Rock mass three-dimensional joint network inversion and blasting parameter adaptive design method

Abstract

The application provides a rock mass three-dimensional joint network inversion and blasting parameter self-adaptive design method, which is based on the microseismic spatial distribution induced by an active seismic source to perform rock mass three-dimensional joint network inversion, has the advantages of simple operation, low cost and high precision, and provides a fine geological model for blasting design. Further, according to the rock mass joint network spatial distribution obtained by inversion, the rock mass in the target region is quantitatively partitioned according to the rock mass blastability, and different grades of rock mass blastability regions are identified. On this basis, the blastability partition is directly used as the basis for the design of the blasting parameter, and the blasting hole network parameters are quantitatively designed. The method of the application significantly improves the engineering adaptability of the blasting parameters, and realizes the accurate matching of the blasting energy and the rock mass structure.

Description

Technical Field

[0001] This invention relates to the field of mining blasting, and in particular to a method for inverting three-dimensional joint networks of rock mass and adaptive design of blasting parameters. Background Technology

[0002] Currently, blasting operations in fields such as mining, tunneling, and geotechnical engineering still widely employ experience-based parameter design methods. These methods rely primarily on engineering analogies, empirical formulas, and the professional judgment of designers, resulting in problems such as poor geological adaptability, weak supporting data, and a lack of dynamic feedback.

[0003] First, traditional blasting design only considers the physical and mechanical properties of rock, completely ignoring the spatial distribution characteristics of discontinuities such as joints, fissures, and faults. In reality, the blastability of rock mass and the blasting effect are controlled by the occurrence, density, and connectivity of these discontinuities. Within the same blasting area, due to the spatial variability of rock mass structure, using uniform hole network parameters often leads to insufficient fragmentation in some areas (resulting in large blocks and foundations) while excessive fragmentation occurs in other areas (generating flyrock and over-excavation).

[0004] Secondly, existing technologies obtain extremely limited geological information, typically relying only on core logging from a small number of boreholes and limited surface geological surveys. This joint data cannot accurately reflect the three-dimensional spatial distribution of the joint network within the rock mass, resulting in blasting designs being based on incomplete geological models, making it difficult to guarantee the reliability and safety of blasting parameter design.

[0005] Third, the current blasting design, construction, and effect evaluation processes are disconnected, with blasting parameters becoming fixed during construction. Once the blasting design is finalized, it remains essentially fixed throughout the entire construction cycle, unable to be dynamically adjusted based on the actual blasting results. When the post-blasting effects (such as block size distribution, contour quality, and surrounding rock damage) deviate from the design expectations, there is a lack of effective technical means to feed the effect information back to the design phase in real time, making dynamic optimization and adaptive adjustment of parameters impossible. This fixed blasting design typically leads to low blasting efficiency and significant material waste.

[0006] To address the aforementioned issues, this invention provides a method for inverting three-dimensional joint networks in rock masses and adaptively designing blasting parameters, thereby achieving precise matching between blasting energy and rock mass structure. Summary of the Invention

[0007] The purpose of this invention is to provide a method for inverting three-dimensional joint networks in rock masses and adaptively designing blasting parameters, so as to achieve dynamic optimization and adaptive adjustment of parameters.

[0008] To achieve the above-mentioned objectives, this invention provides a method for inverting three-dimensional joint networks in rock masses and adaptively designing blasting parameters, characterized by comprising the following steps: S1. In the target rock mass area, based on geological survey data and surface joint distribution, at least one initial borehole point is set up; at the same time, in the target rock mass area, an array network containing at least four microseismic sensors is set up, and the monitoring array is synchronously connected to the data acquisition and recording system. S2, start drilling equipment to carry out drilling construction at the initial drilling point, use small-yield blasting or electric explosion to create an active seismic source in the initial borehole, the active seismic source radiates stress waves to the surrounding rock mass, and the micro-seismic sensor synchronously records the vibration signal of the entire site. S3, process the waveform data collected in step 2 to obtain a spatial point cloud dataset of microseismic events; S4. Based on the microseismic event spatial point cloud dataset obtained in step 3, clustering algorithms (k-means clustering, single-chain clustering, etc.) are used to analyze the spatial clustering characteristics of microseismic events in the target rock mass area. S5. At the key locations obtained in step 4, set up no fewer secondary drilling points than the number of key areas, and repeat the work of steps 2 and 3 to obtain a secondary updated microseismic event spatial point cloud dataset of the target rock mass area. S6. After the secondary drilling is completed, the joint distribution data is collected. S7. Based on the rock mass joint acquisition database obtained in step 6, establish a geometric model of the three-dimensional joint network in the target area. S8. Based on the geometric model of the three-dimensional joint network collected in step 7, and combined with geological exploration data, a three-dimensional numerical model of the rock mass in the target area is established, and a virtual rock mass joint network is output. S9. Based on the virtual rock mass joint network in step 8, conduct a simulated micro-seismic experiment and output the corrected virtual rock mass joint network. S10, Based on the virtual rock mass joint network corrected in step 9, the parameters of the blasting hole network are designed based on the energy criterion; S11, Perform blasting drilling operations and evaluate the blasting effect.

[0009] Furthermore, in step S1, it is required that the four microseismic sensors are not coplanar.

