Multi-field coupling simulation method and system for blasting permeation-increasing in-situ leaching mining of sandstone uranium ore

Abstract

The invention provides a multi-field coupling simulation method and system for blasting permeation-increasing in-situ leaching mining of sandstone uranium ore. According to the method, blasting drilling space coordinates, charge density data and ore body in-situ permeability data of a deep sandstone uranium ore are obtained, charge density and permeability are superposed to a three-dimensional space grid, and a blasting energy permeation coupling map is generated. And based on a region with an energy gradient change rate exceeding a threshold value in the atlas, marking the region as a signal target acquisition region, acquiring an elastic wave signal, extracting position coordinates of a fracture event and a vibration energy value from the elastic wave signal, and generating multi-field coupling state data through field coupling association. And the position of a high-permeability channel formed after blasting is further identified, a leaching liquid flow control instruction is generated accordingly, and dynamic regulation and control of a leaching liquid flow path are achieved. According to the technical scheme provided by the invention, the efficiency and precision of multi-field coupling simulation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of multi-field coupling simulation, and in particular to a multi-field coupling simulation method and system for blasting-enhanced in-situ leaching mining of sandstone uranium mines. Background Art

[0002] In the mining of deep, tight sandstone uranium deposits, the synergistic effects of blasting permeability enhancement and leaching solution penetration require precise control through dynamic simulation. These ore bodies often suffer from weak permeability and unevenly distributed fractures, making it difficult to achieve effective diffusion of the leaching agent and efficient uranium recovery using traditional mining methods. In engineering practice, a simulation system is needed that can reflect the dynamic interaction of stress states, permeability characteristics, and chemical reactions in real time. This can dynamically optimize blasting parameter design and predict the evolution of ore body permeability characteristics, thereby improving resource recovery and reducing environmental risks.

[0003] To address these needs, an existing solution employs a hybrid physical and numerical simulation approach to dynamically reconstruct the blasting permeability enhancement process. This solution uses 3D printing technology to replicate the in-situ geological structure of the ore body. Combined with a laboratory-scale experimental setup, it integrates high-precision stress sensors, permeability monitoring probes, and a leaching solution composition analysis module. The system collects real-time blasting vibration responses and crack propagation morphology, and employs finite element numerical simulation algorithms to establish a dynamic feedback mechanism, enabling the combined simulation of the blasting permeability enhancement effect and the leaching process.

[0004] This existing approach has limitations in terms of dynamic response accuracy and data characterization. The scaling effect of the physical model causes deviations between the propagation characteristics of the blasting stress wave and the actual ore body, making it difficult to accurately reproduce the complex mechanical response of deep strata. The data fusion algorithm does not model the chemical reaction kinetics of the leachate deeply enough to effectively characterize the nonlinear relationship between increased permeability and changes in uranium solubility. Furthermore, the experimental device's real-time monitoring capabilities are limited by the density of sensor placement and sampling frequency, making it difficult to capture the dynamic evolution of the transient fracture network after the blast. Summary of the Invention

[0005] The present application provides a multi-field coupling simulation method and system for blasting-enhanced in-situ leaching of sandstone uranium mines, which are used to solve the problems of low efficiency and poor precision of multi-field coupling simulation in the prior art.

[0006] In a first aspect, the present application provides a multi-field coupling simulation method for sandstone uranium blasting and in-situ leaching mining, comprising:

[0007] Obtain blasting borehole spatial coordinates, charge density data, and in-situ permeability data for deep sandstone uranium deposits;

[0008] Superimposing the charge density data and the in-situ permeability data of the ore body onto a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map;

[0009] Marking the area where the energy gradient change rate in the blasting energy penetration coupling map exceeds a preset threshold as a signal target acquisition area, and synchronously acquiring elastic wave signals in the signal target acquisition area;

[0010] extracting the rupture event position coordinates and the vibration energy value from the elastic wave signal, performing field coupling correlation on the rupture event position coordinates and the vibration energy value to generate multi-field coupling state data;

[0011] The position coordinates of the high permeability channel generated after the blasting are identified according to the multi-field coupling state data, and based on the position coordinates, the leaching liquid flow control instructions of the multi-field coupling simulation are generated.

