CN116243379A - Strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration - Google Patents
Strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration Download PDFInfo
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Abstract
The invention provides a strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration, which comprises the steps of firstly collecting microseismic data generated in the coal mine mining process, determining a target earthquake focus group based on the microseismic data, acquiring earthquake focus mechanism solutions of all earthquake focus in the target earthquake focus group, and acquiring an earthquake focus azimuth angle and an earthquake focus inclination angle based on the earthquake focus mechanism solutions; performing positioning error analysis on all the seismic sources in the target seismic source group to obtain the positioning distribution probability density of all the seismic sources; and performing aggregation analysis on all the seismic sources in the target seismic source group based on the seismic source azimuth angles and the seismic source inclination angles of all the seismic sources and the positioning distribution probability density of all the seismic sources, and constructing a strong mineral earthquake prediction index to perform strong mineral earthquake prediction. The method can quantitatively analyze the aggregation condition of the microseismic event induced by the coal mine mining activity, and realize the accurate prediction of strong mine earthquake.
Description
Technical Field
The invention relates to the technical field of coal mining and coal mine safety, in particular to a strong mine earthquake prediction method based on earthquake focus mechanism and positioning error calibration.
Background
The strong mine earthquake is taken as a strong dynamic phenomenon in the coal mine, has short occurrence time and wide sweep range, and is easy to cause the generation of heavy coal mine secondary disasters such as rock burst, gas explosion, water burst and the like. In the coal mine mining process, vibration wave disturbance around a mining working face can cause development and expansion of cracks in a coal rock mass, so that the coal rock mass is damaged and deteriorated, and the overall strength is reduced; meanwhile, a large number of geological weaknesses, primary cracks, fault structures and the like are formed on the coal rock body, further initiation, expansion, convergence and the like of cracks are caused under mining disturbance, large-scale destabilization damage of the coal rock body can be caused when a plurality of cracks are converged together to form a main crack, so that strong mineral vibration is induced, and the development condition of the cracks can be effectively predicted by utilizing a relevant analysis technology to conduct research and judgment.
A large number of microseismic signals can be generated in the expansion process of cracks around the mining working face, the microseismic signals can be captured by using a microseismic system, clear waveforms of the vibration signals can be obtained by the microseismic system, and accurate calculation of earthquake focus parameters such as earthquake focus time, space coordinates, earthquake focus energy, earthquake focus mechanism and the like can be realized. After the seismic source parameters are acquired, abnormal aggregation and development of the seismic source can be judged by using a cluster analysis technology, but the existing cluster analysis technology only considers the information of the time and the space coordinates of the seismic source, but does not consider the inherent connection of a seismic source mechanism of the seismic source and the unavoidable positioning error problem generated during positioning of a microseismic system, so that the effect of the cluster analysis technology is limited when the impact danger is predicted, and the development of the cluster analysis technology in the field of strong mineral earthquake prediction is greatly limited.
Disclosure of Invention
The invention aims to provide a strong mineral earthquake prediction method based on a earthquake focus mechanism and positioning error calibration, so as to solve the problems of the prior art, quantitatively analyze the probability of microseism event aggregation in the coal mine mining process and realize accurate prediction of the strong mineral earthquake.
In order to achieve the above object, the present invention provides the following technical solutions:
the invention provides a strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration, which comprises the following steps:
s1, acquiring microseismic data generated in the coal mine mining process;
in the invention, the microseismic data is preferably obtained by calculating a microseismic waveform obtained by a microseismic system; the microseismic data preferably includes X, Y, Z spatial coordinate values { i.e., source coordinates (X, Y, Z) }, source energy E, source waveform, source radius r, and moment of origin for all sources.
S2, determining a target focus group based on the microseismic data in the step S1;
acquiring a source mechanism solution of all the sources in the target source group by utilizing micro-seismic data of all the sources in the target source group;
acquiring the focus azimuth angles and the focus inclination angles of all the focus in the target focus group based on the focus mechanism solution;
s3, analyzing positioning errors of all the seismic sources in the target seismic source group in the step S2, and obtaining positioning distribution probability densities of all the seismic sources;
s4, carrying out aggregation analysis on all microseismic events in the target seismic source group based on the seismic source azimuth angles and the seismic source inclination angles of all seismic sources obtained in the step S2 and the positioning distribution probability density of all seismic sources obtained in the step S3, and constructing a strong mineral earthquake prediction index to carry out strong mineral earthquake prediction.
Preferably, the method in step S2 of acquiring the source mechanism solutions of all the sources in the target source group by using the microseismic data of all the sources in the target source group, and acquiring the source azimuth angles and the source inclination angles of all the sources in the target source group based on the source mechanism solutions includes the following steps:
s21, hierarchical clustering is carried out on microseismic data of all the seismic sources in the target seismic source group, and the seismic sources are divided into different categories, wherein the method comprises the following steps:
s211, setting an initial clustering category;
s212, clustering the microseismic data based on the initial clustering category to obtain a microseismic data clustering result, and calculating the microseismic average aggregation degree of the initial category;
s213, adding a clustering class, clustering the microseismic data again to obtain a new microseismic data clustering result, and calculating a new microseismic average concentration degree after adding the clustering class;
s214, comparing the initial class of the microseismic average aggregation degree in the step S212 with the new microseismic average aggregation degree in the step S213, and if the new microseismic average aggregation degree is more than or equal to the initial class of the microseismic average aggregation degree, continuing to increase the clustering class for clustering; if the new microseism average aggregation degree is smaller than the microseism average aggregation degree of the initial category, clustering is terminated, the current category number is output as the final category number, a clustering result is output, and different category seismic sources included in the target seismic source group are determined.
