Seismic source inversion data driven rock burst prediction method and system

Abstract

The invention discloses a seismic source inversion data driven rock burst prediction method and system, and the method comprises the steps: carrying out the quantitative inversion calculation of the core parameters of a coal rock fracture seismic source mechanism through original sound wave data collected on site; a rock burst seismic source data prediction model is constructed based on an Informer algorithm, and the occurrence time and position of rock burst are accurately predicted. A rock burst risk evaluation model is constructed by combining the predicted occurrence time and position of a future fracture source and seismic source mechanism core parameters, the magnitude and property of rock burst are predicted, and a dual-drive rock burst intelligent model for seismic source data prediction and rock burst risk evaluation is established; and multi-dimensional intelligent prediction of sequential response, spatial distribution and evolution attributes in the rock burst catastrophe process is realized. According to the method, seismic source data prediction and risk evaluation are combined, the rock burst catastrophe risk can be predicted, and the rock burst evolution process and the disaster-causing mechanism can be visually represented.

Description

Technical Field

[0001] The present invention relates to the field of rock burst prediction, and in particular to a rock burst prediction method and system driven by earthquake source inversion data. Background Art

[0002] Rock burst is one of the most serious dynamic disasters in underground projects such as coal mines. Its prediction and prevention are crucial to ensuring the safety and efficiency of coal mine production. With the continuous increase in the depth of coal mining, the incidence and degree of damage of rock burst disasters are also increasing. The mechanism of rock burst disasters is complex, involving multiple factors such as the physical properties of the rock mass, the ground stress state, and mining disturbances, which hinders the prediction of rock burst disasters. With the development of monitoring technology, monitoring methods such as microseismic monitoring, geoacoustic monitoring, and electromagnetic radiation technology have begun to become popular, providing faster and more accurate early warning information for real-time monitoring of coal mine rock burst disasters.

[0003] The combination of big data technology with monitoring methods such as microseismic monitoring, ground sound monitoring and electromagnetic radiation technology has become an important technical method for predicting rock burst disasters. CN118584542A proposed to build a prediction model through TFT neural network, and use b value, energy index, cumulative apparent volume and Schmidt number multivariate to predict rock burst, thereby improving the accuracy of rock burst prediction. CN117708705A provides a rock burst early warning method based on the comparison of long-term and short-term characteristics of microseismic data, and introduces prior knowledge into the algorithm design to automatically extract the implicit early warning indicators of rock burst early warning. This method improves the generalization ability of handling rock burst disasters in different mines. CN117849855A proposed a rock burst prediction method based on the comparison of monitoring data with different hazards. Changing the algorithm structure solves the problem of poor algorithm generalization ability caused by data imbalance, and effectively improves the early warning effect of rock burst events. These prediction methods effectively improve the quality of data prediction by improving the algorithm structure, but do not extract data information directly related to rock burst disasters. In order to more accurately predict rock burst disasters, a data and physics dual-driven model is constructed. By directly analyzing the source mechanism of the original monitoring data to predict rock burst disasters, it can better capture the precursor information of rock burst and improve the generalization ability of rock burst disaster prediction. Summary of the invention

[0004] In response to the problems and needs raised above, this solution proposes a method and system for predicting rock burst pressure driven by earthquake source inversion data. By adopting the following technical features, it can achieve the above technical objectives and bring about many other technical effects.

[0005] An object of the present invention is to provide a rock burst prediction method driven by earthquake source inversion data, comprising the following steps:

[0006] S10: Arrange a microseismic monitoring system according to the actual monitoring range of the mine site, collect original acoustic wave data of the mine using the microseismic monitoring system, perform positioning calculation and focal mechanism inversion on the original acoustic wave data, and obtain core parameters of the coal and rock fracture source directly related to the fracture; wherein the microseismic monitoring system includes microseismic sensors arranged in the coal seam of the working face;

[0007] S20: preprocess the core parameters of the rupture source using a standardized processing method, and divide the preprocessed data set into a training set, a test set, and a validation set;

[0008] S30: Construct a rock burst source data prediction model based on the Informer algorithm, input the training set into the rock burst source data prediction model, use the optimized loss function back propagation to train the rock burst source data prediction model, use the validation set to test and evaluate the trained rock burst source data prediction model, and continuously adjust the training parameters until the requirements are met; predict the time, direction, magnitude and core parameters of the future rupture source through the rock burst source data prediction model;

[0009] S40: Construct a rock burst risk assessment model, using the predicted values ​​of the core parameters of the source mechanism obtained by the rock burst source data prediction model as the input set, and establish a risk rating index by calculating the cumulative difference index of the core parameters of each rupture source, and evaluate the rock burst disaster prediction risk by combining the rock burst time, direction and risk rating index;

[0010] S50: First, the predicted occurrence time, orientation, magnitude and core parameters of the rupture source are used to reproduce the cross-scale gradual changes of the rock burst disaster process from the fracture surface to the fracture field; then the risk rating indicators are used to predict the evolution degree of the rock burst disaster and further identify the development trend of the rock burst.

[0011] In addition, the rock burst prediction method driven by earthquake source inversion data according to the present invention may also have the following technical features:

[0012] In one example of the present invention, in step S10, positioning calculation and focal mechanism inversion are performed on the original acoustic wave data to obtain the focal mechanism core parameters directly related to the rupture, including the following steps:

[0013] S11: post-processing the collected acoustic wave data, and extracting the first wave arrival time and amplitude information using the red-latency information criterion method;

[0014] S12: Using the arrival time data of the first wave of the sound wave at different sensors of the microseismic monitoring system, the simplex algorithm is used to calculate the spatial location of the earthquake source;

[0015] S13: Based on the constraints of the tensile-shear rupture source model, the six components M of the source rupture moment tensor are solved by the acoustic first wave amplitude data collected by sensors at different positions. pq ;

[0016] S14: According to the calculated moment tensor components, the core parameters of the coal and rock fracture source are quantitatively inverted and calculated, wherein the core parameters of the coal and rock fracture source include fracture area, orientation azimuth, tension-shear properties, moment magnitude and released energy.

