Calculation method of peak value of slope vibration velocity based on blasting

Abstract

The present application relates to the field of slope blasting, and particularly relates to a slope vibration velocity peak value calculation method based on blasting. The method comprises the following steps: based on obtained topographic surveying and mapping data and geological exploration data of a target slope, the target slope is divided into one or more monitoring areas with different geological and topographic characteristics, and a geological space model reflecting the division of the monitoring areas is constructed; according to the differences in the geological and topographic characteristics of each monitoring area, monitoring points and experimental blasting points are arranged in the geological space model, and the explosive quantity of the experimental blasting points is set. The present application is aimed at a slope with large geological differences, a nodal network is constructed by selecting representative monitoring points, vibration attenuation parameters are calibrated based on measured data in different areas and boundaries, and vibration peak values are recursively calculated along the network topology path, thereby realizing accurate quantification of the vibration propagation law of non-homogeneous rock mass; and the vibration transmission model constructed can be locally updated, and has high precision and practicability.

Description

Technical Field

[0001] This invention relates to the field of slope blasting, and in particular to a method for calculating the peak vibration velocity of a slope based on blasting. Background Technology

[0002] Vibration protection during blasting is a core aspect of mine blasting operations safety. The vibration waves generated by blasting propagate through the rock mass, and if their intensity exceeds a safe threshold, they can lead to serious consequences such as slope instability and damage to structures. Therefore, precise monitoring and effective control of blasting vibrations are crucial prerequisites for ensuring the safe and efficient operation of mining activities.

[0003] Currently, the mainstream methods for monitoring and controlling blasting vibrations in the industry mainly rely on a combination of on-site data acquisition and classical theoretical formulas. In practice, technicians first deploy blasting vibration monitoring instruments around the slope to be protected and key structures to collect peak vibration velocity data in real time. Then, based on this on-site monitoring data, they use the classic Sadovsky empirical formula to perform inversion calculations, thereby dynamically adjusting the maximum amount of explosives per blast in subsequent blasting operations. Through this feedback control, the vibration velocity is strictly limited within a preset safety threshold, avoiding destructive loosening of the slope rock mass structure caused by blasting vibrations. However, this mainstream method is based on the ideal assumption that the rock mass medium is a uniform, continuous, semi-infinite space. In reality, the existence of slope topography significantly alters the propagation and attenuation patterns of vibration waves. The mainstream empirical formula does not take into account the influence of the slope angle, a key topographical factor, leading to errors in the calculated peak vibration velocity data.

[0004] To address this issue, Chinese Patent Publication No. CN109214108A proposes an improved method for calculating peak ground vibration velocity (PGV). This method utilizes conformal mapping to transform complex slope terrain into a simple planar half-space, thus incorporating the slope's boundary conditions into the calculation model. This allows the modified PPV calculation to still utilize the Sadovsky formula. This method is ingeniously conceived and highly practical, significantly improving the calculation accuracy when considering topographic factors.

[0005] However, many technical solutions, including the aforementioned optimization methods, still fundamentally rely on theoretical simulation and calculation. Their common premise is the assumption that geological parameters along the vibration propagation path are relatively clear and uniform. But in actual mining engineering geological environments, this premise is often difficult to uphold. Different mining areas, and even different locations within the same mining area, often exhibit significant differences in rock mass structure, joint development, weathering conditions, and mechanical properties, displaying strong spatial variability and uncertainty. This is especially true in geological structural boundaries, where lithology can change drastically even over short distances. This widespread heterogeneity of geological conditions causes deviations between the input conditions and actual field conditions in simulation calculation methods based on fixed-parameter models, ultimately leading to a decrease in the reliability and accuracy of prediction results, making it difficult to fully meet the practical requirements of high-precision blasting safety control. Summary of the Invention

[0006] This invention provides a method for calculating peak vibration velocity of slopes based on blasting, in order to solve the problem that it is difficult to achieve accurate peak vibration velocity calculation for slopes in areas with large geological differences through simulation calculation methods based on fixed parameter models.

[0007] To solve the above-mentioned technical problems, this application provides the following technical solution: The method for calculating peak vibration velocity of slopes based on blasting includes the following steps: S10: Based on the topographic mapping data and geological exploration data of the target slope, the target slope is divided into one or more monitoring areas with different geological and topographic features, and a geological spatial model reflecting the division of the monitoring areas is constructed; according to the differences in the geological and topographic features of each monitoring area, monitoring points and experimental blasting points are set in the geological spatial model, and the amount of explosive charge at the experimental blasting points is set. S20: After the experimental blasting point is detonated, the peak vibration velocity of each monitoring point is collected as experimental monitoring data and correlated with the position of the monitoring point in the geological space model. S30: For the monitoring area with the experimental blasting point, based on the amount of explosive charge at the experimental blasting point, the distance between the experimental blasting point and the monitoring point, and the corresponding experimental monitoring data, the intrinsic vibration parameters characterizing the generation and attenuation of vibrations within the monitoring area are obtained by fitting the Sadovsky formula; for two adjacent monitoring areas, based on the transmission direction of the vibration wave after the blasting of the experimental blasting point, the experimental monitoring data collected at adjacent monitoring points in the two monitoring areas respectively, and the distance between the two monitoring points, the boundary vibration parameters characterizing the attenuation of the vibration wave when it crosses the boundary of the corresponding monitoring area are analyzed; taking the monitoring points in the monitoring area as nodes and the connection relationship between nodes in adjacent monitoring areas as directed edges, the intrinsic vibration parameters are assigned to the nodes, and the boundary vibration parameters are assigned to the corresponding edges to construct a vibration transmission model. S40: Based on the positions of the target blasting point and the target monitoring point in the vibration transmission model, obtain the propagation path of the vibration wave from the target blasting point to the target monitoring point during blasting, as well as the intrinsic parameters of each node and the boundary vibration parameters of each side along the propagation path. Combined with the amount of explosive charge at the target blasting point, perform recursive calculations to obtain the peak slope vibration velocity at the target monitoring point.

[0008] The basic principle and beneficial effects of this invention are as follows: This invention divides geologically heterogeneous slopes into multiple monitoring zones, and deploys monitoring points and experimental blasting points within each zone. Based on field blasting experimental data, unique intrinsic vibration parameters are dynamically calibrated within each monitoring zone, and unique boundary vibration parameters are calibrated for the boundaries between adjacent monitoring zones. Furthermore, a parameterized vibration transmission model is constructed using representative monitoring points selected from each monitoring zone as network nodes and the connections between nodes as edges. During prediction, instead of using the globally unified Sadovsky empirical formula, the topological path of vibration propagation is identified based on the specific positional relationship between the target monitoring point and the target blasting point in the vibration transmission model, and the node affiliation of the target blasting point and the target monitoring point in the network model. The pre-calibrated node (intrinsic) and edge (boundary) parameters are then applied recursively along this path for calculation. Through this method, this invention addresses and overcomes the deficiency of inaccurate prediction of peak vibration velocity on slopes due to the assumption of homogeneous media, thereby significantly improving the accuracy and reliability of peak vibration velocity prediction on slopes with complex and variable geological conditions.

[0009] Based on the principles of monitoring area centering and networked modeling, this invention can flexibly and stably map arbitrarily complex geological spatial structures. Unlike traditional methods that fail when dealing with boundaries of drastically changing lithology, this invention transforms such boundaries into edges connecting two nodes in a vibration transmission model. By independently calibrating boundary vibration parameters using measured data from the nearest monitoring point pairs near the boundary, it achieves precise quantification and characterization of the abnormal attenuation or amplification effects of vibration waves traversing different geological units, thus enabling a more realistic simulation of the complex propagation behavior of vibration waves in heterogeneous bodies.

[0010] Since the parameters of the vibration transmission model are derived from on-site measurements and reflect the actual geological structure, engineers can conduct multiple simulations of blasting schemes within the vibration transmission model. This allows for precise evaluation of the vibration impact on key protected targets under different experimental blasting point locations and charge combinations. Consequently, the blasting design scheme with the lowest vibration risk can be selected before construction. This breaks through the traditional passive mode centered on monitoring and adjustment, and constructs an active technical path based on prediction and guided by design, thereby improving blasting safety and operational efficiency.

[0011] Furthermore, this solution decomposes the overall slope into a network model (vibration transmission model) built around the monitoring area center, which is easy to update and maintain long-term. When mining progresses to a new geological section, it is only necessary to set up monitoring points in the new area, select new nodes, and calibrate the new parameters connecting them with the existing network through supplementary experiments to seamlessly expand the model. When the local rock mass properties change, only the parameters of the corresponding nodes need to be updated, without reconstructing the global model. This invention can provide a technical solution for blasting vibration safety management that can continuously adapt to dynamic changes in engineering, effectively overcoming the technical defects of traditional fixed parameter methods that cannot achieve adaptive optimization as the project progresses.

[0012] In summary, this invention addresses slopes with significant geological variations by constructing a node-based network using representative monitoring points. Based on measured data, vibration attenuation parameters are calibrated by region and boundary, and the vibration peak value is calculated recursively along the network topology path. This achieves precise quantification of the vibration propagation law in heterogeneous rock masses. Furthermore, the constructed vibration transmission model can be locally updated, combining high accuracy with practicality.

[0013] Furthermore, the geological and topographic features include the spatial distribution characteristics of rock mass lithology, joint development degree, weathering grade, topographic slope, and elevation. In step S10, based on the similarity of geological and topographic features at different locations on the slope, the target slope is divided into monitoring areas with different geological and topographic features. Using the horizontal plane as a reference, the coverage area corresponding to the bottom of the slope within the monitoring area is obtained as the topographic coverage surface, and the shape of the topographic coverage surface is obtained as the topographic coverage shape of the slope. Combined with the geological and topographic features of the slope within the monitoring area, the geological and topographic change trend of the slope is obtained. The similarity of the topographic coverage shape, the distribution of geological and topographic features in the topographic coverage shape, and the geological and topographic change trend of the slope in two adjacent monitoring areas is calculated to provide a basis for selecting monitoring points in each monitoring area.

[0014] By incorporating the similarity between topographic cover shape and geological feature distribution for zoning, the regional division more closely reflects the true differences in geological structures in spatial projection. Traditional methods rely on contour lines or single lithology for division, easily overlooking vertical structural differences, leading to the erroneous merging of areas with similar surface morphology but completely different internal compositions. For example, an ancient landslide may have a gentle surface slope, with its overlying soil layer and underlying bedrock exhibiting drastically different distribution characteristics in horizontal projection. This invention can accurately identify and separately zone these areas based on such distribution differences, thus providing a correct basis for subsequent differential parameter calibration.