S22, calculating the source mechanism solutions of the different types of sources included in the target source group in the step S21, calculating the source azimuth angles and the source inclination angles of the corresponding types, and acquiring the source mechanism solutions, the source azimuth angles and the source inclination angles of all the sources in the target source group based on the source mechanism solutions, the source azimuth angles and the source inclination angles of the different types.
Preferably, the method for calculating the microseismic average aggregation degree comprises the following steps:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the average degree of aggregation of microseismic events of a certain cluster; n is the number of microseismic events of a certain cluster; a, a i ＝(x,y,z,c 2 t) is the time-space coordinate of the ith seismic source, and X, Y, Z and t are the X, Y and Z space coordinate values of the seismic source and the time of initiating the seismic; i, j are the source numbers respectively; />
The geometric center coordinates of all the seismic sources in a certain cluster; c 2 Is the time-space coefficient of variation; var (X), var (Y), var (Z) and Var (T) are the variances of all microseismic time-space coordinates X, Y, Z and T under a certain cluster respectively.
The method provided by the invention carries out iterative cluster analysis on microseismic data of all the seismic sources in the target seismic source group so as to determine different types of seismic sources included in the target seismic source group, solves a seismic source mechanism solution, and then calculates the seismic source azimuth angle and the seismic source inclination angle, namely solves the seismic source azimuth angle and the seismic source inclination angle through an iterative method.
Preferably, in the step S3, a P-wave first arrival positioning algorithm is adopted in the process of performing positioning error analysis on all the seismic sources in the target seismic source group, and the manual standard wave error and the speed model error are considered.
Preferably, in the step S4, a microseismic aggregation principle is required to be constructed before all the seismic sources in the target seismic source group are aggregated and analyzed;
the microseism aggregation principle is constructed by considering a microseism event focus mechanism and a positioning error analysis result.
Preferably, the method of calculating the source mechanism solutions of the sources of different categories included in the target source group in the step S22, and calculating the source azimuth angles and the source dip angles of the respective categories includes the steps of:
s51, screening the seismic sources in the same category, and removing the seismic sources which do not meet far-field conditions to obtain the seismic sources to be analyzed in the category;
s52, solving a seismic source mechanism of the seismic source to be analyzed in the step S51;
s53, calculating theoretical displacement and error coefficients generated by all the seismic sources to be analyzed at different stations, judging whether the error coefficients are larger than a preset value, and if so, returning to S51; if not, terminating the cycle, outputting the corresponding seismic source mechanism solution, and calculating a seismic source azimuth angle and a seismic source inclination angle based on the seismic source mechanism solution;
s54, repeating S51-S53 to calculate the mechanism solutions of the seismic sources of different categories, and calculating the azimuth angles and the inclination angles of the seismic sources of corresponding categories.
Preferably, the method for solving the source mechanism of the source to be analyzed in the step S52 includes the following steps:
s61, calculating far-field displacement of the seismic source to be analyzed;
s62, calculating a focus moment tensor based on the far-field displacement in the step S61;
s63, decomposing and analyzing the focus moment tensor in the step S62 to obtain the focus mechanism.
Preferably, the method for calculating far-field displacement of the seismic source to be analyzed in step S61 includes the following steps:
s71, shearing a P-wave time domain waveform from the waveform of the seismic source to be analyzed;
s72, carrying out Fourier transform on the P-wave time domain waveform obtained in the step 71 by combining the sampling frequency of the microseismic recorder, and converting the P-wave time domain waveform into a frequency domain waveform;
s73, performing attenuation correction on the frequency domain waveform obtained in the step S72, and calculating far-field displacement of the seismic source to be analyzed.
Preferably, the method for calculating the direction angle and the dip angle of the seismic source based on the mechanical solution of the seismic source in the step S53 includes the following steps:
based on the seismic source mechanism solution, constructing the relation between the characteristic vector of the fracture surface of the coal rock mass and the motion direction and normal direction of the fracture surface, acquiring the space vector value of the normal direction of the fracture surface, constructing the geometric equation model of the fracture surface based on the space vector value of the normal direction of the fracture surface, and calculating the seismic source direction angle and the seismic source inclination angle.
The invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the Jiang Kuang earthquake prediction method of the technical scheme is realized when the processor executes the computer program.
The invention also provides a computer readable storage medium, which stores a computer program for executing the Jiang Kuang earthquake prediction method according to the technical scheme.
