CN110780342A - Rock slope deformation early warning method - Google Patents
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Abstract
The invention provides a rock slope deformation early warning method which comprises the steps of obtaining 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 lose stability or not according to the variation trend of the multi-fractal spectrum width and the variation trend of the proportion of the large fluctuation and the small fluctuation in the waveform, and if the target detection space range can deform and lose stability, carrying out early warning prompt, thereby realizing the technical effect of better carrying out early warning on the deformation of the side slope in the process of excavating the side slope.
Description
Technical Field
The application relates to the technical field of geotechnical engineering, in particular to a rock slope deformation early warning method.
Background
The monitoring and early warning of deformation instability of large rock slopes is a heat point and a difficult point of research in the fields of rock mechanics and engineering all the time. At present, the conventional detection mainly adopts one-dimensional or two-dimensional monitoring means such as a stress meter, a displacement meter and the like, has limitation in space, generally has time lag in time, and cannot predict the occurrence of disasters in advance.
Disclosure of Invention
The invention aims to provide a rock slope deformation early warning method which is used for achieving the technical effect of better early warning of slope deformation in the process of slope excavation.
The invention provides a rock slope deformation early warning method which comprises the steps of obtaining 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 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.
In the implementation process, during analysis, the multi-fractal spectrum of the microseismic signal in the target detection space range in a preset time period is divided into a plurality of equal-length sections, then the re-fractal spectrum width of each section and the proportion of the size fluctuation in the waveform are analyzed, whether the target detection space range is deformed or not is analyzed 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 is deformed or not, early warning prompt is carried out. Through analysis of the variation trend of the multi-fractal spectrum width and the variation trend of the proportion of the size fluctuation in the waveform, the slope deformation early warning can be better carried out in the process of slope excavation.
Further, the step of dividing the multi-fractal spectrum of the microseismic signal into a plurality of intervals with 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 length; simultaneously partitioning the microseismic signal profile starting from the tail of the microseismic signal profileIs composed of
N sA section with the same length is arranged in the middle of the section,
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.
In the implementation process, considering that the obtained time sequence length cannot be completely divided according to the preset interval length, in order to fully utilize the sequence data length, the time sequence is divided into equal time lengths by using a positive and negative bidirectional division mode.
Further, the step of analyzing the ratio of the fractal spectrum width and the size fluctuation in the waveform of each of the intervals includes: performing linear fitting on the data of each interval to obtain a fitting sequence corresponding to each interval, and 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 interval length 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.
In the implementation process, firstly, a straight line fitting algorithm is used for performing straight line fitting on the data of each interval to obtain a fitting sequence corresponding to each interval; secondly, analyzing the fitting sequence according to a preset analysis mode to obtain a corresponding fluctuation function; thirdly, according to the fluctuation function and the interval length obtained by analysis, making a double-logarithm diagram and analyzing the double-logarithm diagram to obtain a corresponding generalized Hurst index; and finally, obtaining the multi-fractal spectral width of each interval and the proportion of size fluctuation in the waveform according to the generalized Hurst index analysis.
Further, the step of performing straight line fitting on the data of each interval to obtain a fitting sequence corresponding to each interval includes: 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.
In the implementation process, the data in each interval are subjected to linear fitting by using a least square method according to the preset fitting sequence order, so that the method is more convenient to use.
Further, the step of obtaining the multi-fractal spectrum width of each interval and the proportion of size fluctuation in the waveform according to the generalized Hurst index 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 the derivative of the generalized Hurst index,
f（
α) Representing a multi-fractal spectral function.
In the implementation process, the fluctuation function of each order of the interval can be obtained through calculation of the generalized index, so that the change rule can be analyzed more conveniently.
Further, the step of analyzing the fitting sequence according to a preset analysis manner to obtain a corresponding fluctuation function further includes: 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.
In the implementation process, the fluctuation function corresponding to each interval is calculated according to the average value of the first variance and the second variance, so that the influence of the non-stationary trend of the time sequence is eliminated, and the result is more accurate.
Further, the step of making a log-log graph according to the fluctuation function and the interval length and analyzing the log-log graph to obtain a corresponding generalized Hurst index further includes: 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.
In the implementation process, 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, the preset fluctuation function order change rule and the like, each order of fluctuation function corresponding to each interval under each interval length is obtained through analysis, a corresponding double logarithmic graph is made, and the corresponding Hurst index is obtained through analysis of the double logarithmic graph. And analyzing the scale behaviors of the time sequence on different levels by using different-order fluctuation functions, and revealing the multi-fractal characteristics hidden in the non-stationary time sequence by finely depicting the fractal characteristics of the time sequence.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that are required to be used in the present invention will be briefly described below, it should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be considered as limiting the scope, and it will be apparent to those skilled in the art that other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a schematic diagram of an exemplary microseismic signal provided by the present invention;
FIG. 2 is a schematic flow chart of a rock slope deformation early warning method provided by the invention;
FIG. 3 is a schematic diagram illustrating an embodiment of a rock slope deformation warning method according to the present invention;
FIG. 4 is a schematic representation of the present invention
A log-log graph;
FIG. 5 is a schematic diagram of a typical multi-fractal spectrum of microseismic events provided by the present invention;
fig. 6 is a diagram of a fractal spectral width time-varying response rule provided by the present invention;
fig. 7 is a time-varying response law diagram of the proportion of the size fluctuation in the waveform provided by the present invention.