[0010] Furthermore, the specific steps in step S3 to process the waveform data acquired in step 2 to obtain the microseismic event spatial point cloud dataset are as follows: S3-1 uses signal preprocessing and feature extraction techniques to identify and separate secondary microseismic event signals induced by blasting vibrations. Because the frequency distribution ranges of microseismic signals generated by rock fracturing differ from those of blasting / drilling signals, this difference can be used as a basis for separation, and it is now a mature technology.

[0011] S3-2, based on the acquired microseismic signals, uses microseismic positioning algorithms (such as double-difference positioning method, ray travel time tracking positioning method, etc.) and combines the known sensor positions to calculate the high-precision coordinates (X, Y, Z) of each microseismic event in three-dimensional space. S3-3 aggregates all located microseismic events to generate a spatial point cloud dataset of induced microseismic events corresponding to the borehole.

[0012] Furthermore, the specific steps for analyzing the spatial clustering characteristics of microseismic events in the target rock mass region in step S4 are as follows: S4-1, calculate the spatial microseismic event cluster density and spatial microseismic event rate. The spatial microseismic event cluster density is defined as the number of microseismic events per unit space, and the spatial microseismic event rate is defined as the rate of increase of microseismic events per unit space. S4-2. Based on the calculated spatial microseismic event cluster density and spatial microseismic event rate, draw a spatial distribution map of the spatial microseismic event cluster density and spatial microseismic event rate in the target rock mass area. S4-3. In the spatial distribution map of spatial microseismic event cluster density and spatial microseismic event rate in the target rock mass area, identify several key locations where spatial microseismic event cluster density and spatial microseismic event rate are highly concentrated.

[0013] Furthermore, the specific steps for collecting joint distribution data in step S6 after the secondary drilling is completed are as follows: S6-1 uses borehole television to observe and record the joints inside the borehole, and measures and identifies the attitude (dip, dip angle), trace length, opening and other information of each joint surface according to depth; S6-2, Collect joint features of exposed rock masses in the project area to obtain statistical features of surface joint networks. A three-dimensional laser scanner can be used for the collection. S6-3, spatial coordinate correlation is performed between borehole joint data and exposed rock mass joint data to form a rock mass joint acquisition database.

[0014] Furthermore, the specific steps for establishing the geometric model of the target region's three-dimensional joint network in step S7 are as follows: S7-1: Based on step 5, obtain the spatial point cloud dataset of microseismic events in the target rock mass area after secondary updates, and calculate the spatial microseismic event cluster density, spatial microseismic event rate, single-chain cluster association length and box fractal dimension. S7-2, calculate the joint probability based on microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length and box fractal dimension; S7-3, set a threshold for the joint probability rate. By manually inputting the threshold, data with a joint probability rate lower than the threshold will be removed. S7-4, Based on the spatial distribution pattern of the retained joints and the rock mass joint acquisition database obtained in step 6, set the preliminary connection path of the rock mass joints. S7-5, based on the preliminary connection path of the rock mass joints and combined with the collected data of the rock mass joints, outputs the collected three-dimensional joint network geometric model of the target area.

[0015] The joint probability probability refers to the estimated probability of a joint structure existing at a specified spatial location, based on microseismic monitoring data. It is an evaluation index of joint likelihood formed by weighted synthesis of multiple characteristic parameters of microseismic events closely related to the spatial distribution of joints. Specifically, it is a quantitative value representing the probability of joint existence, obtained by linearly synthesizing multiple characteristic parameters reflecting the spatial correlation between microseismic events and joint structures, assigning different weights according to their importance.

[0016] Furthermore, the method for calculating the joint probability is as follows: S7-2-1 Select several parameters that can characterize the spatial distribution of microseismic events: microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length, and box fractal dimension; S7-2-2, assign a weight coefficient to each selected feature parameter; S7-2-3, multiply each feature parameter by its corresponding weight, and sum all the products to obtain the joint probability.

[0017] Furthermore, the specific steps for establishing a three-dimensional numerical model of the rock mass in the target area and outputting a virtual rock mass joint network in step S8 are as follows: S8-1, Import the three-dimensional joint network geometric model of the target area output in step 7 into the numerical simulation software; S8-2, Determine the size of the numerical model based on the size of the target rock mass region, ensuring that the size of the numerical model exceeds 5 times the size of the target rock mass region; S8-3, Based on geological exploration data (such as fault and rock layer distribution) and the engineering structures involved, establish key influencing factors such as faults, rock layers, and the engineering structures involved in the model; S8-4, Based on the in-situ stress field test data, apply the corresponding in-situ stress initialization curve to the model boundary; S8-5 embeds rock fracture criteria (such as the Griffith criterion and the Mohr-Coulomb criterion) into the model and runs quasi-static mechanical calculations to simulate the fracture initiation, propagation and interaction process of rock mass under in-situ stress. S8-6, Adjust the inversion parameters (in-situ stress field, rock parameters) so that the statistical characteristics of the simulated virtual joint network (including but not limited to attitude distribution, density, connectivity) match the actual observed statistical characteristics within the preset tolerance range; S8-7 outputs a virtual rock mass joint network.