The invention provides a strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration, which comprises the steps of firstly collecting microseismic data generated in the coal mine mining process, determining a target earthquake focus group based on the microseismic data, acquiring earthquake focus mechanism solutions of all earthquake focus in the target earthquake focus group, and acquiring an earthquake focus azimuth angle and an earthquake focus inclination angle based on the earthquake focus mechanism solutions; performing positioning error analysis on all the seismic sources in the target seismic source group to obtain the positioning distribution probability density of all the seismic sources; and carrying out aggregation analysis on all micro-seismic events in the target seismic source group based on the seismic source azimuth angles and the seismic source inclination angles of all the seismic sources and the positioning distribution probability density of all the seismic sources, and constructing a strong mineral earthquake prediction index to carry out strong mineral earthquake prediction. According to the strong mineral earthquake prediction method based on the earthquake focus mechanism and the positioning error calibration, the required original microseismic data come from a coal mine microseismic system, the microseismic data are subjected to real-time post-processing in the mining construction process, and the obtained result can be used for analyzing the aggregation condition of microseismic events in a monitoring range (namely a research area) in real time to obtain a strong mineral earthquake prediction index, so that the auxiliary analysis of the strong mineral earthquake danger is realized, and the accuracy rate of the strong mineral earthquake prediction of the coal mine is improved; the method has definite physical meaning and is suitable for realizing the intellectualization of programming. The method can quantitatively analyze the aggregation condition of the microseismic events induced by the coal mine mining activities, and realize the accurate prediction of strong mine shocks. In the embodiment of the invention, the later-occurring high-energy event falls into the region (I) with higher risk index of strong mine earthquake P >0.8 The method provided by the invention can be used for accurately predicting the strong mineral earthquake based on the strong mineral earthquake risk index calibrated by the earthquake focus mechanism and the positioning error.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration in an embodiment of the invention;
FIG. 2 is a schematic diagram of fracture development and induction of strong mineral earthquakes after mining of a working surface according to an embodiment of the present invention;
FIG. 3 is a diagram of six types of coal mine induced strong mineral earthquakes in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a relationship between the clustering probability after considering the source mechanism and the positioning error according to an embodiment of the present invention;
FIG. 5 is a flow chart of cluster analysis based on source mechanism and positioning error calibration in an embodiment of the invention;
FIG. 6 is a simulated repositioning result of a microseismic event in an embodiment of the present invention after a manual standard wave error and a velocity model error are considered;
FIG. 7 is a graph showing probability density distribution calculated from the result of a microseismic event relocation in accordance with an embodiment of the present invention;
FIG. 8 is a schematic diagram of a region of investigation and microseismic station arrangement in an embodiment of the present invention;
FIG. 9 is a diagram of a microseismic event aggregation result distribution based on calibration of the source mechanism and positioning error, wherein the star map is marked as a high-energy event;
fig. 10 is a graph of strong mineral earthquake prediction index distribution and strong mineral earthquake prediction results based on the calibration of a earthquake focus mechanism and a positioning error in the embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in the following in connection with the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The embodiment of the invention provides a strong mineral earthquake prediction method based on an earthquake focus mechanism and positioning error calibration, and the following is a detailed description of each step related to the embodiment of the invention with reference to figures 1-10.
Fig. 1 is a flowchart of a strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration in the embodiment of the invention, specifically, firstly, acquiring microseismic data generated in the coal mine mining process, then determining a target earthquake focus group based on the microseismic data, then acquiring earthquake focus mechanism solutions of all the earthquake focus by utilizing the microseismic data of all the earthquake focus in the target earthquake focus group (namely, performing iterative cluster analysis on the microseismic data of all the earthquake focus in the target earthquake focus group to determine different types of earthquake focus included in the target earthquake focus group, solving the earthquake focus mechanism solutions), then acquiring earthquake focus azimuth angles and earthquake focus inclination angles of all the earthquake focus based on the earthquake focus mechanism solutions, and also can be called as solving the azimuth angles and the earthquake focus inclination angles by a repeated iterative method), performing positioning error analysis on all the earthquake focus in the target earthquake focus group, finally performing polymerization analysis on all the microseismic events in the target earthquake focus group based on the positioning distribution probability densities of all the earthquake focus and the earthquake focus inclination angles, and constructing a strong earthquake prediction index to perform strong mineral prediction.
FIG. 2 is a schematic diagram of fracture development and induction of strong mineral earthquakes after mining of a working surface in an embodiment of the invention. Under the original ground stress condition, the original rock is in a quasi-hydrostatic pressure state, after the coal body is extracted, a supporting pressure area is transversely formed in front of the coal wall, the supporting pressure in the coal body gradually rises to peak stress from a three-way isobaric hydrostatic pressure state along with the pushing of a working face, then the original rock enters a pressure relief state along with the breaking of the coal body, during the period, the original cracks of the coal rock further germinate, expand and even converge (as shown in fig. 2), and the continuous expansion and convergence of the cracks can lead to the occurrence of a large-range breaking of a top plate, so that a strong mineral vibration event is induced. During the process, the microseism system can be used for positioning the mine earthquake events to obtain the coordinates of the mine earthquake source, so that the development condition of cracks in the working face mining process can be further analyzed, the aggregation condition of the microseism events is judged, and then the strong mine earthquake events are predicted.
In the embodiment of the invention, microseismic data (table 1) of a mining face of an inner Mongolian 2215 is selected for calculation. As shown in fig. 8, in the embodiment of the present invention, the investigation region of the 2215 extraction working surface and the arrangement condition of the peripheral microseismic probes (or microseismic stations) are shown, and the microseismic waveforms can be obtained and calculated by the microseismic system to obtain a microseismic database for subsequent analysis and calculation.
