A deep hole blasting fracture evolution evaluation method and system based on microseismic monitoring

Abstract

The application discloses a deep-hole blasting fracture evolution evaluation method and system based on microseismic monitoring, which comprises the following steps: obtaining the time-space evolution process of fracture expansion and the final fracture distribution characteristics; collecting microseismic signals in the blasting process in real time, combining the time-space evolution process of fracture expansion and the final fracture distribution characteristics, pre-processing the microseismic signals, and obtaining the pre-processed microseismic waveform data; calculating the spatial position coordinates of the microseismic events, and generating a complete three-dimensional microseismic positioning image; according to the generated complete three-dimensional microseismic positioning image, combining the obtained spatial distribution density and energy release rate of the microseismic events, and drawing a fracture density cloud chart; establishing a three-dimensional visualization model of fracture evolution, obtaining evaluation indexes; establishing a comprehensive index system of fracture evolution evaluation, performing weight distribution and quantitative scoring on the evaluation indexes, generating a quantitative evaluation report of fracture evolution, and completing the deep-hole blasting fracture evolution evaluation. The application improves the accuracy and reliability of the evaluation.

Description

Technical Field

[0001] The present 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] Accurately predicting and monitoring fracture evolution in real time during deephole blasting presents significant challenges. Traditional numerical simulation methods struggle to accurately depict the dynamic processes of blasting energy propagation and rock failure under complex geological conditions, while in-situ microseismic monitoring systems face numerous technical bottlenecks in signal acquisition, processing, and interpretation. A key challenge is to effectively integrate theoretical models with measured data to construct a high-precision, high-resolution, three-dimensional visualization model of fracture evolution. Specifically, the following challenges must be overcome: multi-scale, multi-field coupled numerical simulation of blast fracture propagation; high-sensitivity acquisition and high signal-to-noise ratio processing of microseismic signals; precise source location 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 assessment index system for fracture evolution. These technical challenges are interconnected and mutually constrained, forming a complex system of technical contradictions. Overcoming these bottlenecks to achieve accurate prediction, real-time monitoring, and quantitative assessment of fracture evolution during deephole blasting is crucial for guiding blasting engineering design, optimizing blasting parameters, and evaluating blasting effectiveness. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a deep hole blasting fracture 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 cracks 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 and the known velocity model and sensor layout geometry information, the spatial position coordinates of the microseismic event are calculated to generate a complete three-dimensional microseismic positioning image;

[0008] According to the generated complete three-dimensional microseismic positioning image, combined with the spatial distribution density and energy release rate of the obtained microseismic events, a fracture density cloud chart is drawn;

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

[0010] A comprehensive index system for fracture evolution evaluation is established, and the evaluation indexes are weighted, quantified and scored to generate a quantitative evaluation report of fracture evolution, and deep hole blasting fracture evolution evaluation is completed.

[0011] Optionally, obtaining the spatio-temporal evolution process of fracture propagation and the final fracture distribution characteristics comprises:

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

[0013] According to the blasting energy release law, time history blasting load is applied in 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 relation of the rock mass, the stress and strain state of the rock mass under the action of the blasting load is judged to determine the position and direction of the fracture initiation;

[0015] The stress intensity factor of the fracture tip is calculated to determine whether the fracture meets the expansion condition, and the expansion path and length of the fracture are simulated;

[0016] The fracture expansion results at different times are post-processed and visualized to obtain the spatio-temporal evolution process of fracture propagation and the final fracture distribution characteristics.

[0017] Optionally, obtaining the preprocessed microseismic waveform data comprises:

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

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

[0020] Time-frequency analysis is performed on the digital microseismic signals to obtain the time-frequency characteristics of the digital microseismic signals;

[0021] A deep learning model is constructed to automatically detect and classify the digital microseismic signals, identify microseismic events, and determine the type of microseismic events;

[0022] According to the field geological conditions and the blasting parameters, a microseismic positioning inversion model is established, spatial positions of the microseismic events are determined, and the preprocessed microseismic waveform data are obtained.

