CN115144269B - Rock burst prediction method and device based on acoustic emission time-varying multi-fractal spectrum - Google Patents
Rock burst prediction method and device based on acoustic emission time-varying multi-fractal spectrum Download PDFInfo
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Abstract
The invention discloses a rock burst prediction method and device based on acoustic emission time-varying multi-fractal spectrum, and belongs to the technical field of rock burst prediction. The method comprises the following steps: intercepting the time-series acoustic emission absolute energy data by using a sliding event window according to an acoustic emission time series to obtain intercepted data of a plurality of acoustic emission absolute energies; calculating a multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain a multi-fractal spectrum and a singular index, and calculating the spectrum width of each multi-fractal spectrum; comparing the spectrum widths of all multi-fractal spectrums to obtain a starting point of continuously increasing the spectrum width, and taking the starting point as a characteristic point of rock burst prediction and forecast; and taking the ratio of the maximum principal stress value loaded in the rock burst experiment corresponding to the characteristic point to the uniaxial compressive strength of the rock test piece as the criterion for predicting and forecasting the rock burst. The method is attached to the acoustic emission time-varying characteristics in the rock deformation damage process, so that quantitative prediction of rock burst damage is realized, rock burst prediction is more accurate, and engineering practice is guided.
Description
Technical Field
The invention relates to the technical field of rock burst prediction, in particular to a rock burst prediction method and device based on acoustic emission time-varying multi-fractal spectrum.
Background
Rock burst is a nonlinear dynamic phenomenon that energy is instantaneously released by an energy rock body along an excavation free face in the excavation process of deep underground engineering. Because the problem of rock burst disasters is increasingly prominent and the mechanism of rock burst occurrence is highly complex, how to adopt an effective rock burst prediction means to quickly capture and judge the precursor information of rock burst occurrence becomes a research hotspot and difficulty of the rock burst problem.
Rock acoustic emission is the elastic wave that internal crack takes place the action such as closure, slides, expansion, link up when rock atress and releases, contains numerous information that rock inner structure and state evolution, consequently in indoor rock burst experiment, acoustic emission test technique is widely used in monitoring rock deformation destruction process to based on the analysis to acoustic emission parameter, obtain the interior micro-cracked characteristic of rock and the fracture process of depicting rock.
The rock fracture often has the characteristics of discontinuity and multiple scales, and the acquired rock burst acoustic emission signal is a complex nonlinear and non-stable signal, so that the nonlinear characteristics of the acoustic emission signal can be analyzed by combining a multi-fractal theory, and different local conditions on a fractal structure or special structural behaviors and characteristics caused by different data levels of the fractal structure in the evolution process can be described through a multi-fractal spectral function.
At present, a method for analyzing a rock burst acoustic emission signal based on a multi-fractal theory generally describes time period characteristics (stage characteristics) of the acoustic emission signal of a rock in a rock burst destruction process by constructing acoustic emission multi-fractal spectrums corresponding to different stress loading stages of a rock burst experiment and analyzing the multi-fractal spectrums at different stages. However, the internal damage of the rock in the process of rock burst inoculation and generation has dynamic evolution characteristics, so that the rock burst acoustic emission signal shows time-varying property, and therefore, the existing multi-fractal method based on the rock burst acoustic emission signal cannot analyze the acoustic emission parameter characteristics of key time nodes in the process of rock burst damage and realize the prediction and forecast of the rock burst.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides the following technical scheme.
The invention provides a rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum, which comprises the following steps:
acquiring time sequence data of acoustic emission absolute energy in the whole process of a rock burst experiment;
intercepting time sequence data of acoustic emission absolute energy by using a sliding event window according to an acoustic emission time sequence to obtain intercepted data of a plurality of acoustic emission absolute energies;
calculating a multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain a multi-fractal spectrum and a singular index
And calculating the spectral width of each multi-fractal spectrum by using the following formula
Wherein, in the process,
the maximum value of the singular index is represented,
represents the minimum of the singular index;
comparing the spectrum widths of all the multi-fractal spectrums to obtain an initial point with the continuously increased spectrum width, and taking the initial point as a characteristic point of rock burst prediction and forecast;
and taking the ratio of the loaded maximum principal stress value of the rock burst experiment corresponding to the characteristic point to the uniaxial compressive strength of the rock test piece as a criterion for rock burst prediction.
