Deep hole blasting crack evolution evaluation method and system based on micro-seismic monitoring

Abstract

The invention discloses a micro-seismic monitoring-based deep hole blasting crack evolution evaluation method and system. The method comprises the following steps of: obtaining a spatio-temporal evolution process of crack extension and a final crack distribution characteristic; microseismic signals in the blasting process are collected in real time, the spatial-temporal evolution process of crack expansion and the final crack distribution characteristics are combined, the microseismic signals are preprocessed, and preprocessed microseismic waveform data are obtained; calculating a spatial position coordinate of the micro-seismic event, and generating a complete three-dimensional micro-seismic positioning image; drawing a fracture density cloud picture according to the generated complete three-dimensional micro-seismic positioning image in combination with the acquired spatial distribution density and energy release rate of the micro-seismic event; a three-dimensional visualization model of fracture evolution is established, and evaluation indexes are obtained; and establishing a comprehensive index system for crack evolution evaluation, carrying out weight distribution and quantitative scoring on evaluation indexes, generating a quantitative evaluation report of crack evolution, and completing deep hole blasting crack evolution evaluation. The accuracy and reliability of evaluation are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of deep hole blasting gap evolution assessment, and in particular relates to a deep hole blasting gap evolution assessment method and system based on microseismic monitoring. Background Art

[0002] The accurate prediction and real-time monitoring of fracture evolution during deep hole blasting face major challenges. Traditional numerical simulation methods are difficult to accurately depict the dynamic process of blasting energy propagation and rock mass fracture under complex geological conditions, and there are many technical bottlenecks in the field microseismic monitoring system in terms of signal acquisition, processing and interpretation. How to organically combine theoretical models with measured data to construct a high-precision, high-resolution three-dimensional visualization model of fracture evolution has become a key issue that needs to be solved urgently. Specifically, the following difficulties need to be overcome: multi-scale, multi-field coupled numerical simulation of blasting fracture expansion; high-sensitivity acquisition and high signal-to-noise ratio processing of microseismic signals; accurate source positioning based on double difference positioning and waveform correlation; quantitative inversion of fracture density and connectivity; fusion and visualization of multi-source heterogeneous data; and a comprehensive evaluation index system for fracture evolution. These technical difficulties are interrelated and mutually constrained, forming a complex technical contradiction system. How to break through these bottlenecks and realize the accurate prediction, real-time monitoring and quantitative evaluation of deep hole blasting fracture evolution is of great significance for guiding blasting engineering design, optimizing blasting parameters and evaluating blasting effects. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a deep hole blasting rift evolution assessment method and system based on microseismic monitoring to improve the accuracy and reliability of the assessment.

[0004] On the one hand, to achieve the above-mentioned purpose, the present invention provides a method for evaluating the evolution of deep hole blasting rift based on microseismic monitoring, comprising:

[0005] Construct a numerical simulation model for deep hole blasting crack expansion, simulate the propagation and attenuation of blasting energy in the rock mass, predict the initiation and expansion range of cracks, and obtain the spatiotemporal evolution process of crack expansion and the final crack distribution characteristics;

[0006] Real-time acquisition of microseismic signals during the blasting process, combined with the spatiotemporal evolution of the crack expansion and the final crack distribution characteristics, preprocessing the microseismic signals to obtain preprocessed microseismic waveform data;

[0007] Based on the preprocessed microseismic waveform data combined with known velocity models and sensor layout geometric information, the spatial position coordinates of the microseismic event are calculated to generate a complete three-dimensional microseismic positioning image;

[0008] Based on the generated complete three-dimensional microseismic positioning image combined with the spatial distribution density and energy release rate of the acquired microseismic events, a fracture density cloud map is drawn;

[0009] Based on the fracture density cloud map combined with microseismic monitoring data and numerical simulation results, a three-dimensional visualization model of fracture evolution is established to obtain evaluation indicators;

[0010] A comprehensive indicator system for fracture evolution assessment is established, weights are assigned and quantitative scores are given to the assessment indicators, a quantitative assessment report on fracture evolution is generated, and the fracture evolution assessment of deep hole blasting is completed.