[0017] In an example of the present invention, in step S14, the specific calculation formula of the core parameter of the coal rock fracture source is as follows:

[0018] Broken area:

[0019]

[0020] Where ΔV is the rupture area, M1 and M3 are the moment tensors M kl The characteristic value of , μ is the Lame constant, ξ is the proportional coefficient of the rupture area and displacement;

[0021] Orientation:

[0022]

[0023] n=(cosβ,0,±sinβ)

[0024]

[0025] Where β is the angle between the movement direction of the earthquake source rupture surface and the normal direction, M1, M2, and M3 are the moment tensors M kl The eigenvalues ​​of , μ and λ are the Lame constants, n is the spatial orientation, and b is the direction of motion;

[0026] Shearing properties:

[0027]

[0028]

[0029]

[0030] Where V t is the tension volume, V s is the shear volume, K is the tension-shear ratio: K>1, the tensile rupture property is dominant; K<1, the corresponding shear rupture property is dominant;

[0031] Magnitude:

[0032]

[0033] Where M1, M2, and M3 are moment tensors M kl The characteristic value of

[0034] Release Energy:

[0035]

[0036] Where: t is the tensile strength, σ s For shear strength.

[0037] In one example of the present invention, in step S20, a standardized processing method is used to pre-process the core parameters of the rupture source, including:

[0038] For preprocessing parameters x1, x2, ..., x n-1 , x n The normalization formula is as follows:

[0039]

[0040] In the formula, is the minimum value of the preprocessing parameter, is the maximum value of the preprocessing parameter, y i ∈[0,1].

[0041] In one example of the present invention, in step S30, a rock burst source data prediction model is constructed based on the Informer algorithm, and the rock burst source data prediction model includes: an encoder, a decoder and a fully connected layer,

[0042] The decoder includes a masked multi-head probabilistic sparse self-attention mechanism module and a multi-head attention mechanism module, wherein the masked multi-head probabilistic sparse self-attention mechanism layer is configured to ensure the autoregressive nature of the model, prevent future information leakage, and improve computational efficiency; the multi-head attention mechanism layer is configured to capture long-distance dependencies in the sequence, and process information in different subspaces in parallel through a multi-head mechanism, thereby enhancing the model's ability to understand complex time series data;

[0043] The encoder includes a multi-head probabilistic sparse self-attention module and a self-attention distillation module, wherein the multi-head probabilistic sparse self-attention module is configured to reduce the computational complexity of the model by randomly selecting sequence blocks and calculating attention scores only within these blocks, while enhancing the model's ability to capture local temporal dependencies; the self-attention distillation module is configured to enhance the model's ability to handle long-range dependencies of long-sequence inputs by performing feature compression through a distillation step, so that the model can better capture and utilize long-term dependency information in long-sequence data;

[0044] The fully connected layer is configured to map the input into a higher dimensional space and then transform it back to the original dimension after filtering.

[0045] In one example of the present invention, in step S30, using the optimized loss function back propagation to train the rock burst source data prediction model, and using the validation set to test and evaluate the trained rock burst source data prediction model includes the following steps:

[0046] S301: Calculate the data sample error and construct a loss function. The expression of the loss function is as follows:

[0047]

[0048] In the formula, n is the number of samples; l is the true value; is the predicted value;

[0049] S302: Back propagation training model: calculate the gradient of the loss function with respect to the weight and bias of each layer of the output layer from the output layer backwards layer by layer;

[0050] S303: Gradient descent optimization loss function: Calculate the loss function L with respect to a i The partial derivative, or gradient And update the core parameters of each rupture source: Where α is the learning rate that controls the step size of each update; the gradient is calculated and the weights and biases of the prediction model are updated repeatedly until the stopping condition is met;

[0051] S304: Repeat S301 to S303 to determine whether the model meets the requirements. If not, adjust the parameters accordingly and continue training until the requirements are met and use the test set for prediction.

[0052] In an example of the present invention, step S40 specifically includes the following steps:

[0053] S401: Assume that the core parameter set of the earthquake source mechanism for dynamic prediction of rock burst pressure is P = (m, K, ΔV, bV t …);

[0054] S402: Calculate the cumulative difference index, assuming that the data sequence of the parameters in the time window is q1, q2, ..., q n-1 ,q n (n>3) where q n is the parameter when the time series is n; subtract the previous and next terms of the sequence, Δq2=q2-q1,…,Δq n-1 =q n-1 -q n-2 , Δq n =q n -q n-1 , let Gj =min{Δq n ,0},H j =max{Δq n , 0}, then

[0055]

[0056]

[0057] Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference values ​​of each parameter in the core parameter P of the focal mechanism for dynamic prediction of rock burst pressure, and obtain the cumulative difference set of rock burst pressure risk assessment parameters [R m , R K , R ΔV , R bVt …R w ];

[0058] S403: Determine the risk level evaluation index expression as:

[0059] F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w

[0060] In the formula, a1, a2, a3, a4, ... a w The dynamic risk assessment weight of rock burst is used to classify the rock burst risk level. p Evaluate the risk of rock burst and predict the level of rock burst risk in the future.

[0061] In an example of the present invention, step S50 specifically includes the following steps:

[0062] The time, orientation and focal mechanism parameters obtained by the rock burst source data prediction model are used to describe the gradual evolution process of the source fracture surface generation, expansion, penetration to the cross-scale fracture field.