[0018] Furthermore, in step S9, based on the virtual rock mass joint network of step 8, the specific steps for conducting simulated microseismic experiments and outputting the corrected virtual rock mass joint network are as follows: S9-1, In the virtual rock mass joint network, at the current geostress level, at the spatial location corresponding to the actual borehole in step 5, apply a dynamic disturbance load similar to the measured active source characteristics. S9-2 performs dynamic simulation, calculates virtual microseismic events in the model where joint surfaces slide or crack due to disturbance, records their locations, and generates a simulated induced microseismic spatial point cloud dataset. S9-3, quantitatively compare the simulated induced microseismic spatial point cloud dataset with the microseismic event spatial point cloud dataset of the target rock mass area obtained in step 5 and updated twice; the comparison indicators include but are not limited to: microseismic event cluster density, spatial microseismic event rate, single-chain cluster association length, box fractal dimension, etc. S9-4: Based on existing evaluation methods, establish a similarity model and set a similarity threshold. If the similarity of the comparison results is less than the similarity threshold, return to step 5 to plan the next round of verification drilling. If the similarity of the comparison results is greater than or equal to the similarity threshold, the virtual rock mass joint network is considered to have passed dynamic verification and possesses high confidence. S9-5 outputs the corrected virtual rock mass joint network.

[0019] Furthermore, the specific steps for designing the blasting hole mesh parameters in step S10 are as follows: S10-1, based on ground stress, rock properties (compressive strength, tensile strength, etc.) and virtual rock mass joint network, the rock mass in the target area is divided into several rock regions with different blastability. S10-2 For tunnel / roadway rock mass blasting, blast holes typically include cut holes, auxiliary holes, and peripheral holes. Based on blastability, a non-uniform blasting hole network parameter design is adopted. Specifically: identify areas with high blastability and place cut holes in these areas, avoiding placing cut holes at the edge of the target rock mass as much as possible. Place auxiliary holes outside the cut area and peripheral holes along the outline of the target rock mass. The spacing between cut holes, auxiliary holes, and peripheral holes is set according to the blastability of the rock mass. If the rock mass has high blastability, the hole spacing can be appropriately increased; if the area has low blastability, the hole spacing can be appropriately decreased, and the spacing between each hole is not necessarily the same. S10-3, the borehole spacing can be designed based on energy principles and the extent of the fracture zone, while the borehole depth is set manually: First, based on the virtual rock mass joint network, target fragment size, and fractured rock mass volume, the energy required for rock fragmentation at the target fragment size is calculated using the surface energy calculation formula; then, the energy generated by the explosive explosion is calculated based on the target charge and explosive parameters; then, based on energy conservation and considering energy dissipation during the blasting process, the energy generated by the explosive explosion is ensured to exceed 2.5 times the energy required for rock fragmentation at the target fragment size; simultaneously, the theoretical fracture zone extent is calculated based on rock and explosive parameters to ensure that the borehole spacing is less than the fracture zone extent.

[0020] The beneficial effects of this invention are: 1. To address the problem of mismatch between design schemes and actual complex three-dimensional geological structures revealed by traditional blasting design methods that rely on limited geological information (local boreholes / surface scans), a method for reconstructing three-dimensional joint networks based on active-source-induced microseismic inversion is proposed. Using low-yield blasts or electrical explosions as known active sources, and leveraging the high-precision location point cloud of the secondary microseismic events induced by these sources, the three-dimensional spatial distribution of the joint network within the rock mass is directly inverted. This method boasts advantages such as simple operation, low cost, and high accuracy, providing a refined geological model for blasting design.

[0021] 2. In this application, the selection of borehole locations is scientifically and quantitatively determined through the spatial distribution of microseismic events or the spatial distribution of characterizing parameter errors, avoiding the blind selection of boreholes. For example, drilling and controlled excitation are first carried out at the initial borehole location, using the generated vibrations as a known source to induce and collect microseismic signals within the rock mass; by locating and spatially statistically analyzing these microseismic events, a spatial distribution map of characterizing parameters is generated. The locations of subsequent boreholes are determined based on the extreme value areas and anomalous areas of these quantitative spatial parameter distributions, thus transforming borehole layout from relying on geological inference and experience to a scientific model based on the spatial distribution of measured data.

[0022] 3. To address the shortcomings of current blasting design methods, such as fixed parameters and a disconnect between parameters and construction results, an adaptive design method for blasting hole network parameters based on a three-dimensional joint network is proposed. Based on the spatial distribution of the joint network obtained through inversion, the target area rock mass is quantitatively zoned according to its explosiveness, identifying high and low explosiveness zones. On this basis, the explosiveness zoning is used as a direct design basis for blasting parameters, enabling quantitative design of the blasting hole network parameters. This significantly improves the engineering adaptability of blasting parameters and achieves precise matching between blasting energy and rock mass structure. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to the present invention.

[0024] Figure 2 This is a flowchart of the intelligent design method for precise blasting in tunnels / roadways / chambers according to the present invention.

[0025] Figure 3 This is a schematic diagram illustrating the spatial clustering feature analysis of microseismic events in the target rock mass region of this invention.

[0026] Figure 4 The flowchart illustrates the process of establishing a geometric model of the three-dimensional joint network for the target region in this invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] It should also be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and / or processing steps closely related to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0029] Additionally, it should be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] Please see Figure 1-2 As shown, this invention provides a method for inverting three-dimensional joint networks in rock masses and adaptively designing blasting parameters, comprising the following steps: S1. In the target rock mass area, based on geological survey data and surface joint distribution, at least one initial borehole point is set up; at the same time, in the target rock mass area, an array network containing at least four microseismic sensors is set up, and the monitoring array is synchronously connected to the data acquisition and recording system. S2, start drilling equipment to carry out drilling construction at the initial drilling point, use small-yield blasting or electric explosion to create an active seismic source in the initial borehole, the active seismic source radiates stress waves to the surrounding rock mass, and the micro-seismic sensor synchronously records the vibration signal of the entire site. S3, process the waveform data collected in step 2 to obtain a spatial point cloud dataset of microseismic events; S4. Based on the microseismic event spatial point cloud dataset obtained in step 3, a clustering algorithm is used to analyze the spatial clustering characteristics of microseismic events in the target rock mass area. The clustering algorithm can be an existing algorithm such as k-means clustering or single-chain clustering.

