CN104615846A - Wavelet recognition method for landslide deformation sudden change abnormity - Google Patents

Abstract

The invention discloses a wavelet recognition method for the landslide deformation sudden change abnormity and relates to the technical field of landslides. With the combination of the wavelet multi-resolution analysis and sudden change point recognition principle, the wavelet recognition method comprises the specific steps that (1) signals are preprocessed, wherein interpolation and other processing are conducted on the signals; (2) wavelet multi-scale decomposition is conducted on the signals, small decomposition coefficients are obtained, and a wavelet function and decomposition levels of the wavelet function are reasonably selected according to the characteristics of the signals and wavelet transformation multi-scale decomposition properties; (3) the changing characteristics of the high-frequency wavelet coefficients are analyzed, the maximum value of the wavelet coefficients is calculated, and the sudden change signal generation time is determined by detecting the change characters and the maximum value of the wavelet coefficients. By means of the wavelet recognition method, multi-scale analysis can be conducted on abnormal forewarning information of landside deformation sudden change, the sudden change point where a landslide enters an accelerating deformation stage can be accurately obtained, and the wavelet recognition method is an effect recognition method which is used for landslide deformation abnormity forewarning and is worthy of popularization and further study.

Description

The Wavelets of Landslide Deformation Sudden Anomalies

Technical field

What the present invention relates to is landslide technical field, is specifically related to a kind of Wavelets of Landslide Deformation Sudden Anomalies.

Background technology

At present enter to landslide the Study of recognition achievement of accelerating deformation stage catastrophe point and few, traditional recognition methods mainly contains two kinds: a kind of is the stage of the deformation monitoring data on landslide and macroscopical geological analysis and skew prired phenomenon combined to carry out aggregate qualitative differentiation; Another kind is the landslide accumulation displacement monitoring sequential data after foundation filtering process, and the variation characteristic according to rate of deformation carries out rational judgment.In recent years, some scholars is had also to conduct in-depth research this problem, so strong grade realizes coordinate in length and breadth, propose slope accelerate the quantitative criteria for classifying of deformation stage and three sub-phase thereof and sliding early warning criterion is faced on landslide with dimension according to the improvement grazing angle obtained by carrying out coordinate transform to slope accumulative displacement-time curve.The Negative Selection Algorithm thought that artificial immune system band makes a variation is introduced the Study of recognition of Landslide Deformation catastrophe point by Yuan Yong etc.Once the deformation evolution stage of index to landslide such as abundant flat interest landslide cumulative acceleration differentiated.These researchs provide theory and foundation for landslide enters the differentiation accelerating deformation stage undoubtedly.But due to the interference of rainfall, earthquake, Human dried bloodstains and other enchancement factor, cause the accumulation displacement duration curve complexity that comes down various, wanting to utilize existing method to identify exactly, that landslide enters the catastrophe point accelerating deformation stage is still very difficult.

Wavelet analysis is the powerful carrying out time-varying signal processing closely grown up during the last ten years.It can carry out multiscale analysis to signal, has very strong noise removal function and feature extraction functions, particularly evident to the process advantage of jump signal.Because landslide has multiple dimensioned characteristic in deformation evolutionary process, contain multi-level sudden change, be described as the wavelet analysis of " school microscop ", be particularly suitable for carrying out multiscale analysis, partial analysis and Singularity Analysis to signal.This is undoubtedly for the research of Landslide Deformation sudden change provides new method.At present, wavelet analysis method has achieved plentiful and substantial achievement in research in signal transacting, seismic prospecting, atmospheric science and many nonlinear science field.In hazard forecasting forecast field, landslide, wavelet analysis has just started the concern causing domestic and international associated specialist scholar, and its research method is single, and achievement in research is few, there is obvious limitation.

Summary of the invention

For the deficiency that prior art exists, the present invention seeks to the Wavelets being to provide a kind of Landslide Deformation Sudden Anomalies, wavelet analysis method can carry out multiscale analysis to the abnormal precursor information of Landslide Deformation sudden change, more adequately can obtain landslide and enter the catastrophe point accelerating deformation stage, be a kind of being worthy to be popularized and effective recognition methods of the Landslide Deformation precursory anomaly furtherd investigate.

To achieve these goals, the present invention realizes by the following technical solutions: the Wavelets of Landslide Deformation Sudden Anomalies, and in conjunction with the principle of wavelet multiresolution analysis and catastrophe point identification, its concrete grammar step is:

1, Signal Pretreatment: signal is carried out to the process such as interpolation.