TABLE 1 microseismic data for 2215 working surface at certain recovery time in an embodiment of the invention
According to the method provided by the invention, the azimuth angle and the inclination angle of the seismic source are solved by a repeated iteration method, all seismic sources in the table 1 are firstly required to be classified, and compared with the characteristic that errors are easy to generate in artificial classification, the clustering method based on the data characteristics can fully consider the data distribution characteristics and avoid errors generated by subjective judgment, so that the classification is performed by adopting a hierarchical clustering method, and the specific rule of determining the target seismic source group by the hierarchical clustering method is as follows:
classifying all acquired microseismic data by using a hierarchical clustering algorithm, firstly determining the target class as i class (i initial value is 1) in the clustering process, and calculating microseismic average aggregation degree q under different clusters after clustering i The method comprises the steps of carrying out a first treatment on the surface of the Then the target class is defined as i+1 class, and then clustering is carried out and the microseismic average aggregation degree q is calculated i+1 The method comprises the steps of carrying out a first treatment on the surface of the Pair q i And q i+1 Comparing the sizes of (1) if q i+1 ≥q i Then the clustering class is continuously added for clustering; q i+1 ＜q i The clustering is stopped, the current category number is used as the final category number, and the corresponding microseismic data under different categories is used as an independent database.
In the clustering method, the average microseismic aggregation degree is calculated as shown in formula (1):
in the method, in the process of the invention,
the average degree of aggregation of microseismic events of a certain cluster; n is the number of microseismic events of a certain cluster; a, a i ＝(x,y,z,c 2 t) is the time-space coordinate of the ith seismic source, and X, Y, Z and t are the X, Y and Z space coordinate values of the seismic source and the time of initiating the seismic; i, j are the source numbers respectively; />
The geometric center coordinates of all the seismic sources in a certain cluster; c 2 Is the time-space coefficient of variation; var (X), var (Y), var (Z) and Var (T) are the variances of all microseismic time-space coordinates X, Y, Z and T under a certain cluster respectively. The clustering degree of clusters under different clusters is measured by adopting the microseismic average aggregation degree, so that the difficulty in determining the cluster type and the cluster result by the common hierarchical clustering method is effectively overcomeAnd the method has the characteristics of poor interpretability and the like, can determine the source classification according to the time-space distribution characteristics of the source, and lays a foundation for the accurate calculation of the subsequent source mechanism solution.
Adopting the formula (1), carrying out hierarchical clustering analysis on all microseismic data in the table 1, firstly determining the target class as 1, and simultaneously calculating the average aggregation degree q i And by analogy, continuing to increase the target categories to cluster and calculate the average aggregation degree respectively; the average aggregation degree corresponding to different target categories is calculated to be 8882.51, 7302.67, 6618.06, 5435.02, 5328.18, 4531.91 and 9618.61 respectively, so that the number of clustering categories can be determined to be 6.
Therefore, in the embodiment of the invention, all the micro-seismic sources in the table 1 can be divided into 6 groups, and the micro-seismic sources of different categories adopt a method of repeated iteration to solve the source mechanism solution, and the embodiment of the invention firstly carries out the source mechanism solution on the source of the 1 st category, and the specific method is as follows:
1) And (5) performing seismic source screening according to far field conditions required by inversion. For a single seismic source, selecting at least 6 relatively clear microseismic waveforms in the monitored microseismic records, wherein the distance between all channels and the seismic source is required to be more than 500m, and eliminating the seismic sources which do not meet the far-field condition;
2) Calculating far-field displacement of a seismic source;
according to the screened microseismic waveform, the primary amplitude value of the P wave is picked up and recorded as U 1 And calculates the low frequency displacement of each waveform, denoted as U 2 The method comprises the following steps:
(1) Shearing the P-wave time domain waveform in the screened microseismic waveform;
(2) Carrying out Fourier transform on the P-wave time domain waveform sheared in the last step by combining the inherent sampling frequency of the microseismic recorder, and converting the P-wave time domain waveform into a frequency domain waveform;
(3) Performing attenuation correction on the frequency domain waveform obtained in the previous step, as shown in a formula (2):
wherein A (f) is the result of FFT transformation of the time domain velocity spectrum; f is a phaseThe response frequency; v is the P wave velocity; q is an attenuation factor; a is that new (f) The corrected amplitude is attenuated for the time domain velocity spectrum; d is the distance between the seismic source and the microseismic recorder.
(4) Calculating far field displacement of recorded waveform of certain microseismic recorder
In the embodiment of the invention, the far-field displacement of rock fracture is represented by adopting low-frequency displacement amplitude, and the calculation of the low-frequency displacement amplitude is shown in formulas (3) - (6):
wherein, the liquid crystal display device comprises a liquid crystal display device,
multiplying the modified velocity power spectrum by 1/4 to account for free-face effects; />
A corresponding displacement power spectrum; u (U) 2 Recording the low-frequency displacement of the waveform for the microseismic recorder, and approximately representing the low-frequency displacement as the P-wave far-field displacement corresponding to the waveform; s is S V2 、S D2 No special meaning is given to intermediate variables generated in the calculation process.
If U is 1 >0, and the microseismic recorder is above the seismic source Z axis, U 2 Correcting the value; if U is 1 >0, and the microseismic recorder is below the Z axis of the seismic source, U 2 Taking negative value; if U is 1 <0, and slight shake is recordedThe recorder is arranged above the Z axis of the seismic source, U 2 Taking negative value; if U is 1 <0, and the microseismic recorder is below the Z axis of the seismic source, U 2 The value is positive.