[0031] S5. At the key locations obtained in step 4, set up no fewer secondary drilling points than the number of key areas, and repeat the work of steps 2 and 3 to obtain a secondary updated microseismic event spatial point cloud dataset of the target rock mass area. Among them, the key locations are the areas where microseismic signals are concentrated, reflecting the locations of dense joints. Secondary drilling in these areas can detect more comprehensive microseismic and joint data.

[0032] S6. After the secondary drilling is completed, the joint distribution data is collected. S7. Based on the rock mass joint acquisition database obtained in step 6, establish a geometric model of the three-dimensional joint network in the target area. S8. Based on the geometric model of the three-dimensional joint network collected in step 7, and combined with geological exploration data, a three-dimensional numerical model of the rock mass in the target area is established, and a virtual rock mass joint network is output. S9. Based on the virtual rock mass joint network in step 8, conduct a simulated micro-seismic experiment and output the corrected virtual rock mass joint network. S10, Based on the virtual rock mass joint network corrected in step 9, the parameters of the blasting hole network are designed based on the energy criterion.

[0033] S11, Perform blasting operations and evaluate the blasting effect.

[0034] Furthermore, in step S1, the four microseismic sensors are required to be non-coplanar.

[0035] Regarding the deployment of the monitoring array, at least four microseismic sensors should be arranged around the target rock mass area, ensuring that the sensors are not coplanar. The sensors should be placed as close as possible to the target rock mass area to improve the signal-to-noise ratio. Sensors should also be installed on the walls of the excavated area and behind the working area to form a monitoring network capable of spatially locating the seismic source.

[0036] Furthermore, the specific steps in step S3 to process the waveform data acquired in step 2 to obtain the microseismic event spatial point cloud dataset are as follows: S3-1 uses signal preprocessing and feature extraction techniques to identify and separate secondary microseismic event signals induced by blasting vibrations. S3-2. For the identified microseismic signals, a microseismic localization algorithm is used, combined with the known locations of the microseismic sensors, to calculate the high-precision coordinates (X, Y, Z) in the three-dimensional space of each microseismic event. Commonly used localization algorithms include: double-difference localization, ray-tracing localization, grid search, waveform offset superposition, machine learning localization, Geiger localization, least squares linear iterative algorithm, and Inglada algorithm.

[0037] S3-3 aggregates all located microseismic events to generate a spatial point cloud dataset of induced microseismic events corresponding to the borehole.

[0038] Furthermore, such as Figure 3 As shown, the specific steps for analyzing the spatial clustering characteristics of microseismic events in the target rock mass region in step S4 are as follows: S4-1 calculates the spatial microseismic event cluster density and spatial microseismic event rate. The spatial microseismic event cluster density is defined as the number of microseismic events per unit space, and the spatial microseismic event rate is defined as the rate of increase of microseismic events per unit space. 1) Density of space microseismic events The engineering rock mass is divided into numerous square spaces by several planes parallel to the x, y, and z axes. The number of microseisms within each square space is counted, and N is the number of microseisms within each square space. Mi Divide by the volume V of the square i The density of space microseismic events was obtained.

[0039] 2) Microseismic spatial event rate The microseismic spatial event rate describes the average frequency of N consecutive events occurring within a unit space over a time window. The calculation process for the microseismic spatial event rate is as follows.

[0040] in, It is the microseismic spatial event rate. It is the number of changes in the AE event. It has a square volume. and The first and the The instantaneous time of occurrence of each AE event.

[0041] S4-2. Based on the calculated spatial microseismic event cluster density and spatial microseismic event rate, draw a spatial distribution map of the spatial microseismic event cluster density and spatial microseismic event rate in the target rock mass area. S4-3. In the spatial distribution map of spatial microseismic event cluster density and spatial microseismic event rate in the target rock mass area, identify several key locations where spatial microseismic event cluster density and spatial microseismic event rate are highly concentrated.

[0042] Furthermore, the specific steps for collecting joint distribution data in step S6 after the secondary drilling is completed are as follows: S6-1 uses borehole television to observe and record the joints inside the borehole, and measures and identifies the attitude (dip, dip angle), trace length, opening and other information of each joint surface according to depth; S6-2, Collect joint features of exposed rock masses in the project area to obtain statistical features of surface joint networks. A three-dimensional laser scanner can be used for the collection. S6-3, spatial coordinate correlation is performed between borehole joint data and exposed rock mass joint data to form a rock mass joint acquisition database.

[0043] Furthermore, such as Figure 4 As shown, the specific steps for establishing the geometric model of the target region's three-dimensional joint network in step S7 are as follows: S7-1: Based on step 5, obtain the spatial point cloud dataset of microseismic events in the target rock mass area after secondary updates, and calculate the spatial microseismic event cluster density, spatial microseismic event rate, single-chain cluster association length and box fractal dimension. The calculation methods for the spatial microseismic event cluster density and spatial microseismic event rate are the same as in step S4-1.