Preferably, the rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum provided by the invention further comprises the following steps:
acquiring a loading stress value and uniaxial compressive strength of a rock to be detected in a rock burst experiment process;
calculating the ratio of the maximum main stress value to the uniaxial compressive strength;
and if the calculated ratio reaches the prediction criterion of rock burst damage, predicting that the rock to be detected can be subjected to rock burst damage.
Preferably, the sliding event window comprises: window length and sliding step, where window length is set to 1000 events and sliding step is set to 100 events.
Preferably, the intercepting the time-series data of acoustic emission absolute energy by using the sliding event window comprises: a sliding event window is used to intercept forward from the end of the time series data of the acoustic emission absolute energy.
Preferably, the rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum provided by the invention further comprises the following steps:
according to the time sequence of acoustic emission, drawing each multi-fractal spectrum function obtained by calculation in a three-dimensional coordinate system to obtain a time-varying multi-fractal spectrum, wherein the coordinate axes of the three-dimensional coordinate system comprise: time, singular index and multifractal spectrum;
and analyzing the spectrum width change rule of the time-varying multi-fractal spectrum to obtain a starting point of the continuously increased spectrum width, and taking the starting point as a characteristic point of rock burst prediction.
Preferably, when the time-varying multi-fractal spectrum is drawn by using each multi-fractal spectrum function obtained by calculation, the weight factor
The value range of (a) is-50 to 50.
The invention provides a rock burst prediction device based on acoustic emission time-varying multi-fractal spectrum, which comprises:
the time sequence data acquisition module of the acoustic emission absolute energy is used for acquiring time sequence data of the acoustic emission absolute energy in the whole process of the rock burst experiment;
the acoustic emission absolute energy time sequence data intercepting module is used for intercepting the acoustic emission absolute energy time sequence data by utilizing a sliding event window according to an acoustic emission time sequence to obtain a plurality of pieces of acoustic emission absolute energy intercepting data;
the spectrum width calculation module of each multi-fractal spectrum is used for calculating the multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain the multi-fractal spectrum and the singular index
And calculating the spectrum width of each multi-fractal spectrum by using the following formula
Wherein, in the process,
the maximum value of the singular index is represented,
represents the minimum of the singular index;
the characteristic point determining module is used for comparing the spectrum widths of all the multi-fractal spectrums to obtain an initial point of the continuously increased spectrum width and taking the point as a characteristic point of rock burst prediction and forecast;
and the prediction criterion determining module is used for taking the ratio of the maximum main stress value loaded in the rock burst experiment corresponding to the characteristic points to the uniaxial compressive strength of the rock test piece as a prediction criterion for the rock burst damage.
A third aspect of the invention provides a memory storing a plurality of instructions for implementing the method according to the first aspect.
A fourth aspect of the present invention provides an electronic device comprising a processor and a memory coupled to the processor, the memory storing a plurality of instructions that are loadable and executable by the processor to enable the processor to carry out the method according to the first aspect.
The beneficial effects of the invention are: the acoustic emission time-varying multi-fractal spectrum established by the method is more suitable for the acoustic emission time-varying characteristics in the rock deformation and damage process, the characteristic points of rock burst prediction and forecast can be determined by analyzing the characteristic parameter change rule of the acoustic emission time-varying multi-fractal spectrum, the criterion of rock burst prediction and forecast is determined according to the characteristic points, quantitative prediction of the rock burst damage process is realized, and compared with the prior art in which rock burst is predicted and forecasted qualitatively by the acoustic emission time-varying multi-fractal spectrum, the rock burst prediction and forecast can be more accurate and the engineering practice is guided. In addition, the absolute energy of the rock burst acoustic emission is selected as an analysis object, the internal damage or damage condition of the sample can be reflected, and the internal acoustic emission characteristics of the sample in the rock burst experimental process can be visually evaluated.