[0012] Optionally, performing field coupling association on the rupture event position coordinates and the vibration energy value to generate multi-field coupling state data includes:

[0013] Constructing a spatiotemporal distribution dataset containing the spatial density of the rupture event based on the position coordinates of the rupture event, and analyzing the attenuation law of the vibration energy value over time to generate a vibration energy attenuation feature set that characterizes the vibration energy attenuation rate and the vibration energy fluctuation amplitude;

[0014] Constructing a spatial correspondence matrix according to the spatial position mapping relationship between the spatiotemporal distribution data set and the vibration energy attenuation feature set;

[0015] Dynamically coupling the spatial density of the rupture events with the vibration energy attenuation rate through the spatial corresponding correlation matrix to obtain a penetration enhancement coefficient of the blasting energy field on the ore body penetration field;

[0016] The permeability enhancement coefficient is superimposed and correlated with the vibration energy fluctuation amplitude to generate multi-field coupling state data representing the interaction intensity between the blasting energy field and the ore body permeability field.

[0017] Optionally, dynamically coupling the spatial density of the rupture events with the vibration energy attenuation rate through the spatial corresponding correlation matrix to obtain a penetration enhancement coefficient of the blasting energy field on the ore body penetration field includes:

[0018] Calculating a spatial density distribution interval based on the spatial density of rupture events at each associated position point in the spatial corresponding association matrix, and simultaneously calculating an energy attenuation distribution interval of the vibration energy attenuation rate corresponding to the associated position point;

[0019] The spatial density distribution interval and the energy attenuation distribution interval are superimposed and combined, and the penetration influence parameters of each associated position point are calculated based on the superimposed and combined results. The penetration influence parameters are spatially integrated to generate the penetration enhancement coefficient of the blasting energy field on the ore body penetration field.

[0020] Optionally, marking an area in the blasting energy penetration coupling map where the energy gradient change rate exceeds a preset threshold as a signal target acquisition area includes:

[0021] Traversing each three-dimensional space grid in the blasting energy penetration coupling map, and calculating the energy gradient change rate between the current three-dimensional space grid and the adjacent three-dimensional space grid according to the spacing and energy intensity values ​​between the three-dimensional space grids;

[0022] The three-dimensional space grids whose energy gradient change rate exceeds a preset threshold are screened in the blasting energy penetration coupling map, and the screened three-dimensional space grids are marked as signal target acquisition areas.

[0023] Optionally, the charge density data and the in-situ permeability data of the ore body are superimposed on a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map, including:

[0024] Dividing the three-dimensional ore body space into three-dimensional spatial grids based on the blasting borehole spatial coordinates, and mapping the charge density data and the ore body in-situ permeability data into corresponding three-dimensional spatial grids to generate a grid attribute data set;

[0025] Calculating the energy intensity value of the three-dimensional space grid according to the charge density data in the grid attribute data set, and weightedly superimposing the energy intensity value and the in-situ permeability data of the ore body according to a preset weight ratio to generate an energy permeability fusion value set;

[0026] A blasting energy penetration coupling map is constructed based on the energy penetration fusion value set, and the blasting energy penetration coupling map includes a spatial coupling relationship between the blasting energy field and the ore body penetration field.

[0027] Optionally, calculating the energy intensity value of the three-dimensional space grid according to the charge density data in the grid attribute data set includes:

[0028] Mapping the charge density data in the grid attribute data set to corresponding coordinate positions of a three-dimensional space grid divided based on the blasting borehole spatial coordinates to obtain a position charge density distribution map;

[0029] The charge density value corresponding to each coordinate position in the position charge density distribution diagram is converted into an energy intensity value according to a preset energy conversion ratio.