[0044] The following describes the methods for calculating the association length of a single-chain cluster and the fractal dimension of the box-counting method: In microseismic event analysis, microseismic events do not occur completely independently in space and time; they are often correlated within a certain spatiotemporal range. The single-chain cluster correlation length is an indicator used to measure the strength of this correlation.

[0045] 3) Single-chain cluster association length Single-chain cluster analysis provides an effective means to quantify the spatiotemporal correlation of micro-fracture events during rock fracturing. Based on the principle of spatial proximity, the single-chain cluster method constructs a hierarchical clustering structure of acoustic emission events through an iterative nearest-neighbor connection algorithm. Specifically, for a set of three-dimensional acoustic emission events, the Euclidean distance matrix between all point pairs is first calculated. Then, the nearest neighbor points or point clusters are iteratively connected to form single chains, and this process is recursively merged until all events are connected into a complete single-chain cluster structure. The cumulative link length in the single-chain cluster structure follows a Weibull distribution, expressed as: in, It is the cumulative length of links shorter than the link length threshold. This is the cumulative length of all links. (Take...) The length corresponding to the time is taken as the spatial correlation length L. SLC , is used to characterize the spatial correlation strength of micro-fracture events, i.e., the correlation length of single-chain clusters.

[0046] Fractal theory is a classic theory describing the complexity of the spatial distribution of microseismic events. The spatial distribution of microseismic events is usually regarded as a discrete set of points (each point being the location of an event), and methods such as box-counting dimension or correlation dimension are commonly used to quantitatively characterize the spatial distribution characteristics of microseismic events. Among these, box-counting dimension has become a widely used analytical tool in related research due to its advantages such as simple algorithm and clear geometric meaning.

[0047] 4) Box fractal dimension For time series, a "box" is a micro-unit of a time interval; for three-dimensional spatial coordinates and energy dissipation data sources, a "box" is a cubic micro-unit.

[0048] Where D is the box fractal dimension, N(ε) is the statistical value of the internal elements of the "box" with unit scale ε, and ε is the length of the time statistical unit or the equivalent side length of the three-dimensional cuboid.

[0049] S7-2, calculate the joint probability based on microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length and box fractal dimension; The joint probability probability refers to the estimated probability of a joint structure existing at a specified spatial location, based on microseismic monitoring data. It is an evaluation index of joint likelihood formed by weighted synthesis of multiple characteristic parameters of microseismic events closely related to the spatial distribution of joints. Specifically, it is a quantitative value representing the probability of joint existence, obtained by linearly synthesizing multiple characteristic parameters reflecting the spatial correlation between microseismic events and joint structures, assigning different weights according to their importance.

[0050] Microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length, and box-counting fractal dimension were selected as calculation parameters primarily because these four parameters reflect the spatial distribution pattern of microseismic events from different perspectives, and the distribution pattern of microseismic events is often influenced by existing underground discontinuous structures such as joints. Specifically, microseismic event cluster density and microseismic spatial event rate reflect the number of events per unit volume and the number of microseismic events per unit time and per unit volume, respectively, characterizing the clustering characteristics and spatial activity of microfractures within the rock mass. Since joint surfaces often act as channels for stress concentration, microseismic events tend to concentrate along joint surfaces, leading to a significant increase in event density in this area. Therefore, the likelihood of joint existence can be inferred from the cluster density. Single-chain cluster association length refers to the average spatial association distance between events within a cluster when microseismic events are distributed in clusters. A shorter single-chain cluster association length often indicates a more compact spatial distribution of events within the microseismic cluster, suggesting a denser joint structure in the area. Similarly, the box-counting fractal dimension is used to characterize the complexity of the spatial distribution of microseismic events. A lower fractal dimension usually reflects the tendency of microseismic events to cluster in space, and can also be used to reflect the concentrated development of joints. In summary, the four parameters—microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length, and box-counting fractal dimension—reflect the spatial distribution pattern of microseismic events from different perspectives, and indirectly reflect the three-dimensional distribution pattern of rock mass joints. Therefore, these four parameters are selected to calculate the joint probability.

[0051] Specifically, the method for calculating the probability of joint existence is as follows: S7-2-1 Select four parameters that can characterize the spatial distribution of microseismic events: microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length, and box fractal dimension; S7-2-2, assign a weight coefficient to each selected feature parameter; Existing weight determination methods can be used here, such as subjective weighting methods (analytic hierarchy process, etc.), objective weighting methods (entropy weighting, CRITIC method, etc.), or manual input.

[0052] S7-2-3, multiply each feature parameter by its corresponding weight, and sum all the products to obtain the joint probability.

[0053] Because microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length, and box fractal dimension are all data with three-dimensional spatial distribution, the calculated joint probability is also three-dimensional data. By calculating the data in the "box" at each spatial location separately, the joint probability at that location can be obtained.

[0054] S7-3, set a threshold for the joint probability rate. By manually inputting the threshold, microseismic data with a joint probability rate lower than the threshold are removed. S7-4, Based on the spatial distribution pattern of the retained joints and the rock mass joint acquisition database obtained in step 6, set up the preliminary connection path network of the rock mass joints. Specifically, the steps for obtaining the preliminary connection path of rock mass joints are as follows: S7-4-1, Extract the spatial distribution of the possible existence rate of the retained joints and the spatial coordinates of the rock mass joint acquisition data obtained in step 6, and construct a set of key nodes; S7-4-2, Based on spatial proximity and geological similarity, determine all node pairs that need to be connected in the key node set; S7-4-3 is a method for finding the connection path with the minimum total travel cost for each node to be connected in a 3D field. The travel cost of any segment on the path is defined as the geometric length of that segment divided by the probability of joints at its midpoint; the cost is low when passing through high-probability regions and high when passing through low-probability regions. S7-4-4, forming a preliminary set of paths; S7-4-5, Spatial proximity analysis is performed on the preliminary path set, and related paths are merged to finally generate a preliminary connection path network for rock mass joints; S7-5, based on the preliminary connection path network of rock mass joints and combined with the collected data of rock mass joints, outputs the collected three-dimensional joint network geometric model of the target area.