Detailed Description
The technical solution of the present invention will be described below with reference to the accompanying drawings.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1 and 2, fig. 1 is a schematic diagram of a typical microseismic signal provided by the present invention; FIG. 2 is a schematic flow chart of a rock slope deformation early warning method provided by the invention.
As shown in FIG. 1, the rock fracture signal collected by the microseismic monitoring system is a complex non-linear, non-stationary time series. The slope rock fracture often has the characteristic of non-continuity and multi-scale, and the traditional simple fractal dimension has poor detection effect on the time series, so the rock slope deformation early warning method is provided by the application and used for better performing slope deformation early warning in the slope excavation process. The specific content is shown in fig. 2:
step S101, acquiring a multi-fractal spectrum of the microseismic signal in a target detection space range within a preset time period.
In one embodiment, the multi-fractal spectrum of the microseismic signal can be detected using a microseismic signal detector. And selecting a multi-fractal spectrum of the microseismic signals detected in a detection space range in a certain time period for analysis, and early warning in advance.
Step S102, dividing the multi-fractal spectrum of the microseismic signal into a plurality of intervals with equal time length.
In one embodiment, to fully utilize the sequence data length, the time sequence is divided into equal time lengths by forward and backward bidirectional division, and the microseismic signal contour is divided into equal time lengths from the head of the microseismic signal contour
N sIntervals of equal time length; simultaneously, the microseismic signal profile is divided into the same sections from the tail part 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.
And step S103, analyzing the multi-fractal spectrum width of each interval and the proportion of size fluctuation in the waveform.
In one embodiment, a least square method is used for performing straight line fitting on the data in each interval according to the set order; secondly, respectively calculating the division from the head part of the microseismic signal contour
N sThe first variance of each interval is obtained by dividing from the tail part of the microseismic signal profile
N sA second variance of the intervals; thirdly, calculating the average value of the first variance and the second variance and the preset fluctuation function order to obtain the corresponding fluctuation function, and making the fluctuation function corresponding to the q-order fluctuation function
F q (s) -s log-log graph and analyzing to obtain corresponding generalized Hurst index; and finally, obtaining the multi-fractal spectrum width of each interval and the proportion of size fluctuation in the waveform according to a preset calculation mode.
And step S104, analyzing whether the target detection space range is deformed and destabilized or not according to the variation trend of the multi-fractal spectrum width and the variation trend of the proportion of the large fluctuation and the small fluctuation in the waveform, and if the target detection space range is deformed and destabilized, carrying out early warning prompt.
In the process, after the multi-fractal spectrum width of each interval and the proportion of the size fluctuation in the waveform are obtained through calculation, comprehensive analysis can be carried out according to the change trend of the multi-fractal spectrum width of each interval and the change trend of the proportion of the size fluctuation in the waveform, if the result obtained through analysis is that deformation instability can occur in the detection space range, early warning prompt is timely carried out, constructors are timely notified to carry out reinforcement, continuous growth of cracks is timely controlled, and deformation instability damage of the slope is prevented.
Referring to fig. 3, fig. 3 is a schematic view of an embodiment of a rock slope deformation warning method provided by the present invention.
In one embodiment provided herein, the multi-fractal spectrum of microseismic signals is analyzed using an MF-DFA algorithm. The details are as follows.
(1) Firstly, acquiring a microseismic signal time sequence of a multifractal spectrum of a microseismic signal, and then obtaining a microseismic signal time sequence according to a formula
Constructing a microseismic signal profile wherein
x k Representing respective times in a time series;
representing a time sequence
Mean value of (i)
；
Y(
i) Representing the signal profile.
(2) Signal profile
Is divided into
N s Between cells of equal time length, i.e.
N sInt (N/s). Since N is not necessarily an integer multiple of s, the signal profile during the division process
There will be a remainder. To make full use of the data, the remainder is retained, possibly from the signal profile
Is repeated, this time 2 is obtained
N sAnd (4) a plurality of equally long cells.
(3) And (3) fitting the local trend of the data among each cell in the step (2) by using a least square method, and then calculating the variance of the local trend. Specifically, the calculation is performed by the following formula.
When in use
v=1，2，...，
N sThe method comprises the following steps:
wherein,
an m (m =1,2, 3..) order fit polynomial representing the v-th interval. Namely, it is
；
A signal profile representing a vth cell interval;
represents the variance of the v-th cell interval; s represents the length of each section.
When in use
v=N s+1，
N s+2，...，2
N sThe method comprises the following steps:
(4) computing
qOrder of ripple function
：
（5）Make a
qOf order of a ripple function
Double logarithmic graph, specifically, please refer to fig. 4, fig. 4 is provided by the present invention
A log-log plot. After the double logarithm diagram is made, the double logarithm diagram can be analyzed to determine the generalized Hurst index
h（
q），
h（
q) Which represents the correlation of the original sequence and,
h（
q) Is determined by
qThe value size.