[0015] Furthermore, within the monitoring area, based on the geological spatial model, quantitative features of the topographic dimension and geological dimension are extracted respectively. The quantitative features of the topographic dimension are processed into a topographic complexity component, and the quantitative features of the geological dimension are processed into a geological complexity component. The topographic complexity component and the geological complexity component are weighted and fused to obtain the topographic and geological complexity. In step S30, based on the topographic and geological complexity of the monitoring area, one or more monitoring points are selected as candidate monitoring points within the monitoring area. The geometric center is determined based on the spatial distribution of all candidate monitoring points, and the candidate monitoring point closest to the geometric center is designated as the center of the monitoring area. For two adjacent monitoring areas, near the boundary line between the two monitoring areas, a pair of monitoring points located within the two monitoring areas and whose positions are closest are selected as the boundary monitoring point pair. Based on the experimental monitoring data collected from the boundary monitoring point pair and the distance between the two monitoring points, the boundary vibration parameters are calculated using the vibration transmission attenuation model.

[0016] By quantifying the complexity of topography and geology and using this to drive the automatic selection of key nodes in the monitoring network, the selected monitoring points are better adapted to changes in topography and geology, thus enabling more accurate capture of the attenuation patterns of vibration transmission. Traditional point placement relies on the personal experience of engineers, which can easily lead to the omission of key locations under complex and concealed geological conditions. For example, a limestone slope with hidden caves and faults inside has a complex geological structure that causes abnormal vibration propagation. This invention automatically identifies the monitoring area as a high-value zone by calculating complexity and designates the actual monitoring point near its geometric center as a representative point. At the same time, it automatically pairs the nearest monitoring points on both sides of the fault to capture boundary effects. This data-driven method of automatic key point location and pairing can solve the problem of the difficulty in accurately capturing the influence of hidden anomalies manually.

[0017] Furthermore, in step S10, the layout location and number of experimental blasting points are optimized to ensure that the effective monitoring range of the blasting vibration at each experimental blasting point can cover the target slope and that the overlapping area of ​​the covered ranges is minimized.

[0018] This invention optimizes the layout of experimental blasting points to ensure full coverage of the target slope and minimize the overlapping area of ​​the coverage regions. This reduces blasting costs and improves data validity, overcoming the shortcomings of incomplete coverage or data redundancy caused by traditional random layout. For example, on large slopes, unreasonable blasting point layout can easily lead to the problem of no measured data in some lithological areas. This invention can optimize the layout of blasting points in slopes with multiple different lithological zones, allowing a small number of experimental blasting points to cover all areas with minimal overlap, avoiding repeated blasting of the same geological unit. It is particularly suitable for mine slopes with narrow terrain and scattered geological zones, significantly improving experimental efficiency and data utilization.

[0019] Furthermore, after constructing the vibration transmission model, supplementary blasting points are set up in the monitoring area where experimental blasting has been carried out; the supplementary blasting points are then detonated, and experimental monitoring data are collected as supplementary experimental data. The supplementary experimental data is used to verify and calibrate the intrinsic vibration parameters or boundary vibration parameters.

[0020] By supplementing the blasting verification and calibration parameters, this invention overcomes the shortcomings of traditional models with fixed parameters that cannot adapt to dynamic changes in rock mass. For example, if rock mass loosening after blasting leads to subsequent prediction deviations, this invention can add supplementary blasting points in the already blasted granite monitoring area, using new data to calibrate intrinsic vibration parameters, accurately capturing changes in the attenuation characteristics of the rock mass caused by blasting disturbance. This is particularly suitable for mine slopes subjected to multiple blasting rounds, avoiding parameter failure due to changes in rock mass mechanical properties, ensuring that the vibration transmission model always conforms to actual geological conditions, and improving the stability of long-term predictions.

[0021] Furthermore, in step S20, after the experimental blasting, the experimental monitoring data collected from each monitoring point within the same monitoring area are summarized, and the ratio of the standard deviation to the arithmetic mean of each experimental monitoring data is calculated to obtain the spatial variation coefficient of the vibration data in the monitoring area. Then, the topographic and geological complexity of any monitoring area is obtained according to the geological spatial model. If the topographic and geological complexity exceeds a preset complexity threshold, or the estimated spatial variation coefficient of the vibration data exceeds a preset variation threshold, monitoring points are added at key topographic turning points or abrupt changes in geological features within the monitoring area. The key topographic turning points are obtained by analyzing the digital elevation model data of the topographic mapping data, calculating the topographic curvature, and extracting the curvature extreme points. The abrupt changes in geological features are obtained by analyzing geological exploration data, performing spatial interpolation, and calculating attribute gradients, or by extracting lithological boundaries and structural lines from geological exploration data.

[0022] By using both the coefficient of variation and complexity threshold to determine the addition of monitoring points, this invention overcomes the limitations of traditional fixed-point systems that cannot adapt to complex scenarios. For example, vibration data may be missing at terrain bends due to the lack of monitoring points. This invention can add monitoring points at extreme points of terrain curvature displayed in the digital elevation model (such as slope inflection points) or at abrupt changes in lithological boundaries. This solves the problem of large spatial variability and large calculation errors in vibration data in areas with abrupt changes in terrain and geology. It is particularly suitable for slopes with fault structures and abrupt changes in weathering layer thickness, making experimental monitoring data more comprehensive and reducing parameter fitting bias.

[0023] Furthermore, in step S10, when determining the location and number of experimental blasting points, uniform grid points can be generated in the target slope area at a preset density as candidate points; the area of ​​the slope surface in the monitoring area can be obtained from the topographic and geological model as the slope surface area, and the ratio of the topographic cover shape of the monitoring area to the slope surface area can be used as the area weight; the topographic and geological complexity of the monitoring area can be obtained from the geological spatial model, and the number of experimental blasting points in each monitoring area can be allocated according to the area weight and topographic and geological complexity of the monitoring area, and the point closest to the center of the monitoring area can be selected from the candidate points as the experimental blasting point according to the number of points.

[0024] By combining area weighting and topographic / geological complexity in allocating experimental blasting points, this invention enables differentiated deployment, avoiding the data shortage problem in key areas caused by traditional uniform deployment. For example, sharing the same number of blasting points between large areas of low complexity and small areas of high complexity leads to a lack of data in high-complexity areas. This invention can allocate more blasting points to fractured zones in slopes containing small fractured zones and large areas of intact rock mass according to complexity and area weighting, accurately capturing the attenuation patterns in special areas, while reducing the number of blasts in intact rock mass areas, balancing data effectiveness and blasting costs, and is more suitable for scenarios with large differences in geological zoning area.

[0025] Furthermore, step S40 also includes the following steps: S41: Based on the spatial location of the target blasting point and the target monitoring point, determine a spatial baseline from the target blasting point to the target monitoring point; S42: In the vibration transmission model, determine the multiple nodes and directed edges that are closest to the spatial baseline, and generate a matching propagation path that is closest to the direction of the spatial baseline based on the topological connection relationship in the vibration transmission model. S43: Divide the matching propagation path into segments based on the different nodes and directed edges it passes through to obtain segmented paths, obtain the geometric length of each segmented path, and match the corresponding intrinsic vibration parameters or boundary vibration parameters for the segmented paths. S44: Based on the amount of explosive charge at the target blasting point, and according to the length of each segment on the matching propagation path and the matching vibration parameters, the segmented recursive attenuation calculation is performed to finally obtain the peak slope vibration velocity at the target monitoring point.

[0026] This invention addresses the shortcomings of traditional models that ignore the actual propagation direction of vibration waves and rely solely on topological relationships for calculations by matching propagation paths using spatial baselines. For example, when a slope is obstructed by a protruding mountain, fixed-path calculations can deviate significantly from reality. This invention can select paths that closely approximate the actual propagation direction based on the spatial connection between the target blasting point and the monitoring point. In slopes with significant topographic relief and scattered monitoring areas, it accurately matches the vibration parameters of each segment, avoiding attenuation calculation distortion caused by path deviations. It is particularly suitable for complex scenarios such as long and narrow slopes or multiple obstacles, improving the accuracy of peak vibration velocity calculations.

[0027] Furthermore, it also includes step S50: After constructing the vibration transmission model in step S30, at least one monitoring area that has undergone experimental blasting is selected, and a secondary experimental blasting point is set up at the same location. The amount of explosive charge at the secondary experimental blasting point is the same as that of the previous experimental blasting at the same location, or is a preset ratio. After the secondary experimental blasting point is detonated, experimental monitoring data of monitoring points in the monitoring area where the secondary experimental blasting point is located is collected. The changes in experimental monitoring data obtained from the two experimental blasts at the same monitoring point are compared and analyzed, and the change in the attenuation characteristics of the rock mass caused by the disturbance of the first blast is calculated. Based on the calculated change in attenuation characteristics, the intrinsic vibration parameters and associated boundary vibration parameters of the corresponding monitoring area nodes in the vibration transmission model are corrected and optimized.

[0028] By calibrating parameters through secondary blasting, this invention can capture changes in the attenuation characteristics of rock masses caused by blasting disturbances, overcoming the limitations of traditional models that only consider initial geological conditions. For example, after multiple blasts, the rock mass may loosen but the parameters may not be updated, leading to subsequent predicted values ​​being lower than actual values. This invention can perform secondary blasting with the same charge in the already blasted sandstone monitoring area, compare and analyze data changes, and calibrate intrinsic and boundary vibration parameters. It is particularly suitable for slopes in phased mining operations, accurately quantifying the cumulative impact of blasting on the rock mass, avoiding safety risks caused by changes in the attenuation characteristics of the rock mass, and improving the safety of blasting scheme design.

[0029] Furthermore, in step S50, based on data from multiple repeated blasting experiments, a predictive model is established to show how the rock mass attenuation parameters in key areas change with the number of blasts and the cumulative amount of explosive charge. The predictive model is then integrated into the vibration transmission model, and the boundary vibration parameters and intrinsic vibration parameters in the vibration transmission model are adjusted according to the amount of explosive charge and the location of the blasting point in the experimental blasting.

