CN115048821B - Prediction method of lagging rock burst - Google Patents
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Abstract
The invention provides a prediction method of lagging rock burst, and belongs to the technical field of rock burst prediction. The invention discloses a prediction method of lagging rock burst, which comprises the following steps: carrying out an acoustic emission experiment on the rock sample to construct a classification model of the crack; performing an indoor hysteretic rockburst experiment on the rock sample to obtain an RA value and an AF value of acoustic emission of hysteretic rockburst; substituting the RA value and the AF value of the acoustic emission of the lag rock burst into a classification model to obtain the crack type distribution of the whole process of the lag rock burst; calculating the accumulated proportion of the cracks in the whole process of the lag rock burst to obtain the corresponding relation between the accumulated proportion of the cracks and time; and monitoring the corresponding relation in real time to judge whether the lagging rock burst occurs. The invention provides a prediction method of a lag rock burst, which is used for realizing accurate prediction of the lag rock burst.
Description
Technical Field
The invention relates to the technical field of rockburst prediction, in particular to a prediction method of lagging rockburst.
Background
The lag rock burst is a common rock burst type, is usually generated in a deep space after excavation is finished, and is different from hours to months, the hazard of the lag rock burst is strong, the generation time is unknown, and the generation mechanism and the influence factors are complex.
In the prior art, the grade of the rock burst which is possible to occur is mostly predicted based on lithology, engineering geological conditions and the stress state of surrounding rocks, the method is mostly based on judgment of experience, and has great subjective factors, for example, in on-site engineering application, the micro-seismic monitoring is often adopted to predict the rock burst, but a gap exists between the performance and the actual requirement, the gap can be influenced by a plurality of factors such as exploitation factors, geological factors, station measuring factors, monitoring factors and algorithm factors, and therefore the precision of the micro-seismic monitoring has errors; when rock burst occurs, the micro-seismic energy or event number increases sharply, but rock burst cannot occur when the micro-seismic energy or event number increases sharply, so the micro-seismic monitoring method is only a necessary condition but not a sufficient condition for rock burst occurrence. In addition, due to the complexity of the actual engineering, the stress state of the surrounding rock is often in an unknown state, so the rock burst cannot be predicted accurately in real time.
Therefore, how to accurately predict the hysteresis rock burst is a technical problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a method for predicting a lagging rock burst, which is used for improving the accuracy of predicting the lagging rock burst.
The above object of the present invention can be achieved by the following technical solutions:
the invention provides a prediction method of lagging rock burst, which comprises the following steps:
carrying out an acoustic emission experiment on the rock sample to construct a classification model of the cracks;
performing an indoor hysteretic rockburst experiment on the rock sample to obtain an RA value and an AF value of acoustic emission of hysteretic rockburst;
substituting the RA value and the AF value of the acoustic emission of the lag rock burst into a classification model to mark out the crack type to which the acoustic emission signal generated at each time point in the whole process of the lag rock burst belongs;
calculating the accumulated proportion of the cracks in the whole process of the lag rock burst to obtain the corresponding relation between the accumulated proportion of the cracks and time;
and monitoring the corresponding relation in real time to judge whether the lagging rock burst occurs.
In an embodiment of the invention, the crack is a shear crack and/or a tension crack.
In an embodiment of the invention, a direct shear acoustic emission test is performed on a rock sample to obtain an RA value and an AF value of the direct shear acoustic emission test;
drawing a first density cloud picture according to the obtained RA value and AF value of the direct shear acoustic emission test;
defining an area with a slope of 0-40 in the first density cloud picture as a shear crack aggregation area S1, wherein a label of a data point in the shear crack aggregation area S1 is {1};
and training data points in the shear crack aggregation zone S1 through a support vector machine to construct a classification model of the shear cracks.
