CN110780342A - Rock slope deformation early warning method - Google Patents

Abstract

The invention provides a method for early warning of rock slope deformation, comprising: acquiring a multifractal spectrum of a microseismic signal in a target detection space range within a preset time period; dividing the multifractal spectrum of the microseismic signal into a plurality of intervals of equal time length; analyzing The multifractal spectrum width of each of the intervals and the proportion of size fluctuations in the waveform; according to the change trend of the multifractal spectrum width and the change trend of the proportion of size fluctuations in the waveform, analyze whether the target detection space range will Deformation and instability, if deformation and instability occurs, an early warning prompt is provided, which realizes the technical effect of better early warning of slope deformation during the slope excavation process.

Description

A kind of rock slope deformation early warning method

technical field

The present application relates to the technical field of geotechnical engineering, and in particular, to a method for early warning of rock slope deformation.

Background technique

Deformation and instability monitoring and early warning of large rock slopes have always been a hot and difficult research topic in the field of rock mechanics and engineering. At present, routine detection mainly adopts one-dimensional or two-dimensional monitoring methods such as stress gauges and displacement gauges, which are limited in space and usually have a time lag in time, so it is impossible to predict the occurrence of disasters in advance.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method for early warning of rock slope deformation, so as to realize the technical effect of better early warning of slope deformation in the process of slope excavation.

The invention provides a method for early warning of rock slope deformation, which includes acquiring the multi-fractal spectrum of microseismic signals in a target detection space range within a preset time period; dividing the multi-fractal spectrum of microseismic signals into a plurality of intervals of equal length; analyzing each The multifractal spectrum width of the interval and the proportion of size fluctuations in the waveform; analyze whether the target detection space range will be deformed according to the change trend of the multifractal spectrum width and the change trend of the size fluctuation proportion in the waveform Instability, if deformation and instability occur, an early warning prompt will be given.

In the above implementation process, during the analysis, the multifractal spectrum of the microseismic signal in the target detection space within the preset time period is divided into multiple intervals of equal length, and then the width of the refractal spectrum in each interval and the proportion of fluctuations in the waveform are analyzed. , and then analyze whether the target detection space will be deformed and unstable according to the change trend of the above-mentioned multifractal spectrum width and the change trend of the proportion of large and small fluctuations in the waveform. If deformation and instability occurs, a warning will be given. By analyzing the variation trend of the multifractal spectrum width and the variation trend of the proportion of large and small fluctuations in the waveform, the slope deformation early warning can be better performed during the slope excavation process.

Further, the step of dividing the microseismic signal multifractal spectrum into a plurality of intervals of equal time length includes: acquiring a microseismic signal time series; constructing a corresponding microseismic signal profile according to the microseismic signal time series; The head of the signal profile divides the microseismic signal profile into N _s equal-length intervals; at the same time, the microseismic signal profile is divided into N _s equal-length intervals from the tail of the microseismic signal profile, N _s =int ( N/s), where N represents the length of the time series, s represents the length of each interval, and int represents an integer.

In the above implementation process, considering that the length of the obtained time series cannot be completely divided according to the preset interval length, in order to make full use of the length of the sequence data, the time series is divided into equal time lengths by means of forward and reverse bidirectional division.

Further, the step of analyzing the multifractal spectrum width of each of the intervals and the proportion of size fluctuations in the waveform includes: performing linear fitting on the data of each of the intervals to obtain a fitting sequence corresponding to each of the intervals, and analyze the fitting sequence according to the preset analysis method to obtain the corresponding fluctuation function; make a logarithmic graph according to the fluctuation function and the interval length and analyze the logarithmic graph to obtain the corresponding generalized Hurst index ; According to the analysis of the generalized Hurst exponent, the multifractal spectrum width of each interval and the proportion of size fluctuations in the waveform are obtained.

In the above implementation process, firstly, a straight line fitting algorithm is used to perform straight line fitting on the data of each interval to obtain a fitting sequence corresponding to each interval; secondly, the fitting sequence is analyzed according to a preset analysis method to obtain the corresponding wave function; thirdly, according to the obtained wave function and interval length, make a double logarithmic graph and analyze the double logarithmic graph to obtain the corresponding generalized Hurst index; finally, according to the analysis of the generalized Hurst index, the multifractal spectrum width and The proportion of large and small fluctuations in the waveform.