[0030] Optionally, identifying the position coordinates of the high permeability channel generated after the blasting according to the multi-field coupling state data, and generating the leaching liquid flow control instructions for the multi-field coupling simulation based on the position coordinates, including:

[0031] Analyzing the permeability distribution characteristics of the multi-field coupling state data to generate a permeability distribution map containing permeability values, and defining the spatial regions corresponding to the permeability values ​​exceeding a preset threshold in the permeability distribution map as high permeability channel candidate regions;

[0032] Performing spatial connectivity analysis on the candidate high permeability channel area, screening spatial regions containing a through-going fracture network, and marking the screened spatial regions as the location coordinates of the high permeability channel;

[0033] Calculating a gradient flow control parameter of the leaching liquid in the high permeability channel according to the spatial distribution density of the position coordinates, and calculating a time control parameter of the leaching liquid in the low permeability region based on the permeability value of the low permeability region in the permeability distribution map;

[0034] The gradient flow control parameter is correspondingly associated with the duration control parameter to generate an immersion liquid flow control instruction for multi-field coupling simulation.

[0035] In a second aspect, the present application provides a multi-field coupling simulation system for sandstone uranium blasting and in-situ leaching mining, comprising:

[0036] Acquisition module, which obtains the spatial coordinates of blasting drill holes, charge density data and in-situ permeability data of ore bodies in deep sandstone uranium mines;

[0037] a superposition module, superimposing the charge density data and the in-situ permeability data of the ore body onto a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map;

[0038] a marking module for marking an area in the blasting energy penetration coupling map where the energy gradient change rate exceeds a preset threshold as a signal target acquisition area, and synchronously acquiring elastic wave signals in the signal target acquisition area;

[0039] a correlation module, extracting the rupture event position coordinates and the vibration energy value from the elastic wave signal, performing field coupling correlation on the rupture event position coordinates and the vibration energy value, and generating multi-field coupling state data;

[0040] A generation module identifies the position coordinates of the high permeability channel generated after the blasting according to the multi-field coupling state data, and generates a leaching liquid flow control instruction for the multi-field coupling simulation based on the position coordinates.

[0041] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-field coupling simulation method for blasting-enhanced in-situ leaching mining of sandstone uranium ore as described in the first aspect above.

[0042] In a fourth aspect, the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multi-field coupling simulation method for blasting-enhanced in-situ leaching mining of sandstone uranium ore as described in the first aspect.

[0043] In an embodiment of the present application, the spatial coordinates of the blasting borehole, charge density data and in-situ permeability data of the ore body of a deep sandstone uranium mine are obtained; the charge density data and the in-situ permeability data of the ore body are superimposed on the three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy penetration coupling map; the area in the blasting energy penetration coupling map where the energy gradient change rate exceeds a preset threshold is marked as a signal target acquisition area, and the elastic wave signal of the signal target acquisition area is synchronously collected; the position coordinates of the rupture event and the vibration energy value are extracted from the elastic wave signal, and the position coordinates of the rupture event are associated with the vibration energy value through field coupling to generate multi-field coupling state data; the position coordinates of the high permeability channel generated after the blasting are identified according to the multi-field coupling state data, and based on the position coordinates, the leaching liquid flow control instructions of the multi-field coupling simulation are generated.

[0044] The technical solution of this application has the following beneficial effects:

[0045] This application provides high-precision basic data support for subsequent three-dimensional modeling by accurately collecting the borehole spatial coordinates, charge density and in-situ permeability data of deep sandstone uranium mines, thereby ensuring the authenticity of the simulated geological parameters. The charge density and permeability data are superimposed on the three-dimensional grid to intuitively present the spatial coupling relationship between blasting energy and ore body permeability, providing a quantitative basis for the prediction of high permeability channels. Based on the energy gradient threshold, key areas are screened to reduce redundant signal interference and improve the targeting and data acquisition efficiency of elastic wave monitoring. Through the spatiotemporal correlation of the rupture event coordinates and vibration energy, a dynamic interaction model of the blasting energy field and the permeability field is established to reveal the physical mechanism of crack expansion and permeability enhancement. Based on the high permeability channel positioning results, the flow path of the leaching solution is optimized to improve the efficiency of in-situ leaching mining and reduce the amount of leaching agent and environmental risks.