3) Calculating a focus moment tensor;
the far-field displacement of the P wave can be obtained according to the elastic wave theory deduction, as shown in the formula (7):
v p the propagation speed of the P wave; r is the distance from the seismic source to the microseismic recorder; ρ is the rock density; k is the kth (k=1, 2, 3) component of the microseismic recorder; gamma ray i The component of the vibration wave rays corresponding to each coordinate axis, namely gamma, of the vibration source to the microseismic recorder i ＝(x i -x 0i )/r(x i For each coordinate component, x of the microseismic recorder 0i I=1, 2,3 for each coordinate component of the source); m is M ij Is a moment tensor acting on the seismic source; u (u) p,k The far field displacement of the kth (k=1, 2, 3) component of the P wave recorded for the microseismic recorder.
The above formula can be expressed as shown in formula (8) without performing polarization treatment for the single-component microseismic recorder:
u p the P wave far field displacement recorded by the single component microseismic recorder.
The matrix is expressed as shown in formula (9):
wherein the gamma superscript indicates the channel number and the following table indicates the coordinate component.
The source moment tensor may be represented as shown in equation (10):
4) Decomposing and analyzing the source moment tensor;
in the main coordinate axis (a 1 ，a 2 ，a 3 ) In which the moment tensor matrix may be diagonalized as shown in equation (11):
a 1 ，a 2 ，a 3 is the eigenvector of matrix M 1 ，M 2 And M 3 For the corresponding eigenvalues, the above formula can be decomposed into the following formula (12):
P＝(M 1 +M 2 +M 3 ) I is an identity matrix, PI is an isotropic part of a moment tensor, M' is a partial tensor part of the moment tensor, and eigenvalue is M i '＝M-P，i＝1,2,3。
For the research of the earthquake focus rupture mechanism of the mine mining induced impact microseismic, the moment tensor can be decomposed into an isotropic part, a compensating linear vector dipole and a dual couple, as shown in a formula (13):
wherein: f= -M 1 '/M 3 ' and 0.ltoreq.F.ltoreq.1/2, the pure bias part comprising only the double couple part when F=0, and the bias part comprising only the compensating linear vector dipole when F=0.5.
The moment tensor M is decomposed to obtain M '(partial tensor part), and the M' is represented by the principal axis (b 1 ，b 2 ，b 3 ) In which the moment tensor matrix may be diagonalized as shown in equation (14):
wherein b 1 ，b 2 ，b 3 Is the eigenvector of matrix M', M 1 ′，M 2 ' and M 3 ' is the corresponding eigenvalue. Let M be 1 ′＜M 2 ′＜M 3 ', b 1 Corresponding to stretching direction axis T, b 3 Corresponding to the compression direction axis P.
5) Calculating theoretical displacements of all the seismic sources under the classification generated at different stations, and calculating error coefficients, wherein the error coefficients are differences between the theoretical displacements and the observed displacements calculated in each iteration, and if the error coefficients are greater than 5%, the steps 1) to 4) solve the seismic source moment tensors M again for all the seismic sources under the classification; if the error coefficient is not more than 5%, the solved source moment tensor error is considered to be acceptable, and then the source moment tensor results of all the sources are output, wherein the source moment tensor is a source mechanism solution. The accuracy of the solution of the seismic source mechanism can be guaranteed to the greatest extent through repeated iteration, and reliable data guarantee can be provided for the subsequent analysis of the main fracture azimuth.
6) Solving the parameters of the azimuth angle and the inclination angle of the seismic source according to the result of the moment tensor of the seismic source:
because of the symmetry of the moment tensors of the seismic source, the result of the moment tensors of the seismic source is consistent due to reciprocity of a sliding vector v of a fracture surface (or a seismic source) and a normal vector n of the fracture surface, and according to the magnitude relation of characteristic values of the moment tensors, the following relation between the characteristic vectors and the motion direction and the normal direction of the fracture surface can be obtained, as shown in a formula (15):
in the formula e 1 ⊥e 2 ⊥e 3 Absolute value symbols represent vector sizes; x represents vector multiplication; e, e 1 、e 2 、e 3 The maximum eigenvalue, the intermediate eigenvalue, and the minimum eigenvalue of the source moment tensor, respectively.
Let the vector v and the vectorn has an included angle of beta, v and e 1 Included angle of n and e 1 The included angles of (2) are beta/2, and then the formulas (16) - (18) are shown:
then, the relationship between the direction and normal direction of the fracture surface movement and the feature vector corresponding to the maximum feature value and the feature vector corresponding to the minimum feature value of the moment tensor can be obtained as shown in the following formulas (19) to (20):
the geometrical equation expression of the fracture surface can be obtained according to the space vector value of the normal direction of the fracture surface, and the azimuth angle and the inclination angle of the fracture surface (or the seismic source) can be determined.
On the basis of solving the normal vector and the sliding vector of the fracture surface, the azimuth and the inclination angle of the fracture surface (or the seismic source) can be solved:
inclination angle of seismic source:
wherein n (3) is the normal vector Z-axis component of the fracture surface, |n| represents the modulus of the normal vector, and if dip >90 °, the fracture surface azimuth is (180 ° -dip)
Azimuth angle of seismic source:
where n (1) is the fracture surface normal vector X-axis component and n (2) is the fracture surface normal vector Y-axis component. And the moment tensor inversion theory (namely the inversion of the seismic source mechanism solution) is adopted to solve the parameters of the fracture surface of the seismic source (namely the azimuth angle of the seismic source and the dip angle of the seismic source), so that the physical meaning is clear, the occurrence of the fracture surface can be solved more accurately, and an accurate data basis can be provided for judging the development characteristics of the fracture.