[0055] Furthermore, the specific steps for establishing a three-dimensional numerical model of the rock mass in the target area and outputting a virtual rock mass joint network in step S8 are as follows: S8-1, Import the three-dimensional joint network geometric model of the target area output in step 7 into the numerical simulation software; S8-2, Determine the size of the numerical model based on the size of the target rock mass region, ensuring that the size of the numerical model exceeds 5 times the size of the target rock mass region; S8-3, Based on geological exploration data (such as fault and rock layer distribution) and the engineering structures involved, establish key influencing factors such as faults, rock layers, and the engineering structures involved in the model; S8-4, Based on the in-situ stress field test data, apply the corresponding in-situ stress initialization curve to the model boundary; S8-5 embeds rock fracture criteria (such as the Griffith criterion and the Mohr-Coulomb criterion) into the model and runs quasi-static mechanical calculations to simulate the fracture initiation, propagation and interaction process of rock mass under in-situ stress. S8-6, Adjust the inversion parameters (in-situ stress field, rock parameters) so that the statistical characteristics of the simulated virtual joint network (including but not limited to attitude distribution, density, connectivity) match the actual observed statistical characteristics within the preset tolerance range; S8-7 outputs a virtual rock mass joint network.

[0056] Furthermore, in step S9, based on the virtual rock mass joint network of step 8, the specific steps for conducting simulated microseismic experiments and outputting the corrected virtual rock mass joint network are as follows: S9-1, In the virtual rock mass joint network, at the current geostress level, at the spatial location corresponding to the actual borehole in step 5, apply a dynamic disturbance load similar to the measured active source characteristics. S9-2 performs dynamic simulation, calculates virtual microseismic events in the model where joint surfaces slide or crack due to disturbance, records their locations, and generates a simulated induced microseismic spatial point cloud dataset. S9-3, a quantitative comparison is made between the simulated induced microseismic spatial point cloud dataset and the microseismic event spatial point cloud dataset of the target rock mass area obtained in step 5 and updated twice. The comparison indicators include, but are not limited to: microseismic event cluster density, spatial microseismic event rate, single-chain cluster association length, and box-counting fractal dimension, etc. S9-4: Based on existing evaluation methods, establish a similarity model and set a similarity threshold. If the similarity of the comparison results is less than the similarity threshold, return to step 5 to plan the next round of verification drilling. If the similarity of the comparison results is greater than or equal to the similarity threshold, the virtual rock mass joint network is considered to have passed dynamic verification and possesses high confidence. The next round of verification drilling refers to supplementing drilling in key areas with large data errors, then collecting and analyzing the data from the existing and new drilling to obtain a point cloud dataset, re-collecting joint data, and then building a model and conducting simulation experiments.

[0057] S9-5 outputs the corrected virtual rock mass joint network.

[0058] Furthermore, the specific steps for designing the blasting hole mesh parameters in step S10 are as follows: S10-1, based on ground stress, rock properties (compressive strength, tensile strength, etc.) and virtual rock mass joint network, the rock mass in the target area is divided into several rock regions with different blastability. S10-2 For tunnel / roadway rock mass blasting, blast holes typically include cut holes, auxiliary holes, and peripheral holes. Based on blastability, a non-uniform blasting hole network parameter design is adopted. Specifically: identify areas with high blastability and place cut holes in these areas, avoiding placing cut holes at the edge of the target rock mass as much as possible. Place auxiliary holes outside the cut area and peripheral holes along the outline of the target rock mass. The spacing between cut holes, auxiliary holes, and peripheral holes is set according to the blastability of the rock mass. If the rock mass has high blastability, the hole spacing can be appropriately increased; if the area has low blastability, the hole spacing can be appropriately decreased, and the spacing between each hole is not necessarily the same. S10-3, the borehole spacing can be designed based on energy principles and the extent of the fracture zone, while the borehole depth is set manually: First, based on the virtual rock mass joint network, target fragment size, and fractured rock mass volume, the energy required for rock fragmentation at the target fragment size is calculated using the surface energy calculation formula; then, the energy generated by the explosive explosion is calculated based on the target charge and explosive parameters; simultaneously, based on energy conservation and considering energy dissipation during the blasting process, it is required that the energy generated by the explosive explosion exceeds 2.5 times the energy required for rock fragmentation at the target fragment size; at the same time, the theoretical fracture zone extent is calculated based on rock parameters and explosive parameters to ensure that the borehole spacing is less than the fracture zone extent.

[0059] The specific calculation method is as follows: a. Energy required for rock fracturing at the target fragmentation size : Among them, the newly added surface area of ​​the fractured rock under the target fragmentation size , For rock surface energy, existing formulas can be used to calculate it. .

[0060] b. The energy generated by the explosion of explosives can be theoretically estimated using the classical detonation model, or numerically calculated using LS-DYNA numerical simulation.