[0011] Optionally, obtaining the spatiotemporal evolution of crack expansion and the final crack distribution characteristics includes:

[0012] According to the deep hole blasting design parameters and geological conditions, a three-dimensional rock mass model of deep hole blasting crack expansion is established, and the model is meshed and boundary conditions are set;

[0013] According to the law of blasting energy release, a time-course blasting load is applied to the model to calculate the propagation and attenuation process of blasting energy in the rock mass.

[0014] According to the dynamic mechanical parameters and constitutive relations of the rock mass, the stress-strain state of the rock mass under the blasting load is judged, and the location and direction of crack initiation are determined;

[0015] Calculate the stress intensity factor at the crack tip, determine whether the crack meets the expansion conditions, and simulate the crack expansion path and length;

[0016] The crack extension results at different times are post-processed and visualized to obtain the spatiotemporal evolution of crack extension and the final crack distribution characteristics.

[0017] Optionally, obtaining pre-processed microseismic waveform data includes:

[0018] Real-time collection of microseismic signals during blasting to expand the dynamic range of microseismic signals;

[0019] Convert the amplified analog microseismic signals into digital microseismic signals and determine the sampling rate and quantization accuracy of high-speed analog-to-digital conversion technology;

[0020] Performing time-frequency analysis on the digital microseismic signal to obtain time-frequency characteristics of the digital microseismic signal;

[0021] Constructing a deep learning model to automatically detect and classify the digital microseismic signals, identify microseismic events, and determine the types of microseismic events;

[0022] According to the on-site geological conditions and blasting parameters, a microseismic positioning inversion model is established to determine the spatial location of the microseismic event and obtain the pre-processed microseismic waveform data.

[0023] Optionally, generating a complete three-dimensional microseismic location image includes:

[0024] According to the waveform-related characteristics of the microseismic event, combined with the preset velocity model and sensor layout geometry information, the spatial position coordinates of the microseismic event are calculated;

[0025] Draw a three-dimensional microseismic location image based on the determined spatial position coordinates of the microseismic event;

[0026] Classify the waveform-related features of microseismic events to obtain the spatial distribution patterns of different types of microseismic events;

[0027] Cluster analysis is performed on the spatial position coordinates of microseismic events to obtain the spatial distribution area of ​​cracks;

[0028] According to the spatial distribution area of ​​the cracks, a crack expansion prediction model is established to predict the expansion trend and range of the cracks;

[0029] A complete three-dimensional microseismic location image is generated based on the spatial position coordinates of the microseismic event, the spatial distribution characteristics of the cracks, and the expansion trend and range of the cracks.

[0030] Optionally, the fracture density cloud map is drawn based on the spatial distribution density and energy release rate of microseismic events, including:

[0031] According to the spatial coordinate information of microseismic events, the spatial distribution of microseismic events is fitted to obtain the spatial distribution density of microseismic events;

[0032] According to the magnitude information of the microseismic event, the energy release rate of each microseismic event is calculated by using the energy release rate model to obtain the energy release rate of the microseismic event;

[0033] According to the spatial distribution density and energy release rate of microseismic events, a quantitative relationship model between crack density and the spatial distribution density and energy release rate of microseismic events is established, and the crack density distribution is inverted and calculated;

[0034] A crack density cloud map is drawn according to the crack density distribution.

[0035] Optionally, obtain evaluation metrics including:

[0036] Obtain microseismic monitoring data and numerical simulation results;

[0037] Performing feature extraction on the numerical simulation results to extract crack evolution characteristic parameters;

[0038] The pre-processed microseismic monitoring data and the fracture evolution characteristic parameters are fused to establish a fracture evolution prediction model, and the optimized model parameters are obtained through training;

[0039] According to the fracture evolution prediction model, new microseismic monitoring data are predicted to obtain fracture evolution state data;

[0040] The fracture evolution state data is converted into a three-dimensional visualization model, and according to the three-dimensional visualization model, the blasting effect is evaluated, the blasting parameters are optimized, and the evaluation index is obtained.