Drawings
FIG. 1 is a schematic flow chart of a rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum according to the present invention;
FIG. 2 is a schematic view of an acoustic emission time-varying multifractal spectrum according to the present invention;
FIG. 3 is a schematic diagram of the spectral width variation law of the acoustic emission time-varying multifractal spectrum according to the present invention;
FIG. 4 is a schematic diagram of a rock burst forecast feature point in the stress loading and unloading process of a rock burst experiment according to the present invention;
FIG. 5 is a schematic view of a sliding event window according to the present invention;
FIG. 6 is a graphical representation of the time dependence of the absolute energy of the three-dimensional stress and acoustic emission of a sample according to the present invention;
FIG. 7 is a schematic diagram of a functional module of a rock burst prediction and forecast device based on acoustic emission time-varying multi-fractal spectrum.
Detailed Description
For better understanding of the above technical solutions, the following detailed descriptions will be provided in conjunction with the drawings and the detailed description of the embodiments.
The method provided by the invention can be implemented in the following terminal environment, and the terminal can comprise one or more of the following components: a processor, a memory, and a display screen. Wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the methods described in the embodiments described below.
A processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory, and calling data stored in the memory.
The Memory may include a Random Access Memory (RAM) or a Read-Only Memory (ROM). The memory may be used to store instructions, programs, code, sets of codes, or instructions.
The display screen is used for displaying user interfaces of all the application programs.
In addition, those skilled in the art will appreciate that the above-described terminal configurations are not intended to be limiting, and that the terminal may include more or fewer components, or some components may be combined, or a different arrangement of components. For example, the terminal further includes a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and other components, which are not described herein again.
Example one
As shown in fig. 1, an embodiment of the present invention provides a rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum, including: s101, acquiring time sequence data of acoustic emission absolute energy in the whole process of a rock burst experiment; s102, intercepting time sequence data of acoustic emission absolute energy by using a sliding event window according to an acoustic emission time sequence to obtain intercepted data of a plurality of acoustic emission absolute energies; s103, calculating a multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain a multi-fractal spectrum and a singular index
And calculating the spectral width of each multi-fractal spectrum by using the following formula
Wherein, in the process,
the maximum value of the singular index is represented,
represents the minimum of the singular index; s104, comparing the spectrum widths of all the multi-fractal spectrums to obtain an initial point with the continuously increased spectrum width, and taking the initial point as a characteristic point of rock burst prediction and forecast; and S105, taking the ratio of the maximum principal stress value loaded in the rock burst experiment corresponding to the characteristic point to the uniaxial compressive strength of the rock test piece as a criterion for rock burst prediction.
In the embodiment of the invention, the rock burst acoustic emission absolute energy is used as a parameter, so that the internal damage or damage condition of the sample can be better reflected, and the internal acoustic emission characteristics of the sample in the rock burst experimental process can be more visually evaluated.
In the rock burst process, the acoustic emission signal has the characteristic of dynamic random evolution, namely the acoustic emission has time sequence. The rock burst can be predicted more accurately and quantitatively only by analyzing or reflecting the time sequence characteristics of the acoustic emission.
The existing method for analyzing the rock burst acoustic emission signal based on the multi-fractal theory usually constructs an acoustic emission multi-fractal spectrum by dividing an acoustic emission time interval, and the obtained spectrum parameters can only qualitatively express the characteristics of the acoustic emission signal in the time interval and cannot reflect the acoustic emission signal change rule of a key time node in the rock burst destruction process and realize the prediction of the rock burst. In addition, the acoustic emission time interval division in the currently used method lacks a unified standard, and the obtained result is greatly influenced by human factors.