[0023] Optionally, generating the complete three-dimensional microseismic positioning image comprises:

[0024] According to the waveform-related features of the microseismic events, in combination with a preset velocity model and sensor layout geometry information, spatial position coordinates of the microseismic events are calculated;

[0025] According to the determined spatial position coordinates of the microseismic events, a three-dimensional microseismic positioning image is drawn;

[0026] The waveform-related features of the microseismic events are classified, and spatial distribution rules of different types of microseismic events are obtained;

[0027] The spatial position coordinates of the microseismic events are subjected to cluster analysis, and a spatial distribution area of the fractures is obtained;

[0028] According to the spatial distribution area of the fractures, a fracture expansion prediction model is established, and an expansion trend and range of the fractures are predicted;

[0029] According to the spatial position coordinates of the microseismic events, the spatial distribution features of the fractures, and the expansion trend and range of the fractures, a complete three-dimensional microseismic positioning image is generated.

[0030] Optionally, drawing the fracture density cloud chart through the spatial distribution density and the energy release rate of the microseismic events comprises:

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

[0032] According to the magnitude information of the microseismic events, an energy release rate model is adopted to calculate the energy release rate of each microseismic event, and the energy release rate of the microseismic events is obtained;

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

[0034] According to the fracture density distribution, a fracture density cloud chart is drawn.

[0035] Optionally, obtaining the evaluation index comprises:

[0036] Microseismic monitoring data and numerical simulation results are obtained;

[0037] Feature extraction is performed on the numerical simulation results, and a fracture evolution feature parameter is extracted;

[0038] The preprocessed microseismic monitoring data and the crack evolution characteristic parameter are fused to establish a crack evolution prediction model, and an optimized model parameter is obtained through training;

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

[0040] The crack 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 evaluation indexes are obtained.

[0041] Optionally, the quantitative evaluation report of crack evolution includes:

[0042] A comprehensive index system for crack evolution evaluation is established, the evaluation indexes are compared with each other, a judgment matrix is constructed, and the weights of the indexes are calculated;

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

[0044] The weights of the indexes and the corresponding membership values are weighted and summed to obtain a comprehensive evaluation score of crack evolution;

[0045] According to the comprehensive evaluation score, the crack evolution state is divided into different grades, and a quantitative evaluation report of crack evolution is generated.

[0046] In another aspect to achieve the above object, the application further provides a deep hole blasting crack 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 crack 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 of deep hole blasting crack propagation, simulate the propagation and attenuation law of blasting energy in rock mass, predict the initiation and propagation range of cracks, and obtain the spatio-temporal evolution process of crack propagation 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 spatio-temporal evolution process of crack propagation and the final crack distribution characteristics, and preprocess the microseismic signals to obtain preprocessed microseismic waveform data;

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

[0050] The fracture density inversion module is configured to draw a fracture density cloud chart according to the generated complete three-dimensional microseismic positioning image, the spatial distribution density and the energy release rate of the acquired microseismic event.

[0051] The three-dimensional visualization model module is configured to establish a three-dimensional visualization model of fracture evolution by combining the fracture density cloud chart with the microseismic monitoring data and the numerical simulation result, and obtain an evaluation index.

