sum_{i=1}^{N_\epsilon}m_{[i,\epsilon]} = }$ the total mass or sum of pixels in all boxes for this $\displaystyle{ \epsilon }$

(Eq.1.0)

$\displaystyle{ P_{[i,\epsilon]} = \frac{m_{[i,\epsilon]}}{M_\epsilon} = }$ the probability of this mass at $\displaystyle{ i }$ relative to the total mass for a box size

(Eq.2.0)

$\displaystyle{ P }$ is used to observe how the pixel distribution behaves when distorted in certain ways as in Eq.3.0 and Eq.3.1:

P is used to observe how the pixel distribution behaves when distorted in certain ways as in and :

P 用于观察像素分布在以下特定方式扭曲时的表现:

$\displaystyle{ Q }$ = an arbitrary range of values to use as exponents for distorting the data set
Q = an arbitrary range of values to use as exponents for distorting the data set
q = 一个任意范围的值，用作扭曲数据集的指数
$\displaystyle{ I_{{(Q)}_{[\epsilon]}} = \sum_{i=1}^{N_\epsilon} {P_{[i,\epsilon]}^Q} = }$ the sum of all mass probabilities distorted by being raised to this Q, for this box size

(Eq.3.0)

• When $\displaystyle{ Q=1 }$, Eq.3.0 equals 1, the usual sum of all probabilities, and when $\displaystyle{ Q=0 }$, every term is equal to 1, so the sum is equal to the number of boxes counted, $\displaystyle{ N_\epsilon }$.
$\displaystyle{ \mu_{{(Q)}_{[i,\epsilon]}} = \frac{P_{[i,\epsilon]}^Q}{I_{{(Q)}_{[\epsilon]}}} = }$ how the distorted mass probability at a box compares to the distorted sum over all boxes at this box size

(Eq.3.1)

• When Q=1, equals 1, the usual sum of all probabilities, and when Q=0, every term is equal to 1, so the sum is equal to the number of boxes counted, N_\epsilon.

These distorting equations are further used to address how the set behaves when scaled or resolved or cut up into a series of $\displaystyle{ \epsilon }$-sized pieces and distorted by Q, to find different values for the dimension of the set, as in the following:

These distorting equations are further used to address how the set behaves when scaled or resolved or cut up into a series of \epsilon-sized pieces and distorted by Q, to find different values for the dimension of the set, as in the following:

• An important feature of Eq.3.0 is that it can also be seen to vary according to scale raised to the exponent $\displaystyle{ \tau }$ in Eq.4.0:
$\displaystyle{ I_{{(Q)}_{[\epsilon]}} \varpropto \epsilon^{\tau_{(Q)}} }$

(Eq.4.0)

• An important feature of is that it can also be seen to vary according to scale raised to the exponent \tau in :

• 一个重要的特点是，它也可以被视为根据提高到指数 τ 的比例而变化:

Thus, a series of values for $\displaystyle{ \tau_{(Q)} }$ can be found from the slopes of the regression line for the log of Eq.3.0 versus the log of $\displaystyle{ \epsilon }$ for each $\displaystyle{ Q }$, based on Eq.4.1:

Thus, a series of values for \tau_{(Q)} can be found from the slopes of the regression line for the log of versus the log of \epsilon for each Q, based on :

$\displaystyle{ \tau_{(Q)} = {\lim_{\epsilon\to0}{\left[ \frac {\log{I_{{(Q)}_{[\epsilon]}}}} {\log{\epsilon}} \right ]}} }$

(Eq.4.1)

$\displaystyle{ D_{(Q)} = {\lim_{\epsilon\to0} { \left [ \frac{\log{I_{{(Q)}_{[\epsilon]}}}}{\log{\epsilon^{-1}}} \right ]}} {(1-Q)^{-1}} }$

(Eq.5.0)

$\displaystyle{ D_{(Q)} = \frac{\tau_{(Q)}}{Q-1} }$

(Eq.5.1)

$\displaystyle{ \tau_{{(Q)}_{}} = D_{(Q)}\left(Q-1\right) }$

(Eq.5.2)

$\displaystyle{ \tau_{(Q)} = \alpha_{(Q)}Q - f_{\left(\alpha_{(Q)}\right)} }$

(Eq.5.3)

• For the generalized dimension:

• 关于一般维度:
• $\displaystyle{ \alpha_{(Q)} }$ is estimated as the slope of the regression line for log A$\displaystyle{ \epsilon }$,Q versus log $\displaystyle{ \epsilon }$ where:
• \alpha_{(Q)} is estimated as the slope of the regression line for versus where:

• alpha _ {(q)}估计为回归线的斜率，其中:
$\displaystyle{ A_{\epsilon,Q} = \sum_{i=1}^{N_\epsilon}{\mu_{{i,\epsilon}_{Q}}{P_{{i,\epsilon}_{Q}}}} }$

(Eq.6.0)

• Then $\displaystyle{ f_{\left(\alpha_{{(Q)}}\right)} }$ is found from Eq.5.3.