The method provided by the invention is based on the dynamic random evolution process of the rock burst acoustic emission signal, uses the sliding event window in the acoustic emission time sequence, intercepts the time sequence data of the acoustic emission absolute energy into a plurality of sections by selecting proper window length and sliding step length, and calculates the corresponding multi-fractal spectrum function by utilizing the intercepted data to obtain the multi-fractal spectrum and the singular index. That is, for each intercepted data, a corresponding multi-fractal spectrum and singular index are obtained, and each multi-fractal spectrum is combined to obtain a time-varying multi-fractal spectrum according to the rock burst experiment acoustic emission time series (as shown in fig. 2, each curve is a multi-fractal spectrum in a certain sliding event window, and all curve combinations form a time-varying multi-fractal spectrum). Thus, in embodiments of the invention, an acoustic emission time-varying multifractal spectrum is a combination of multiple multifractal spectra formed in a time series.
In the embodiment of the invention, a box dimension method is utilized to calculate the multi-fractal spectral function
Taking into account the singular index
And multifractal Spectroscopy
With weight factor
The value change tends to be stable, and in the actual calculation process, the value change is generally determined
The value range of (a). In the embodiment of the present invention, the first and second substrates,
the value range is-50 to 50. Therefore, the program is written using-50 to 50 as
The value range of (a). Meanwhile, in order to fully reflect the influence of acoustic emission data in the rock burst stage, the data is intercepted from the tail end of time sequence data of acoustic emission absolute energy in the process of intercepting the data, and the data in the rock burst stage is ensured to be completely involved in data analysis under various scales.
In the embodiment of the invention, the spectral width of each multi-fractal spectrum is used as a characteristic parameter of acoustic emission, the spectral width of the multi-fractal spectrum can be used for describing the fluctuation range of the distribution of acoustic emission signals, the larger the spectral width is, the more uneven the distribution of physical parameters is, the violent index fluctuation is shown, and the events are shown as followsThe small probability constitutes the main. In particular, the spectral width of each multifractal spectrum
Wherein, in the process,
the maximum value of the singular index is represented,
representing the minimum of the singular index.
After the spectrum width of each multi-fractal spectrum is obtained, the spectrum widths of all the multi-fractal spectrums (the change rule of the spectrum width of the time-varying multi-fractal spectrum) can be contrastively analyzed to obtain an initial point of the continuously increased spectrum width, and the initial point is used as a characteristic point for rock burst prediction and forecast. Specifically, each multi-fractal spectrum function obtained by calculation can be drawn in a three-dimensional coordinate system according to the time sequence of acoustic emission to obtain a time-varying multi-fractal spectrum, and the coordinate axes of the three-dimensional coordinate system include: time, singular index and multifractal spectrum; and then analyzing the spectrum width change rule of the time-varying multi-fractal spectrum to obtain a starting point of the continuously increased spectrum width, and taking the starting point as a characteristic point of rock burst prediction. As an embodiment, for example, the spectrum width of the corresponding multi-fractal spectrum may be calculated according to the maximum and minimum of the singular index corresponding to each sliding event window as shown in fig. 3, and each spectrum width may be analyzed, as can be seen in fig. 3,T 1 the point is the turning point of the change of the spectral width rate of the multi-fractal spectrum and is the starting point of the continuous increase of the spectral width. Because the change of the spectral width of the rock burst acoustic emission absolute energy time-varying multi-fractal spectrum has better consistency with the stress loading and unloading process of the rock burst experiment (as shown in figure 4), the spectral width rising point can be known as the starting point of the axial stress concentration loading stage, and the rock burst damage phenomenon occurs in a short time of the axial stress loading stage, so the occurrence of the rock burst can be effectively predicted by taking the spectral width rising point as a characteristic point.
After the characteristic points are obtained, the ratio of the maximum principal stress value of the rock burst experiment loading corresponding to the characteristic points to the uniaxial compressive strength of the rock test piece can be calculated, and the ratio is used as the criterion for rock burst prediction.
The rock burst prediction and forecast by using the criterion provided by the invention can be implemented as follows: firstly, acquiring a loading stress value and uniaxial compressive strength of a rock to be detected in a rock burst experiment process; then calculating the ratio of the maximum main stress value to the uniaxial compressive strength; and if the calculated ratio reaches the prediction criterion of rock burst damage, predicting that the rock to be detected can be subjected to rock burst damage.
In one embodiment of the present invention, the sliding event window comprises a window length and a sliding step size, wherein the window length is set to 1000 events and the sliding step size is set to 100 events. The sliding event window may be as shown in fig. 5.