[0030] This invention integrates an attenuation parameter prediction model, enabling dynamic parameter adjustment and solving the problem that traditional models cannot adapt to the evolution of rock mass properties caused by long-term blasting. For example, if the initial parameters are still used after the cumulative amount of explosive charge increases, prediction deviations will occur. This invention can establish a model of attenuation parameters changing with the number of blasts and the cumulative amount of explosive charge based on multiple blasting data. In long-term mining slopes, the parameters are dynamically updated according to historical blasting records. It is especially suitable for large mines with high-frequency blasting, predicting changes in rock mass attenuation characteristics in advance, ensuring that vibration prediction always matches the real-time state of the rock mass, and breaking through the application limitations of traditional static models. Attached Figure Description

[0031] Figure 1 This is a flowchart of the method for calculating the peak vibration velocity of a slope based on blasting in Example 1. Detailed Implementation

[0032] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Example 1 like Figure 1 As shown, the method for calculating the peak vibration velocity of a slope based on blasting includes the following steps: S10: Based on the topographic mapping data and geological exploration data of the target slope, the target slope is divided into one or more monitoring areas with different geological and topographic features, and a geological spatial model reflecting the division of the monitoring areas is constructed; according to the differences in the geological and topographic features of each monitoring area, monitoring points and experimental blasting points are set in the geological spatial model, and the amount of explosive charge for the experimental blasting points is set.

[0033] This study employs RTK (Real-Time Kinematic) technology combined with UAV oblique photogrammetry to acquire 3D point cloud data of the target slope. In this embodiment, a default sampling density of 0.5 m / point is assumed. The point cloud data is processed using ArcGIS software to generate a 1m resolution Digital Elevation Model (DEM) and Digital Orthophoto Map (DOM). Based on the processed slope DEM, the elevation difference between each grid cell and its surrounding grid cells is calculated using ArcGIS's slope analysis tools, and the terrain slope is converted by combining this with horizontal distance. The elevation values ​​of each grid cell in the DEM are directly extracted and correlated by coordinates to form a slope elevation dataset, quickly obtaining comprehensive slope and elevation data. The survey area is delineated using the slope toe line as the boundary to ensure complete coverage of the slope area. When defining the surveying area, based on the actual location of the slope toe determined by geological exploration, mark the slope toe coordinates in the digital elevation model (DEM) and fit a closed slope toe line; extend outward by 5-10m from the closed slope toe line (this is an assumed value in this embodiment, and the specific value is set by the administrator according to the terrain error) to define a rectangular or polygonal surveying area, and combine the oblique photography field of view of the UAV to ensure coverage of the slope top, slope surface and the outer extension area of ​​the slope toe, without any complete slope being missed.

[0034] A combination of borehole exploration and ground-penetrating radar (GPR) detection was employed. The borehole spacing was set according to the complexity of the slope's topography and geology; in this embodiment, a default value of 20-50m was assumed. The borehole depth penetrated the weathered layer of the slope to the stable bedrock. Core samples were collected from the boreholes, and mechanical parameters such as compressive strength and elastic modulus of the rock mass were determined through laboratory tests. In this embodiment, the GPR detection frequency was assumed to be 100MHz by default, with a detection depth of 30m, identifying joint development, lithological stratification, and structural boundaries. The borehole data and radar detection results were integrated to form a geological profile and a planar geological map.

[0035] Geological and topographic features include the spatial distribution characteristics of rock mass lithology, joint development degree, weathering grade, topographic slope, and elevation. Different geological and topographic features refer to significant differences in the spatial distribution of rock mass lithology, joint development degree, weathering grade, topographic slope, and elevation (the difference threshold is set by industry standards, such as different lithology, joint density difference ≥2 joints / m, weathering grade difference of 1 grade or more, slope difference ≥10°, elevation difference ≥50m, etc.).

[0036] Based on the differences between the core sample identification results in geological exploration data and the ground-penetrating radar wave velocity, lithological zones (such as granite zone, sandstone zone, and shale zone) are divided in GIS, and each zone is assigned a unique lithological code.

[0037] Joint surfaces are identified using ground-penetrating radar data. The number of joints and the average spacing within a unit area (assumed to be 10m×10m in this embodiment) are statistically analyzed. The joints are classified into three levels: dense (assumed to be <1m), medium (assumed to be 1-3m), and sparse (assumed to be >3m), and quantified into a joint density value λ (unit: joints / m).

[0038] Based on the core color and compressive strength attenuation rate, the rock is classified into four levels: fully weathered, strongly weathered, moderately weathered, and slightly weathered, and quantified into a weathering coefficient f (assuming f is 0.25 for fully weathered, 0.5 for strongly weathered, 0.75 for moderately weathered, and 1.0 for slightly weathered).

[0039] Based on DEM data, the slope value θ (unit: degrees) of each raster is calculated using ArcGIS's slope analysis tool.

[0040] Directly extract the elevation value H (unit: m) of the DEM raster.

[0041] When constructing the geological spatial model, the topographic mapping data (DEM, DOM) and geological exploration data (lithological zoning, joint density, weathering coefficient) are first unified into a single coordinate system (using the UTM projection coordinate system), and outlier data (such as rasters with slopes exceeding the 0-90° range) are removed. In ArcGIS, topographic layers (including elevation and slope) and geological layers (including lithological codes, joint density λ, and weathering coefficient f) are created, assuming a uniform raster resolution of 1m for each layer. The topographic and geological layer data are imported into Surfer software to generate a three-dimensional geological spatial model. This model includes information such as slope surface topography, underground lithological distribution, joint development areas, and weathering layer thickness, and supports spatial querying and parameter extraction.

[0042] Within the monitoring area, based on the geological spatial model, quantitative features of the terrain dimension and the geological dimension are extracted respectively. The quantitative features of the terrain dimension are processed into a terrain complexity component, and the quantitative features of the geological dimension are processed into a geological complexity component. The terrain complexity component and the geological complexity component are weighted and fused to obtain the terrain-geological complexity.

[0043] Select terrain slope θ and elevation standard deviation Three indicators: topographic curvature C, elevation standard deviation, and topographic curvature C. The elevation value was obtained by calculating the standard deviation of the elevation values ​​within a 10m × 10m window (the default value assumed in this embodiment); the terrain curvature C was calculated using the second derivative of the DEM data, with the following formula: , Where x and y are planar coordinates.

[0044] Three indicators were selected: joint density λ, weathering coefficient f, and lithological difference coefficient k. The lithological difference coefficient k was calculated by statistically analyzing the proportion of different lithologies within a 10m × 10m window, using the following formula: , in The grid percentage of the nth lithology.

[0045] The min-max normalization method is used to transform each quantized feature into a value in the [0,1] interval. The formula is as follows: , Where x is the original feature value. , These are the minimum and maximum values ​​of the feature, respectively.

[0046] Terrain complexity components , Topographic and geological complexity components , The weights of the terrain complexity component and the terrain-geological complexity component are determined based on the administrator's scoring method.

[0047] Topographical and geological complexity (Topography and geology have equal weights, which can be adjusted according to the actual scenario).

[0048] In step S10, based on the similarity of geological and topographic features at different locations on the slope, the target slope is divided into monitoring areas with different geological and topographic features. Using the horizontal plane as a reference, the coverage area corresponding to the bottom of the slope in the monitoring area is obtained as the topographic coverage surface, and the shape of the topographic coverage surface is obtained as the topographic coverage shape of the slope. Combined with the geological and topographic features of the slope in the monitoring area, the geological and topographic change trend of the slope is obtained. The similarity of the topographic coverage shape, the distribution of geological and topographic features in the topographic coverage shape, and the geological and topographic change trend of the slope in two adjacent monitoring areas is calculated to provide a basis for selecting monitoring points in each monitoring area.

[0049] Using the horizontal plane as a reference, a horizontal section is extracted from the bottom of the slope (the plane where the toe line is located) in the geological space model. The area enclosed by the intersection of this horizontal section and the slope surface is the topographic cover. The coordinates of the intersection line are extracted ( , The polygonal expression of the terrain cover shape is obtained by using a polygon fitting algorithm (such as the least squares method), and quantified into shape factors (such as roundness and elongation). (S is the area of ​​the polygon, L is the perimeter), elongation ( The longest axis length (This is the shortest axis length).

[0050] Feature points (such as elevation extremes and lithological boundaries) within the terrain cover shape are selected, and the elevation variation rate of these feature points is analyzed using linear regression. (ΔH represents the change in elevation, and ΔL represents the change in horizontal distance), thus obtaining the trend of topographic change; the spatial gradient of joint density and weathering coefficient at statistical feature points is also obtained. , This allows us to obtain trends in geological changes.

[0051] The cosine similarity algorithm is used to calculate the comprehensive similarity between adjacent monitoring areas. The formula is: , Terrain cover shape similarity is calculated using the cosine similarity of the shape factors between two monitored areas. ,in, These are the circularity of the terrain cover shape in the two monitoring areas (reflecting the degree to which the shape is close to a circle). These represent the elongation rate of the terrain cover shape in the two monitoring areas (reflecting the degree of narrowness or elongation of the shape).

[0052] Geological feature distribution similarity is calculated based on the cosine similarity of the spatial distribution vectors of joint density and weathering coefficient. Similarity of change trends is calculated using the vector cosine similarity of topographic change rate and geological gradient. when When the preset similarity threshold set by the administrator is adjusted according to the actual situation of the target slope, it is determined to be a different monitoring area.

[0053] In step S10, the layout and number of experimental blasting points are optimized to ensure that the effective monitoring range of blasting vibration at each experimental blasting point can cover the target slope with minimal overlap in the shape of the covered areas. The effective monitoring range of blasting vibration refers to the range within which the monitoring instrument can stably and accurately collect blasting vibration data that meets the analysis requirements. Specifically, the peak vibration velocity at the monitoring point must be greater than or equal to the instrument's minimum measurable threshold (usually 0.1 cm / s-0.5 cm / s, with 0.1 cm / s as the default in this embodiment), and the signal-to-noise ratio must be greater than 20 dB (this is an assumed value in this embodiment to avoid environmental noise interference). Furthermore, the vibration attenuation within the monitoring range must conform to the empirical law of the Sadovsky formula (i.e., the power function relationship between vibration velocity and the distance from the blast center), without any abnormal fluctuations caused by geological abrupt changes.

[0054] Specifically, based on the Sadovsky formula, the effective monitoring range is defined as the radius centered on the experimental blast point. The circular area, in which The effective measurement threshold of the monitoring instrument is assumed to be 0.1 cm / s, and K and α are empirical parameters of the monitoring area (assuming the initial industry average K=200 and α=1.5).