[0063] The risk rating index F obtained by the rock burst risk assessment model p Predict the evolution of rock burst disasters using R K , R ΔV , R bVt Judge the nature of rock bursts that will occur in the future, and further identify their development trends, to achieve multi-dimensional intelligent prediction of the temporal response, spatial distribution, and evolutionary properties of rock burst disaster processes.

[0064] Another object of the present invention is to provide an intelligent rock burst prediction system driven by earthquake source inversion data, comprising:

[0065] The parameter acquisition module is configured to arrange the microseismic monitoring system according to the actual monitoring range of the mine site, collect the original acoustic wave data of the mine by using the microseismic monitoring system, perform positioning calculation and focal mechanism inversion on the original acoustic wave data, and obtain the core parameters of the coal and rock fracture source directly related to the fracture; wherein the microseismic monitoring system includes microseismic sensors arranged in the coal seam of the working face;

[0066] A data partitioning module is configured to pre-process the core parameters of the rupture source using a standardized processing method, and to divide the pre-processed data set into a training set, a test set, and a validation set;

[0067] The prediction model module is configured to construct a rock burst source data prediction model based on the Informer algorithm, input the training set into the rock burst source data prediction model, train the rock burst source data prediction model by back propagation of the optimized loss function, test and evaluate the trained rock burst source data prediction model by using the validation set, and continuously adjust the training parameters until the requirements are met; predict the time, direction, magnitude and core parameters of the future rupture source by using the rock burst source data prediction model;

[0068] The risk model module is configured to construct a rock burst risk assessment model, using the predicted values ​​of the core parameters of the source mechanism obtained by the rock burst source data prediction model as an input set, establishing a risk rating index by calculating the cumulative difference index of the core parameters of each rupture source, and evaluating the rock burst disaster prediction risk in combination with the rock burst time, direction and risk rating index;

[0069] The comprehensive prediction module is configured to first use the predicted time, direction, magnitude and core parameters of the rupture source to reproduce the cross-scale gradual changes in the rock burst disaster process from the fracture surface to the fracture field; then use the risk rating indicators to predict the evolution degree of the rock burst disaster and further identify the development trend of the rock burst.

[0070] In one example of the present invention, the risk model module includes:

[0071] The core parameter setting unit is configured to set the core parameter set of the focal mechanism for dynamic prediction of rock burst pressure P = (m, K, ΔV, bV t …);

[0072] The cumulative difference indicator unit is configured to set the data sequence of the parameters in the time window to q1, q2, ..., q n-1 ,q n (n>3) where qn is the parameter when the time series is n; subtract the previous and next terms of the sequence, Δq2=q2-q1,…,Δq n-1 =q n-1 -q n-2 , Δq n =q n -q n-1 , let G j =min{Δq n ,0},H j =max{Δq n , 0}, then

[0073]

[0074]

[0075] Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference values ​​of each parameter in the core parameter P of the focal mechanism for dynamic prediction of rock burst pressure, and obtain the cumulative difference set of rock burst pressure risk assessment parameters [R m , R K , R ΔV , R bVt …R w ];

[0076] The risk level evaluation unit is configured to determine the risk level evaluation index expression as:

[0077] F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w

[0078] In the formula, a1, a2, a3, a4, ... a w The dynamic risk assessment weight of rock burst is used to classify the rock burst risk level. p Evaluate the risk of rock burst and predict the level of rock burst risk in the future.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] Compared with the traditional rock burst prediction model, which usually directly uses the original acquired acoustic wave time series signal as the data set, this method analyzes the core parameters of the source mechanism directly related to the rock burst disaster through source mechanism inversion. This method can provide data information that more accurately and directly reflects the characteristics of the disaster.

[0081] This method uses the core parameters of the earthquake source mechanism as the data set. Due to the direct correlation between the core parameters and the nature of the disaster, the evolution process of rock burst disasters in different mines is essentially the same, which enables the prediction model to be better generalized to various mine environments, improving the applicability and robustness of the model.

[0082] This method builds a rock burst source data prediction model based on the Informer algorithm, which can efficiently handle long time series prediction problems. By optimizing the loss function and back propagation training, combined with the test and evaluation of the validation set, the parameters are continuously adjusted until the model meets the requirements. This ensures the effectiveness of the prediction model in the actual application of rock burst prediction, and can more accurately predict the time and location of future rupture sources.

[0083] This method constructs a dual-driven rock burst intelligent prediction model for earthquake source data prediction and rock burst risk assessment. It can not only accurately identify and predict the time and location of rock burst disasters, but also further judge the development trend of rock burst disaster evolution. It forms a multi-dimensional intelligent prediction method for rock burst that integrates source parameter prediction and disaster evolution rating, providing more reliable technical support for mine safety.

[0084] The best embodiment for carrying out the present invention will be described in more detail below with reference to the accompanying drawings so that the features and advantages of the present invention can be easily understood. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings of the embodiments of the present invention, wherein the drawings are only used to illustrate some embodiments of the present invention, but not to limit all embodiments of the present invention thereto.