Different from the time period characteristic of multiple fractal of the rock burst acoustic emission parameter expressed by the current research result, the technical scheme provided by the invention reflects the time-varying characteristic of the unsteady dynamic evolution process of the acoustic emission signal on the time sequence. Specifically, the window length and the sliding step length of the acoustic emission parameter are introduced to express the multi-fractal time shifting characteristic of the acoustic emission parameter, so that the expression of the rock burst acoustic emission time-varying response characteristic is more met. In addition, when the time-varying multi-fractal characteristics of rock burst acoustic emission parameters are analyzed, a box-dimension method is mainly utilized to calculate a multi-fractal spectrum function
And are combined with
And
and as a characterization parameter, analyzing the morphological characteristics of the multi-fractal spectrum.
In addition, in the present invention, the spectrum width is set to be wide
As a characterizing featureThe acoustic emission signal can be used for describing the fluctuation amplitude of the acoustic emission signal in the rock burst experiment process, and the acoustic emission signal can reflect the internal damage degree of the sample, namely the internal crack development and expansion conditions of the sample. Before rock burst is damaged, cracks in the sample can accelerate development and expand, so that the spectral width of the varied multi-fractal spectrum is in a sudden increase trend during acoustic emission, and based on the characteristic, a characteristic point for rock burst prediction is established. In addition, the rock burst stress loading and unloading process has better consistency with the acoustic emission parameter change characteristics of the sample, acoustic emission signals can be concentrated in a large amount in the stress loading and unloading stage, meanwhile, a spectral width rising point is the starting point of the axial stress concentration loading stage, based on the result, for quantifying the specific stress state corresponding to the spectral width rising point, the ratio of the loading stress value corresponding to the spectral width rising point to the rock uniaxial compressive strength is taken as the criterion for rock burst damage, and the condition that the sample has the rock burst damage tendency in the stress state is shown, so that effective prevention and control are needed.
In a specific embodiment, a PCI-2 acoustic emission system in the united states is used to monitor the whole process of a rock burst experiment, characteristic parameters of acoustic emission in the experiment process are obtained, absolute energy in an acoustic emission signal is used as an analysis object, and as shown in fig. 6, a time-domain characteristic curve of three-dimensional stress applied to a sample and acoustic emission absolute energy can be obtained. The experimental process can be divided into 9 stages according to the experimental stress-time curve, the maximum principal stress and the peak stress, wherein the stress loading process (initial loading to minimum principal stress)
Before unloading) is set as a stage according to every two stages, and the stage is divided into 6 stages (numbers (1) - (6)) and a rock burst process (minimum principal stress)
After unloading, maintaining intermediate principal stress
Constant, maximum principal stress
Rapidly centered) according to the maximum principal stress
Initial maximum principal stress
And peak stress
Definition of
(33.3%, 66.7%, 100%, respectively) into 3 stages, i.e., stages (7) - (9). As can be seen from the graph 6, the absolute energy of acoustic emission in the process of the rock burst experiment has obvious stage characteristics (time period characteristics), the phenomenon that the absolute energy of acoustic emission is concentrated and increased occurs in each stress loading stage, and in the last rock burst stage, along with the full expansion of cracks in rocks and the penetration of the cracks until the overall damage of a test piece, the absolute energy of acoustic emission is concentrated and appears greatly.
In the invention, for describing the dynamic evolution process of an unstable nonlinear system, time sequence data of acoustic emission absolute energy in the whole process of a rock burst experiment is intercepted and divided by selecting a sliding event window with proper window length and sliding step length in an acoustic emission time sequence, a multi-fractal spectrum is calculated based on each intercepted data, all multi-fractal spectrums are utilized to establish a time-varying multi-fractal spectrum of the acoustic emission absolute energy according to the time sequence, and the multi-fractal spectrum is drawn in a three-dimensional coordinate system, as shown in figure 2.