[0055] Establish the optimization objective function: The constraints are ; The effective monitoring range of the i-th blasting point is... (Target slope area).

[0056] Using a genetic algorithm, assuming a population size of 50, 100 iterations, a crossover probability of 0.8, and a mutation probability of 0.1, the optimal placement and number of experimental blasting points were obtained through iteration.

[0057] In step S10, when determining the location and number of experimental blasting points, uniform grid points can be generated in the target slope area at a preset density as candidate points; the area of ​​the slope surface in the monitoring area is obtained from the topographic and geological model as the slope surface area, and the ratio of the topographic cover shape of the monitoring area to the slope surface area is used as the area weight; the topographic and geological complexity of the monitoring area is obtained from the geological spatial model, and the number of experimental blasting points in each monitoring area is allocated according to the area weight and topographic and geological complexity of the monitoring area, and the point closest to the center of the monitoring area is selected from the candidate points as the experimental blasting point according to the number of points.

[0058] Specifically, uniform grid points are generated in the target slope area at a preset density according to a specific size (assuming 5m×5m), and grid points outside the slope area are removed to obtain a set of candidate points.

[0059] slope surface area Obtained using a surface area calculation tool based on DEM data: , This represents the summation of n raster cells in a DEM; , It is the planar side length of a single grid cell (e.g., 1m × 1m). It is the elevation difference between this grid and its adjacent grids; The overall representation is the actual surface area of ​​the slope obtained by accumulating the slope surfaces of the grid.

[0060] Topographical Coverage Shape and Area Calculate using the polygon area formula: , in , This indicates that the polygon's vertices are closed (because the terrain cover shape is a closed polygon, the last vertex, i.e., the nth vertex, needs to be connected to the first vertex in order to fully calculate the area of ​​the polygon). , It represents the planar coordinates of the i-th vertex of the polygon covering the terrain.

[0061] Area weight: , and The ratio reflects the proportion of the topographically covered projected area to the actual slope surface area.

[0062] Distribution coefficient of blasting points in the monitoring area: , Let be the area weight of the i-th monitoring area. The weighting coefficients are 0.4 and 0.6, representing the complexity of the terrain and geology. Based on the administrator's experience, the emphasis is on the impact of terrain and geology complexity on data requirements.

[0063] The total number of blasting points N (set according to the project budget, e.g., N=10).

[0064] Number of blasting points in the i-th monitoring area: , It is the sum of the allocation coefficients of all k monitoring areas, and round is the rounding function.

[0065] For each monitoring area, calculate the Euclidean distance between the candidate point and the center of the monitoring area: , x, y, and H are the three-dimensional coordinates of the candidate point. , , It is the three-dimensional coordinate of the center of the monitoring area. The spatial straight-line distance from the point to the center of the monitoring area is obtained by taking the square root of the sum of the squares of the differences in the three-dimensional coordinates.

[0066] Based on the complexity of the terrain and geology Determine the number of candidate monitoring points. (The basic number is 5, and the number of points increases with higher complexity); Candidate monitoring points are evenly distributed within the area, and the geometric center coordinates of all candidate points are calculated: , The candidate point with the smallest Euclidean distance from the geometric center is selected as the center of the monitoring area.

[0067] S20: After detonating the experimental blasting point (the amount of explosive charge is set according to the S10 setting), collect the peak vibration velocity at each monitoring point as experimental monitoring data, and correlate it with the position of the monitoring point in the geological space model. That is, use a blasting vibration monitoring instrument (assuming the sampling frequency is set to 1000Hz and the range is 0.01-30cm / s) to collect the triaxial vibration velocity (x, y, z axes) at each monitoring point, and take the vector composite value of the triaxial velocity as the peak vibration velocity. The GPS module of the monitoring instrument records the real-time coordinates of the monitoring points and associates the coordinates with the corresponding peak vibration velocity.

[0068] S30: For the monitoring area with the experimental blasting point, based on the amount of explosive charge at the experimental blasting point, the distance between the experimental blasting point and the monitoring point, and the corresponding experimental monitoring data, the intrinsic vibration parameters characterizing the generation and attenuation of vibrations within the monitoring area are obtained by fitting the Sadovsky formula; for two adjacent monitoring areas, based on the transmission direction of the vibration wave after the blasting of the experimental blasting point, the experimental monitoring data collected at adjacent monitoring points in the two monitoring areas respectively, and the distance between the two monitoring points, the boundary vibration parameters characterizing the attenuation of the vibration wave when it crosses the boundary of the corresponding monitoring area are analyzed; taking the monitoring points in the monitoring area as nodes and the connection relationship between nodes in adjacent monitoring areas as directed edges, the intrinsic vibration parameters are assigned to the nodes, and the boundary vibration parameters are assigned to the corresponding edges to construct a vibration transmission model.

[0069] In step S30, based on the topographic and geological complexity of the monitoring area, one or more monitoring points are selected as candidate monitoring points within the monitoring area. The geometric center is determined based on the spatial distribution of all candidate monitoring points, and the candidate monitoring point closest to the geometric center is designated as the center of the monitoring area. For two adjacent monitoring areas, a pair of monitoring points located within each monitoring area and closest to each other are selected near the boundary line between the two areas as a boundary monitoring point pair. Based on the experimental monitoring data collected from the boundary monitoring point pair and the distance between the two monitoring points, the boundary vibration parameters are calculated using a vibration transmission attenuation model.

[0070] Using the Sadovsky formula The intrinsic vibration parameters are fitted, where V is the peak vibration velocity (cm / s), Q is the amount of detonating charge (g), R is the straight-line distance between the experimental blast point and the monitoring point (m), K is the site coefficient, and α is the attenuation coefficient. Based on the data table relating Q, R, and V, K and α are fitted using the least squares method, with the objective function being: The intrinsic vibration parameters (K, α) are obtained by solving the gradient descent method.

[0071] Near the boundary between two adjacent regions, select the pair of monitoring points that are closest to each other (A∈Region 1, B∈Region 2), with a spacing of: , The straight-line distance between points A and B. , The x-coordinates of points A and B in the plane; , The ordinates of points A and B in the plane; , Elevation values ​​of points A and B.

[0072] Based on the direction of vibration wave propagation (from A to B), a vibration transmission attenuation model is adopted: , in , The peak vibration velocities at points A and B are respectively. For boundary site coefficients, The boundary attenuation coefficient is used; by substituting the experimental monitoring data at points A and B, the boundary vibration parameters are obtained through fitting. ).

[0073] Using monitoring points in each monitoring area as nodes (with the area center as the core node), the lines connecting adjacent core nodes are directed edges (with the direction consistent with the direction of vibration wave propagation); the intrinsic vibration parameters (K, α) are assigned to the corresponding nodes, and the boundary vibration parameters (K, α) are assigned to the corresponding nodes. Assign corresponding directed edges and construct a graph-based vibration transmission model in MATLAB (Matrix Laboratory, a core tool used in constructing vibration transmission models). The vibration transmission model stores node coordinates, edge connections, and corresponding vibration parameters.

[0074] S40: Based on the positions of the target blasting point and the target monitoring point in the vibration transmission model, obtain the propagation path of the vibration wave from the target blasting point to the target monitoring point during blasting, as well as the intrinsic parameters of each node and the boundary vibration parameters of each side along the propagation path. Combined with the amount of explosive charge at the target blasting point, perform recursive calculations to obtain the peak slope vibration velocity at the target monitoring point.

[0075] Step S40 also includes the following steps: S41: Based on the spatial location of the target blasting point and the target monitoring point, determine a spatial baseline from the target blasting point to the target monitoring point.

[0076] Based on the target blasting point P ( , , ) and target monitoring point M ( , , Spatial coordinates, constructing spatial baseline vectors The baseline equation is: .

[0077] S42: In the vibration transmission model, determine the multiple nodes and directed edges that are closest to the spatial baseline, and generate a matching propagation path that is closest to the direction of the spatial baseline based on the topological connection relationship in the vibration transmission model.

[0078] Calculate the shortest distance (Euclidean distance from point to line, shortest distance from line segment to line) from each node and directed edge in the vibration transmission model to the spatial baseline; filter nodes and directed edges with distances less than a preset threshold (e.g., 5m), and find the shortest topological path from the node where the target blasting point is located to the node where the target monitoring point is located based on the Dijkstra algorithm. The shortest topological path is the matching propagation path that is closest to the direction of the spatial baseline.

[0079] Each monitoring area's center is designated as a node and encoded. Directed edges connect adjacent nodes, with edge weights equal to the 3D distance between nodes. An adjacency matrix records the edge weights (or infinity if no edge exists). Initialize the distances from the starting point to each node (0 for the starting point, infinity for the rest). Iteratively select the unvisited node with the smallest distance and update the distances to its adjacent nodes and predecessor nodes. The process terminates if the endpoint is visited. Traverse back from the endpoint to the starting point via predecessor nodes to obtain the node sequence, which is the shortest topological path. Verify the path's fit to the spatial baseline and output the path and total weight.

[0080] S43: Divide the matching propagation path into segments based on the different nodes and directed edges it passes through to obtain segmented paths, obtain the geometric length of each segmented path, and match the corresponding intrinsic vibration parameters or boundary vibration parameters for each segmented path.

[0081] The matching propagation path is divided into n segments based on the nodes and directed edges it passes through. If the i-th segment is an internal path of a node, the intrinsic vibration parameters of that node are matched. , The length is the geometric length of the segmented path. If it is a boundary path, match the boundary vibration parameters of the corresponding edge ( The length is the geometric distance between the two boundary nodes. .

[0082] First, extract the complete node sequence that matches the propagation path: ( The node where the target blast point is located. (The node where the target monitoring point is located) is split into nodes according to the topological relationship of "node-edge-node". Segmented path.

[0083] Paragraph 1: (Boundary path) (Internal path of the node) Paragraph 2: (Boundary path) (Internal path of the node) ... Segment i: ( ) ... Segment k: .

[0084] If the start and end points of the segmented path are the same node (e.g., only in... If the internal propagation path is a node's internal path, then the node's internal path is considered a segmented path type; if the starting and ending points of the segmented path are two adjacent different nodes (e.g., ... (For cross-regional paths), the boundary path will be treated as a segmented path type.

[0085] Directly retrieve this node Intrinsic vibration parameters (site coefficient) attenuation coefficient ); Calculate the geometric length: , This path is at node The starting point's three-dimensional coordinates within; This path is at node The three-dimensional coordinates of the endpoint within.