[0041] Optionally, generate a report to quantitatively assess the fracture evolution including:

[0042] Establish a comprehensive indicator system for fracture evolution assessment, compare the assessment indicators pairwise, construct a judgment matrix, and calculate the weight of each indicator;

[0043] According to the fuzzy comprehensive evaluation method, a comment set is set for each evaluation index and a corresponding membership value is assigned;

[0044] The weight of each indicator is weighted and summed with the corresponding membership value to obtain the comprehensive evaluation score of fracture evolution;

[0045] According to the comprehensive evaluation score, the fracture evolution status is divided into different levels, and a quantitative evaluation report of the fracture evolution is generated.

[0046] On the other hand, to achieve the above-mentioned purpose, the present invention also provides a deep hole blasting fracture evolution evaluation system based on microseismic monitoring, comprising: a deep hole blasting numerical simulation module, a microseismic monitoring system module, a microseismic event positioning module, a fracture density inversion module, a three-dimensional visualization model module and a comprehensive evaluation index system module;

[0047] The deep hole blasting numerical simulation module is used to construct a numerical simulation model for deep hole blasting crack expansion, simulate the propagation and attenuation law of blasting energy in the rock mass, predict the initiation and expansion range of cracks, and obtain the spatiotemporal evolution process of crack expansion and the final crack distribution characteristics;

[0048] The microseismic monitoring system module is used to collect microseismic signals in the blasting process in real time, combine the spatiotemporal evolution of the crack expansion and the final crack distribution characteristics, pre-process the microseismic signals, and obtain pre-processed microseismic waveform data;

[0049] The microseismic event positioning module is used to calculate the spatial position coordinates of the microseismic event based on the preprocessed microseismic waveform data combined with the known velocity model and sensor layout geometric information, and generate a complete three-dimensional microseismic positioning image;

[0050] The fracture density inversion module is used to draw a fracture density cloud map based on the generated complete three-dimensional microseismic positioning image combined with the acquired spatial distribution density and energy release rate of the microseismic events;

[0051] The three-dimensional visualization model module is used to combine the fracture density cloud map with microseismic monitoring data and numerical simulation results to establish a three-dimensional visualization model of fracture evolution and obtain evaluation indicators;

[0052] The comprehensive evaluation index system module is used to establish a comprehensive index system for fracture evolution evaluation, perform weight distribution and quantitative scoring on the evaluation indicators, generate a quantitative evaluation report on fracture evolution, and complete deep hole blasting fracture evolution evaluation.

[0053] Technical effect of the invention: The present invention discloses a method and system for evaluating the evolution of deep hole blasting gaps based on microseismic monitoring. First, a numerical simulation model of deep hole blasting gap expansion is established to predict the theoretical fracture distribution. Then, a microseismic monitoring system is deployed at the blasting site to collect high-quality microseismic signals in real time. The double-difference positioning algorithm is used to accurately calculate the spatial position of microseismic events, and a three-dimensional positioning map is drawn to reflect the fracture distribution. Based on the microseismic positioning results, the degree of fracture development is evaluated by the fracture density inversion algorithm. Finally, the microseismic monitoring data and numerical simulation results are integrated to establish a three-dimensional visualization model of fracture evolution, and the whole process is dynamically displayed. The present invention also establishes a comprehensive evaluation index system and uses a fuzzy comprehensive evaluation method to form a quantitative evaluation report. The method realizes the precise monitoring and evaluation of the evolution of deep hole blasting gaps, and provides a reliable basis for optimizing blasting parameters and subsequent projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0055] Figure 1 A schematic diagram of a flow chart of a method for evaluating deep hole blasting fracture evolution based on microseismic monitoring according to an embodiment of the present invention;