In an embodiment of the invention, a Brazilian acoustic emission test is performed on a rock sample to obtain an RA value and an AF value of the Brazilian acoustic emission test;
drawing a second density cloud picture according to the obtained RA value and AF value of the Brazilian splitting acoustic emission test;
defining the region with the slope larger than 400 in the second density cloud map as a tension crack aggregation region S2, wherein the label of the data point is {2};
and training data points in the tension crack aggregation area S2 through a support vector machine to construct a classification model of the tension cracks.
In an embodiment of the present invention, the classification model isfIt is represented as:
(ii) a Wherein the content of the first and second substances,
an input set I { (RA, AF) | RA ∈ S1 { (RA, AF) | S2, AF ∈ S1 { (RA, AF) | S2};
output set O = { "1", "2" }.
In the embodiment of the invention, in the RA-AF density cloud picture corresponding to the classification model, the boundary line of the tension crack and the shear crack
The acoustic emission signal in the area below the boundary is a shear crack, and the acoustic emission signal in the area above the boundary is a tension crack.
In the embodiment of the invention, the loading mode of the hysteresis rock burst test is three-way six-sided loading-single-sided sudden unloading-load retention-axial loading.
In an embodiment of the present invention, the substrate is,
the initial stress level of the loading was: sigma v =26MPa，σ h =33MPa，σ H =21MPa, the whole loading process is divided into five stages; wherein the content of the first and second substances,
the first stage is loading three-way stress to initial ground stress, the second stage is loading holding stage after loading to initial ground stress state, and the third stage is sigma h Unloading to 0MPa, and the fourth stage is sigma h After unloading to 0MPa, the fifth stage is loading sigma v And finally, a rock burst stage.
In the embodiment of the present invention, the correspondence relationship is a crack cumulative percentage map obtained based on the cumulative percentage of cracks and the time corresponding to the cumulative percentage of cracks.
In the embodiment of the invention, the crack cumulative proportion graph is a line graph, the abscissa of the line graph is time, and the ordinate of the line graph is the cumulative proportion;
and monitoring the trend of the accumulated proportion in the line graph changing along with time in real time to judge whether the lag rock burst occurs.
The invention has the characteristics and advantages that:
the prediction method of the lag rock burst disclosed by the invention starts from the angle of crack evolution, quantitatively describes the change of the accumulated occupation ratios of different types of cracks in the whole process of the lag rock burst, can accurately realize the prediction of the lag rock burst through the change trend of the accumulated occupation ratios of the cracks, and does not predict the possible grade of the rock burst based on lithology, engineering geological conditions and the stress state of surrounding rocks in the prior art, namely, the prediction method is not determined by experience and is not easily influenced by factors such as mining factors, geological factors, station measuring factors, monitoring factors and algorithm factors, so that the prediction method has the advantages of small error and convenience in application, and can accurately realize the prediction of the lag rock burst.
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The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 is a flow chart of a method of predicting a lag rock burst in accordance with the present invention;
FIG. 2 is another flow chart of a method of predicting a lag rock burst in accordance with the present invention;
FIG. 3 is a stress path diagram of a lag rock burst in accordance with the present invention;
FIG. 4 shows the results of the classification of cracks of a lag rock burst according to the present invention;
FIG. 5 is a cumulative event number distribution characteristic of different types of cracks in the present invention;
FIG. 6 is a cumulative percentage of cracks curve according to the present invention;
fig. 7 is an enlarged view of the cumulative percentage of tensile cracks curve in stage v of fig. 6.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their detailed description will be omitted.
The terms "a," "an," "the," "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.