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

[0053] The technical effect of the present application is that the present application discloses a deep hole blasting fracture evolution evaluation method and system based on microseismic monitoring, which first establishes a numerical simulation model of deep hole blasting fracture expansion to predict the theoretical fracture distribution. Then, a microseismic monitoring system is arranged at the blasting site to collect high-quality microseismic signals in real time. A double-difference positioning algorithm is used to accurately calculate the spatial position of the microseismic event and draw a three-dimensional positioning chart to reflect the fracture distribution. Based on the microseismic positioning result, a fracture development degree is evaluated by a fracture density inversion algorithm. Finally, a three-dimensional visualization model of fracture evolution is established by combining the microseismic monitoring data and the numerical simulation result, and the whole process is dynamically displayed. The present application also establishes a comprehensive evaluation index system and uses a fuzzy comprehensive evaluation method to form a quantitative evaluation report. The method realizes accurate monitoring and evaluation of deep hole blasting fracture evolution, and provides a reliable basis for optimizing the blasting parameters and subsequent engineering. BRIEF DESCRIPTION OF DRAWINGS

[0054] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application and serve to explain the illustrative embodiments of the present application and their descriptions, and are not intended to constitute an improper limitation of the present application. In the drawings:

[0055] Figure 1 A flowchart of a deep hole blasting fracture evolution evaluation method based on microseismic monitoring according to an embodiment of the present application is shown in the figure.

[0056] Figure 2 A structural schematic diagram of a deep hole blasting fracture evolution evaluation system based on microseismic monitoring according to an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0057] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. 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 flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0059] As shown in Figure 1 The embodiment provides a deep hole blasting fracture evolution evaluation method based on microseismic monitoring, which comprises the following steps: constructing a numerical simulation model of deep hole blasting fracture expansion, simulating the propagation and attenuation law of blasting energy in the rock mass, predicting the initiation and expansion range of the fracture, and obtaining the spatio-temporal evolution process and final fracture distribution characteristics of the fracture expansion;

[0060] Real-time acquisition of microseismic signals during the blasting process in combination with the spatio-temporal evolution process and final fracture distribution characteristics of the fracture expansion, pre-processing of the microseismic signals, and obtaining pre-processed microseismic waveform data;

[0061] Based on the pre-processed microseismic waveform data in combination with the known velocity model and sensor layout geometry information, the spatial position coordinates of the microseismic event are calculated, and a complete three-dimensional microseismic positioning image is generated;

[0062] According to the generated complete three-dimensional microseismic positioning image in combination with the spatial distribution density and energy release rate of the microseismic event, a fracture density cloud chart is drawn;

[0063] Based on the fracture density cloud chart in combination with the microseismic monitoring data and numerical simulation results, a three-dimensional visualization model of fracture evolution is established, and an evaluation index is obtained;

[0064] A comprehensive index system for fracture evolution evaluation is established, the evaluation index is weighted and quantitatively scored, a quantitative evaluation report of fracture evolution is generated, and deep hole blasting fracture evolution evaluation is completed.

[0065] Further, obtaining the spatio-temporal evolution process and final fracture distribution characteristics of the fracture expansion comprises:

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

[0067] According to the blasting energy release law, time history blasting load is applied in the model, and the propagation and attenuation process of blasting energy in the rock mass is calculated;

[0068] According to the dynamic mechanical parameters and constitutive relation of the rock mass, the stress and strain state of the rock mass under the action of the blasting load is judged, and the position and direction of the fracture initiation are determined;

[0069] The stress intensity factor of the crack tip is calculated, whether the crack meets the expansion condition is judged, and the expansion path and length of the crack are simulated;

[0070] The crack expansion results at different times are post-processed and visually analyzed to obtain the spatio-temporal evolution process of crack expansion and the final crack distribution characteristics.

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

[0072] Real-time acquisition of microseismic signals during blasting, and extension of the dynamic range of the microseismic signals;

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

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

[0075] Building a deep learning model to automatically detect and classify the digital microseismic signals, identifying microseismic events and determining the type of microseismic events;

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

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

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

[0079] According to the determined spatial position coordinates of the microseismic event, a three-dimensional microseismic positioning image is drawn;

[0080] Classifying the waveform-related characteristics of the microseismic event to obtain the spatial distribution of different types of microseismic events;

[0081] Cluster analysis is performed on the spatial position coordinates of the microseismic event to obtain the spatial distribution area of the crack;

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

[0083] According to the spatial position coordinates of the microseismic event, the spatial distribution characteristics of the crack, and the expansion trend and range of the crack, a complete three-dimensional microseismic positioning image is generated.