After the solving of the focus azimuth angle and the focus inclination angle under the category 1 is completed, the focus azimuth angle and the inclination angle parameters of other 5 groups are calculated according to the steps 1) to 6), and the final calculation results are summarized as shown in the table 2.
TABLE 2 microseismic data of embodiments of the present invention and calculated source azimuth and source dip
Then, aggregate analysis is performed on all source events in the target source cluster based on the source azimuth angles and source inclination angles of all sources and the positioning distribution probability densities of all sources.
FIG. 3 shows six types of coal mine induced strong mineral earthquakes in an embodiment of the present invention. As shown in fig. 3, the source breaking mechanism can be divided into 6 types according to the source acting force mode and the relative position of the broken coal rock and the working surface: roof fall-off type, coal pillar destabilization type, roof break type, roof dislocation type, floor dislocation type, and roof horizontal slip type. The first three seismic source cracking modes are mainly stress leading, and the last three are mainly caused by slippage and dislocation of geological weaknesses in coal and rock mass. Whether stress-dominant or weak-surface-dislocation, coal and rock masses induce a series of microseismic events before a large fracture occurs, which often follow certain rules in time and spatial location of occurrence, i.e., precursor information before a high-energy fracture event occurs.
In addition, the research shows that the positioning error of the microseismic system is difficult to avoid, for example, the horizontal positioning error of the microseismic system commonly equipped in domestic rock burst mines is within 20m, and the vertical positioning error is about 50 m. It is not difficult to find that the presence of positioning errors results in a large uncertainty in the process of using the source positioning information analysis. For example, in cluster analysis, different standard wave results change event positioning, so that the same seismic source may belong to different cluster categories. In addition, from the standpoint of fracture-through induced coal rock destabilization or high energy events, it is not desirable to consider only source positioning and ignore source mechanisms in cluster analysis. For example, when the source breaking mechanism is dominated by implosion or implosion (i.e., the shear component is relatively low), it is feasible to directly group the sources into one type when the two sources are spatially located close enough to each other; however, when two seismic source mechanisms are shear fracture dominant (i.e. the shear component is relatively high), there is an obvious fracture surface at the seismic source, and the fracture tends to spread or shift along the fracture surface during fracture, for example, in the case of seismic source fracture corresponding to (d), (e) and (f) in fig. 3. Even if the two seismic sources are sufficiently close in space, if the fracture surfaces are parallel, the fracture surfaces are difficult to penetrate each other, and obviously, it is not reasonable to group the two seismic sources into one type. Therefore, the positioning error and the focus cracking mechanism need to be considered during clustering.
And a microseism aggregation principle is required to be constructed before all the seismic sources in the target seismic source group are aggregated and analyzed, and the aggregation principle is constructed by considering a microseism event seismic source mechanism and a positioning error analysis result.
FIG. 4 is a schematic diagram of the clustering probability after considering the source mechanism and the positioning error in the embodiment of the present invention. As shown in fig. 4, there are three relative relationships for sources a and B when considering positioning errors and source break-up mechanisms. Where the positions a and B are the true positions of the sources, respectively, and a 'and B' are the possible positions of the sources due to positioning errors, respectively, and three relative positional relationships of the sources a and B distributed in the black elliptical range (error ellipse) can be described as: (1) the distance D (or D ') between the seismic source A (or A ') and the seismic source B (or B ') is larger than the shortest distance D which can be penetrated by the crack, and the rupture mechanism of the seismic source A and the rupture mechanism of the seismic source B are the same; (2) the distance D (or D ') between the seismic source A (or A ') and the seismic source B (or B ') is not greater than the shortest distance D through which the crack can penetrate and the rupture mechanism of the seismic source A and the seismic source B is the same; (3) the distance between source A (or A ') and B (or B ') is not greater than the shortest distance d (or d ') that the fracture may intersect and the rupture mechanism of source A and source B is different.
The method is characterized in that a microseism event source mechanism and a positioning error analysis result are considered for construction to obtain an aggregation principle, namely: when the sources a and B satisfy the relation (1), the fracture mechanisms generated due to the distant distance are difficult to affect each other; when the vibration sources A and B meet the relation (2), the two vibration sources are in the range that the cracks can be mutually communicated, but the crack surfaces generated by the vibration sources A and B are parallel to each other because the cracking mechanisms of the vibration sources A and B are the same, and the cracks are difficult to be mutually communicated; only when sources A and B satisfy relationship (3) will the sources satisfy the condition that the slots are mutually penetrated. It can be derived that the conditions for the interpenetration of the source A and B fracture are:
wherein d is the distance between sources A and B; d is the shortest distance that the cracks can penetrate when the seismic sources A and B break; r is (r) A And r B Source radius of sources A and B, respectively; f (F) A And F B The fracture mechanisms of sources a and B, respectively.
Thus, the aggregate analysis is performed on all microseismic events of the target seismic source library according to the aggregate principle set forth in the formula (21), and a specific flowchart is shown in fig. 5.