As shown in fig. 1 to 7, the present invention provides a method for predicting a lag rock burst, including: carrying out an acoustic emission experiment on the rock sample to construct a classification model of the crack, wherein the crack can be a shear crack and/or a tension crack; performing an indoor lag rockburst experiment on the rock sample to obtain an RA value and an AF value of acoustic emission of lag rockburst; substituting the RA value and the AF value of the acoustic emission of the lag rock burst into a classification model (or a crack signal identification library) to mark out the crack type to which the acoustic emission signal generated at each time point in the whole process of the lag rock burst belongs; calculating the accumulated proportion of the cracks in the whole process of the lag rock burst to obtain the corresponding relation between the accumulated proportion of the cracks and time; and monitoring the corresponding relation in real time, for example, observing the change trend of the cumulative percentage of the cracks to judge whether the lagging rock burst occurs.
The prediction method of the lag rock burst can quantitatively describe the change of the accumulated occupation ratios of different types of cracks in the whole process of the lag rock burst from the perspective of crack evolution, can accurately predict the lag rock burst through the change trend of the accumulated occupation ratios of the cracks, and does not predict the possible grade of the rock burst based on lithology, engineering geological conditions and the stress state of surrounding rocks in the prior art, namely, the prediction method is not determined by experience and is not easily influenced by factors such as mining factors, geological factors, station measuring factors, monitoring factors, algorithm factors and the like, so that the prediction method has the advantages of small error and convenience in application, and can accurately predict the lag rock burst.
In one embodiment of the present invention, performing an acoustic emission experiment on a rock sample to construct a classification model of cracks includes: performing a direct shear acoustic emission test on the rock sample to obtain an RA value and an AF value of the direct shear acoustic emission test; drawing a first density cloud picture according to the obtained RA value and AF value of the direct shear acoustic emission test; defining an area with a slope ranging from 0 to 40 in the first density cloud chart as a shear crack accumulation zone S1, wherein a label of a data point in the shear crack accumulation zone S1 is {1}; most acoustic emission signals of the direct shear acoustic emission test are shear signals so as to construct a classification model of the shear cracks; the support vector machine can train data points in the shear crack gathering area S1 to construct a classification model of the shear cracks, has good advantages in dividing nonlinear data as a machine learning algorithm with supervised learning, and can accurately divide the shear cracks.
Further, carrying out Brazilian splitting acoustic emission test on the rock sample to obtain an RA value and an AF value of the Brazilian splitting acoustic emission test; drawing a second density cloud picture according to the obtained RA value and AF value of the Brazilian splitting acoustic emission test, wherein most of signals obtained from the Brazilian splitting test are tension crack signals so as to construct a classification model of tension cracks; defining a region with a slope larger than 400 in the second density cloud image as a tension crack aggregation region S2, wherein the label of a data point is {2}; and training data points in the tension crack aggregation area S2 through a support vector machine to construct a classification model of the tension cracks.
In the present invention, the classification model isfIt is expressed as:
(ii) a Wherein, the input set I = { (RA, AF) | RA ∈ S1 { (RA, AF) | S2}, AF ∈ S1 { (RA, AF) }; output set O = { "1", "2" }.
Specifically, in the RA-AF density cloud chart corresponding to the classification model, the boundary line of the tension crack and the shear crack
The acoustic emission signal in the area below the boundary is a shear crack, and the acoustic emission signal in the area above the boundary is a tension crack.
Specifically, as shown in fig. 3, the indoor hysteresis rockburst experiment performed on the rock sample includes: the red sandstone can be selected for carrying out a lag rock burst test, and the loading mode of the lag rock burst test is three-dimensional six-surface loading, single-surface sudden unloading, load retention and axial loading.
More specifically, the initial stress level of loading may be: sigma v =26MPa，σ h =33MPa，σ H =21MPa, the whole loading process is divided into five stages; wherein the content of the first and second substances,
the first stage is loading three-way stress to initial ground stress, the second stage is loading holding stage after loading to initial ground stress state, and the third stage is sigma h And unloading to 0MPa, wherein the fourth stage is a load-holding stage after single-side unloading, and the fifth stage is a rock burst stage.