[0056] Figure 2 The present invention is a schematic structural diagram of a deep hole blasting fracture evolution assessment system based on microseismic monitoring according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0058] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] like Figure 1 As shown, in this embodiment, a method for evaluating the evolution of deep hole blasting cracks based on microseismic monitoring is provided, including: constructing a numerical simulation model for deep hole blasting crack expansion, simulating the propagation and attenuation law of blasting energy in the rock mass, predicting the initiation and expansion range of cracks, and obtaining the spatiotemporal evolution process of crack expansion and the final crack distribution characteristics;

[0060] Real-time acquisition of microseismic signals during the blasting process, combined with the spatiotemporal evolution of the crack expansion and the final crack distribution characteristics, preprocessing the microseismic signals to obtain preprocessed microseismic waveform data;

[0061] Based on the preprocessed microseismic waveform data combined with known velocity models and sensor layout geometric information, the spatial position coordinates of the microseismic event are calculated to generate a complete three-dimensional microseismic positioning image;

[0062] Based on the generated complete three-dimensional microseismic positioning image combined with the spatial distribution density and energy release rate of the acquired microseismic events, a fracture density cloud map is drawn;

[0063] Based on the fracture density cloud map combined with microseismic monitoring data and numerical simulation results, a three-dimensional visualization model of fracture evolution is established to obtain evaluation indicators;

[0064] A comprehensive indicator system for fracture evolution assessment is established, weights are assigned and quantitative scores are given to the assessment indicators, a quantitative assessment report on fracture evolution is generated, and the fracture evolution assessment of deep hole blasting is completed.

[0065] Furthermore, the spatiotemporal evolution of crack expansion and the final crack distribution characteristics are obtained, including:

[0066] According to the deep hole blasting design parameters and geological conditions, a three-dimensional rock mass model of deep hole blasting crack expansion is established, and the model is meshed and boundary conditions are set;

[0067] According to the law of blasting energy release, a time-course blasting load is applied to the model to calculate the propagation and attenuation process of blasting energy in the rock mass.

[0068] According to the dynamic mechanical parameters and constitutive relations of the rock mass, the stress-strain state of the rock mass under the blasting load is judged, and the location and direction of crack initiation are determined;

[0069] Calculate the stress intensity factor at the crack tip, determine whether the crack meets the expansion conditions, and simulate the crack expansion path and length;

[0070] The crack extension results at different times are post-processed and visualized to obtain the spatiotemporal evolution of crack extension and the final crack distribution characteristics.

[0071] Furthermore, obtaining the pre-processed microseismic waveform data includes:

[0072] Real-time collection of microseismic signals during blasting to expand the dynamic range of microseismic signals;

[0073] Convert the amplified analog microseismic signals into digital microseismic signals and determine the sampling rate and quantization accuracy of high-speed analog-to-digital conversion technology;

[0074] Performing time-frequency analysis on the digital microseismic signal to obtain time-frequency characteristics of the digital microseismic signal;

[0075] Constructing a deep learning model to automatically detect and classify the digital microseismic signals, identify microseismic events, and determine the types of microseismic events;

[0076] According to the on-site geological conditions and blasting parameters, a microseismic positioning inversion model is established to determine the spatial location of the microseismic event and obtain the pre-processed microseismic waveform data.

[0077] Furthermore, generating a complete three-dimensional microseismic positioning image includes:

[0078] According to the waveform-related characteristics of the microseismic event, combined with the preset velocity model and sensor layout geometry information, the spatial position coordinates of the microseismic event are calculated;

[0079] Draw a three-dimensional microseismic location image based on the determined spatial position coordinates of the microseismic event;

[0080] Classify the waveform-related features of microseismic events to obtain the spatial distribution patterns of different types of microseismic events;

[0081] Cluster analysis is performed on the spatial position coordinates of microseismic events to obtain the spatial distribution area of ​​cracks;

[0082] According to the spatial distribution area of ​​the cracks, a crack expansion prediction model is established to predict the expansion trend and range of the cracks;

[0083] A complete three-dimensional microseismic location image is generated based on the spatial position coordinates of the microseismic event, the spatial distribution characteristics of the cracks, and the expansion trend and range of the cracks.