[0061] c. Scope of the cracked area Based on the attenuation law of stress waves in rock mass, the peak value of tangential stress at any point in the rock is... The decay law is as follows in, Lateral pressure coefficient, The pressure decay index, The peak blast pressure on the borehole wall is denoted by 'a', and 'a' is the borehole radius. The distance between the center and the burst point.

[0062] Rock mass, as a typical brittle material, has a tensile strength far lower than its compressive strength. Under blasting loads, the stress waves generated by the explosive detonation propagate within the rock mass. When this tensile stress exceeds the dynamic tensile strength of the rock mass, it will lead to tensile failure, thus forming a fracture zone dominated by tensile fractures, satisfying the following conditions: in, This refers to the dynamic tensile strength of the rock mass.

[0063] make The critical value obtained at the time of the explosion center distance is the radius of the fracture zone: in, To determine the dynamic Poisson's ratio of the rock, the borehole spacing must be less than the radius of the fracture zone. R C .

[0064] In summary, this invention proposes an adaptive design method for non-uniform blasting hole network parameters based on energy matching of a three-dimensional joint network. Based on the spatial distribution of the joint network obtained through inversion, the rock mass in the target area is quantitatively partitioned according to its blastability, identifying high and low blastability zones. On this basis, the blastability partitioning is used as a direct basis for blasting parameter design, quantitatively designing the blasting hole network parameters. This significantly improves the engineering adaptability of the blasting parameters and achieves precise matching between blasting energy and rock mass structure. The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for inverting three-dimensional joint networks in rock masses and adaptively designing blasting parameters, characterized in that, Includes the following steps: S1. In the target rock mass area, based on geological survey data and surface joint distribution, at least one initial borehole point is set up; at the same time, in the target rock mass area, an array network containing at least four microseismic sensors is set up, and the monitoring array is synchronously connected to the data acquisition and recording system. S2, start drilling equipment to carry out drilling construction at the initial drilling point, use small-yield blasting or electric explosion to create an active seismic source in the initial borehole, the active seismic source radiates stress waves to the surrounding rock mass, and the micro-seismic sensor synchronously records the vibration signal of the entire site. S3, process the waveform data collected in step 2 to obtain a spatial point cloud dataset of microseismic events; S4. Based on the microseismic event spatial point cloud dataset obtained in step 3, a clustering algorithm is used to analyze the spatial clustering characteristics of microseismic events in the target rock mass area. S5. At the key locations obtained in step 4, set up no fewer secondary drilling points than the number of key areas, and repeat the work of steps 2 and 3 to obtain a secondary updated microseismic event spatial point cloud dataset of the target rock mass area. S6. After the secondary drilling is completed, the joint distribution data is collected. S7. Based on the rock mass joint acquisition database obtained in step 6, establish a geometric model of the three-dimensional joint network in the target area. S8. Based on the geometric model of the three-dimensional joint network collected in step 7, and combined with geological exploration data, a three-dimensional numerical model of the rock mass in the target area is established, and a virtual rock mass joint network is output. S9. Based on the virtual rock mass joint network in step 8, conduct a simulated micro-seismic experiment and output the corrected virtual rock mass joint network. S10, Based on the virtual rock mass joint network corrected in step 9, the parameters of the blasting hole network are designed based on the energy criterion; S11, Perform blasting operations and evaluate the blasting effect.

2. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: In step S1, it is required to ensure that the four microseismic sensors are not coplanar.

3. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: The specific steps in step S3 to process the waveform data acquired in step 2 to obtain the spatial point cloud dataset of microseismic events are as follows: S3-1 uses signal preprocessing and feature extraction techniques to identify and separate secondary microseismic event signals induced by blasting vibrations. S3-2. For each identified microseismic event, a microseismic location algorithm is used, combined with the known real-time position of the drill bit as a constraint, to calculate the time of occurrence of each induced microseismic event and its high-precision coordinates (X, Y, Z) in three-dimensional space. S3-3 aggregates all located microseismic events to generate a spatial point cloud dataset of induced microseismic events corresponding to the borehole.

4. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: The specific steps for analyzing the spatial clustering characteristics of microseismic events in the target rock mass region in step S4 are as follows: S4-1, calculate the spatial microseismic event cluster density and spatial microseismic event rate. The spatial microseismic event cluster density is defined as the number of microseismic events per unit space, and the spatial microseismic event rate is defined as the rate of increase of microseismic events per unit space. S4-2. Based on the calculated spatial microseismic event cluster density and spatial microseismic event rate, draw a spatial distribution map of the spatial microseismic event cluster density and spatial microseismic event rate in the target rock mass area. S4-3. In the spatial distribution map of spatial microseismic event cluster density and spatial microseismic event rate in the target rock mass area, identify several key locations where spatial microseismic event cluster density and spatial microseismic event rate are highly concentrated.

5. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: The specific steps for collecting joint distribution data in step S6 after the secondary drilling is completed are as follows: S6-1 uses borehole television to observe and record the joints inside the borehole, and measures and identifies the attitude (dip, dip angle), trace length, opening and other information of each joint surface according to depth; S6-2, Collect joint features of exposed rock masses in the project area to obtain statistical features of surface joint networks. A three-dimensional laser scanner can be used for the collection. S6-3, spatial coordinate correlation is performed between borehole joint data and exposed rock mass joint data to form a rock mass joint acquisition database.

6. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: The specific steps for establishing the geometric model of the target region's three-dimensional joint network in step S7 are as follows: S7-1: Based on step 5, obtain the spatial point cloud dataset of microseismic events in the target rock mass area after secondary updates, and calculate the spatial microseismic event cluster density, spatial microseismic event rate, single-chain cluster association length and box fractal dimension. S7-2, calculate the joint probability based on microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length and box fractal dimension; S7-3, set a threshold for the joint probability rate. By manually inputting the threshold, microseismic data with a joint probability rate lower than the threshold are removed. S7-4, Based on the spatial distribution pattern of the retained joints and the rock mass joint acquisition database obtained in step 6, set the preliminary connection path of the rock mass joints. S7-5, based on the preliminary connection path of the rock mass joints and combined with the collected data of the rock mass joints, outputs the collected three-dimensional joint network geometric model of the target area.

7. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 6, characterized in that: The method for calculating the probability of joint presence is as follows: S7-2-1 Select several parameters that can characterize the spatial distribution of microseismic events: microseismic event cluster density, microseismic spatial event rate, single-chain cluster association length, and box fractal dimension; S7-2-2, assign a weight coefficient to each selected feature parameter; S7-2-3, multiply each feature parameter by its corresponding weight, and sum all the products to obtain the joint probability.

8. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: The specific steps for establishing a three-dimensional numerical model of the target area rock mass and outputting a virtual rock mass joint network in step S8 are as follows: S8-1, Import the three-dimensional joint network geometric model of the target area output in step 7 into the numerical simulation software; S8-2, Determine the size of the numerical model based on the size of the target rock mass region, ensuring that the size of the numerical model exceeds 5 times the size of the target rock mass region; S8-3, Based on geological exploration data (such as fault and rock layer distribution) and the engineering structures involved, establish key influencing factors such as faults, rock layers, and the engineering structures involved in the model; S8-4, Based on the in-situ stress field test data, apply the corresponding in-situ stress initialization curve to the model boundary; S8-5 embeds rock fracture criteria (such as the Griffith criterion and the Mohr-Coulomb criterion) into the model and runs quasi-static mechanical calculations to simulate the fracture initiation, propagation and interaction process of rock mass under in-situ stress. S8-6, Adjust the inversion parameters (in-situ stress field, rock parameters) so that the statistical characteristics of the simulated virtual joint network (including but not limited to attitude distribution, density, connectivity) match the actual observed statistical characteristics within the preset tolerance range; S8-7 outputs a virtual rock mass joint network.

9. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: In step S9, based on the virtual rock mass joint network from step 8, the specific steps for conducting simulated microseismic experiments and outputting the corrected virtual rock mass joint network are as follows: S9-1, In the virtual rock mass joint network, at the current geostress level, at the spatial position corresponding to the actual borehole in step 5, apply a dynamic disturbance load similar to the measured active source vibration energy spectrum; S9-2 performs dynamic simulation, calculates virtual microseismic events in the model where joint surfaces slide or crack due to disturbance, records their locations, and generates a simulated induced microseismic spatial point cloud dataset. S9-3, quantitatively compare the simulated induced microseismic spatial point cloud dataset with the microseismic event spatial point cloud dataset of the target rock mass area obtained in step 5 and updated twice; the comparison indicators include but are not limited to: microseismic event cluster density, spatial microseismic event rate, single-chain cluster association length, box fractal dimension, etc. S9-4. Based on the existing evaluation method, establish a similarity model and set a similarity threshold. If the similarity of the comparison result is less than the similarity threshold, return to step 5 and plan the next round of verification drilling. If the similarity of the comparison result is greater than or equal to the similarity threshold, it is considered that the virtual rock mass joint network has passed dynamic verification and has high confidence. S9-5 outputs the corrected virtual rock mass joint network.

10. The method for inverting three-dimensional joint networks in rock mass and adaptive design of blasting parameters according to claim 1, characterized in that: The specific steps for designing the blasting hole mesh parameters in step S10 are as follows: S10-1, based on ground stress, rock properties (compressive strength, tensile strength, etc.) and virtual rock mass joint network, the rock mass in the target area is divided into several rock regions with different blastability. S10-2 In rock blasting of tunnels / roadways, blast holes typically include cut holes, auxiliary holes, and peripheral holes. Based on the blastability, a non-uniform blasting hole network parameter design is adopted. Specifically: identify areas with high blastability and place cut holes in these areas, avoiding placing cut holes at the edge of the target rock mass as much as possible. Place auxiliary holes outside the cut area and peripheral holes along the outline of the target rock mass. The spacing between cut holes, auxiliary holes, and peripheral holes is set according to the blastability of the rock mass. If the rock mass has high blastability, the hole spacing can be appropriately increased; if the area has low blastability, the hole spacing can be appropriately decreased, and the spacing between each hole is not necessarily the same. S10-3, the borehole spacing can be designed based on energy principles and the extent of the fracture zone, while the borehole depth is set manually: First, based on the virtual rock mass joint network, target fragment size, and fractured rock mass volume, the energy required for rock fragmentation at the target fragment size is calculated using the surface energy calculation formula; then, the energy generated by the explosive explosion is calculated based on the target charge and explosive parameters; simultaneously, based on energy conservation, energy dissipation during the blasting process is considered to ensure that the energy generated by the explosive explosion exceeds 2.5 times the energy required for rock fragmentation at the target fragment size; at the same time, the theoretical fracture zone extent is calculated based on rock parameters and explosive parameters to ensure that the borehole spacing is less than the fracture zone extent.

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