[0086] Figure 1 A flowchart of a rock burst prediction method driven by earthquake source inversion data according to an embodiment of the present invention;

[0087] Figure 2 A schematic diagram of a structure for constructing a rock burst source data prediction model based on the Informer algorithm according to an embodiment of the present invention;

[0088] Figure 3 Schematic diagram of focal mechanism inversion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solution and advantages of the technical solution of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described in conjunction with the drawings of specific embodiments of the present invention. The same figure marks in the drawings represent the same parts. It should be noted that the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] Unless otherwise defined, the technical terms or scientific terms used herein shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar words used in the patent application specification and claims of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not necessarily indicate a quantity limitation. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0091] According to a first aspect of the present invention, a rock burst prediction method driven by earthquake source inversion data is as follows: Figure 1 and Figure 3 As shown, the following steps are included:

[0092] S10: Arrange a microseismic monitoring system according to the actual monitoring range of the mine site, collect original acoustic wave data of the mine using the microseismic monitoring system, perform positioning calculation and focal mechanism inversion on the original acoustic wave data, and obtain core parameters of the coal and rock fracture source directly related to the fracture; wherein the microseismic monitoring system includes microseismic sensors arranged in the coal seam of the working face;

[0093] S20: preprocess the core parameters of the rupture source using a standardized processing method, and divide the preprocessed data set into a training set, a test set, and a validation set;

[0094] S30: Construct a rock burst source data prediction model based on the Informer algorithm, input the training set into the rock burst source data prediction model, use the optimized loss function back propagation to train the rock burst source data prediction model, use the validation set to test and evaluate the trained rock burst source data prediction model, and continuously adjust the training parameters until the requirements are met; predict the time, direction, magnitude and core parameters of the future rupture source through the rock burst source data prediction model;

[0095] S40: Construct a rock burst risk assessment model, using the predicted values ​​of the core parameters of the source mechanism obtained by the rock burst source data prediction model as the input set, and establish a risk rating index by calculating the cumulative difference index of the core parameters of each rupture source, and evaluate the rock burst disaster prediction risk by combining the rock burst time, direction and risk rating index;

[0096] S50: First, the predicted occurrence time, orientation, magnitude and core parameters of the rupture source are used to reproduce the cross-scale gradual changes of the rock burst disaster process from the fracture surface to the fracture field; then the risk rating indicators are used to predict the evolution degree of the rock burst disaster, further identify the development trend of the rock burst, realize the intelligent prediction of rock burst driven by earthquake source data prediction and rock burst risk assessment, and comprehensively predict the evolution process of rock burst disaster risk.

[0097] Compared with the traditional rock burst prediction model, which usually directly uses the original acquired acoustic wave time series signal as the data set, this method analyzes the core parameters of the source mechanism directly related to the rock burst disaster through source mechanism inversion. This method can provide data information that more accurately and directly reflects the characteristics of the disaster.

[0098] This method uses the core parameters of the earthquake source mechanism as the data set. Due to the direct correlation between the core parameters and the nature of the disaster, the evolution process of rock burst disasters in different mines is essentially the same, which enables the prediction model to be better generalized to various mine environments, improving the applicability and robustness of the model.

[0099] This method builds a rock burst source data prediction model based on the Informer algorithm, which can efficiently handle long time series prediction problems. By optimizing the loss function and back propagation training, combined with the test and evaluation of the validation set, the parameters are continuously adjusted until the model meets the requirements. This ensures the effectiveness of the prediction model in the actual application of rock burst prediction, and can more accurately predict the time and location of future rupture sources.

[0100] This method constructs a dual-driven rock burst intelligent prediction model for earthquake source data prediction and rock burst risk assessment. It can not only accurately identify and predict the time and location of rock burst disasters, but also further judge the development trend of rock burst disaster evolution. It forms a multi-dimensional intelligent prediction method for rock burst that integrates source parameter prediction and disaster evolution rating, providing more reliable technical support for mine safety.

[0101] In one example of the present invention, in step S10, positioning calculation and focal mechanism inversion are performed on the original acoustic wave data to obtain the focal mechanism core parameters directly related to the rupture, including the following steps:

[0102] S11: post-processing the collected acoustic wave data, and extracting the first wave arrival time and amplitude information using the red-latency information criterion method;

[0103] S12: Using the arrival time data of the first wave of the sound wave at different sensors of the microseismic monitoring system, the simplex algorithm is used to calculate the spatial location of the earthquake source;

[0104] S13: Based on the constraints of the tensile-shear rupture source model, the six components M of the source rupture moment tensor are solved by the acoustic first wave amplitude data collected by sensors at different positions. pq ;

[0105] S14: According to the calculated moment tensor components, the core parameters of the coal and rock fracture source are quantitatively inverted and calculated, wherein the core parameters of the coal and rock fracture source include fracture area, orientation azimuth, tension-shear properties, moment magnitude and released energy.

[0106] In an example of the present invention, in step S14, the specific calculation formula of the core parameter of the coal rock fracture source is as follows:

[0107] Broken area:

[0108]

[0109] Where ΔV is the rupture area, M1 and M3 are the moment tensors M kl The characteristic value of , μ is the Lame constant, ξ is the proportional coefficient of the rupture area and displacement;

[0110] Orientation:

[0111]

[0112] n=(cosβ,0,±sinβ)

[0113]

[0114] Where β is the angle between the movement direction of the earthquake source rupture surface and the normal direction, M1, M2, and M3 are the moment tensors Mkl The eigenvalues ​​of , μ and λ are the Lame constants, n is the spatial orientation, and b is the direction of motion;

[0115] Shearing properties:

[0116]

[0117]

[0118]

[0119] Where V t is the tension volume, V s is the shear volume, K is the tension-shear ratio: K>1, the tensile rupture property is dominant; K<1, the corresponding shear rupture property is dominant;

[0120] Magnitude:

[0121]

[0122] Where M1, M2, and M3 are moment tensors M kl The characteristic value of

[0123] Release Energy:

[0124]

[0125] Where: t is the tensile strength, σ s For shear strength.