[0084] Furthermore, the crack density cloud map is drawn based on the spatial distribution density and energy release rate of microseismic events, including:

[0085] According to the spatial coordinate information of microseismic events, the spatial distribution of microseismic events is fitted to obtain the spatial distribution density of microseismic events;

[0086] According to the magnitude information of the microseismic event, the energy release rate of each microseismic event is calculated by using the energy release rate model to obtain the energy release rate of the microseismic event;

[0087] According to the spatial distribution density and energy release rate of microseismic events, a quantitative relationship model between crack density and the spatial distribution density and energy release rate of microseismic events is established, and the crack density distribution is inverted and calculated;

[0088] A crack density cloud map is drawn according to the crack density distribution.

[0089] Specifically, according to the spatial coordinate information of microseismic events, the least squares method is used to fit the spatial distribution of microseismic events. By fitting the spatial coordinate data of 500 microseismic events, the spatial density distribution function of microseismic events is obtained as f(x, y, z) = 02x + 015y + 01z + 5. According to the magnitude information of microseismic events, the energy release rate model E = 10^(5M+8), where M is the magnitude, is used to calculate the average energy release rate of microseismic events to be 5×10^6J. According to the spatial density distribution function and energy release rate distribution function of microseismic events, the fracture density inversion algorithm is used to establish a quantitative relationship model between the fracture density ρ and the spatial density f of microseismic events and the energy release rate E: ρ = 8f + 2lgE. The fracture density distribution cloud map is obtained by inversion calculation. The maximum value of the fracture density is 2, which is greater than the preset threshold value of 0, and it is judged that the degree of fracture development is high. The fracture connectivity analysis algorithm is used to calculate the connectivity index of the fracture network to be 85, which is greater than the preset threshold value of 6, and it is judged that the fracture connectivity is good.

[0090] Furthermore, the evaluation indicators include:

[0091] Obtain microseismic monitoring data and numerical simulation results;

[0092] Performing feature extraction on the numerical simulation results to extract crack evolution characteristic parameters;

[0093] The pre-processed microseismic monitoring data and the fracture evolution characteristic parameters are fused to establish a fracture evolution prediction model, and the optimized model parameters are obtained through training;

[0094] According to the fracture evolution prediction model, new microseismic monitoring data are predicted to obtain fracture evolution state data;

[0095] The fracture evolution state data is converted into a three-dimensional visualization model, and according to the three-dimensional visualization model, the blasting effect is evaluated, the blasting parameters are optimized, and the evaluation index is obtained.

[0096] Specifically, the preprocessed microseismic monitoring data and the crack evolution characteristic parameters are fused to construct a support vector machine prediction model. The radial basis kernel function is selected, and the penalty factor C and the kernel function parameter γ of the model are optimized through grid search and cross-validation, and the optimal parameters C = 10, γ = 1 are obtained. According to the trained support vector machine model, the newly collected microseismic monitoring data are predicted to obtain the real-time evolution state of the crack. According to the parameters such as the size, direction, and density of the crack, it is judged whether the crack is in the initiation, expansion, or penetration stage. The crack evolution state data is converted into a three-dimensional visualization model, and the Marching Cubes algorithm is used to extract the triangular facets of the crack and render the three-dimensional morphology of the crack. The three-dimensional model of the crack is dynamically updated to show the initiation, expansion, and penetration process of the crack. Finally, the blasting effect is evaluated based on the three-dimensional visualization model of the crack. If the crack density is less than 1 / m 3 , then increase the blasting charge; if the crack size is less than 10cm 2 , then reduce the spacing between blast holes; if the crack direction deviates from the design direction by more than 20°, adjust the arrangement of blast holes. Apply the optimized blasting parameters to actual projects, and combine with the crack evolution model to predict the permeability of the rock mass after blasting, and formulate targeted grouting reinforcement and seepage control plans.