• Then f_{\left(\alpha_模板:(Q)\right)} is found from .
• 然后 f _ { left (alpha _ {{(q)} right)}从。
• The mean $\displaystyle{ \tau_{(Q)} }$ is estimated as the slope of the log-log regression line for $\displaystyle{ \tau_{{(Q)}_{[\epsilon]}} }$ versus $\displaystyle{ \epsilon }$, where:
• The mean \tau_{(Q)} is estimated as the slope of the log-log regression line for \tau_{{(Q)}_{[\epsilon]}} versus \epsilon, where:

• 平均 tau _ {(q)}被估计为 tau _ {{(q)} _ {[ epsilon ]}}相对于 epsilon 的对数对数回归线的斜率，其中:
$\displaystyle{ \tau_{(Q)_{[\epsilon]}} = \frac{\sum_{i=1}^{N_\epsilon} {P_{[i,\epsilon]}^{Q-1}}} {N_\epsilon} }$

(Eq.6.1)

In practice, the probability distribution depends on how the dataset is sampled, so optimizing algorithms have been developed to ensure adequate sampling.[22]

In practice, the probability distribution depends on how the dataset is sampled, so optimizing algorithms have been developed to ensure adequate sampling.

## Applications

Multifractal analysis has been successfully used in many fields, including physical, information, and biological sciences.[25] For example, the quantification of residual crack patterns on the surface of reinforced concrete shear walls.[26]

Multifractal analysis has been successfully used in many fields, including physical, information, and biological sciences. For example, the quantification of residual crack patterns on the surface of reinforced concrete shear walls.

### Dataset distortion analysis

Multifractal analysis is analogous to viewing a dataset through a series of distorting lenses to home in on differences in scaling. The pattern shown is a Hénon map.

Multifractal analysis has been used in several scientific fields to characterize various types of datasets.[27][5][8] In essence, multifractal analysis applies a distorting factor to datasets extracted from patterns, to compare how the data behave at each distortion. This is done using graphs known as multifractal spectra, analogous to viewing the dataset through a "distorting lens", as shown in the illustration.[22] Several types of multifractal spectra are used in practise.

Multifractal analysis has been used in several scientific fields to characterize various types of datasets. In essence, multifractal analysis applies a distorting factor to datasets extracted from patterns, to compare how the data behave at each distortion. This is done using graphs known as multifractal spectra, analogous to viewing the dataset through a "distorting lens", as shown in the illustration. Several types of multifractal spectra are used in practise.

#### DQ vs Q

DQ vs Q spectra for a non-fractal circle (empirical box counting dimension = 1.0), mono-fractal Quadric Cross (empirical box counting dimension = 1.49), and multifractal Hénon map (empirical box counting dimension = 1.29).

One practical multifractal spectrum is the graph of DQ vs Q, where DQ is the generalized dimension for a dataset and Q is an arbitrary set of exponents. The expression generalized dimension thus refers to a set of dimensions for a dataset (detailed calculations for determining the generalized dimension using box counting are described below).

One practical multifractal spectrum is the graph of DQ vs Q, where DQ is the generalized dimension for a dataset and Q is an arbitrary set of exponents. The expression generalized dimension thus refers to a set of dimensions for a dataset (detailed calculations for determining the generalized dimension using box counting are described below).

#### Dimensional ordering

The general pattern of the graph of DQ vs Q can be used to assess the scaling in a pattern. The graph is generally decreasing, sigmoidal around Q=0, where D(Q=0) ≥ D(Q=1) ≥ D(Q=2). As illustrated in the figure, variation in this graphical spectrum can help distinguish patterns. The image shows D(Q) spectra from a multifractal analysis of binary images of non-, mono-, and multi-fractal sets. As is the case in the sample images, non- and mono-fractals tend to have flatter D(Q) spectra than multifractals.