[0086] Retrieve the directed edge of this segment ( The corresponding boundary vibration parameters (boundary site coefficients) Boundary attenuation coefficient ); Calculate the geometric length: , : Starting node The center three-dimensional coordinates; End point The center three-dimensional coordinates.

[0087] The segment path types, matching parameters (intrinsic vibration parameters or boundary vibration parameters), and geometric lengths of all segmented paths are compiled into a data table to provide a basis for subsequent segmented recursive attenuation calculations.

[0088] S44: Based on the amount of explosive charge at the target blasting point, and according to the length of each segment on the matching propagation path and the matching vibration parameters, the segmented recursive attenuation calculation is performed to finally obtain the peak slope vibration velocity at the target monitoring point.

[0089] Initial vibration velocity of the node where the target blast point is located ,in , These are the intrinsic vibration parameters of the node. Let Q be the distance from the target blasting point to the center of the node, and let Q be the amount of explosive charge at the target blasting point.

[0090] For the i-th path segment (i from 1 to n): If it is an internal path of a node: ; If it is a boundary path: ; The final result This refers to the peak slope vibration velocity at the target monitoring point M.

[0091] In practice, the administrator first uses real-time dynamic positioning technology combined with UAV oblique photography to acquire 3D point cloud data of the target slope at a density of 0.5 meters per point. This data is then processed by geographic information system software to generate a 1-meter resolution digital elevation model and digital orthophoto, extracting the overall terrain slope and elevation data. Based on the actual location of the slope toe determined by geological exploration, coordinates are marked in the digital elevation model, and a closed slope toe line is fitted. The mapping range is then extended outward by 5-10 meters to ensure coverage of the slope top, slope surface, and the area extending beyond the slope toe.

[0092] Simultaneous drilling and ground-penetrating radar (GPR) exploration were conducted. The borehole spacing was set at 20-50 meters based on the complexity of the terrain and geology, penetrating the weathered layer to the stable bedrock. Core samples were collected to determine mechanical parameters. Assuming a 100 MHz GPR frequency was used, joint development, lithological stratification, and tectonic boundaries were identified, and the data was integrated to form geological maps. Lithological zones were delineated in the geographic information system, and indicators such as joint density and weathering coefficient were quantified. Topographic and geological layers were constructed, and a geological spatial model containing information such as slope topography and lithological distribution was generated using 3D modeling software.

[0093] The topographic and geological dimensional quantitative features are extracted, and the topographic and geological complexity is calculated by normalization and weighting. The cosine similarity algorithm (assuming it is less than 0.7) is used to divide the monitoring area and determine the topographic cover shape and geological topographic change trend.

[0094] There are two methods for setting up experimental blasting points: one is to reverse-engineer the effective monitoring range and optimize the layout using a genetic algorithm; the other is to generate 5m×5m candidate grid points, allocate the number of blasting points according to area weight and complexity, and select the point closest to the center of the area. The number of monitoring points is determined according to the complexity of the terrain and geology, and after deployment, monitoring point pairs between the area center and the boundary are selected.

[0095] After the experimental detonation point, a monitoring instrument with a sampling frequency of 1000 Hz was used to collect the triaxial vibration velocities at each monitoring point. The vector composite value was taken as the peak value and associated with the coordinates. The intrinsic vibration parameters of the region were fitted using empirical formulas, and the boundary vibration parameters were calculated based on the data from the boundary monitoring points. A vibration transmission model was then constructed in the matrix laboratory software.

[0096] Based on the coordinates of the target blasting point and the monitoring point, a spatial baseline is determined. A path optimization algorithm is used to generate a matching propagation path and segment it, distinguishing between paths within nodes and those at boundaries. The initial velocity is calculated using the initial vibration velocity formula, and then recursively calculated according to the segmented path type to finally obtain the peak slope vibration velocity at the target monitoring point.

[0097] Example 2 The only difference between this embodiment and Embodiment 1 is that, after constructing the vibration transmission model, supplementary blasting points are set up in the monitoring area where experimental blasting has been carried out; the supplementary blasting points are then detonated, and experimental monitoring data is collected as supplementary experimental data. The supplementary experimental data is used to verify and calibrate the intrinsic vibration parameters or boundary vibration parameters.

[0098] After collecting experimental monitoring data in step S20, the data for all monitoring points within the same monitoring area is summarized. The peak vibration velocity data at each monitoring point is denoted as... Calculate the arithmetic mean of this set of data. : , The average peak value of the regional vibration velocity (cm / s); For the first Peak vibration velocity at each monitoring point; This represents the number of monitoring points within the region.

[0099] Calculate the standard deviation of this set of data. : , The peak standard deviation of the vibration velocity is (cm / s).

[0100] Calculate the spatial variation coefficient of vibration data : , It is a dimensionless parameter that reflects the degree of dispersion of vibration data within the region.

[0101] Retrieve the topographic and geological complexity of the monitoring area calculated in Example 1. Set a preset complexity threshold (Assuming this embodiment) (0.6) and preset mutation threshold (Assuming this embodiment) (0.25). If satisfied. or If so, it is determined that additional monitoring points need to be set up in the area, and the additional points should be set up at key topographic turning points or places where geological features change abruptly.

[0102] In step S20, after the experimental blasting, the experimental monitoring data collected from each monitoring point within the same monitoring area are summarized, and the ratio of the standard deviation to the arithmetic mean of each experimental monitoring data is calculated to obtain the spatial variation coefficient of the vibration data in the monitoring area. Then, based on the geological spatial model, the topographic and geological complexity of any monitoring area is obtained. If the topographic and geological complexity exceeds a preset complexity threshold, or the estimated spatial variation coefficient of the vibration data exceeds a preset variation threshold, additional monitoring points are set up within the monitoring area at key topographic turning points or locations of abrupt geological changes.

[0103] Key topographic inflection points are obtained by analyzing digital elevation model (DEM) data from topographic mapping data, calculating topographic curvature, and extracting curvature extrema. First, the topographic curvature C of each raster in the DEM is calculated. Then, a 3×3 window mean filter is used to filter and denoise the curvature data. The formula is as follows: , After filtering The curvature value of the raster; This represents the curvature value of the neighboring raster before filtering.

[0104] Set curvature threshold (Assuming this embodiment takes) ),Will or The grid is determined to be the curvature extreme points, which are the key terrain turning points (such as ridge lines, valley lines, and steep slopes).

[0105] Geological abrupt changes are identified by analyzing geological exploration data, performing spatial interpolation, and calculating attribute gradients, or by extracting lithological boundaries and structural lines from geological exploration data. Joint density is selected. Weathering coefficient Two core geological attributes are used to generate a spatially continuous raster of attributes using the inverse distance weighted interpolation (IDW) method, with the following formula: , interpolation point Joint density; For the first Joint density at each exploration point; For the interpolation point to the th Distance between exploration points; For distance weighting coefficients (assuming that in this embodiment, we take...) (2) The number of neighboring exploration points involved in the interpolation.

[0106] Similarly, the weathering coefficient is generated. The interpolated raster. Calculate the magnitude of the attribute gradient: , Set gradient threshold (Assuming this embodiment takes) (0.5 strips / m·m) The area is where the joint density changes abruptly; similarly, the area where the weathering coefficient changes abruptly is determined.

[0107] Alternatively, lithological boundaries, fault lines, and boundaries of densely jointed zones can be directly extracted from geological profile maps and planar geological maps. Significant differences in geological properties exist on both sides of these boundaries, which are the points where geological characteristics abruptly change.

[0108] The supplementary blasting points must be located within the already identified area for additional monitoring points, prioritizing locations that simultaneously cover key topographical turning points and areas of abrupt geological changes. The distance between the supplementary blasting points and the original experimental blasting points should be no less than half the radius of the original effective monitoring range to avoid data overlap and interference. The amount of explosive used at the supplementary blasting points should be... The amount of explosive charge at the original experimental blasting point is kept consistent to ensure comparability of vibration intensity.

[0109] After detonating the supplementary blasting points, the same blasting vibration monitoring instrument as in Example 1 (assuming a sampling frequency of 1000Hz and a range of 0.01-30cm / s) was used to collect the peak vibration velocity data of the original monitoring points and the newly added monitoring points, which were recorded as the supplementary experimental dataset. ,in The total number of monitoring points (the sum of the original number and the newly added number) is used to associate the three-dimensional coordinates of each monitoring point.

[0110] For the intrinsic vibration parameters of a single monitoring area ( First, integrate the original experimental data with the supplementary experimental data to form a joint dataset. ( arrive , For combined data volume; This refers to the amount of detonating explosive; The distance between the centers of the explosion; (The peak value of the vibration velocity). The Sadovsky formula is refitted using the least squares method, with the objective function being: , The calibrated site coefficient; This is the calibrated attenuation coefficient.

[0111] Recalculate the parameter calibration correction: , like and If the original parameters do not need to be corrected, then the original parameters are determined to be correct; otherwise, the following applies. Replace the existing parameters.

[0112] For the boundary vibration parameters of two adjacent monitoring areas ( The data selected is the joint data of boundary monitoring point pairs (including newly added boundary monitoring points), including the original data. With supplementary data The boundary vibration transmission attenuation model is refitted, with the objective function being: , The combined data volume for the boundary monitoring point pairs; These are the calibrated boundary field coefficients; This is the calibrated boundary attenuation coefficient; For the first Peak vibration velocity of the boundary point pair; For the first The spacing between the boundary point pairs of the group.

[0113] Similarly, calculate the correction amount. , The original parameters are replaced based on a 5% deviation threshold.

[0114] By linking the spatial variation coefficient of vibration data with topographic and geological complexity, areas requiring supplementary monitoring can be accurately identified, avoiding the blind addition of monitoring points. The locations of additional monitoring points are spatially matched with key topographic inflection points and abrupt geological changes to obtain effective data from highly discrete areas. The supplementary experimental data is used to iteratively calibrate the original intrinsic or boundary vibration parameters, updating the node and edge attributes of the vibration transmission model.

[0115] In practice, after the vibration transmission model is constructed, the process proceeds based on supplementary blasting and parameter calibration optimization. First, the peak vibration velocity data from each monitoring point within the same monitoring area are collected, and the arithmetic mean and standard deviation are calculated to obtain the spatial variation coefficient of the vibration data. The topographic and geological complexity of the area is then retrieved and compared with a preset complexity threshold (assumed to be 0.6) and a preset variation threshold (assumed to be 0.25). If either threshold condition is met, additional monitoring points need to be added.