In fig. 2, from the morphology of the multi-fractal spectrum, in the stress loading stage after the initiation of the rockburst experiment, the morphology of the multi-fractal spectrum does not change much, the spectrum width gradually decreases, and when the minimum principal stress applied to the sample is unloaded
And then, the widths of the fractal spectrums are basically unchanged, the multi-fractal spectrums are basically overlapped together, the axial stress concentration loading stage is started, the spectrum width of the multi-fractal spectrums is suddenly increased, and the maximum spectrum width is reached at the rock burst moment. In the embodiment of the present invention, the window length is set to 1000 events, and the sliding step length of the adjacent window reflecting different degrees is set to 100 events.
In the embodiment of the invention, a box dimension method can be utilized to calculate the multi-fractal spectrum function
Wherein the weight factor
The value range is-50 to 50. Meanwhile, in order to fully reflect the influence of acoustic emission data in the rock burst stage, the data is intercepted from the tail end of original data in the process of intercepting the data, the data in the rock burst stage is ensured to be completely involved in data analysis under various scales, the multi-fractal spectrum cannot be directly used for representing signals, and the spectrum width of the multi-fractal spectrum is extracted
As a characteristic quantity, it can be used to describe the fluctuation amplitude of the signal distribution,
the larger the distribution of the physical parameter, the more uneven the distribution of the physical parameter, the more the index fluctuates, and the events are mainly composed with a small probability.
Comparing the spectral width change rule of acoustic emission multi-fractal spectrum (shown in figure 3) with the stress loading process of rock burst experiment (shown in figure 4), and determining the turning point of the spectral width rate change of the multi-fractal spectrumT 1 Can be used as a characteristic point for rock burst prediction and forecast, and the ratio of the loading stress value corresponding to the characteristic point to the uniaxial compressive strength of the rock (
) Can be used as a prediction criterion for the occurrence of rock burst. Figure 4 rock burstThe stress value corresponding to the predicted characteristic point is 56MPa, and the uniaxial compressive strength of the rock adopted in the invention is 93MPa, so that the ratio of the loading stress value corresponding to the characteristic point to the uniaxial compressive strength of the rock can be determined
Is 0.6, and the ratio is used as the criterion for the rock burst to be destroyed.
Example two
As shown in fig. 7, an embodiment of the present invention further provides a rock burst prediction and forecast apparatus based on acoustic emission time-varying multi-fractal spectrum, including: the time sequence data acquisition module 701 for the acoustic emission absolute energy is used for acquiring time sequence acoustic emission absolute energy data in the whole process of the rock burst experiment; the acoustic emission absolute energy time sequence data intercepting module 702 is configured to intercept the acoustic emission absolute energy time sequence data by using a sliding event window according to an acoustic emission time sequence to obtain a plurality of acoustic emission absolute energy intercepted data; a spectrum width calculating module 703 of each multi-fractal spectrum, configured to calculate, by using a box-dimension method, a multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain a multi-fractal spectrum and a singular index
And calculating the spectrum width of each multi-fractal spectrum by using the following formula
Wherein, in the step (A),
the maximum value of the singular index is represented,
represents the minimum of the singular index; a characteristic point determining module 704, configured to compare the spectrum widths of all the multi-fractal spectrums to obtain an initial point where the spectrum width continuously increases, and use the point as a characteristic point for rock burst prediction and forecast; a prediction criterion determining module 705 for loading the maximum principal responses to the rockburst experiments corresponding to the feature pointsAnd taking the ratio of the force value to the uniaxial compressive strength of the rock test piece as a prediction criterion for the occurrence of the rock burst.
Further, the device provided by the invention also comprises a rock burst prediction module, which is used for: acquiring a rock burst experiment process loading stress value and uniaxial compressive strength of a rock to be detected; calculating the ratio of the maximum main stress value to the uniaxial compressive strength; and if the calculated ratio reaches the prediction criterion of rock burst damage, predicting that the rock to be detected can be subjected to rock burst damage.
Further, the sliding event window includes: window length and sliding step, where window length is set to 1000 events and sliding step is set to 100 events.
Further, the intercepting the time-series data of the acoustic emission absolute energy by using the sliding event window comprises: a sliding event window is utilized to cut forward starting from the end of the time series data of acoustic emission absolute energy.