The general pattern of the graph of DQ vs Q can be used to assess the scaling in a pattern. The graph is generally decreasing, sigmoidal around Q=0, where D(Q=0) ≥ D(Q=1) ≥ D(Q=2). As illustrated in the figure, variation in this graphical spectrum can help distinguish patterns. The image shows D(Q) spectra from a multifractal analysis of binary images of non-, mono-, and multi-fractal sets. As is the case in the sample images, non- and mono-fractals tend to have flatter D(Q) spectra than multifractals.

= = = 维序 = = = = DQ 与 q 图的一般模式可以用来评估一个模式中的标度。图一般是递减的，在 q = 0左右，其中 d (q = 0)≥ d (q = 1)≥ d (q = 2)。如图所示，这个图形光谱的变化可以帮助区分模式。图像显示了 d (q)谱从非，单分形和多分形集的二进制图像的多重分形分析。正如在样本图像的情况，非和单分形往往比多分形有更平坦的 d (q)光谱。

The generalized dimension also gives important specific information. D(Q=0) is equal to the capacity dimension, which—in the analysis shown in the figures here—is the box counting dimension. D(Q=1) is equal to the information dimension, and D(Q=2) to the correlation dimension. This relates to the "multi" in multifractal, where multifractals have multiple dimensions in the D(Q) versus Q spectra, but monofractals stay rather flat in that area.[22][23]

The generalized dimension also gives important specific information. D(Q=0) is equal to the capacity dimension, which—in the analysis shown in the figures here—is the box counting dimension. D(Q=1) is equal to the information dimension, and D(Q=2) to the correlation dimension. This relates to the "multi" in multifractal, where multifractals have multiple dimensions in the D(Q) versus Q spectra, but monofractals stay rather flat in that area.

#### $\displaystyle{ f(\alpha) }$ versus $\displaystyle{ \alpha }$

Another useful multifractal spectrum is the graph of $\displaystyle{ f(\alpha) }$ versus $\displaystyle{ \alpha }$ (see calculations). These graphs generally rise to a maximum that approximates the fractal dimension at Q=0, and then fall. Like DQ versus Q spectra, they also show typical patterns useful for comparing non-, mono-, and multi-fractal patterns. In particular, for these spectra, non- and mono-fractals converge on certain values, whereas the spectra from multifractal patterns typically form humps over a broader area.

Another useful multifractal spectrum is the graph of f(\alpha) versus \alpha (see calculations). These graphs generally rise to a maximum that approximates the fractal dimension at Q=0, and then fall. Like DQ versus Q spectra, they also show typical patterns useful for comparing non-, mono-, and multi-fractal patterns. In particular, for these spectra, non- and mono-fractals converge on certain values, whereas the spectra from multifractal patterns typically form humps over a broader area.

= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =.这些曲线通常会上升到最大值，接近于 q = 0的分形维数，然后下降。与 DQ 和 q 光谱一样，它们也显示了用于比较非分形、单分形和多分形模式的典型模式。特别是，对于这些光谱来说，非分形和单分形集中在某些特定的值上，而多分形图案的光谱通常在更广的区域形成峰。

### Generalized dimensions of species abundance distributions in space

One application of Dq versus Q in ecology is characterizing the distribution of species. Traditionally the relative species abundances is calculated for an area without taking into account the locations of the individuals. An equivalent representation of relative species abundances are species ranks, used to generate a surface called the species-rank surface,[28] which can be analyzed using generalized dimensions to detect different ecological mechanisms like the ones observed in the neutral theory of biodiversity, metacommunity dynamics, or niche theory.[28][29]

One application of Dq versus Q in ecology is characterizing the distribution of species. Traditionally the relative species abundances is calculated for an area without taking into account the locations of the individuals. An equivalent representation of relative species abundances are species ranks, used to generate a surface called the species-rank surface, which can be analyzed using generalized dimensions to detect different ecological mechanisms like the ones observed in the neutral theory of biodiversity, metacommunity dynamics, or niche theory.

= = = 空间物种多度分布的广义维数 = = = = Dq 与 q 在生态学中的一个应用是描述物种的分布。传统上计算一个地区的相对物种丰度时没有考虑个体的位置。相对物种丰度的等价表示是物种等级，用于生成一个称为物种等级面的表面，这个表面可以用广义维度来分析，以发现不同的生态机制，如生物多样性中性理论、协整动力学或生态位理论中观察到的机制。

• Fractional Brownian motion
• Detrended fluctuation analysis
• Tweedie distributions
• Markov switching multifractal
• Weighted planar stochastic lattice (WPSL)

# = =

• 分数布朗运动
• 去趋势涨落分析
• Tweedie 分布
• Markov 切换多重分形
• 加权平面随机晶格(WPSL)

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