[0116] Analyze the digital elevation model data, calculate the topographic curvature of each grid, and then use a 3×3 window mean filter to remove noise. Extract extreme points according to a curvature threshold of ±0.05 as key topographic turning points. For geological exploration data, select joint density and weathering coefficient for inverse distance weighted interpolation (distance weight coefficient is 2), calculate attribute gradient magnitude, and determine abrupt changes according to a threshold of 0.5 lines / m·m, or directly extract lithological boundaries, fault lines, etc. as abrupt changes in geological features.

[0117] Supplementary blasting points are deployed in the area where additional monitoring points are established, prioritizing coverage of dual critical locations. The distance between these points and the existing blasting points should be no less than half the effective monitoring radius, and the amount of explosive charge should remain consistent. After detonation, a monitoring instrument with a sampling frequency of 1000Hz is used to collect peak vibration velocity data from both the existing and newly added monitoring points, forming a supplementary experimental dataset and correlating the coordinates.

[0118] When calibrating intrinsic vibration parameters, integrate existing and supplementary data, refit empirical formulas using the least squares method, calculate parameter corrections, and replace existing parameters if the deviation exceeds 5%. Boundary vibration parameter calibration requires selecting joint data including newly added boundary monitoring point pairs, refitting the transmission attenuation model, and determining whether to update based on the same deviation threshold.

[0119] During implementation, the spatial variation coefficient of vibration data and the complexity of terrain and geology are used to accurately delineate supplementary areas. New monitoring points are matched with key terrain and geological abrupt changes to obtain data in highly discrete areas. By supplementing the data, parameters are iteratively calibrated, and the nodes and edge attributes of the vibration transmission model are updated to improve the model accuracy.

[0120] Example 3 The only difference between this embodiment and embodiments 1-2 is that it also includes step S50: after constructing the vibration transmission model in step S30, at least one monitoring area that has undergone experimental blasting is selected, and secondary experimental blasting points are set up at the same location. The amount of explosives used in the secondary experimental blasting points is the same as that used in the previous experimental blasting at the same location, or is a preset ratio.

[0121] Examples 1-2 were selected based on high terrain and geological complexity ( The vibration data exhibits high dispersion. In the monitoring area, a second experimental blasting point is set up at the same spatial location as the original experimental blasting point to ensure that the blasting center position of the two blasts is completely consistent and to eliminate the influence of spatial deviation on the experimental results.

[0122] Set the amount of secondary detonating charge Compared with the amount of explosives used in the initial detonation The proportional relationship is a fixed proportional coefficient. The expression is: , This is the dosage ratio coefficient, with a value range of [value range missing]. (Assuming this embodiment selects) Two gradients, corresponding to equal-charge secondary blasting and increased-charge secondary blasting, respectively. The amount of explosive used in the initial experimental blasting (in grams); The amount of explosive used in the secondary experimental blasting (in grams).

[0123] In particular, when When the explosive charges are exactly the same in both blasts, the changes in attenuation characteristics before and after rock mass disturbance can be directly compared; when At this time, the influence of the dosage on the vibration intensity needs to be corrected by a proportionality coefficient.

[0124] After the secondary experimental blasting point is detonated, experimental monitoring data are collected from monitoring points within the monitoring area where the secondary experimental blasting point is located. The changes in experimental monitoring data obtained from the two experimental blasts at the same monitoring point are compared and analyzed, and the change in the attenuation characteristics of the rock mass caused by the disturbance of the first blast is calculated.

[0125] After detonating the secondary experimental blasting point, the same blasting vibration monitoring instrument as in Example 1 (sampling frequency 1000Hz, range 0.01-30cm / s) was used to collect the peak vibration velocity data of all monitoring points in the monitoring area, which was recorded as the secondary experimental dataset. ( , (Number of monitoring points). The dataset from the first experiment was also recorded. .

[0126] when At that time, based on the dosage correlation of the Sadovsky formula, the secondary vibration data were corrected to the condition of equal dosage. The correction formula is as follows: , The peak vibration velocity (in cm / s) of the second experiment after correction. The measured peak vibration velocity (unit: cm / s) is from the second experiment.

[0127] when hour, No calibration is required.

[0128] The change in attenuation characteristics is characterized by two dimensions: the ratio of vibration velocity after two corrections at the same monitoring point and the change in attenuation coefficient.

[0129] Calculate the first Vibration velocity change rate at each monitoring point This reflects the degree of change in the vibration response at that point after the rock mass is disturbed. , For dimensionless parameters, This indicates an increase in vibration intensity. This indicates a decrease in vibration intensity.

[0130] Fit the regional intrinsic attenuation coefficients of the first and second experiments respectively. (The fitting method is the same as in Example 1), calculate the change in attenuation coefficient. : , This represents the change in the attenuation coefficient. This indicates that the attenuation effect is enhanced after the rock mass is disturbed. This indicates that the attenuation effect has weakened.

[0131] Based on the calculated changes in attenuation characteristics, the intrinsic vibration parameters and associated boundary vibration parameters of the corresponding monitoring area nodes in the vibration transmission model are corrected and optimized.

[0132] Regarding the intrinsic parameters of the monitoring area where the secondary experiment was conducted ( ), combined with the change in attenuation coefficient The correction is performed using the following formula: , To correct and optimize the intrinsic site coefficient and attenuation coefficient.

[0133] The change rate of regional average vibration velocity is corrected to reflect the change in the vibration excitation capacity after rock mass disturbance; The change in the attenuation coefficient is directly corrected, reflecting the change in the damping characteristics of the rock mass.

[0134] Vibration parameters at the boundary between the monitoring area and adjacent areas ( (This requires a linkage correction based on the proportion of changes in intrinsic parameters, and the formula is:) .

[0135] To correct and optimize the boundary site coefficient and attenuation coefficient.

[0136] Boundary parameters are positively correlated with intrinsic parameters of adjacent regions, and the influence of rock mass disturbance is transmitted through the proportion of change in intrinsic parameters.

[0137] Set parameter correction threshold If the following conditions are met: If the original nodal intrinsic parameters and associated boundary parameters in the vibration transmission model are replaced with the corrected parameters, then the parameters are determined to have no significant change and the original parameters are retained.

[0138] In step S50, based on data from multiple repeated blasting experiments, a predictive model is established to show how the rock mass attenuation parameters in the key area change with the number of blasts and the cumulative amount of explosive charge. The predictive model is then integrated into the vibration transmission model, and the boundary vibration parameters and intrinsic vibration parameters in the vibration transmission model are adjusted according to the amount of explosive charge and the location of the blasting point in the experimental blasting.

[0139] Multiple (assuming 3 groups in this example) or more repeated blasting experiments with different cumulative detonation charges were selected, and the number of experiments was recorded. Total amount of explosives detonated And the corresponding regional attenuation coefficient after each experiment. .

[0140] A univariate nonlinear regression model is used to establish the attenuation coefficient. With cumulative detonation charge The association relationship can be expressed as follows: , The regression fitting coefficients are obtained by fitting multiple sets of experimental data using the least squares method. Total amount of explosive charge (kg); This is the predicted attenuation coefficient.

[0141] The fitting objective function is: , The number of repeated blasting experiments; For the first The measured attenuation coefficient after the experiment; For the first The cumulative amount of explosives detonated after each experiment.

[0142] In the vibration transmission model built in MATLAB, a new data input interface was added to associate the cumulative amount of explosive charge and the attenuation coefficient, and to associate the historical blasting data of each monitoring area.

[0143] After inputting the amount and location of the detonating charge in the experimental blast, the vibration transmission model automatically calls the prediction model to calculate the predicted attenuation coefficient for the corresponding area based on the current cumulative charge. Update the model parameters according to the following formula: , After each new blasting experiment, experimental data is added to the prediction model, regression coefficients are refitted, and the prediction model is iteratively optimized to ensure that the vibration transmission model parameters are dynamically adjusted according to the degree of rock mass disturbance.

[0144] In practice, a new step, S50, was added to optimize the vibration transmission model parameters. Monitoring areas with high topographical and geological complexity and large dispersion in vibration data were selected, and secondary experimental blasting points were set up at the same locations as the original experimental blasting points. The amount of explosive used in the secondary blasting was set according to a fixed proportional coefficient with the initial blasting, ranging from 0.5 to 2.0. Two gradients, 1.0 and 1.5, were selected to ensure consistent blast center positions and eliminate spatial deviations.

[0145] After the secondary detonation point is detonated, the peak vibration velocity of all monitoring points is collected using a monitoring instrument with a sampling frequency of 1000Hz to form a secondary experimental dataset, while the data from the first experiment is retrieved simultaneously. If the charge ratio coefficient is not 1.0, the secondary data is corrected to the equal charge condition according to the Sadovsky formula correlation; if the coefficient is 1.0, the original data is used directly.

[0146] The rate of change of vibration velocity at a single point is calculated to reflect the change in vibration response after rock mass disturbance. The regional intrinsic attenuation coefficients from the two experiments are fitted separately to obtain the change in attenuation coefficients. Combining these two indicators, the intrinsic vibration parameters of the monitoring area are corrected. The site coefficient is corrected according to the rate of change of the regional average vibration velocity, and the attenuation coefficient is directly adjusted according to the change. The vibration parameters of the associated boundary are corrected in conjunction with the changes in intrinsic parameters. A parameter correction threshold of 5% is set; if the deviation exceeds the threshold, the original parameters are replaced.

[0147] Three or more repeated blasting experiments were selected, and the number of experiments, cumulative explosive charge, and corresponding attenuation coefficients were recorded. A univariate nonlinear regression model was used to fit the correlation. A data input interface was added to the vibration transmission model constructed in MATLAB. After inputting the explosive charge and location of the blasts already performed, the model automatically called the prediction model to calculate the predicted attenuation coefficient and updated the intrinsic and boundary parameters. After each new blasting experiment, supplementary data was added, and the regression coefficients were refitted to achieve dynamic adjustment of model parameters according to the degree of rock mass disturbance.

[0148] Example 4 The only difference between this embodiment and embodiments 1-3 is that it also includes the following steps: S60: Based on the geological spatial model, the potential distribution range of fractures is initially predicted, and fracture-specific monitoring points are set up within the predicted range; the initial experimental blasting point is set and the initial detonation charge is determined. After detonation, the vibration velocity data of the fracture-specific monitoring points are collected. Combining the location of the initial experimental blasting point, the initial detonation charge, and the vibration velocity data, a fracture vibration model is constructed.