Further, the apparatus provided by the present invention further includes a time-varying multi-fractal spectrum drawing module, configured to: according to the time sequence of acoustic emission, drawing each multi-fractal spectrum function obtained by calculation in a three-dimensional coordinate system to obtain a time-varying multi-fractal spectrum, wherein the coordinate axis of the three-dimensional coordinate system comprises: time, singular index and multi-fractal spectrum; and analyzing the spectrum width change rule of the time-varying multi-fractal spectrum to obtain a starting point of the continuously increased spectrum width, and taking the starting point as a characteristic point of rock burst prediction.
Furthermore, when the time-varying multi-fractal spectrum is drawn by utilizing each multi-fractal spectrum function obtained by calculation, the weight factor
The value range of (a) is-50 to 50.
The device can be realized by the rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum provided in the first embodiment, and specific implementation methods can be referred to the description in the first embodiment, and are not described herein again.
The invention also provides a memory storing a plurality of instructions for implementing the method according to the first embodiment.
The invention also provides an electronic device comprising a processor and a memory connected to the processor, wherein the memory stores a plurality of instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the method according to the first embodiment.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A rock burst prediction method based on acoustic emission time-varying multi-fractal spectrum is characterized by comprising the following steps:
acquiring time sequence data of acoustic emission absolute energy in the whole process of a rock burst experiment;
intercepting time sequence data of acoustic emission absolute energy by using a sliding event window according to an acoustic emission time sequence to obtain intercepted data of a plurality of acoustic emission absolute energies;
calculating a multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain a multi-fractal spectrum and a singular index
And calculating the spectrum width of each multi-fractal spectrum by using the following formula
Wherein, in the process,
the maximum value of the singular index is represented,
represents the minimum of the singular index;
comparing the spectrum widths of all the multi-fractal spectrums to obtain a starting point with the continuously increased spectrum width, and taking the starting point as a characteristic point of rock burst prediction and forecast;
and taking the ratio of the loaded maximum principal stress value of the rock burst experiment corresponding to the characteristic point to the uniaxial compressive strength of the rock test piece as a criterion for rock burst prediction.
2. The method for rock burst prediction based on acoustic emission time-varying multi-fractal spectrum as claimed in claim 1, further comprising:
acquiring a loading stress value and uniaxial compressive strength of a rock to be detected in a rock burst experiment process;
calculating the ratio of the maximum main stress value to the uniaxial compressive strength;
and if the calculated ratio reaches the prediction criterion of rock burst damage, predicting that the rock to be detected can be subjected to rock burst damage.
3. The method for rock burst predictive forecasting based on acoustic emission time-varying multi-fractal spectra according to claim 1, characterized in that the sliding event window comprises: window length and sliding step, where window length is set to 1000 events and sliding step is set to 100 events.
4. The method for forecasting rock burst prediction based on acoustic emission time-varying multifractal spectrum according to claim 1, wherein said intercepting time series data of acoustic emission absolute energy using a sliding event window includes: a sliding event window is used to intercept forward from the end of the time series data of the acoustic emission absolute energy.
5. The method for rock burst prediction based on acoustic emission time-varying multi-fractal spectrum as claimed in claim 1, further comprising:
according to the time sequence of acoustic emission, drawing each multi-fractal spectrum function obtained by calculation in a three-dimensional coordinate system to obtain a time-varying multi-fractal spectrum, wherein the coordinate axis of the three-dimensional coordinate system comprises: time, singular index and multifractal spectrum;
and analyzing the spectrum width change rule of the time-varying multi-fractal spectrum to obtain a starting point of the continuously increased spectrum width, and taking the starting point as a characteristic point of rock burst prediction.
6. The method as claimed in claim 5, wherein the weighting factors are used to obtain time-varying multi-fractal spectrum by plotting the calculated multi-fractal spectrum functions
The value range of (a) is-50 to 50.