[0046] Furthermore, by constructing a spatiotemporal distribution dataset of fracture events and a set of vibration energy attenuation characteristics, combined with a spatially corresponding correlation matrix, the dynamic coupling of fracture density and energy attenuation rate is achieved, and the vibration energy fluctuation amplitude is superimposed to generate multi-field coupling state data. The vibration energy attenuation law is analyzed to quantify the enhancement effect of blasting energy on the permeability field, and a spatial position mapping is established using the correlation matrix. Finally, the coupling strength between the blasting energy field and the permeability field is accurately characterized through the interaction between the permeability enhancement coefficient and the fluctuation amplitude. This significantly improves the accuracy and reliability of the multi-field coupling simulation. By dynamically quantifying the enhancement effect of blasting energy on the permeability of the ore body, an in-depth analysis of the formation mechanism of high-permeability channels is achieved. At the same time, based on the correlation analysis of vibration energy attenuation characteristics and spatial mapping, the prediction of blasting parameters and the simulation of permeability field evolution are optimized, providing a scientific basis for the precise control of in-situ leaching mining of deep sandstone uranium mines and the improvement of resource recovery rates.

[0047] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A flow chart of a multi-field coupling simulation method for blasting-enhanced in-situ leaching of sandstone uranium ore provided in this application is shown;

[0050] Figure 2 The present invention provides a schematic structural diagram of a multi-field coupling simulation system for blasting-enhanced in-situ leaching of sandstone uranium ore;

[0051] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0053] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0054] In the mining of deep, tight sandstone-type uranium deposits, traditional blasting permeability enhancement and leaching solution penetration coordinated control technology face multiple challenges. On the one hand, physical simulation experiments are difficult to reproduce the response of the actual formation due to the model scaling effect, resulting in significant differences between the blasting stress wave propagation characteristics and the actual ore body; on the other hand, the data integration method does not have enough depth to model the correlation between permeability evolution and uranium dissolution dynamics, and cannot reveal the complex nonlinear relationship between the two. In addition, due to the limitations of the spatial resolution and temporal response speed of the monitoring equipment, the dynamic evolution process of the transient fracture network is difficult to be fully captured, which limits the accurate analysis of the formation mechanism of high permeability channels. These technical bottlenecks lead to delayed optimization of blasting parameters and inaccurate prediction of leaching paths, which ultimately affect resource recovery efficiency and increase environmental risks.

[0055] In response to the above problems, this application proposes a method for blasting-enhanced in-situ leaching of sandstone uranium mines based on multi-field coupling simulation, which achieves precise control through three-dimensional spatial data integration and dynamic fracture network identification. The method first maps the charge density and permeability data to the borehole spatial coordinates, constructs an energy and permeability coupling map, and quantifies the transformation effect of blasting energy on the ore body; then, combined with targeted elastic wave signal acquisition and fracture event positioning, a correlation model between vibration energy attenuation characteristics and spatial fracture distribution is established to dynamically generate the spatial position of high permeability channels; finally, the flow path of the leaching solution is optimized based on multi-field coupling state data. This technology breaks through the scale limitations of the physical model, improves the simulation accuracy of fracture evolution through unscaled data superposition and precise positioning of transient signals, and uses the nonlinear correlation between vibration energy fluctuation characteristics and spatial mapping to achieve coordinated optimization of permeability enhancement effect and uranium dissolution efficiency, thereby significantly improving the resource recovery rate of deep ore bodies and reducing mining environmental risks.

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0057] Figure 1 The present invention provides a flowchart of a multi-field coupling simulation method for blasting-in-situ leaching of sandstone uranium ore, as shown in FIG. Figure 1 As shown, the method includes:

[0058] 101. Obtain the spatial coordinates of blasting boreholes, charge density data, and in-situ permeability data of ore bodies in deep sandstone uranium mines;

[0059] In this step, deep sandstone uranium deposits refer to exogenous epigenetic uranium deposits occurring in sandstone or conglomerate at greater depths underground.