[0149] Specifically, based on geological exploration data in the geological spatial model, areas with high joint density, abrupt lithological changes, and abnormal geological characteristics are identified. Combined with topographic mapping data of extreme curvature points and elevation anomalies, the potential range of fractures is delineated. Within the predicted range, dedicated fracture monitoring points are deployed at specific intervals (pre-set according to geology; in this embodiment, it is set to 5 meters), prioritizing coverage of abrupt geological changes and topographic turning points, and the three-dimensional coordinates of each monitoring point are recorded simultaneously.

[0150] An initial experimental blasting point is set outside the potential fracture range. The amount of explosive charge is determined according to the complexity of the regional terrain and geology; for example, 500g is used if the complexity exceeds 0.6, and 800g is used otherwise. After detonation, vibration velocity data of each dedicated monitoring point is collected using a blasting vibration monitoring instrument to establish a correlation dataset between the initial explosive charge, blasting point location, monitoring point location, and vibration velocity.

[0151] A fracture vibration model was constructed using a multiple linear regression model, with the distance between the blasting point and the monitoring point, the amount of explosive charge, and the topographic and geological complexity of the monitoring point as input variables, and vibration velocity as the output variable. The fracture vibration model was trained using at least 30 sets of valid data (this is the default value in this embodiment; the specific value can be adjusted by the administrator based on geological survey results, such as based on topographic and geological complexity). The model coefficients were determined, ultimately enabling the mapping function of predicting the vibration velocity of the fracture monitoring point based on the input blasting parameters, laying the foundation for subsequent fracture location.

[0152] By predicting the potential fracture range through multi-dimensional features of a geological spatial model, this invention accurately locates the core monitoring area, eliminates invalid data interference from non-fracture areas, and specifically deploys fracture-specific monitoring points and scientifically sets the initial detonation charge according to the complexity of the terrain and geology. This avoids the risk of premature fracture amplification caused by blind deployment of monitoring points and improper initial charge, while efficiently collecting local vibration response data in the fracture area. The constructed fracture vibration model is specifically adapted to the vibration propagation law of the fracture area, laying a reliable foundation for subsequent accurate analysis and significantly improving the quantitative accuracy of the correlation between blasting parameters and fracture vibration.

[0153] S70: Based on the vibration transmission model, obtain the correlation between different detonation locations and the amount of explosive charge, add supplementary experimental blasting points, determine the amount of explosive charge for each supplementary experimental blasting point based on the fracture vibration model, detonate all supplementary experimental blasting points at the same time, collect multi-dimensional vibration data from fracture-specific monitoring points, analyze the correlation between vibration data differences, difference orientation and vibration intensity, and inversely deduce the precise location of the fracture in the geological space model.

[0154] Specifically, based on the intrinsic vibration parameters and boundary vibration parameters of each monitoring area in the vibration transmission model, the vibration influence intensity of different detonation locations on the fracture area is obtained. In the geological space model, supplementary experimental blasting points are added around the predicted range of potential fractures, according to the administrator's preset method. In this embodiment, the default is a ring distribution. That is, with the center of the predicted range as the center, multiple (3 in this embodiment) supplementary experimental blasting points are set on each of the ring lines with different radii (30m, 60m, and 90m in this embodiment) to ensure that blasting points are covered in all directions.

[0155] Based on the fracture vibration model constructed in step S60, and referencing the safety conditions set by the administrator, such as the predicted vibration velocity at the fracture center monitoring point not exceeding a safety threshold (assumed to be 0.5 cm / s in this example) when each supplementary blasting point is detonated individually, the amount of explosive charge at each supplementary experimental blasting point is determined. For example, if the distance between a supplementary blasting point and the fracture center monitoring point is 40m, and the monitoring point complexity is 0.7, assuming that the calculated explosive charge is 300g, the predicted vibration velocity is 0.45 cm / s, which meets the constraint requirements, thus the explosive charge at this supplementary point is determined to be 300g.

[0156] Simultaneously, all supplementary experimental detonation points were detonated, and multi-dimensional vibration data were collected from each fracture-specific monitoring point, including peak vibration velocity, vibration duration, and vibration waveform phase difference. The vibration data differences (vibration velocity difference from the initial experimental detonation point alone), the direction of difference (which supplementary detonation point contributed the most to the vibration), and the vibration intensity (peak vibration velocity magnitude) of each monitoring point were recorded.

[0157] The location of the cracks is obtained primarily through multi-dimensional vibration data, using a combination of K-means clustering and azimuth analysis to achieve precise localization. Specifically, the data input dimensions are first defined by selecting the vibration intensity (standardized peak vibration velocity), the azimuth angle of the difference in orientation (the orientation angle of the supplementary blasting point corresponding to the largest vibration difference), and the three-dimensional distance to each supplementary blasting point at each crack monitoring point. This forms a complete input dataset, ensuring that the data covers the three core elements of vibration response, spatial orientation, and distance relationship.

[0158] Next, the K-means clustering algorithm is used to set a specific number of clusters (2-3 in this embodiment). Vibration intensity is used as the core clustering index, and the cluster centers are iteratively optimized to divide the monitoring points into high-intensity, medium-intensity, and low-intensity clusters. The high-intensity cluster corresponds to the area most significantly affected by the crack, as its vibration propagation is more significantly affected by crack expansion and reflection, making it the core target for location. Then, focusing on the monitoring points of the high-intensity cluster, the azimuth distribution of their different orientations is statistically analyzed. By drawing an azimuth histogram, the concentrated azimuth interval with a proportion exceeding a preset proportion (60% in this embodiment) is identified. This interval is the dominant orientation with the highest vibration intensity, reflecting the main extension direction of the crack.

[0159] Finally, by combining the three-dimensional distances between the dominant azimuth area and each supplementary blasting point, and overlaying the vibration attenuation law in the vibration transmission model, the core range of abnormal vibration energy transmission was deduced by comparing the differences in vibration attenuation at different supplementary blasting points in the region. Combined with topographic and geological data from the geological spatial model, the distribution range of the fractures in the three-dimensional coordinate system was finally determined, clarifying their horizontal extension range along the x and y axes and their elevation span along the z axis. Simultaneously, key parameters such as the fracture length, initial width, and depth were quantified, achieving precise and scientific location of the fractures.

[0160] Supplementary experimental blasting points were deployed in a ring around potential fractures to achieve comprehensive vibration coverage. This effectively eliminated azimuth deviations and local geological anomalies caused by unidirectional blasting. Based on a fracture vibration model, the amount of explosives used at the supplementary blasting points was precisely controlled, ensuring a balance between data comparability and construction safety. By collecting multi-dimensional vibration data and combining K-means clustering and azimuth analysis to focus on the core area and dominant azimuth of the fracture, the vibration of the fracture and surrounding rock mass was accurately isolated. The vibration transmission model attenuation law was superimposed to form a closed-loop back-inference logic, achieving high-precision positioning of the fracture's three-dimensional coordinates (with errors controlled within a reasonable range). Furthermore, the reliability of the positioning results was improved through multi-data cross-validation.

[0161] S80: Based on the monitoring data of the supplementary experimental blasting, quantify the impact of simultaneous detonation on the amplification of fracture length, depth, and width; combine the position of the target blasting point in the geological space model, adjust the amount of explosive charge at the target blasting point, and calculate the predicted vibration data of the fracture during the blasting of the target blasting point.

[0162] Specifically, based on the monitoring data from the supplementary experimental blasting, the changes in vibration velocity at specific monitoring points of the fracture before and after synchronous detonation were compared. Combined with fracture morphology data detected by ground-penetrating radar (GPR detection was repeated after detonation), the amplification effect of the fracture was quantified, including length amplification, depth amplification, and width amplification. Length amplification was calculated by measuring the extension distance at both ends of the fracture detected by GPR, determining the increase in fracture length after synchronous detonation compared to the initial length (e.g., initial length 50m, after detonation 55m, an amplification of 5m). Depth amplification was calculated by measuring the vertical extension depth of the fracture detected by GPR (e.g., initial depth 30m, after detonation 33m, an amplification of 3m). Width amplification was calculated by measuring the width data on the fracture surface (average width at multiple monitoring points) (e.g., initial average width 1.5m, after detonation 2.0m, an amplification of 0.5m).

[0163] Based on the location of the target blasting point in the geological space model (distance from the core region of the fracture), the initial detonation charge at the target blasting point is adjusted. For example, assuming the distance between the target blasting point and the core region of the fracture is 45m, and supplementing the experiment where a blasting point at a distance of 40m had a detonation charge of 300g resulted in a fracture expansion of 0.5m, the detonation charge at the target blasting point is proportionally adjusted to 270g to initially reduce the impact on the fracture.

[0164] Substitute the adjusted target blasting point detonation charge (assumed to be 270g), the distance between the target blasting point and each crack monitoring point, and the complexity of the monitoring point into the crack vibration model constructed in step S60 to calculate the predicted vibration velocity of each monitoring point. For example, the predicted vibration velocity of a certain monitoring point is 0.38cm / s.

[0165] By combining supplementary blasting monitoring data with ground-penetrating radar detection results, the amplification of fracture length, depth, and width is precisely quantified, clarifying the degree of impact of blasting on fractures and avoiding the blind adjustment of explosive dosage based solely on experience. Based on the distance between the target blasting point and the core area of ​​the fracture, and the correlation between distance, explosive dosage, and fracture amplification, the target explosive dosage is adjusted in a targeted manner. This balances construction efficiency and safety risks, and by using a fracture vibration model to predict target blasting fracture vibration data in advance, it effectively eliminates the risk of vibration exceeding standards due to improper explosive dosage, ensuring that the target blasting vibration effect meets safety threshold requirements.

[0166] S90: Select vibration data from all directions that have the least impact on the fracture, and based on this data, set up maintenance detonation points and corresponding detonation charges. Detonate the maintenance detonation points simultaneously when detonating the target blasting point to offset or weaken the impact of the target blasting on the expansion of the fracture.

[0167] Analyze the predicted vibration data after adjusting the charge at the target blasting point, and screen for vibration data in each direction that has the least impact on the fracture. That is, find the region with the lowest vibration velocity in each direction and whose vibration waveform phase is opposite to that of the target blasting vibration. For example, in the northwest direction of the fracture, the predicted vibration velocity is 0.25 cm / s, and the phase is 180° different from that of the target blasting vibration. The vibration data in this direction is optimal.