[0084] Further, drawing a crack density cloud map through the spatial distribution density and energy release rate of the microseismic event includes:

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

[0086] According to the magnitude information of the microseismic events, the energy release rate model is used to calculate the energy release rate of each microseismic event, and the energy release rate of the microseismic events is obtained;

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

[0088] According to the fracture density distribution, a fracture density cloud chart is drawn.

[0089] Specifically, according to the spatial coordinate information of the microseismic events, the least square method is used to fit the spatial distribution of the microseismic events. By fitting the spatial coordinate data of 500 microseismic events, the spatial density distribution function of the microseismic events is obtained as f(x, y, z) = 0 2x + 0 15y + 0 1z + 5. According to the magnitude information of the microseismic events, the energy release rate model E = 10^(5M+8) is used, where M is the magnitude, and the average energy release rate of the microseismic events is calculated as 5*10^6J. According to the spatial density distribution function and the energy release rate distribution function of the microseismic events, the fracture density inversion algorithm is used to establish a quantitative relationship model of the fracture density p and the spatial density f and the energy release rate E of the microseismic events: p = 8f + 2lgE. The fracture density distribution cloud chart is inversely calculated, the maximum value of the fracture density is 2, which is greater than the preset threshold value 0, and it is judged that the fracture development degree is high. The connectivity index of the fracture network is calculated by using the fracture connectivity analysis algorithm, which is 85, which is greater than the preset threshold value 6, and it is judged that the fracture connectivity is good.

[0090] Further, the evaluation index includes:

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

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

[0093] Data fusion is performed on the preprocessed microseismic monitoring data and the fracture evolution feature parameters to establish a fracture evolution prediction model, and optimized model parameters are obtained through training;

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

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

[0096] Specifically, the pre-processed microseismic monitoring data and the fracture evolution characteristic parameters are fused to construct a support vector machine prediction model. A 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 to obtain the optimal parameters C=10 and γ=1. According to the trained support vector machine model, the newly collected microseismic monitoring data is predicted to obtain the real-time evolution state of the fracture. Through the parameters such as the size, direction and density of the fracture, it is determined whether the fracture is in the initiation, expansion or penetration stage. The fracture evolution state data is converted into a three-dimensional visualization model, the Marching Cubes algorithm is used to extract the triangular facets of the fracture, and the three-dimensional morphology of the fracture is rendered. The three-dimensional model of the fracture is dynamically updated to show the initiation, expansion and penetration process of the fracture. Finally, according to the three-dimensional visualization model of the fracture, the blasting effect is evaluated. If the fracture density is less than 1 / m 3 , the blasting explosive quantity is increased; if the fracture size is less than 10cm 2 , the blasting hole spacing is reduced; and if the fracture direction deviates from the design direction by more than 20°, the blast hole arrangement mode is adjusted. The optimized blasting parameters are applied to actual engineering, and combined with the fracture evolution model, the permeability of the rock mass after blasting is predicted, and a targeted grouting reinforcement and seepage control scheme is developed.

[0097] Further, the quantitative evaluation report of the fracture evolution includes:

[0098] A comprehensive index system for evaluating the fracture evolution is established, the evaluation indexes are compared with each other, a judgment matrix is constructed, and the weights of the indexes are calculated;

[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 weights of the indexes and the corresponding membership values are weighted and summed to obtain the comprehensive evaluation score of the fracture evolution;