[0126] In one example of the present invention, in step S20, a standardized processing method is used to pre-process the core parameters of the rupture source, including:

[0127] For preprocessing parameters x1, x2, ..., x n-1 , x n The normalization formula is as follows:

[0128]

[0129] In the formula, is the minimum value of the preprocessing parameter, is the maximum value of the preprocessing parameter, y i ∈[0,1].

[0130] In one example of the present invention, Figure 2 As shown, in step S30, a rock burst source data prediction model is constructed based on the Informer algorithm. The rock burst source data prediction model includes: an encoder, a decoder and a fully connected layer.

[0131] The decoder includes a masked multi-head probabilistic sparse self-attention mechanism module and a multi-head attention mechanism module, wherein the masked multi-head probabilistic sparse self-attention mechanism layer is configured to ensure the autoregressive nature of the model, prevent future information leakage, and improve computational efficiency; the multi-head attention mechanism layer is configured to capture long-distance dependencies in the sequence, and process information in different subspaces in parallel through a multi-head mechanism, thereby enhancing the model's ability to understand complex time series data;

[0132] The encoder includes a multi-head probabilistic sparse self-attention module and a self-attention distillation module, wherein the multi-head probabilistic sparse self-attention module is configured to reduce the computational complexity of the model by randomly selecting sequence blocks and calculating attention scores only within these blocks, while enhancing the model's ability to capture local temporal dependencies; the self-attention distillation module is configured to enhance the model's ability to handle long-range dependencies of long-sequence inputs by performing feature compression through a distillation step, so that the model can better capture and utilize long-term dependency information in long-sequence data;

[0133] The fully connected layer is configured to map the input into a higher dimensional space and then transform it back to the original dimension after filtering.

[0134] The detailed process of data processing by the rock burst source data prediction model is as follows:

[0135] The original data set is input into the encoder and decoder respectively. In the encoder, the multi-head probabilistic sparse self-attention module reduces the computational complexity of the model by randomly selecting sequence blocks and calculating the attention scores only in these blocks, while enhancing the model's ability to capture local time dependencies; the self-attention distillation module improves the model's ability to input long-distance dependencies of long sequences, and performs feature compression through the distillation step, so that the model can better capture and utilize long-term dependency information in long sequence data. The original data processed by the encoder is input into the decoder and processed by the decoder together with the original data set initially input into the decoder, that is, the masked multi-head probabilistic sparse self-attention mechanism layer ensures the autoregressive nature of the model, prevents future information leakage, and improves computational efficiency. By adding a mask to the attention block, it is ensured that the model can only access current and past information when predicting, but not future information; the multi-head attention mechanism layer captures the long-distance dependencies in the sequence, and processes information in different subspaces in parallel through the multi-head mechanism, enhancing the model's ability to understand complex time series data; finally, the fully connected layer maps the input to a higher-dimensional space, and after screening, it is transformed back to the original dimension. This step uses a fully connected network (replaced by CNN in Informer) to enhance the model's ability to transform features nonlinearly, further improving the model's predictive performance.

[0136] In one example of the present invention, in step S30, using the optimized loss function back propagation to train the rock burst source data prediction model, and using the validation set to test and evaluate the trained rock burst source data prediction model includes the following steps:

[0137] S301: Calculate the data sample error and construct a loss function. The expression of the loss function is as follows:

[0138]

[0139] In the formula, n is the number of samples; l is the true value; is the predicted value;

[0140] S302: Back propagation training model: calculate the gradient of the loss function with respect to the weight and bias of each layer of the output layer from the output layer backwards layer by layer;

[0141] S303: Gradient descent optimization loss function: Calculate the loss function L with respect to a i The partial derivative, or gradient And update the core parameters of each rupture source: Where α is the learning rate that controls the step size of each update; the gradient is calculated and the weights and biases of the prediction model are updated repeatedly until the stopping condition is met;

[0142] S304: Repeat S301 to S303 to determine whether the model meets the requirements. If not, adjust the parameters accordingly and continue training until the requirements are met and use the test set for prediction.

[0143] In an example of the present invention, step S40 specifically includes the following steps:

[0144] S401: Assume that the core parameter set of the earthquake source mechanism for dynamic prediction of rock burst pressure is P = (m, K, ΔV, bV t …);

[0145] S402: Calculate the cumulative difference index, assuming that the data sequence of the parameters in the time window is q1, q2, ..., q n-1 ,q n (n>3) where q n is the parameter when the time series is n; subtract the previous and next terms of the sequence, Δq2=q2-q1,…,Δq n-1 =q n-1 -q n-2 , Δq n =q n -q n-1 , let G j =min{Δq n ,0},H j =max{Δq n, 0}, then

[0146]

[0147]

[0148] Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference values ​​of each parameter in the core parameter P of the focal mechanism for dynamic prediction of rock burst pressure, and obtain the cumulative difference set of rock burst pressure risk assessment parameters [R m , R K , R ΔV , R bVt …R w ];

[0149] S403: Determine the risk level evaluation index expression as:

[0150] F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w

[0151] In the formula, a1, a2, a3, a4, ... a w The dynamic risk assessment weight of rock burst is used to classify the rock burst risk level. p Evaluate the risk of rock burst and predict the level of rock burst risk in the future.

[0152] In an example of the present invention, step S50 specifically includes the following steps:

[0153] The time, orientation and focal mechanism parameters obtained by the rock burst source data prediction model are used to describe the gradual evolution process of the source fracture surface generation, expansion, penetration to the cross-scale fracture field.

[0154] The risk rating index F obtained by the rock burst risk assessment model p Predict the evolution of rock burst disasters using R K , R ΔV , R bVt Judge the nature of rock bursts that will occur in the future, and further identify their development trends, to achieve multi-dimensional intelligent prediction of the temporal response, spatial distribution, and evolutionary properties of rock burst disaster processes.