(6) Singular index of
(Multifractal spectral Width) and Multifractal spectral function
：
Please refer to fig. 5, fig. 6, and fig. 7, in which fig. 5 is a diagram of a typical multifractal spectrum of a microseismic event provided by the present invention, and fig. 6 is a diagram of a time-varying response rule of a multifractal spectrum width provided by the present invention; fig. 7 is a time-varying response law diagram of the proportion of the size fluctuation in the waveform provided by the present invention.
Obtaining singular index in calculation
(Multifractal spectral Width) and Multifractal spectral function
Then, the maximum singular index in each interval can be obtainedAnd calculating the value and the minimum value to obtain the multi-fractal spectrum width, and calculating the proportion of size fluctuation in the waveform in each interval according to the multi-fractal spectrum function. And finally, analyzing the variation trend of the multi-fractal spectrum width and the variation trend of the proportion of the size fluctuation in the waveform to determine whether the target detection space range can deform and destabilize, and if so, performing early warning prompt. Specifically, if the multi-fractal spectrum width shows an increasing trend and the proportion of the size fluctuation in the corresponding waveform shows a decreasing trend, the early warning is performed.
During analysis, the interval lengths s and q can be assigned first. And setting the change rule of the cyclic calculation of s and q at the same time, repeating the process to obtain each order of fluctuation function corresponding to each interval length, making the double-logarithm diagram according to the fluctuation function and the interval length, and analyzing the double-logarithm diagram to obtain the corresponding Hurst index. And analyzing the scale behaviors of the time sequence on different levels by using different-order fluctuation functions, and revealing the multi-fractal characteristics hidden in the non-stationary time sequence by finely depicting the fractal characteristics of the time sequence.
In summary, the invention provides a rock slope deformation early warning method, which includes 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; whether the target detection space range can deform and lose stability is analyzed according to the variation trend of the multi-fractal spectrum width and the variation trend of the proportion of the large fluctuation and the small fluctuation in the waveform, if the target detection space range can deform and lose stability, early warning prompt is carried out, and the technical effect of better early warning of slope deformation in the process of slope excavation is achieved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to 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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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111381276A (en) * | 2020-03-24 | 2020-07-07 | 长江水利委员会长江科学院 | Monitoring method for dam leakage damage occurrence development and positioning |
| CN114718793A (en) * | 2022-04-22 | 2022-07-08 | 西安理工大学 | Method for identifying cavitation state of through-flow turbine |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104614717A (en) * | 2015-01-28 | 2015-05-13 | 南京信息工程大学 | Small target fractal detection method under sea clutter background |
| CN105974469A (en) * | 2016-06-30 | 2016-09-28 | 马克 | Micro-seismic monitoring early warning analysis system and method under action of rock slope excavating perturbation |
| CN108498106A (en) * | 2018-02-08 | 2018-09-07 | 陕西师范大学 | CNV brain electricity lie detecting methods based on multi-fractal detrend fluctuation analysis |
| CN109001807A (en) * | 2018-07-19 | 2018-12-14 | 四川大学 | Arch dam body crack advanced early warning method based on micro seismic monitoring |
-
2019
- 2019-12-31 CN CN201911407192.7A patent/CN110780342A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104614717A (en) * | 2015-01-28 | 2015-05-13 | 南京信息工程大学 | Small target fractal detection method under sea clutter background |
| CN105974469A (en) * | 2016-06-30 | 2016-09-28 | 马克 | Micro-seismic monitoring early warning analysis system and method under action of rock slope excavating perturbation |
| CN108498106A (en) * | 2018-02-08 | 2018-09-07 | 陕西师范大学 | CNV brain electricity lie detecting methods based on multi-fractal detrend fluctuation analysis |
| CN109001807A (en) * | 2018-07-19 | 2018-12-14 | 四川大学 | Arch dam body crack advanced early warning method based on micro seismic monitoring |
Non-Patent Citations (4)
| Title |
|---|
| 李楠 等: ""冲击破坏过程微震波形多重分形及其时变响应特征"", 《中国矿业大学学报》 * |
| 杨景文 等: ""基于多重分形的大坝裂缝整体性态分析"", 《水电能源科学》 * |
| 郑丰 著: "《金融市场分形特征研究》", 31 December 2015 * |
| 金宁德 等编著: "《非线性信息处理技术》", 31 January 2017, 天津大学出版社 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111381276A (en) * | 2020-03-24 | 2020-07-07 | 长江水利委员会长江科学院 | Monitoring method for dam leakage damage occurrence development and positioning |
| CN111381276B (en) * | 2020-03-24 | 2021-06-01 | 长江水利委员会长江科学院 | Monitoring method for dam leakage damage occurrence development and positioning |
| CN114718793A (en) * | 2022-04-22 | 2022-07-08 | 西安理工大学 | Method for identifying cavitation state of through-flow turbine |
| CN114718793B (en) * | 2022-04-22 | 2024-02-06 | 西安理工大学 | Through-flow turbine cavitation state identification method |
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Application publication date: 20200211 |