[0168] A maintenance detonation point is added at the location corresponding to the optimal vibration data (e.g., northwest of the fracture, 35m from the core region of the fracture). The vibration velocity generated after the maintenance detonation point is detonated is equal in magnitude and opposite in phase to the vibration velocity of the target blast at that location. For example, if the predicted vibration velocity of the target blast at that location is 0.25cm / s, by reverse calculation using the fracture vibration model, setting the detonation charge at the maintenance detonation point to 150g can generate a reverse vibration of 0.25cm / s.

[0169] Simultaneously with the detonation of the target blasting point, all maintenance detonation points are also detonated. The reverse vibration generated by the maintenance detonation points will offset or weaken the vibration impact of the target blasting on the fracture, ultimately achieving a balance between the normal progress of the target blasting operation and minimizing the risk of fracture propagation. This avoids the slowdown in construction progress caused by simply reducing the amount of explosives used in the target blasting, and also reduces the risk of safety accidents caused by fracture propagation.

[0170] By selecting optimal azimuth vibration data and adding maintenance detonation points, the vibration energy of the target blast on the fractures is actively offset by reverse vibration. Compared with passive reduction of explosives, this approach minimizes the impact of fracture amplification while ensuring construction efficiency. The multi-azimuth maintenance detonation points form a collaborative protection network, preventing excessive fracture amplification caused by local vibration superposition. Furthermore, the parameters can be updated iteratively through model iteration based on dynamic changes in the rock mass, ensuring that the vibration offsetting effect always adapts to geological conditions. This effectively eliminates the risk of protection failure, ultimately achieving a balance between normal target blasting progress and controllable fracture risk, reducing the number of construction interruptions, and lowering the probability of safety accidents such as slope instability.

[0171] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for calculating peak vibration velocity of slopes based on blasting, characterized in that, Includes the following steps: S10: Based on the topographic mapping data and geological exploration data of the target slope, the target slope is divided into one or more monitoring areas with different geological and topographic features, and a geological spatial model reflecting the division of the monitoring areas is constructed; according to the differences in the geological and topographic features of each monitoring area, monitoring points and experimental blasting points are set in the geological spatial model, and the amount of explosive charge at the experimental blasting points is set. S20: After the experimental blasting point is detonated, the peak vibration velocity of each monitoring point is collected as experimental monitoring data and correlated with the position of the monitoring point in the geological space model. S30: For the monitoring area with the experimental blasting point, based on the amount of explosive charge at the experimental blasting point, the distance between the experimental blasting point and the monitoring point, and the corresponding experimental monitoring data, the intrinsic vibration parameters characterizing the generation and attenuation of vibrations within the monitoring area are obtained by fitting the Sadovsky formula; for two adjacent monitoring areas, based on the transmission direction of the vibration wave after the blasting of the experimental blasting point, the experimental monitoring data collected at adjacent monitoring points in the two monitoring areas respectively, and the distance between the two monitoring points, the boundary vibration parameters characterizing the attenuation of the vibration wave when it crosses the boundary of the corresponding monitoring area are analyzed; taking the monitoring points in the monitoring area as nodes and the connection relationship between nodes in adjacent monitoring areas as directed edges, the intrinsic vibration parameters are assigned to the nodes, and the boundary vibration parameters are assigned to the corresponding edges to construct a vibration transmission model. S40: Based on the positions of the target blasting point and the target monitoring point in the vibration transmission model, obtain the propagation path of the vibration wave from the target blasting point to the target monitoring point during blasting, as well as the intrinsic parameters of each node and the boundary vibration parameters of each side along the propagation path. Combined with the amount of explosive charge at the target blasting point, perform recursive calculations to obtain the peak slope vibration velocity at the target monitoring point.

2. The method for calculating peak vibration velocity of slopes based on blasting according to claim 1, characterized in that: The geological and topographic features include the spatial distribution characteristics of rock mass lithology, joint development degree, weathering grade, topographic slope, and elevation; In step S10, the target slope is divided into monitoring areas with different geological and topographic features based on the similarity of geological and topographic features at different locations on the slope. Using the horizontal plane as a reference, the coverage area corresponding to the bottom of the slope within the monitoring area is obtained as the topographic coverage surface. The shape of the topographic coverage surface is obtained as the topographic coverage shape of the slope. Combined with the geological and topographic features of the slope within the monitoring area, the geological and topographic change trend of the slope is obtained. The similarity of the topographic coverage shape, the distribution of geological and topographic features in the topographic coverage shape, and the geological and topographic change trend of the slope in two adjacent monitoring areas is calculated to provide a basis for selecting monitoring points in each monitoring area.

3. The method for calculating peak vibration velocity of slopes based on blasting according to claim 2, characterized in that: Within the monitoring area, based on the geological spatial model, quantitative features of the terrain dimension and the geological dimension are extracted respectively. The quantitative features of the terrain dimension are processed into a terrain complexity component, and the quantitative features of the geological dimension are processed into a geological complexity component. The terrain complexity component and the geological complexity component are weighted and fused to obtain the terrain-geological complexity. In step S30, one or more monitoring points are selected as candidate monitoring points within the monitoring area based on the topographic and geological complexity of the monitoring area. The geometric center is determined based on the spatial distribution of all candidate monitoring points, and the candidate monitoring point closest to the geometric center is designated as the center of the monitoring area. For two adjacent monitoring areas, near the boundary line between the two monitoring areas, a pair of monitoring points located within the two monitoring areas and closest in position are selected as the boundary monitoring point pair; based on the experimental monitoring data collected from the boundary monitoring point pair and the distance between the two monitoring points, the boundary vibration parameters are calculated using the vibration transmission attenuation model.

4. The method for calculating peak vibration velocity of slopes based on blasting according to any one of claims 1-3, characterized in that: In step S10, the layout location and number of experimental blasting points are optimized to ensure that the effective monitoring range of blasting vibration at each experimental blasting point can cover the target slope and that the overlapping area of ​​the covered ranges is minimized.

5. The method for calculating peak vibration velocity of slopes based on blasting according to any one of claims 1-3, characterized in that: After constructing the vibration transmission model, supplementary blasting points are set up in the monitoring area where experimental blasting has been carried out; the supplementary blasting points are then detonated, and experimental monitoring data are collected as supplementary experimental data. The supplementary experimental data is used to verify and calibrate the intrinsic vibration parameters or boundary vibration parameters.

6. The method for calculating peak vibration velocity of slopes based on blasting according to claim 1, characterized in that: In step S20, after the experimental blasting, the experimental monitoring data collected from each monitoring point in the same monitoring area are summarized, and the ratio of the standard deviation to the arithmetic mean of each experimental monitoring data is calculated to obtain the spatial variation coefficient of the vibration data in the monitoring area. Then, based on the geological spatial model, the topographic and geological complexity of any monitoring area is obtained. If the topographic and geological complexity exceeds the preset complexity threshold, or the estimated spatial variation coefficient of vibration data exceeds the preset variation threshold, then monitoring points are added at key topographic turning points or abrupt changes in geological features within the monitoring area. The key terrain turning points are obtained by analyzing the digital elevation model data of the terrain mapping data, calculating the terrain curvature, and extracting the curvature extreme points. The locations of abrupt changes in geological features are obtained by analyzing geological exploration data, performing spatial interpolation and calculating attribute gradients, or by extracting lithological boundaries and structural lines from geological exploration data.

7. The method for calculating peak vibration velocity of slopes based on blasting according to claim 4, characterized in that: In step S10, when determining the location and number of experimental blasting points, uniform grid points can be generated in the target slope area according to a preset density as candidate points; the area of ​​the slope surface in the monitoring area is obtained from the topographic geological model as the slope surface area, and the ratio of the topographic cover shape of the monitoring area to the slope surface area is used as the area weight. The topographic and geological complexity of the monitoring area is obtained from the geological spatial model. Based on the area weight and topographic and geological complexity of the monitoring area, the number of experimental blasting points in each monitoring area is allocated. Based on the number of points, the point closest to the center of the monitoring area is selected from the candidate points as the experimental blasting point.

8. The method for calculating peak vibration velocity of slopes based on blasting according to claim 1, characterized in that: Step S40 also includes the following steps: S41: Based on the spatial location of the target blasting point and the target monitoring point, determine a spatial baseline from the target blasting point to the target monitoring point; S42: In the vibration transmission model, determine the multiple nodes and directed edges that are closest to the spatial baseline, and generate a matching propagation path that is closest to the direction of the spatial baseline based on the topological connection relationship in the vibration transmission model. S43: Divide the matching propagation path into segments based on the different nodes and directed edges it passes through to obtain segmented paths, obtain the geometric length of each segmented path, and match the corresponding intrinsic vibration parameters or boundary vibration parameters for the segmented paths. S44: Based on the amount of explosive charge at the target blasting point, and according to the length of each segment on the matching propagation path and the matching vibration parameters, the segmented recursive attenuation calculation is performed to finally obtain the peak slope vibration velocity at the target monitoring point.

9. The method for calculating peak vibration velocity of slopes based on blasting according to claim 1, characterized in that: It also includes step S50: After constructing the vibration transmission model in step S30, select at least one monitoring area that has undergone experimental blasting, and set up secondary experimental blasting points at the same location. The amount of explosives used in the secondary experimental blasting points is the same as that used in the previous experimental blasting at the same location, or is a preset ratio. After the secondary experimental blasting point is detonated, experimental monitoring data are collected from monitoring points within the monitoring area where the secondary experimental blasting point is located. The changes in experimental monitoring data obtained from the two experimental blasts at the same monitoring point are compared and analyzed, and the change in the attenuation characteristics of the rock mass caused by the disturbance of the first blast is calculated. Based on the calculated changes in attenuation characteristics, the intrinsic vibration parameters and associated boundary vibration parameters of the corresponding monitoring area nodes in the vibration transmission model are corrected and optimized.

10. The method for calculating peak vibration velocity of slopes based on blasting according to claim 9, characterized in that: In step S50, based on data from multiple repeated blasting experiments, a predictive model is established to show how the rock mass attenuation parameters in the key area change with the number of blasts and the cumulative amount of explosive charge. The predictive model is then integrated into the vibration transmission model, and the boundary vibration parameters and intrinsic vibration parameters in the vibration transmission model are adjusted according to the amount of explosive charge and the location of the blasting point in the experimental blasting.

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