In specific application, an acoustic emission test can be carried out according to the hysteresis rockburst test to obtain acoustic emission data, an RA value and an AF value of an acoustic emission parameter are calculated, and the calculated RA value and AF value of the acoustic emission parameter are substituted into the classification modelfThe crack type distribution of the whole process of the lagging rock burst can be obtained.
Further, as shown in fig. 4 and 5, the crack evolution characteristic of the entire hysteresis rock burst process can be obtained by calculating the cumulative crack occupation ratio of the entire hysteresis rock burst process according to the following formulas (1) and (2) based on the crack species distribution (or the crack distribution characteristic).
Wherein p is t And p s Respectively the cumulative percentage of the tensile cracks and the cumulative percentage of the shear cracks from the beginning of loading to the time t; m is a group of t The cumulative number of the tension cracks from the beginning of loading to the time t; m is a group of s The accumulated number of the shear cracks from the beginning of loading to the moment t is shown; m is the total number of acoustic emission events from the start of loading to time t.
Monitoring the corresponding relation in real time to judge whether the hysteresis rock burst occurs comprises the following steps: the corresponding relation is a crack accumulative ratio graph obtained based on the accumulative ratio of the cracks and the time corresponding to the accumulative ratio of the cracks, the relation of the accumulative ratio of the cracks changing along with the time can be observed according to the crack accumulative ratio graph, and the relation of the accumulative ratio of the cracks changing along with the time is monitored in real time to judge whether the lag rock burst occurs.
In a possible embodiment, the crack cumulative proportion map is a line graph, the abscissa of the line graph is time, and the ordinate of the line graph is the cumulative proportion; and monitoring the trend of the accumulated proportion in the line graph along with the change of time in real time to judge whether the hysteresis rock burst occurs, and in specific application, observing the trend (such as slope) of the line graph along with the change of time to judge whether the hysteresis rock burst occurs.
In the present invention, as can be seen from fig. 6, the cumulative percentage of cracks curve can show different variation trends in different stages, taking the cumulative percentage of tensile cracks as an example, stage i is a stage of loading to initial stress, and the cumulative percentage of tensile cracks is fasterLifting; with the application of stress, the red sandstone gradually enters an elastic stage, and the cumulative percentage of tensile cracks is slowed down and kept stable; the stage II is a load-holding stage, the stress state of the red sandstone is not changed, so that an acoustic emission signal is not generated, and a crack accumulated proportion curve is kept unchanged; stage III is σ h When the crack is quickly unloaded to the 0MPa stage, the accumulated crack occupation ratio curve is kept stable firstly, and the crack occupation ratio curve is slightly reduced along with the falling of the cushion block and is kept stable along with the stretching of the empty surface; the stage IV is a load-holding stage, the stress state of the red sandstone is not changed, so that an acoustic emission signal is not generated, and a crack accumulated proportion curve is kept unchanged; stage V is loading σ v And at the rock burst stage, the cumulative occupation ratio curve of the tension cracks shows three processes of keeping stability, slowly descending and violently and continuously descending, and reaches the lowest point when the rock burst occurs.
The key of rock burst prediction is to facilitate the distinction of the catastrophe points, as shown in fig. 6 and 7, the cumulative occupancy curve of the tension cracks and the shear crack curve have distinct changing trends in different stages, taking the cumulative occupancy curve of the tension cracks as an example, in the stages i to iv, the cumulative occupancy curve of the tension cracks increases with the application of stress or keeps unchanged with the increase of stress, and in the stage v, the cumulative occupancy curve of the tension cracks decreases with the application of stress, which is different from the first four stages and is in strong contrast with the phenomenon in the first four stages, so that once the cumulative occupancy curve of the tension cracks is severely and continuously decreased, the occurrence of the lagging rock burst can be represented.
In embodiments of the present invention, the term "plurality" means two or more unless explicitly defined otherwise. The terms "mounted," "connected," "fixed," and the like are to be construed broadly and include, for example, fixed connections, removable connections, or integral connections. Specific meanings of the above terms in the embodiments of the present invention can be understood by those of ordinary skill in the art according to specific situations.