[0155] According to the second aspect of the present invention, a rock burst intelligent prediction system driven by earthquake source inversion data comprises:

[0156] The parameter acquisition module is configured to arrange the microseismic monitoring system according to the actual monitoring range of the mine site, collect the original acoustic wave data of the mine by using the microseismic monitoring system, perform positioning calculation and focal mechanism inversion on the original acoustic wave data, and obtain the core parameters of the coal and rock fracture source directly related to the fracture; wherein the microseismic monitoring system includes microseismic sensors arranged in the coal seam of the working face;

[0157] A data partitioning module is configured to pre-process the core parameters of the rupture source using a standardized processing method, and to divide the pre-processed data set into a training set, a test set, and a validation set;

[0158] The prediction model module is configured to construct a rock burst source data prediction model based on the Informer algorithm, input the training set into the rock burst source data prediction model, train the rock burst source data prediction model by back propagation of the optimized loss function, test and evaluate the trained rock burst source data prediction model by using the validation set, and continuously adjust the training parameters until the requirements are met; predict the time, direction, magnitude and core parameters of the future rupture source by using the rock burst source data prediction model;

[0159] The risk model module is configured to construct a rock burst risk assessment model, using the predicted values ​​of the core parameters of the source mechanism obtained by the rock burst source data prediction model as an input set, establishing a risk rating index by calculating the cumulative difference index of the core parameters of each rupture source, and evaluating the rock burst disaster prediction risk in combination with the rock burst time, direction and risk rating index;

[0160] The comprehensive prediction module is configured to first use the predicted time, direction, magnitude and core parameters of the rupture source to reproduce the cross-scale gradual changes of the rock burst disaster process from the fracture surface to the fracture field; then use the risk rating indicators to predict the evolution degree of the rock burst disaster, further identify the development trend of the rock burst, realize the dual-driven intelligent prediction of rock burst by earthquake source data prediction and rock burst risk assessment, and comprehensively predict the evolution process of rock burst disaster risk.

[0161] Compared with the traditional rock burst prediction model, which usually directly uses the original acquired acoustic wave time series signal as the data set, this system analyzes the core parameters of the source mechanism directly related to the rock burst disaster through source mechanism inversion. This method can provide data information that more accurately and directly reflects the characteristics of the disaster.

[0162] The system uses the core parameters of the earthquake source mechanism as the data set. Due to the direct correlation between the core parameters and the nature of the disaster, the evolution process of rock burst disasters in different mines is essentially the same, which enables the prediction model to be better generalized to various mine environments, improving the applicability and robustness of the model.

[0163] The system builds a rock burst source data prediction model based on the Informer algorithm, which can efficiently handle long time series prediction problems. By optimizing the loss function and back propagation training, combined with the test and evaluation of the validation set, the parameters are continuously adjusted until the model meets the requirements. This ensures the effectiveness of the prediction model in the actual application of rock burst prediction, and can more accurately predict the time and location of future rupture sources.

[0164] The system constructs a dual-driven rock burst intelligent prediction model for earthquake source data prediction and rock burst risk assessment. It can not only accurately identify and predict the time and location of rock burst disasters, but also further judge the development trend of rock burst disaster evolution. It forms a multi-dimensional intelligent prediction method for rock burst that integrates source parameter prediction and disaster evolution rating, providing more reliable technical support for mine safety.

[0165] In one example of the present invention, the prediction model module includes:

[0166] The loss function unit is configured to construct a loss function and calculate the data sample error. The expression of the loss function is as follows:

[0167]

[0168] In the formula, n is the number of samples; l is the true value; is the predicted value;

[0169] A back-propagation training unit configured to calculate the gradient of the loss function with respect to the weights and biases of each layer of the output layer from the output layer backwards layer by layer;

[0170] Loss function optimization unit: configured to calculate the loss function L with respect to a i The partial derivative, or gradient And update the core parameters of each rupture source: Where α is the learning rate that controls the step size of each update; the gradient is calculated and the weights and biases of the prediction model are updated repeatedly until the stopping condition is met;

[0171] The model judgment unit is configured to determine whether the model meets the requirements. If not, the parameters are adjusted accordingly and training is continued until the requirements are met and prediction is performed using the test set.

[0172] In one example of the present invention, the risk model module includes:

[0173] The core parameter setting unit is configured to set the core parameter set of the focal mechanism for dynamic prediction of rock burst pressure P = (m, K, ΔV, bV t …);

[0174] The cumulative difference indicator unit is configured to set the data sequence of the parameters in the time window to q1, q2, ..., q n-1 ,q n (n>3) where q n is the parameter when the time series is n; subtract the previous and next terms of the sequence, Δq2=q2-q1,…,Δq n-1 =q n-1 -q n-2 , Δq n =q n -q n-1 , let G j =min{Δq n ,0},H j =max{Δq n , 0}, then

[0175]

[0176]

[0177] Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference values ​​of each parameter in the core parameter P of the focal mechanism for dynamic prediction of rock burst pressure, and obtain the cumulative difference set of rock burst pressure risk assessment parameters [R m , R K , R ΔV , R bVt …R w ];

[0178] The risk level evaluation unit is configured to determine the risk level evaluation index expression as:

[0179] F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w

[0180] In the formula, a1, a2, a3, a4, ... a w The dynamic risk assessment weight of rock burst is used to classify the rock burst risk level. p Evaluate the risk of rock burst and predict the level of rock burst risk in the future.