[0097] Furthermore, a quantitative assessment report of crack evolution is generated including:

[0098] Establish a comprehensive indicator system for fracture evolution assessment, compare the assessment indicators pairwise, construct a judgment matrix, and calculate the weight of each indicator;

[0099] According to the fuzzy comprehensive evaluation method, a comment set is set for each evaluation index and a corresponding membership value is assigned;

[0100] The weight of each indicator is weighted and summed with the corresponding membership value to obtain the comprehensive evaluation score of fracture evolution;

[0101] According to the comprehensive evaluation score, the fracture evolution status is divided into different levels, and a quantitative evaluation report of the fracture evolution is generated.

[0102] Specifically, in order to establish a comprehensive indicator system for fracture evolution assessment, four key parameters, namely fracture density, fracture scale, fracture direction and energy release, were selected. The analytic hierarchy process was used to compare the four evaluation indicators in pairs and construct a judgment matrix. By calculating the eigenvector and normalizing it, the weights of fracture density, fracture scale, fracture direction and energy release were 35, 28, 22 and 15 respectively. A comment set was set for each evaluation indicator, which were "high, relatively high, medium, relatively low and low", and the corresponding membership values ​​were 9, 7, 5, 3 and 1. The weighted sum of the indicator weight obtained by the analytic hierarchy process and the membership value of the fuzzy comprehensive evaluation was performed to obtain the comprehensive evaluation score of fracture evolution. According to the comprehensive evaluation score, the fracture evolution state was divided into five levels, namely, level I, level II, level III, level IV and level V. The support vector machine algorithm was used to train the fracture evolution assessment model with parameters such as fracture density, fracture scale, fracture direction and energy release as input and fracture evolution level as output. Through 10-fold cross validation, the accuracy of the model reached 92%. Using the trained fracture evolution assessment model to conduct predictive analysis on new fracture data can quickly and accurately determine the state and trend of fracture evolution, providing a reliable basis for engineering decision-making.

[0103] like Figure 2 As shown, this embodiment also provides a deep hole blasting fracture evolution evaluation system based on microseismic monitoring, including: a deep hole blasting numerical simulation module, a microseismic monitoring system module, a microseismic event positioning module, a fracture density inversion module, a three-dimensional visualization model module and a comprehensive evaluation index system module;

[0104] The deep hole blasting numerical simulation module is used to construct a numerical simulation model for deep hole blasting crack expansion, simulate the propagation and attenuation of blasting energy in the rock mass, predict the initiation and expansion range of cracks, and obtain the spatiotemporal evolution process of crack expansion and the final crack distribution characteristics;

[0105] The microseismic monitoring system module is used to collect microseismic signals in the blasting process in real time, combine the spatiotemporal evolution of crack expansion and the final crack distribution characteristics, pre-process the microseismic signals, and obtain the pre-processed microseismic waveform data;

[0106] The microseismic event location module is used to calculate the spatial position coordinates of microseismic events based on the preprocessed microseismic waveform data combined with the known velocity model and sensor layout geometry information, and generate a complete three-dimensional microseismic location image;

[0107] The fracture density inversion module is used to draw a fracture density cloud map based on the generated complete three-dimensional microseismic positioning image combined with the spatial distribution density and energy release rate of the acquired microseismic events;

[0108] The 3D visualization model module is used to combine the fracture density cloud map with microseismic monitoring data and numerical simulation results to establish a 3D visualization model of fracture evolution and obtain evaluation indicators;

[0109] The comprehensive evaluation index system module is used to establish a comprehensive index system for fracture evolution evaluation, assign weights and quantify scores for evaluation indicators, generate a quantitative evaluation report on fracture evolution, and complete deep hole blasting fracture evolution evaluation.