FIG. 5 shows the present inventionIn the embodiment, an aggregate analysis flow chart based on a seismic source mechanism and positioning error calibration is provided, wherein first, the seismic source coordinates (x, y, z), the seismic source energy E, the seismic source waveform and the seismic source radius r of a micro-seismic event are taken as input data, and then the seismic source mechanism inversion is calculated to obtain the seismic source fracture surface parameters (seismic source azimuth angle and seismic source inclination angle); simultaneously performing a positioning error simulation test and calculating the probability density of the seismic source distribution, dividing a research area into grids formed by a plurality of nodes in the test, and firstly calculating the probability of aggregation of the jth (j initial value is 1) seismic source and the h (h initial value is 1) seismic source in the g (g initial value is 1) grid nodes
Traversing all the seismic sources until all the seismic sources are calculated, and obtaining the aggregation number N at the g-th node g The method comprises the steps of carrying out a first treatment on the surface of the Then, carrying out circulation traversal on all the nodes, and calculating the aggregation number of all the nodes according to the steps; then all the nodes are normalized to obtain a danger prediction index (namely, a strong ore earthquake prediction index) I P Risk prediction index of g-th node
The value calculation formula is shown as formula (22), according to the I of the grid node P The values can be interpolated to draw the predicted cloud image.
Wherein N is gmin To study region N g Is the minimum of (2); n (N) gmax To study region N g Is a maximum value of (a).
Wherein p is j To consider the distribution probability of the jth source and node g satisfying equation (21) under the positioning error condition; p is p h To consider the condition that the j-th source and node g satisfy the formula (21) under the condition of positioning errorProbability of distribution.
In the microseismic positioning error test, the embodiment of the invention adopts the most widely used P-wave first arrival positioning algorithm in the microseismic positioning algorithm of the coal mine to carry out the positioning error simulation test, and the core formulas are shown in formulas (24) to (26). Because the factors influencing the positioning accuracy mainly comprise artificial standard wave error and speed model accuracy, in order to quantify the influence of the two on the positioning error, the embodiment of the invention specifically introduces an artificial standard wave error factor delta p And a velocity model error factor delta v Quantifying the effect of both (wherein δ p And delta v Random numbers which are subjected to normal distribution), and on the basis, numerical simulation (namely a positioning error simulation test) is carried out, the distribution probability of microseismic events under the influence of standard wave and speed model errors is calculated, on-site actual measurement is carried out on the numerical simulation parameters of the working face of the embodiment 2215 of the invention, and the values of key parameters are shown in Table 3 in detail.
r α ＝t α +δ αp -t 0 -T α (x 0 ,y 0 ,z 0 ) (25)
In which Φ (t) 0 ,x 0 ,y 0 ,z 0 ) For locating an objective function; r is (r) α Residues for theoretical arrival time and marked arrival time for the alpha-th station; t is t α Marking arrival time for P wave of alpha-th station; t is t 0 The time of the vibration is the vibration starting time of the vibration source; t (T) α (x 0 ,y 0 ,z 0 ) A theoretical arrival time for the alpha-th station; (x) 0 ,y 0 ,z 0 ) Is the space coordinates of the seismic source; v p The average wave velocity of the P wave.
Table 3 key parameter values for numerical simulation test
Fig. 6 shows the result of 2000 relocations and probability density distribution (simulated relocations taking into account artificial standard wave errors and velocity model errors) of a microseismic event of the working surface of an inner mine 2215. From fig. 6, it can be seen that 80% of the seismic sources repositioned after numerical simulation are distributed within 20m from the real seismic sources in the X-axis and Y-axis directions, which illustrates that the positioning error simulation test results are consistent with the positioning error of the microseismic system. From fig. 6, the probability density distribution of the post-relocation microseismic events can be calculated as shown in fig. 7.
Fig. 7 is a probability density distribution diagram calculated according to the relocation result of the microseismic event in the embodiment of the present invention, as can be seen from fig. 7, the probability density is higher as the probability density is closer to the real position, the probability density is lower as the probability density is farther from the real position, and the probability density distribution is further calculated to relocate the seismic source according to the situation of real standard wave location. And so on, the distribution probability of all microseismic events in the target seismic source library can be calculated.
After the source mechanism and the positioning probability density distribution of all the microseismic events of the target source library are obtained, according to the formula (21) and the aggregation analysis flow in fig. 5, the aggregation possibility of all the microseismic events can be calculated, so that the microseismic event aggregation distribution can be obtained as shown in fig. 9,
FIG. 9 is a diagram of a microseismic event aggregation result distribution based on calibration of a seismic source mechanism and a positioning error, wherein a star-shaped graph is marked as a high-energy event, and a strong mine earthquake risk index distribution diagram is shown in FIG. 10, which is obtained by further normalizing the result calculated according to FIG. 9. As can be seen from FIG. 10, the later-occurring high-energy event falls into the region with high risk index of strong mine earthquake (I P >0.8 It can be seen that the strong mineral earthquake risk index based on the calibration of the earthquake focus mechanism and the positioning error can realize the accurate prediction of the strong mineral earthquake.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A strong mineral earthquake prediction method based on earthquake focus mechanism and positioning error calibration comprises the following steps:
s1, acquiring microseismic data generated in the coal mine mining process;
s2, determining a target focus group based on the microseismic data in the step S1;
acquiring a source mechanism solution of all the sources in the target source group by utilizing micro-seismic data of all the sources in the target source group;
acquiring the focus azimuth angles and the focus inclination angles of all the focus in the target focus group based on the focus mechanism solution;
s3, analyzing positioning errors of all the seismic sources in the target seismic source group in the step S2, and obtaining positioning distribution probability densities of all the seismic sources;
s4, carrying out aggregation analysis on all microseismic events in the target seismic source group based on the seismic source azimuth angles and the seismic source inclination angles of all seismic sources obtained in the step S2 and the positioning distribution probability density of all seismic sources obtained in the step S3, and constructing a strong mineral earthquake prediction index to carry out strong mineral earthquake prediction.