Further, the step of performing linear fitting on the data of each of the intervals to obtain a fitting sequence corresponding to each of the intervals includes: acquiring a preset fitting sequence order of each of the intervals; The fitting sequence order uses the least squares method to perform straight line fitting to obtain the corresponding fitting sequence.

In the above implementation process, the present invention uses the least squares method to perform linear fitting on the data in each interval according to the preset fitting sequence order, which is more convenient to use.

计算得到所述多重分形谱宽度；根据公式

计算得到所述波形中大小波动所占比例；其中α表示多重分形谱宽度，

表示广义Hurst指数，q表示波动函数阶数，表示广义Hurst指数的导数，f（α）表示多重分形谱函数。Further, the step of obtaining the multifractal spectrum width of each of the intervals and the proportion of size fluctuations in the waveform according to the generalized Hurst exponent analysis includes: according to the formula

Calculate the multifractal spectrum width; according to the formula

Calculate the proportion of size fluctuations in the waveform; where α represents the multifractal spectrum width,

represents the generalized Hurst exponent, q represents the order of the wave function, represents the derivative of the generalized Hurst exponent and f ( α ) represents the multifractal spectral function.

In the above implementation process, the fluctuation functions of each order in the interval can be obtained by calculating the generalized index, which is more convenient to analyze the variation law.

Further, the step of analyzing the fitting sequence to obtain the corresponding wave function according to the preset analysis method also includes: calculating the first variance of N _s intervals divided from the head of the microseismic signal profile. ; Calculate the second variance divided from the tail of the microseismic signal profile to obtain N _s intervals; calculate the average value of the first variance and the second variance to obtain the corresponding wave function.

In the above implementation process, the fluctuation function corresponding to each interval is calculated according to the average value of the first variance and the second variance, which eliminates the influence of the non-stationary trend of the time series, and the result is more accurate.

Further, the step of making a logarithmic graph according to the fluctuation function and the interval length and analyzing the logarithmic graph to obtain a corresponding generalized Hurst index further includes: obtaining an initial fluctuation function order and a maximum fluctuation function order. time, the initial interval length, the maximum interval length, the variation rule of the preset interval length, and the variation rule of the preset fluctuation function order; according to the initial fluctuation function order, the maximum fluctuation function order, the initial interval length, the Set the interval length variation rule and the preset fluctuation function order variation rule to calculate each order wave function corresponding to each interval length; make the double logarithmic graph according to the wave function and the interval length, and analyze the double logarithm Figure to get the corresponding Hurst index.

In the above implementation process, according to the initial wave function order, the maximum wave function order, the initial interval length, the maximum interval length, the change rule of the preset interval length, and the change rule of the preset wave function order, etc. Each order fluctuation function corresponding to the interval and a corresponding double logarithmic graph are made, and the corresponding Hurst exponent is obtained by analyzing the double logarithmic graph. Using fluctuation functions of different orders to analyze the scaling behavior of time series at different levels, the fractal characteristics of the time series are finely described, and the multifractal characteristics hidden in the non-stationary time series are revealed.

Description of drawings

In order to illustrate the technical solutions of the present invention more clearly, the following drawings are briefly introduced to be used in the present invention. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be viewed as As a limitation of the scope, for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

1 is a schematic diagram of a typical microseismic signal provided by the present invention;

2 is a schematic flowchart of a method for early warning of rock slope deformation provided by the present invention;

3 is a schematic diagram of an embodiment of a method for early warning of rock slope deformation provided by the present invention;

双对数图；Figure 4 is provided by the present invention

double logarithmic plot;

5 is a schematic diagram of a multifractal spectrum of a typical microseismic event provided by the present invention;

Fig. 6 is a multifractal spectrum width time-varying response law diagram provided by the present invention;

FIG. 7 is a time-varying response law diagram of the proportion of large and small fluctuations in the waveform provided by the present invention.