7. A rock burst prediction and forecast device based on acoustic emission time-varying multi-fractal spectrum is characterized by comprising the following components:
the acoustic emission absolute energy time sequence data acquisition module is used for acquiring acoustic emission absolute energy time sequence data in the whole process of a rock burst experiment;
the acoustic emission absolute energy time sequence data intercepting module is used for intercepting the acoustic emission absolute energy time sequence data by utilizing a sliding event window according to an acoustic emission time sequence to obtain a plurality of acoustic emission absolute energy intercepting data;
a spectrum width calculation module of each multi-fractal spectrum, which is used for calculating a multi-fractal spectrum function corresponding to each acoustic emission absolute energy interception data to obtain a multi-fractal spectrum and a singular index
And calculating the spectral width of each multi-fractal spectrum by using the following formula
Wherein, in the process,
the maximum value of the singular index is represented,
represents the minimum of the singular index;
the characteristic point determining module is used for comparing the spectrum widths of all the multi-fractal spectrums to obtain an initial point of the continuously increased spectrum width and taking the point as a characteristic point of rock burst prediction and forecast;
and the prediction criterion determining module is used for taking the ratio of the maximum main stress value loaded in the rock burst experiment corresponding to the characteristic points to the uniaxial compressive strength of the rock test piece as a prediction criterion for the rock burst damage.
8. A memory having stored thereon a plurality of instructions for implementing the method of any one of claims 1-6.
9. An electronic device comprising a processor and a memory coupled to the processor, the memory storing a plurality of instructions that are loadable and executable by the processor to enable the processor to perform the method according to any of claims 1-6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102749660A (en) * | 2012-06-26 | 2012-10-24 | 中国人民解放军第二炮兵工程设计研究所 | Method for comprehensively forecasting approximately horizontal stratum rock burst in high geostress regions |
| CN204462405U (en) * | 2015-04-03 | 2015-07-08 | 中国矿业大学(北京) | A kind of rock burst omen early warning system based on acoustic emission |
| CN114486548A (en) * | 2022-02-15 | 2022-05-13 | 菏泽学院 | Tunnel rock burst grade assessment method considering internal and external factors |
| US11422076B1 (en) * | 2021-06-04 | 2022-08-23 | Beijing University Of Civil Engineering And Architecture | K-nearest neighbour rock burst prediction method and device based on big data visualization analysis |
-
2022
- 2022-09-02 CN CN202211071006.9A patent/CN115144269B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102749660A (en) * | 2012-06-26 | 2012-10-24 | 中国人民解放军第二炮兵工程设计研究所 | Method for comprehensively forecasting approximately horizontal stratum rock burst in high geostress regions |
| CN204462405U (en) * | 2015-04-03 | 2015-07-08 | 中国矿业大学(北京) | A kind of rock burst omen early warning system based on acoustic emission |
| US11422076B1 (en) * | 2021-06-04 | 2022-08-23 | Beijing University Of Civil Engineering And Architecture | K-nearest neighbour rock burst prediction method and device based on big data visualization analysis |
| CN114486548A (en) * | 2022-02-15 | 2022-05-13 | 菏泽学院 | Tunnel rock burst grade assessment method considering internal and external factors |
Non-Patent Citations (6)
| Title |
|---|
| Application of Acoustic Measurement Data to Characterize Initiation and Development of Disintegration Focus in a Rock Mass;Rasskazov, IY等;《JOURNAL OF MINING SCIENCE》;20170331;第53卷(第2期);第224-231页 * |
| 基于微震信号多重分形特征的岩石边坡;毛浩宇等;《岩石力学与工程学报》;20200331;第39卷(第3期);第560-571页 * |
| 花岗岩应变岩爆声发射特征及微观断裂机制;苗金丽等;《岩石力学与工程学报》;20090831;第28卷(第8期);第1593-1603页 * |
| 裂隙砂岩裂纹扩展声发射响应及速率效应研究;王笑然等;《岩石力学与工程学报》;20180315(第06期);第151-163页 * |
| 隧道地应力测试及岩爆预测研究;康勇等;《岩土力学》;20050630(第06期);第122-126页 * |
| 高海拔矿区地应力测量和岩爆倾向性评价;刘建东等;《中国矿业》;20220131;第31卷(第1期);第140-145页 * |
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