2, Multiscale Wavelet Decomposition is carried out to signal, obtain little coefficient of dissociation: according to signal characteristic and wavelet transformation multi-resolution decomposition character, Rational choice wavelet function and decomposition level thereof;

3, the variation characteristic of analysis of high frequency wavelet coefficient, calculates the maximum value of wavelet coefficient, by determining to the variation characteristic of wavelet coefficient and the detection of wavelet coefficient maximum point the time that jump signal occurs.

Wavelet transformation: small echo be within the scope of finite time change and its mean value is the mathematical function of zero, there is frequency and the amplitude of limited duration and sudden change.Satisfy condition small echo be called wavelet or morther wavelet.By mother wavelet function ψ (t) through stretching and after translation, then having:

${\mathrm{\ψ}}_{a,b}\left(t\right)=\frac{1}{\sqrt{2}}\mathrm{\ψ}\left(\frac{t-b}{a}\right),(a,b\∈R,a\≠0)---\left(1\right)$

In formula (1), ψ a, b (t) is called wavelet basis function, and a is scale factor or contraction-expansion factor, and b is shift factor.

Wavelet transformation is defined as one group of wavelet basis function ψ a, the inner product of b (t) and signal f (t) to be analyzed.For continuous wavelet transform, can be expressed as [12]:

${\mathrm{WT}}_f(a,b)=<f\left(t\right),{\mathrm{\&psi;}}_{a,b}\left(t\right)>=\frac{1}{\sqrt{2}}\&Integral;f\left(t\right)\mathrm{\&psi;}\left(\frac{t-b}{a}\right)\mathrm{dt}---\left(2\right)$

Multi-resolution wavelet is analyzed: wavelet transformation has the feature of multiresolution (also crying multiple dimensioned), can progressively analytic signal from coarse to finely.Multiresolution analysis is very similar to the vision of the mankind.When people is when observing a certain target, the distance of people and target can be set as yardstick j, when object observing a long way off, corresponding large-scale dimension, can only see the general picture of target; When coming into target, corresponding to Small-scale Space, careful observation can be carried out to target.Draw near, yardstick from large to small accordingly, can carry out multiple dimensioned observation from coarse to fine to target.Therefore, there is people that wavelet method is described as " school microscop " of signal analysis.

By the definition of multiresolution analysis, if by f (t) ∈ L ²(R) launch by following Spatial Coupling:

$L^2\left(R\right)=\underset{j=-\&infin;}{\overset{J}{\mathrm{\&Sigma;}}}{W}_j\&CirclePlus;V_j---\left(3\right)$

Wherein J is the yardstick of arbitrarily setting, and Vj is metric space, the wavelet space of Wj to be yardstick be j, then

$f\left(t\right)=\underset{j=-\&infin;}{\overset{J}{\mathrm{\&Sigma;}}}\underset{k=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{d}_{j,k}{\mathrm{\&psi;}}_{j,k}\left(t\right)+\underset{k=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{c}_{j,k}{\mathrm{\&phi;}}_{j,k}\left(t\right)---\left(4\right)$

In formula, c _{j, k}=< f (t), φ _{j, k}t () >, is called yardstick expansion coefficient; d _{j, k}=< f (t), ψ _{j, k}t () > is called Wavelet Expansions coefficient, the 1st summation of formula (4) equal sign right-hand member is the approximation component of f (t) on Wj, and the 2nd summation is the wavelet details component on different scale.

Therefore, for arbitrary function, we it can be decomposed into detail section and large scale approaches part, then large scale are approached the nearly step of part and decompose, and so repeat just to obtain approaching partly and detail section on any yardstick (or resolution).The framework of Here it is multiresolution analysis.

The ultimate principle of Wavelet Detection sign mutation point:

Sign mutation has the implication of two aspects: one is the sharply change (namely peak value is unusual) of displacement, and two is sharply changes of frequency, and therefore the mutation analysis of signal also will comprise two aspects: the sudden change moment 1. determining signal; 2. different mutation types (Singularity Detection) is distinguished.If the free locality of the frequency spectrum of signal, the frequency analysis of given time just can be carried out, simultaneously again can the situation of change of As time goes on tracking signal frequency.The important feature of this wavelet transformation just.

From the angle of mathematics, the null point of first order derivative of a function corresponds to this Function Extreme Value point, and second order null point reciprocal corresponds to the flex point of this function, i.e. turning point.Therefore, if be the first order derivative or the second derivative that come from some lowpass functions for the wavelet function of wavelet transformation, so the result of wavelet transformation will embody extreme point or the turning point of signal.