Detailed ways

The technical solutions in the present invention will be described below with reference to the accompanying drawings in the present invention.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

Please refer to FIG. 1 and FIG. 2, FIG. 1 is a schematic diagram of a typical microseismic signal provided by the present invention; FIG. 2 is a schematic flowchart of a method for early warning of rock slope deformation provided by the present invention.

As shown in Figure 1, the rock rupture signal collected by the microseismic monitoring system is a complex nonlinear and non-stationary time series. Slope rock fractures often have discontinuous multi-scale characteristics, and the traditional simple fractal dimension is less effective for this type of time series detection. Therefore, this application provides a rock slope deformation early warning method for use in slopes. Better early warning of slope deformation during excavation. The specific content is shown in Figure 2:

Step S101 , acquiring the multifractal spectrum of the microseismic signal in the target detection spatial range within a preset time period.

In one embodiment, the multifractal spectrum of the microseismic signal can be detected using a microseismic signal detector. Select the multifractal spectrum of the microseismic signal detected in the detection space within a certain time period for analysis, and give early warning.

Step S102, dividing the multifractal spectrum of the microseismic signal into a plurality of intervals of equal time length.

In one embodiment, in order to make full use of the length of the sequence data, the time series is divided into equal time lengths by means of forward and reverse bidirectional division, and the microseismic signal contour is divided into N _s equal time lengths starting from the head of the At the same time, starting from the tail of the microseismic signal profile, the microseismic signal profile is also divided into N _s intervals of equal time length, N _s =int (N/s), where N represents the length of the time series, and s represents the length of each interval. Length, int means to take an integer.

Step S103: Analyze the multifractal spectrum width of each of the intervals and the proportion of fluctuations in the size of the waveform.

In an embodiment, firstly, the least squares method is used to perform linear fitting on the data in each interval according to the set order; secondly, the first part of the N _s intervals obtained by dividing the microseismic signal profile from the head of the microseismic signal profile is calculated separately. The variance and the second variance of N _s intervals are obtained from the tail of the microseismic signal profile; again, the average value of the first variance and the second variance and the preset order of the fluctuation function are calculated to obtain the corresponding fluctuation function , make the F _q (s)-s double logarithmic graph corresponding to the q-order wave function and analyze to obtain the corresponding generalized Hurst exponent; finally, according to the preset calculation method, the multifractal spectrum width of each interval and the proportion of fluctuations in the waveform are obtained. Proportion.

Step S104, according to the change trend of the multifractal spectrum width and the change trend of the proportion of size fluctuations in the waveform, analyze whether the target detection space range will be deformed and unstable, and if deformation and instability will occur, then give an early warning prompt .

In the above process, after calculating the multifractal spectrum width of each interval and the proportion of size fluctuations in the waveform, it is possible to synthesize the variation trend of the multifractal spectrum width in each interval and the proportion of the size fluctuations in the waveform. Analysis, if the result of the analysis is that deformation and instability will occur within the detection space, the early warning prompt will be given in time, the construction personnel will be notified in time for reinforcement, and the continuous growth of cracks will be controlled in time to prevent the slope from deformation, instability and damage.

Please refer to FIG. 3. FIG. 3 is a schematic diagram of an embodiment of a method for early warning of rock slope deformation provided by the present invention.

In an embodiment provided in this application, the MF-DFA algorithm is used to analyze the multifractal spectrum of the microseismic signal. The details are as follows.

构造微震信号轮廓，其中x _k 表示时间序列中的各个时间；

表示时间序列

的均值，即

；Y(i)表示信号轮廓。(1) First obtain the microseismic signal time series of the multifractal spectrum of the microseismic signal, and then according to the formula

Construct the microseismic signal profile, where x _k represents each time in the time series;

represent time series

the mean of , that is

; Y ( i ) represents the signal profile.

会有余值。为了充分利用数据，保留这部分余值，可从信号轮廓的尾部开始，重复上述划分过程，此时会得到2N _s个等长小区间。(2) Contour the signal It is divided into N _s small intervals of equal time length, that is, N _s =int(N/s). Since N is not necessarily an integer multiple of s, the signal profile during division

There will be residuals. In order to make full use of the data, keep this part of the residual value, which can be obtained from the signal profile Starting from the tail of , repeating the above division process, at this time, 2 N _s equal-length small intervals will be obtained.