[0181] The exemplary implementation scheme of the earthquake source inversion data-driven rock burst prediction method and system proposed in the present invention is described in detail above with reference to the preferred embodiments. However, those skilled in the art will appreciate that, without departing from the concept of the present invention, various modifications and variations can be made to the above-mentioned specific embodiments, and various technical features and structures proposed in the present invention can be combined in various ways without exceeding the protection scope of the present invention, which is determined by the attached claims.

Claims

1. A rock burst prediction method driven by earthquake source inversion data, characterized in that: The steps include: S10: Arrange a microseismic monitoring system according to the actual monitoring range of the mine site, collect original acoustic wave data of the mine using the microseismic monitoring system, perform positioning calculation and focal mechanism inversion on the original acoustic wave data, and obtain core parameters of the coal and rock fracture source directly related to the fracture; wherein the microseismic monitoring system includes microseismic sensors arranged in the coal seam of the working face; S20: preprocess the core parameters of the rupture source using a standardized processing method, and divide the preprocessed data set into a training set, a test set, and a validation set; S30: Construct a rock burst source data prediction model based on the Informer algorithm, input the training set into the rock burst source data prediction model, use the optimized loss function back propagation to train the rock burst source data prediction model, use the validation set to test and evaluate the trained rock burst source data prediction model, and continuously adjust the training parameters until the requirements are met; predict the time, direction, magnitude and core parameters of the future rupture source through the rock burst source data prediction model; S40: Construct a rock burst risk assessment model, using the predicted values ​​of the core parameters of the source mechanism obtained by the rock burst source data prediction model as the input set, and establish a risk rating index by calculating the cumulative difference index of the core parameters of each rupture source, and evaluate the rock burst disaster prediction risk by combining the rock burst time, direction and risk rating index; S50: First, the predicted occurrence time, orientation, magnitude and core parameters of the rupture source are used to reproduce the cross-scale gradual changes of the rock burst disaster process from the fracture surface to the fracture field; then the risk rating indicators are used to predict the evolution degree of the rock burst disaster and further identify the development trend of the rock burst.

2. The rock burst prediction method driven by earthquake source inversion data according to claim 1, characterized in that: In step S10, the original acoustic wave data is subjected to positioning calculation and focal mechanism inversion to obtain the focal mechanism core parameters directly related to the rupture, including the following steps: S11: post-processing the collected acoustic wave data, and extracting the first wave arrival time and amplitude information using the red-latency information criterion method; S12: Using the arrival time data of the first wave of the sound wave at different sensors of the microseismic monitoring system, the simplex algorithm is used to calculate the spatial location of the earthquake source; S13: Based on the constraints of the tensile-shear rupture source model, the six components M of the source rupture moment tensor are solved by the acoustic first wave amplitude data collected by sensors at different positions. pq ; S14: According to the calculated moment tensor components, the core parameters of the coal and rock fracture source are quantitatively inverted and calculated, wherein the core parameters of the coal and rock fracture source include fracture area, orientation azimuth, tension-shear properties, moment magnitude and released energy.

3. The rock burst prediction method driven by earthquake source inversion data according to claim 2, characterized in that: In step S14, the specific calculation formula of the core parameters of the coal rock fracture source is as follows: Broken area: Where ΔV is the rupture area, M1 and M3 are the moment tensors M kl The characteristic value of , μ is the Lame constant, ξ is the proportional coefficient of the rupture area and displacement; Orientation: n=(cosβ,0,±sinβ) Where β is the angle between the movement direction of the earthquake source rupture surface and the normal direction, M1, M2, and M3 are the moment tensors M kl The eigenvalues ​​of , μ and λ are the Lame constants, n is the spatial orientation, and b is the direction of motion; Shearing properties: Where V t is the tension volume, V s is the shear volume, K is the tension-shear ratio: K>1, the tensile rupture property is dominant; K<1, the corresponding shear rupture property is dominant; Magnitude: Where M1, M2, and M3 are moment tensors M kl The characteristic value of Release Energy: Where: t is the tensile strength, σ s For shear strength.

4. The rock burst prediction method driven by source inversion data according to claim 1, characterized in that: In step S20, the core parameters of the rupture source are preprocessed using a standardized processing method, including: For preprocessing parameters x1, x2, ..., x n-1 , x n The normalization formula is as follows: In the formula, is the minimum value of the preprocessing parameter, is the maximum value of the preprocessing parameter, y i ∈[0,1].

5. The rock burst prediction method driven by earthquake source inversion data according to claim 1, characterized in that: In step S30, a rock burst source data prediction model is constructed based on the Informer algorithm. The rock burst source data prediction model includes an encoder, a decoder, and a fully connected layer. The decoder includes a masked multi-head probabilistic sparse self-attention mechanism module and a multi-head attention mechanism module, wherein the masked multi-head probabilistic sparse self-attention mechanism layer is configured to ensure the autoregressive nature of the model, prevent future information leakage, and improve computational efficiency; the multi-head attention mechanism layer is configured to capture long-distance dependencies in the sequence, and process information in different subspaces in parallel through a multi-head mechanism, thereby enhancing the model's ability to understand complex time series data; The encoder includes a multi-head probabilistic sparse self-attention module and a self-attention distillation module, wherein the multi-head probabilistic sparse self-attention module is configured to reduce the computational complexity of the model by randomly selecting sequence blocks and calculating attention scores only within these blocks, while enhancing the model's ability to capture local temporal dependencies; the self-attention distillation module is configured to enhance the model's ability to handle long-range dependencies of long-sequence inputs by performing feature compression through a distillation step, so that the model can better capture and utilize long-term dependency information in long-sequence data; The fully connected layer is configured to map the input into a higher dimensional space and then transform it back to the original dimension after filtering.