[0060] The spatial coordinates of blasting boreholes refer to the specific position coordinates of blasting boreholes in deep sandstone uranium mines in three-dimensional space, which are used to locate the distribution range of blasting energy.

[0061] Charge density data refers to the spatial distribution data of explosive loading in the borehole, which represents the intensity of blasting energy release.

[0062] The in-situ permeability data of the ore body refers to the permeability parameter of the leachate in the undisturbed state of the ore body, reflecting the conductivity of the natural fracture network.

[0063] In this example, the spatial coordinates of blasting boreholes in deep sandstone uranium deposits, including their specific three-dimensional location information, are first acquired through a geological exploration system. Subsequently, based on the blasting design, the spatial distribution parameters of the explosive charge within each borehole are extracted to generate charge density data. Simultaneously, downhole permeability testing equipment is used to measure the permeability of the leachate in an undisturbed state using mercury intrusion or pulse tracer technology, thereby acquiring in-situ permeability data for the ore body.

[0064] During the actual mining of a deep sandstone uranium deposit, technicians used a geological exploration system to obtain the 3D coordinates of multiple blasting boreholes. Combined with the blasting design drawings, they determined the charge density distribution range for each borehole. Subsequently, they used a downhole permeability tester using the pulse tracer method to measure the orebody's in situ permeability. The results showed that the natural permeability ranged from low to high. After spatially aligning the borehole coordinates, charge density, and permeability data, technicians integrated all the data into a unified database, providing the foundation for subsequent 3D modeling.

[0065] 102. Superimposing the charge density data and the in-situ permeability data of the ore body onto a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map;

[0066] In this step, the three-dimensional spatial grid refers to a mathematical model that divides the ore body or geological structure into discrete three-dimensional units for numerical simulation and data analysis.

[0067] The blasting energy-permeability coupling map refers to the visualization result of superimposing charge density and permeability data on a three-dimensional grid, which represents the potential of blasting energy to transform the permeability of the ore body.

[0068] In the embodiment of the present application, first, a spatial interpolation algorithm is used to map the discrete spatial coordinates of the blasting boreholes to a three-dimensional spatial grid to construct a gridded data framework covering the entire ore body. Subsequently, the charge density data is fused with the in-situ permeability data of the ore body through a weighted superposition formula, where the charge density weight reflects the intensity of the blasting energy release and the permeability weight reflects the conductivity of the natural fracture. Finally, after calculating the coupling strength value of each grid cell, a blasting energy-permeability coupling map is generated in the form of a heat map. This map intuitively represents the spatial correspondence between the blasting energy concentration area and the potential high-permeability channel, providing a quantitative basis for subsequent energy gradient analysis.

[0069] Based on the spatial coordinates of the blasting boreholes, charge density data, and in-situ permeability data of the ore body, technicians used an interpolation algorithm to map the discrete borehole data onto three-dimensional spatial grid cells, constructing a gridded data framework covering the entire ore body. The coupling strength of each grid cell was calculated using a weighted superposition formula, and a blasting energy penetration coupling map was generated as a visualization of the heat map. The blasting energy penetration coupling map intuitively illustrates the spatial correspondence between areas of concentrated blasting energy release and potential high-permeability channels. For example, a clear coupling strength gradient is observed in areas with dense borehole clusters, providing a quantitative basis for subsequent energy gradient analysis.

[0070] 103. Marking an area in the blasting energy penetration coupling map where the energy gradient change rate exceeds a preset threshold as a signal target acquisition area, and synchronously acquiring elastic wave signals in the signal target acquisition area;

[0071] In this step, the energy gradient change rate refers to the ratio of the energy density difference of adjacent cells in the three-dimensional spatial grid to the distance, which reflects the spatial change rate of energy distribution.

[0072] The area of ​​the preset threshold refers to the critical range set according to the rate of change of the energy gradient, which is used to screen geological areas that need special attention.