2. The strong mineral earthquake prediction method according to claim 1, wherein: in the step S2, the method for acquiring the source mechanism solutions of all the sources in the target source group by using the microseismic data of all the sources in the target source group and acquiring the source azimuth angles and the source inclination angles of all the sources in the target source group based on the source mechanism solutions includes the following steps:
s21, hierarchical clustering is carried out on microseismic data of all the seismic sources in the target seismic source group, and the seismic sources are divided into different categories, wherein the method comprises the following steps:
s211, setting an initial clustering category;
s212, clustering the microseismic data based on the initial clustering category to obtain a microseismic data clustering result, and calculating the microseismic average aggregation degree of the initial category;
s213, adding a clustering class, clustering the microseismic data again to obtain a new microseismic data clustering result, and calculating a new microseismic average concentration degree after adding the clustering class;
s214, comparing the initial class of the microseismic average aggregation degree in the step S212 with the new microseismic average aggregation degree in the step S213, and if the new microseismic average aggregation degree is more than or equal to the initial class of the microseismic average aggregation degree, continuing to increase the clustering class for clustering; if the new microseism average aggregation degree is smaller than the microseism average aggregation degree of the initial category, clustering is terminated, the current category number is output as the final category number, a clustering result is output, and different category seismic sources included in the target seismic source group are determined.
S22, calculating the source mechanism solutions of the different types of sources included in the target source group in the step S21, calculating the source azimuth angles and the source inclination angles of the corresponding types, and acquiring the source mechanism solutions, the source azimuth angles and the source inclination angles of all the sources in the target source group based on the source mechanism solutions, the source azimuth angles and the source inclination angles of the different types.
3. The strong mineral earthquake prediction method according to claim 1, wherein: in the step S3, a P-wave first arrival positioning algorithm is adopted in the process of performing positioning error analysis on all the seismic sources in the target seismic source group, and the artificial standard wave error and the speed model error are considered.
4. The strong mineral earthquake prediction method according to claim 1, wherein: in the step S4, a microseism polymerization principle is constructed before all the seismic sources in the target seismic source group are subjected to polymerization analysis;
the microseism aggregation principle is constructed by considering a microseism event focus mechanism and a positioning error analysis result.
5. The strong mineral earthquake prediction method according to claim 2, wherein: the method of calculating the source mechanism solutions of the sources of different categories included in the target source group and calculating the source azimuth angles and the source inclination angles of the corresponding categories in the step S22 includes the following steps:
s51, screening the seismic sources in the same category, and removing the seismic sources which do not meet far-field conditions to obtain the seismic sources to be analyzed in the category;
s52, solving a seismic source mechanism of the seismic source to be analyzed in the step S51;
s53, calculating theoretical displacement and error coefficients generated by all the seismic sources to be analyzed at different stations, judging whether the error coefficients are larger than a preset value, and if so, returning to S51; if not, terminating the cycle, outputting the corresponding seismic source mechanism solution, and calculating a seismic source azimuth angle and a seismic source inclination angle based on the seismic source mechanism solution;
s54, repeating S51-S53 to calculate the mechanism solutions of the seismic sources of different categories, and calculating the azimuth angles and the inclination angles of the seismic sources of corresponding categories.
6. The strong mineral earthquake prediction method according to claim 5, wherein: the method for solving the source mechanism of the source to be analyzed in the step S52 includes the following steps:
s61, calculating far-field displacement of the seismic source to be analyzed;
s62, calculating a focus moment tensor based on the far-field displacement in the step S61;
s63, decomposing and analyzing the focus moment tensor in the step S62 to obtain the focus mechanism.
7. The strong mineral earthquake prediction method according to claim 6, wherein: the method for calculating far-field displacement of the seismic source to be analyzed in the step S61 includes the following steps:
s71, shearing a P-wave time domain waveform from the waveform of the seismic source to be analyzed;
s72, carrying out Fourier transform on the P-wave time domain waveform obtained in the step 71 by combining the sampling frequency of the microseismic recorder, and converting the P-wave time domain waveform into a frequency domain waveform;
s73, performing attenuation correction on the frequency domain waveform obtained in the step S72, and calculating far-field displacement of the seismic source to be analyzed.
8. The strong mineral earthquake prediction method according to claim 5, wherein: the method for calculating the direction angle and the dip angle of the seismic source based on the mechanism solution of the seismic source in the step S53 comprises the following steps:
based on the seismic source mechanism solution, constructing the relation between the characteristic vector of the fracture surface of the coal rock mass and the motion direction and normal direction of the fracture surface, acquiring the space vector value of the normal direction of the fracture surface, constructing the geometric equation model of the fracture surface based on the space vector value of the normal direction of the fracture surface, and calculating the seismic source direction angle and the seismic source inclination angle.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 8 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 8.
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