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

[0102] Specifically, to establish a comprehensive evaluation index system of fracture evolution, four key parameters, i.e., fracture density, fracture size, fracture direction and energy release, are selected. The analytic hierarchy process is used to compare the four evaluation indexes with each other to build a judgment matrix. By calculating the characteristic vector and normalizing the processing, the weights of fracture density, fracture size, fracture direction and energy release are obtained as 35, 28, 22 and 15 respectively. For each evaluation index, a comment set is set, which is “high, higher, medium, lower, low”, and the corresponding membership values are 9, 7, 5, 3 and 1 respectively. The index weights obtained by the analytic hierarchy process and the membership values of fuzzy comprehensive evaluation are weighted and summed to obtain the comprehensive evaluation score of fracture evolution. According to the comprehensive evaluation score, the fracture evolution state is divided into five levels, i.e., level I, level II, level III, level IV and level V. The support vector machine algorithm is used to train the fracture evolution evaluation model, with the parameters of fracture density, fracture size, fracture direction and energy release as inputs and the fracture evolution level as output. Through 10-fold cross-validation, the accuracy of the model reaches 92%. The trained fracture evolution evaluation model can be used to predict and analyze new fracture data, so as to quickly and accurately judge the state and trend of fracture evolution and provide a reliable basis for engineering decision-making.

[0103] As shown in Figure 2 The embodiment also provides a deep-hole blasting fracture evolution evaluation system based on microseismic monitoring, which comprises 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 build a numerical simulation model of deep-hole blasting fracture expansion, simulate the propagation and attenuation law of blasting energy in the rock mass, predict the initiation and expansion range of fractures, and obtain the spatio-temporal evolution process of fracture expansion and the final fracture distribution characteristics.

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

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

[0107] The fracture density inversion module is used to draw a fracture density cloud map according to the generated complete three-dimensional microseismic positioning image and the obtained spatial distribution density and energy release rate of microseismic events.

[0108] A three-dimensional visualization model module is configured to establish a three-dimensional visualization model of the fracture evolution by combining the fracture density cloud map with microseismic monitoring data and numerical simulation results, and to obtain evaluation indexes;

[0109] A comprehensive evaluation index system module is configured to establish a comprehensive index system for the fracture evolution evaluation, to perform weight distribution and quantitative scoring on the evaluation indexes, to generate a quantitative evaluation report of the fracture evolution, and to complete the fracture evolution evaluation of the deep-hole blasting.

[0110] The above merely describes the preferred embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed by the present application can be easily conceived by those skilled in the art, and should be encompassed within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the evolution of deep hole blasting cracks 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 pre-processed microseismic waveform data and the known velocity model and sensor layout geometry 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 in combination with the acquired spatial distribution density and energy release rate of the 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 for 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 blasting energy release law, a time-course blasting load is applied to the model to calculate the propagation and attenuation process of the blasting energy in the rock mass. According to the dynamic mechanical parameters and constitutive relations of the rock mass, the stress and 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 includes: Real-time acquisition 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 is 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 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 coordinates of microseismic events to obtain the spatial distribution area of ​​cracks; According to the spatial distribution of cracks, a crack expansion prediction model is established to predict the expansion trend and range of 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 is characterized in that: Drawing a crack density cloud map based on the spatial distribution density and energy release rate of microseismic events includes: 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 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; fusing the pre-processed microseismic monitoring data with the fracture evolution characteristic parameters to establish a fracture evolution prediction model, and obtaining optimized model parameters through training; According to the fracture evolution prediction model, new microseismic monitoring data is predicted to obtain fracture evolution state data; The fracture evolution state data is converted into a three-dimensional visualization model. Based on 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 fracture evolution assessment method based on microseismic monitoring according to claim 1 is characterized in that: Generate a quantitative assessment report of crack evolution including: Establish a comprehensive indicator system for fracture evolution assessment, compare the evaluation 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 indicator and the 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 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 real time during the blasting process, 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 location module is configured to calculate the spatial position coordinates of the microseismic event based on the preprocessed microseismic waveform data in combination with a known velocity model and sensor layout geometry information, and generate a three-dimensional microseismic location 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 the deep hole blasting fracture evolution evaluation.

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