6. The rock burst prediction method driven by earthquake source inversion data according to claim 1, characterized in that: In step S30, the rock burst source data prediction model is trained by back propagation of the optimized loss function, and the trained rock burst source data prediction model is tested and evaluated by the validation set, including the following steps: S301: Calculate the data sample error and construct a loss function. The expression of the loss function is as follows: In the formula, n is the number of samples; l is the true value; is the predicted value; S302: Back propagation training model: calculate the gradient of the loss function with respect to the weight and bias of each layer of the output layer from the output layer backwards layer by layer; S303: Gradient descent optimization loss function: Calculate the loss function L with respect to a i The partial derivative, or gradient And update the core parameters of each rupture source: Where α is the learning rate that controls the step size of each update; the gradient is calculated and the weights and biases of the prediction model are updated repeatedly until the stopping condition is met; S304: Repeat S301 to S303 to determine whether the model meets the requirements. If not, adjust the parameters accordingly and continue training until the requirements are met and use the test set for prediction.

7. The rock burst prediction method driven by earthquake source inversion data according to claim 1, characterized in that: The step S40 specifically includes the following steps: S401: Assume that the core parameter set of the earthquake source mechanism for dynamic prediction of rock burst pressure is P = (m, K, ΔV, bV t …); S402: Calculate the cumulative difference index, assuming that the data sequence of the parameters in the time window is q1, q2, ..., q n-1 ,q n (n>3) where q n is the parameter when the time series is n; subtract the previous and next terms of the sequence, Δq2=q2-q1,…,Δq n-1 =q n-1 -q n-2 , Δq n =q n -q n-1 , let G j =min{Δq n ,0},H j =max{Δq n , 0}, then Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference values ​​of each parameter in the core parameter P of the focal mechanism for dynamic prediction of rock burst pressure, and obtain the cumulative difference set of rock burst pressure risk assessment parameters [R m , R K , R ΔV , R bVt …R w ]; S403: Determine the risk level evaluation index expression as: F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w In the formula, a1, a2, a3, a4, ... a w The dynamic risk assessment weight of rock burst is used to classify the rock burst risk level. p Evaluate the risk of rock burst and predict the level of rock burst risk in the future.

8. The method for predicting rock burst pressure driven by earthquake source inversion data according to claim 1, characterized in that: The step S50 specifically includes the following steps: The time, orientation and focal mechanism parameters obtained by the rock burst source data prediction model are used to describe the gradual evolution process of the source fracture surface generation, expansion, penetration to the cross-scale fracture field. The risk rating index F obtained by the rock burst risk assessment model p Predict the evolution of rock burst disasters using R K , R ΔV , R bVt Judge the nature of rock bursts that will occur in the future, and further identify their development trends, to achieve multi-dimensional intelligent prediction of the temporal response, spatial distribution, and evolutionary properties of rock burst disaster processes.

9. An intelligent rock burst prediction system driven by earthquake source inversion data, characterized in that: include: The parameter acquisition module is configured to arrange the microseismic monitoring system according to the actual monitoring range of the mine site, collect the original acoustic wave data of the mine by using the microseismic monitoring system, perform positioning calculation and focal mechanism inversion on the original acoustic wave data, and obtain the core parameters of the coal and rock fracture source directly related to the fracture; wherein the microseismic monitoring system includes microseismic sensors arranged in the coal seam of the working face; A data partitioning module is configured to pre-process the core parameters of the rupture source using a standardized processing method, and to divide the pre-processed data set into a training set, a test set, and a validation set; The prediction model module is configured to construct a rock burst source data prediction model based on the Informer algorithm, input the training set into the rock burst source data prediction model, train the rock burst source data prediction model by back propagation of the optimized loss function, test and evaluate the trained rock burst source data prediction model by using the validation set, and continuously adjust the training parameters until the requirements are met; predict the time, direction, magnitude and core parameters of the future rupture source by using the rock burst source data prediction model; The risk model module is configured to construct a rock burst risk assessment model, using the predicted values ​​of the core parameters of the source mechanism obtained by the rock burst source data prediction model as an input set, establishing a risk rating index by calculating the cumulative difference index of the core parameters of each rupture source, and evaluating the rock burst disaster prediction risk in combination with the rock burst time, direction and risk rating index; The comprehensive prediction module is configured to first use the predicted time, direction, magnitude and core parameters of the rupture source to reproduce the cross-scale gradual changes in the rock burst disaster process from the fracture surface to the fracture field; then use the risk rating indicators to predict the evolution degree of the rock burst disaster and further identify the development trend of the rock burst.

10. The rock burst intelligent prediction system driven by earthquake source inversion data according to claim 9, characterized in that: The risk model module includes: The core parameter setting unit is configured to set the core parameter set of the focal mechanism for dynamic prediction of rock burst pressure P = (m, K, ΔV, bV t …); The cumulative difference indicator unit is configured to set the data sequence of the parameters in the time window to q1, q2, ..., q n-1 ,q n (n>3) where q n is the parameter when the time series is n; subtract the previous and next terms of the sequence, Δq2=q2-q1,…,Δq n-1 =q n-1 -q n-2 , Δq n =q n -q n-1 , let G j =min{Δq n ,0},H j =max{Δq n , 0}, then Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference values ​​of each parameter in the core parameter P of the focal mechanism for dynamic prediction of rock burst pressure, and obtain the cumulative difference set of rock burst pressure risk assessment parameters [R m , R K , R ΔV , R bVt …R w ]; The risk level evaluation unit is configured to determine the risk level evaluation index expression as: F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w In the formula, a1, a2, a3, a4, ... a w The dynamic risk assessment weight of rock burst is used to classify the rock burst risk level. p Evaluate the risk of rock burst and predict the level of rock burst risk in the future.

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