In the description of the embodiments of the present invention, it should be understood that the terms "upper", "lower", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the embodiments of the present invention and simplifying the description, but do not indicate or imply that the referred devices or units must have a specific direction, be configured in a specific orientation, and operate, and thus, should not be construed as limiting the embodiments of the present invention.
In the description herein, the appearances of the phrase "one embodiment," "a preferred embodiment," or the like, are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the present embodiment by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.
Claims (4)
1. A method for predicting a lag rock burst, comprising:
carrying out an acoustic emission experiment on the rock sample to construct a classification model of the cracks;
performing an indoor hysteretic rockburst experiment on the rock sample to obtain an RA value and an AF value of acoustic emission of hysteretic rockburst;
substituting the RA value and the AF value of the acoustic emission of the lag rock burst into the classification model to mark out the crack type to which the acoustic emission signal generated at each time point in the whole process of the lag rock burst belongs;
calculating the accumulated proportion of the cracks in the whole process of the lag rock burst so as to obtain the corresponding relation between the accumulated proportion of the cracks and time;
monitoring the corresponding relation in real time to judge whether hysteresis rock burst occurs or not;
the cracks are shear cracks and/or tension cracks;
carrying out Brazilian splitting acoustic emission test on the rock sample to obtain an RA value and an AF value of the Brazilian splitting acoustic emission test;
drawing a first density cloud picture according to the obtained RA value and AF value of the Brazilian acoustic emission test;
defining an area with a slope of 0-40 in the first density cloud picture as a tension crack aggregation area S1, wherein a label of a data point in the tension crack aggregation area S1 is {1};
training data points in the tension crack gathering area S1 through a support vector machine to construct a classification model of the tension cracks;
performing a direct shear acoustic emission test on the rock sample to obtain an RA value and an AF value of the direct shear acoustic emission test;
drawing a second density cloud picture according to the obtained RA value and AF value of the direct shear acoustic emission test;
defining a region with a slope larger than 400 in the second density cloud image as a shear crack aggregation zone S2, wherein the label of a data point is {2};
training data points in the shear crack aggregation zone S2 through the support vector machine to construct a classification model of the shear crack;
in the RA-AF density cloud picture corresponding to the classification model, the boundary of the tension crack and the shear crack
Acoustic emission signals in the area above the boundary are shear cracks, and acoustic emission signals in the area below the boundary are tension cracks;
the loading mode of the hysteresis rock burst test is three-dimensional six-surface loading, single-surface sudden unloading, load retention and axial loading;
the initial ground stress level of loading was: sigma v =26MPa，σ h =33MPa，σ H =21MPa, the whole loading process is divided into five stages; wherein, the first and the second end of the pipe are connected with each other,
first orderThe section is a stage of loading three-dimensional stress to initial ground stress, the second stage is a load-holding stage after loading to the initial ground stress state, and the third stage is sigma h Unloading to 0MPa, and the fourth stage is sigma h After unloading to 0MPa, the fifth stage is loading sigma v And finally, a rock burst stage.
2. The method of predicting a lag rock burst of claim 1,
the classification model is f, which is expressed as:
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
the input set I = { (RA, AF) | RA ∈ S1 { (RA, AF) | S2}, AF ∈ S1 { (RA, AF) | S2};
output set O = { "1", "2" }.
3. The method of predicting a lag rock burst of claim 1,
the correspondence relationship is a crack cumulative proportion map obtained based on the cumulative proportion of cracks and the time corresponding to the cumulative proportion of cracks.
4. The method of predicting a lag rock burst of claim 3,
the crack accumulated proportion graph is a line graph, the abscissa of the line graph is time, and the ordinate of the line graph is the accumulated proportion;
and monitoring the trend of the accumulated proportion in the line graph changing along with time in real time to judge whether the lagging rock burst occurs.
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