[0110] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the evolution of deep hole blasting rift based on microseismic monitoring, characterized in that: include: Construct a numerical simulation model for deep hole blasting crack expansion, simulate the propagation and attenuation of blasting energy in the rock mass, predict the initiation and expansion range of cracks, and obtain the spatiotemporal evolution process of crack expansion and the final crack distribution characteristics; Real-time acquisition of microseismic signals during the blasting process, combined with the spatiotemporal evolution of the crack expansion and the final crack distribution characteristics, preprocessing the microseismic signals to obtain preprocessed microseismic waveform data; Based on the preprocessed microseismic waveform data combined with known velocity models and sensor layout geometric information, the spatial position coordinates of the microseismic event are calculated to generate a three-dimensional microseismic positioning image; Drawing a fracture density cloud map based on the three-dimensional microseismic positioning image combined with the spatial distribution density and energy release rate of the acquired microseismic events; The fracture density cloud map is combined with microseismic monitoring data and numerical simulation results to establish a three-dimensional visualization model of fracture evolution and obtain evaluation indicators; A comprehensive indicator system for fracture evolution assessment is established, weights are assigned and quantitative scores are given to the assessment indicators, a quantitative assessment report on fracture evolution is generated, and the fracture evolution assessment of deep hole blasting is completed.

2. The deep hole blasting fracture evolution assessment method based on microseismic monitoring according to claim 1 is characterized in that: The spatiotemporal evolution of crack expansion and the final crack distribution characteristics are obtained including: According to the deep hole blasting design parameters and geological conditions, a three-dimensional rock mass model of deep hole blasting crack expansion is established, and the model is meshed and boundary conditions are set; According to the law of blasting energy release, a time-course blasting load is applied to the model to calculate the propagation and attenuation process of blasting energy in the rock mass. According to the dynamic mechanical parameters and constitutive relations of the rock mass, the stress-strain state of the rock mass under the blasting load is judged, and the location and direction of crack initiation are determined; Calculate the stress intensity factor at the crack tip, determine whether the crack meets the expansion conditions, and simulate the crack expansion path and length; The crack extension results at different times are post-processed and visualized to obtain the spatiotemporal evolution of crack extension and the final crack distribution characteristics.

3. The deep hole blasting fracture evolution assessment method based on microseismic monitoring according to claim 1 is characterized in that: The pre-processed microseismic waveform data include: Real-time collection of microseismic signals during blasting to expand the dynamic range of microseismic signals; Convert the amplified analog microseismic signals into digital microseismic signals and determine the sampling rate and quantization accuracy of high-speed analog-to-digital conversion technology; Performing time-frequency analysis on the digital microseismic signal to obtain time-frequency characteristics of the digital microseismic signal; Constructing a deep learning model to automatically detect and classify the digital microseismic signals, identify microseismic events, and determine the types of microseismic events; According to the on-site geological conditions and blasting parameters, a microseismic positioning inversion model is established to determine the spatial location of the microseismic event and obtain the pre-processed microseismic waveform data.

4. The deep hole blasting fracture evolution assessment method based on microseismic monitoring according to claim 1, characterized in that: Generating a complete 3D microseismic location image includes: According to the waveform-related characteristics of the microseismic event, combined with the preset velocity model and sensor layout geometry information, the spatial position coordinates of the microseismic event are calculated; Draw a three-dimensional microseismic location image based on the determined spatial position coordinates of the microseismic event; Classify the waveform-related features of microseismic events to obtain the spatial distribution patterns of different types of microseismic events; Cluster analysis is performed on the spatial position coordinates of microseismic events to obtain the spatial distribution area of ​​cracks; According to the spatial distribution area of ​​the cracks, a crack expansion prediction model is established to predict the expansion trend and range of the cracks; A complete three-dimensional microseismic location image is generated based on the spatial position coordinates of the microseismic event, the spatial distribution characteristics of the cracks, and the expansion trend and range of the cracks.