(3) Use the least squares method to fit the local trend of the data between each cell in step (2), and then calculate its variance. Specifically, the calculation is performed by the following formula.

When v = 1, 2, ..., N _s :

表示第v个小区间的m（m=1,2,3，...）阶拟合多项式。即

；

表示第v个小区间的信号轮廓；

表示第v个小区间的方差；s表示各个区间的长度。in,

Represents the fitting polynomial of order m (m=1, 2, 3, ...) in the vth cell. which is

;

represents the signal profile of the vth cell;

Represents the variance of the vth small interval; s represents the length of each interval.

When v=N _s +1, N _s +2, ..., 2 N _s :

(4) Calculate the q -order wave function :

双对数图，具体地，请参看图4，图4是本发明提供的双对数图。在作出双对数图以后就可以对其进行分析，确定广义Hurst指数h（q），h（q）表示原始序列的相关性，(5) Make the q -order wave function

Double logarithmic graph, specifically, please refer to Fig. 4, Fig. 4 is provided by the present invention Double logarithmic graph. After making the double logarithmic plot, it can be analyzed to determine the generalized Hurst exponent h ( q ), h ( q ) represents the correlation of the original series,

The size of h ( q ) depends on the size of the q value.

（多重分形谱宽度）和多重分形谱函数

：(6) Find the singularity index

(Multifractal Spectral Width) and Multifractal Spectral Function

:

Please refer to Fig. 5, Fig. 6 and Fig. 7, Fig. 5 is a schematic diagram of the multifractal spectrum of a typical microseismic event provided by the present invention, Fig. 6 is a graph of the time-varying response law of the multifractal spectrum width provided by the present invention; The time-varying response law of the proportion of large and small fluctuations in the waveform.

（多重分形谱宽度）和多重分形谱函数

以后就可以根据各个区间中的奇异指数最大值和最小值计算得到多重分形谱宽度，同时根据多重分形谱函数计算得到各个区间中波形中大小波动所占比例。最后对多重分形谱宽度的变化趋势和波形中大小波动所占比例的变化趋势进行分析确定目标检测空间范围是否会变形失稳，若会发生变形失稳，则进行预警提示。具体地，若多重分形谱宽度呈现增大趋势，对应的波形中大小波动所占比例呈现减小趋势，则进行预警。Calculate the singularity index

(Multifractal Spectral Width) and Multifractal Spectral Function

Afterwards, the width of the multifractal spectrum can be calculated according to the maximum and minimum values of the singular exponents in each interval, and at the same time, the proportion of the size fluctuation in the waveform in each interval can be calculated according to the multifractal spectral function. Finally, the change trend of the multifractal spectrum width and the change trend of the proportion of the size fluctuation in the waveform are analyzed to determine whether the target detection space will deform and become unstable. Specifically, if the width of the multifractal spectrum shows an increasing trend, and the proportion of large and small fluctuations in the corresponding waveform shows a decreasing trend, an early warning is issued.

During the analysis, the interval lengths s and q can also be assigned values first. Simultaneously set the variation rules of the cyclic calculation of s and q, and then repeat the above process to obtain each order wave function corresponding to each interval length, make the double logarithmic graph according to the wave function and the interval length, and analyze the double pair The corresponding Hurst exponent is obtained from the digit map. Using fluctuation functions of different orders to analyze the scaling behavior of time series at different levels, the fractal characteristics of the time series are finely described, and the multifractal characteristics hidden in the non-stationary time series are revealed.