Beneficial effect of the present invention: wavelet analysis method can carry out multiscale analysis to the abnormal precursor information of Landslide Deformation sudden change, more adequately can obtain landslide and enter the catastrophe point accelerating deformation stage.

Accompanying drawing explanation

The present invention is described in detail below in conjunction with the drawings and specific embodiments;

Fig. 1 is the detail signal after the Bai Shi township landslide accumulation displacement monitoring signal of the embodiment of the present invention and the reconstruct of continuous 4 Scale Decompositions thereof.

Embodiment

The technological means realized for making the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with embodiment, setting forth the present invention further.

Embodiment 1: stream (compacted) the alter opinion of Rock And Soil shows, under the continuous action of permanent load (as gravity), the standard displacement duration curve that Slope develops can be divided into three phases: initial creep stage, at the uniform velocity deformation stage and acceleration deformation stage.But due to the interference of rainfall, earthquake, Human dried bloodstains and other enchancement factor, in fact the displacement duration curve on great majority landslide all has fluctuation in various degree and fluctuating.

Below, for several accumulation displacement duration curve types common in the practice of landslide, utilize above-mentioned wavelet method and principle to enter to landslide the catastrophe point accelerating deformation stage and carry out Study of recognition.

Landslide, Yi Baishi township is example.This landslide is positioned at Lao Jiehou mountain, Bai Shi township, Beichuan County, Sichuan Province, landslide volume about 3,000,000 m ³.Landslide area belongs to etching structure senior middle school mountain region looks, and the shallow rotten phyllite of main exposure Silurian upper system Mao County group (Smx) and slate, general about the 1000-1800m of height above sea level, relative relief is about 800m.According to related data, this Landslide Deformation starts from 1986, and in January, 2007, relevant department implemented monitoring to this landslide, and on April 1st, 2007, landslide entered acceleration deformation stage, and landslide main body glided and plugged the plain boiled water river of landslide leading edge on July 28th, 2007.

According to the Theories and methods (wavelet decomposition selects biorl.1 small echo, and wavelet reconstruction selects db5 small echo) of above-mentioned wavelet recognition catastrophe point, the accumulation displacement monitoring signal of 6# monitoring point, landslide, dialogue assorted township is analyzed, and result as shown in Figure 1.

As can be seen from Figure 1, before the position of time sequence number 84 (the corresponding time is on April 4th, 2007), the amplitude of 4 yardstick detail signals is consistent substantially, and the amplitude of 4 yardstick detail signals is in constantly increasing trend afterwards, imply that landslide enters acceleration deformation stage.This and actual landslide enter the April 1 2007 time of accelerating deformation stage and only differ 3 days.

More than show and describe ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and instructions just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.

Claims (2)

1. the Wavelets of Landslide Deformation Sudden Anomalies, is characterized in that, in conjunction with the principle of wavelet multiresolution analysis and catastrophe point identification, its concrete grammar step is:

(1), Signal Pretreatment: signal is carried out to the process such as interpolation;

(2), to signal carry out Multiscale Wavelet Decomposition, obtain little coefficient of dissociation: according to signal characteristic and wavelet transformation multi-resolution decomposition character, Rational choice wavelet function and decomposition level thereof;

(3), the variation characteristic of analysis of high frequency wavelet coefficient, calculate the maximum value of wavelet coefficient, by determining to the variation characteristic of wavelet coefficient and the detection of wavelet coefficient maximum point the time that jump signal occurs.

2. the Wavelets of Landslide Deformation Sudden Anomalies according to claim 1, is characterized in that, the ultimate principle of described Wavelet Detection sign mutation point:

Sign mutation has the implication of two aspects: one is the sharply change of displacement, and two is sharply changes of frequency, and therefore the mutation analysis of signal also will comprise two aspects: (1) determines the sudden change moment of signal; (2) different mutation types is distinguished; If the free locality of the frequency spectrum of signal, the frequency analysis of given time just can be carried out, simultaneously again can the situation of change of As time goes on tracking signal frequency;

From the angle of mathematics, the null point of first order derivative of a function corresponds to this Function Extreme Value point, and second order null point reciprocal corresponds to the flex point of this function, i.e. turning point; Therefore, if be the first order derivative or the second derivative that come from some lowpass functions for the wavelet function of wavelet transformation, so the result of wavelet transformation will embody extreme point or the turning point of signal.