5. The deep hole blasting fracture evolution assessment method based on microseismic monitoring according to claim 1, characterized in that: The fracture density cloud map is drawn based on the spatial distribution density and energy release rate of microseismic events, including: According to the spatial coordinate information of microseismic events, the spatial distribution of microseismic events is fitted to obtain the spatial distribution density of microseismic events; According to the magnitude information of the microseismic event, the energy release rate of each microseismic event is calculated by using the energy release rate model to obtain the energy release rate of the microseismic event; According to the spatial distribution density and energy release rate of microseismic events, a quantitative relationship model between crack density and the spatial distribution density and energy release rate of microseismic events is established, and the crack density distribution is inverted and calculated; A crack density cloud map is drawn according to the crack density distribution.

6. The deep hole blasting fracture evolution assessment method based on microseismic monitoring according to claim 1, characterized in that: The evaluation indicators include: Obtain microseismic monitoring data and numerical simulation results; Performing feature extraction on the numerical simulation results to extract crack evolution characteristic parameters; The pre-processed microseismic monitoring data and the fracture evolution characteristic parameters are fused to establish a fracture evolution prediction model, and the optimized model parameters are obtained through training; According to the fracture evolution prediction model, new microseismic monitoring data are predicted to obtain fracture evolution state data; The fracture evolution state data is converted into a three-dimensional visualization model, and according to the three-dimensional visualization model, the blasting effect is evaluated, the blasting parameters are optimized, and the evaluation index is obtained.

7. The deep hole blasting rift evolution assessment method based on microseismic monitoring according to claim 1 is characterized in that: Generates a quantitative assessment report of fracture evolution including: Establish a comprehensive indicator system for fracture evolution assessment, compare the assessment indicators pairwise, construct a judgment matrix, and calculate the weight of each indicator; According to the fuzzy comprehensive evaluation method, a comment set is set for each evaluation index and a corresponding membership value is assigned; The weight of each indicator is weighted and summed with the corresponding membership value to obtain the comprehensive evaluation score of fracture evolution; According to the comprehensive evaluation score, the fracture evolution status is divided into different levels, and a quantitative evaluation report of the fracture evolution is generated.

8. A system for evaluating deep hole blasting fracture evolution based on microseismic monitoring according to any one of claims 1 to 7, characterized in that: include: Deep hole blasting numerical simulation module, microseismic monitoring system module, microseismic event location module, fracture density inversion module, three-dimensional visualization model module and comprehensive evaluation index system module; The deep hole blasting numerical simulation module is used to construct a numerical simulation model for deep hole blasting crack expansion, simulate the propagation and attenuation law of blasting energy in the rock mass, predict the initiation and expansion range of cracks, and obtain the spatiotemporal evolution process of crack expansion and the final crack distribution characteristics; The microseismic monitoring system module is used to collect microseismic signals in the blasting process in real time, combine the spatiotemporal evolution of the crack expansion and the final crack distribution characteristics, pre-process the microseismic signals, and obtain pre-processed microseismic waveform data; The microseismic event positioning module is used to calculate the spatial position coordinates of the microseismic event based on the preprocessed microseismic waveform data combined with the known velocity model and sensor layout geometric information, and generate a three-dimensional microseismic positioning image; The fracture density inversion module is used to draw a fracture density cloud map based on the three-dimensional microseismic positioning image in combination with the spatial distribution density and energy release rate of the acquired microseismic events; The three-dimensional visualization model module is used to combine the fracture density cloud map with microseismic monitoring data and numerical simulation results to establish a three-dimensional visualization model of fracture evolution and obtain evaluation indicators; The comprehensive evaluation index system module is used to establish a comprehensive index system for fracture evolution evaluation, perform weight distribution and quantitative scoring on the evaluation indicators, generate a quantitative evaluation report on fracture evolution, and complete deep hole blasting fracture evolution evaluation.

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