To sum up, the present invention provides a method for early warning of rock slope deformation, including acquiring a multifractal spectrum of a microseismic signal in a target detection space within a preset time period; dividing the multifractal spectrum of the microseismic signal into a plurality of intervals of equal time length. ;Analyze the multifractal spectrum width in each interval and the proportion of size fluctuations in the waveform; analyze whether the target detection space range will be deformed and unstable according to the change trend of the multifractal spectrum width and the change trend of the size fluctuations in the waveform. In the event of deformation and instability, an early warning prompt is provided, which achieves the technical effect of better early warning of slope deformation during the slope excavation process.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (7)

1. A rock slope deformation early warning method is characterized by comprising the following steps:

acquiring a multi-fractal spectrum of a microseismic signal in a target detection space range within a preset time period;

dividing the multi-fractal spectrum of the microseismic signal into a plurality of intervals with equal time length;

analyzing the multi-fractal spectrum width of each interval and the proportion of size fluctuation in the waveform;

and analyzing whether the target detection space range can deform and destabilize or not according to the variation trend of the multi-fractal spectrum width and the variation trend of the proportion of the size fluctuation in the waveform, and if the target detection space range can deform and destabilize, carrying out early warning prompt.

2. The method of claim 1, wherein the step of dividing the multi-fractal spectrum of microseismic signals into a plurality of intervals of equal time length comprises:

acquiring a microseismic signal time sequence;

constructing a corresponding microseismic signal profile according to the microseismic signal time sequence;

partitioning the microseismic signal profile into segments starting from the beginning of the microseismic signal profile N _sIntervals of equal time length; simultaneously dividing the microseismic signal profile into a plurality of microseismic signal profiles starting from the tail of the microseismic signal profile N _sAn interval of equal time length is formed, N _sint (N/s), where N denotes the length of the time series, s denotes the length of each interval, and int denotes taking an integer.

3. The method of claim 2, wherein the step of analyzing the multifractal spectral width and the proportion of size variations in the waveform for each of the intervals comprises:

performing linear fitting on the data of each interval to obtain a fitting sequence corresponding to each interval;

analyzing the fitting sequence according to a preset analysis mode to obtain a corresponding fluctuation function;

making a double logarithmic graph according to the fluctuation function and the length of the interval and analyzing the double logarithmic graph to obtain a corresponding generalized Hurst index;

and analyzing according to the generalized Hurst index to obtain the multi-fractal spectrum width of each interval and the proportion of size fluctuation in the waveform.

4. The method according to claim 3, wherein the step of performing straight line fitting on the data of each interval to obtain a fitting sequence corresponding to each interval comprises:

acquiring a preset fitting sequence order of each interval;

and performing linear fitting by using a least square method according to the preset fitting sequence order to obtain a corresponding fitting sequence.

5. The method of claim 3, wherein said step of obtaining the multifractal spectral width and the ratio of size variations in the waveform for each of said intervals from said generalized Hurst exponent analysis comprises:

according to the formula

Calculating to obtain the multi-fractal spectrum width;

according to the formula

Calculating to obtain the proportion of the large and small fluctuation in the waveform;

wherein αThe multi-fractal spectral width is represented, which represents the generalized Hurst index of the device, qthe order of the ripple function is represented,

representing generalized Hurst indicesThe derivative(s) of the signal(s), f（ α) Representing a multi-fractal spectral function.

6. The method according to claim 3, wherein the step of analyzing the fitting sequence according to a preset analysis mode to obtain a corresponding fluctuation function comprises:

the calculation is obtained by dividing the microseismic signal contour from the head N _sA first variance of the intervals;

the calculation is obtained by dividing the tail part of the microseismic signal profile N _sA second variance of the intervals;

and calculating the average value of the first variance and the second variance to obtain a corresponding fluctuation function.

7. The method of claim 3, wherein said step of generating a log-log plot based on said volatility function and said interval length and analyzing said log-log plot to obtain a corresponding generalized Hurst exponent further comprises:

acquiring an initial fluctuation function order, a maximum fluctuation function order, an initial interval length, a maximum interval length, a preset interval length change rule and a preset fluctuation function order change rule;

calculating to obtain each order of fluctuation function corresponding to each interval length according to the initial fluctuation function order, the maximum fluctuation function order, the initial interval length, the maximum interval length, the preset interval length change rule and the preset fluctuation function order change rule;

and making the double logarithmic graph according to the fluctuation function and the interval length, and analyzing the double logarithmic graph to obtain a corresponding Hurst index.

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