[0073] The signal targeted acquisition area refers to the area where the energy gradient change rate in the coupling spectrum exceeds the threshold, which represents the sensitive area of ​​blasting energy release.

[0074] Elastic wave signals refer to seismic waves generated by rock mass vibration caused by blasting, which contain transient information of crack expansion.

[0075] In this embodiment, a spatial gradient algorithm is first used to calculate the energy gradient change rate of adjacent grid cells based on the blast energy penetration coupling map. Regions where the energy gradient change rate exceeds a preset threshold are selected as target signal acquisition areas. Subsequently, three-dimensional spatial mapping technology is used to convert these regions into specific geological spatial ranges and mark them as dynamic monitoring areas requiring special attention. Finally, a high-density three-component seismic detector array is deployed within the target signal acquisition area, and the sampling frequency is set to capture elastic wave signals.

[0076] Based on the blasting energy penetration coupling map, technicians calculated the energy gradient change rate of adjacent grid cells and screened out areas where the gradient change exceeded a preset threshold as the signal target acquisition range. In a specific area, technicians deployed multiple groups of three-component seismic detector arrays and set the sampling frequency to capture high-frequency elastic wave signals. When the blasting is carried out, the signal acquisition device is activated through the synchronous trigger system to record the propagation characteristics and spatial distribution state of the elastic wave in the time dimension. After preprocessing, the collected raw signals form a data set containing the temporal and spatial distribution characteristics of microseismic events, providing input conditions for the subsequent rupture event positioning.

[0077] 104. Extracting the rupture event position coordinates and the vibration energy value from the elastic wave signal, performing field coupling correlation on the rupture event position coordinates and the vibration energy value to generate multi-field coupling state data;

[0078] In this step, the rupture event location coordinates refer to the spatial coordinates of the crack extension starting point obtained by inversion of the elastic wave signal.

[0079] The vibration energy value refers to the energy parameter in the elastic wave signal that reflects the crack expansion intensity.

[0080] Coupling correlation refers to integrating the data relationships of different physical fields through mathematical models or algorithms and analyzing their interaction mechanisms.

[0081] Multi-field coupling state data refers to a quantitative data set that integrates the interaction between multiple physical fields such as stress field, penetration field, and energy field.

[0082] In the embodiments of the present application, first, the inversion algorithm is used to analyze the elastic wave signal, and the spatial coordinates of the crack propagation starting point, i.e., the position of the rupture event, are determined by the wave field inversion technology. The vibration energy value reflecting the crack propagation intensity in the signal is extracted by using the Hilbert-Huang transform, and the attenuation characteristic parameters are calculated. Then, a spatial corresponding correlation matrix containing the position coordinates of the rupture event and the vibration energy value is constructed, and the permeability enhancement coefficient representing the intensity of the permeability reconstruction of the ore body by the blasting energy is generated by the dynamic coupling model. Finally, the vibration energy fluctuation amplitude information is further superimposed to form the multi-field coupling state data integrating the interaction relationship of the stress field, the energy field and the permeability field.

[0083] The skilled person uses the inversion algorithm to analyze the elastic wave signal, determines the spatial coordinates of the crack propagation starting point by the wave field inversion technology, and extracts the vibration energy value reflecting the crack propagation intensity by the signal processing method. After further calculating the vibration energy attenuation characteristics, the spatial corresponding correlation matrix is constructed, the rupture event density is dynamically coupled with the vibration energy attenuation rate, and the parameter representing the intensity of the permeability reconstruction of the ore body by the blasting energy is generated. After superimposing the vibration energy fluctuation amplitude information, the multi-field coupling state data set integrating the interaction relationship of the stress field, the energy field and the permeability field is formed, which provides key input for high permeability channel identification.

[0084] 105. Identify the position coordinates of the high permeability channel generated after blasting according to the multi-field coupling state data, and generate the leaching liquid flow control instruction of the multi-field coupling simulation based on the position coordinates.

[0085] In this step, the position coordinates of the high permeability channel refer to the region coordinates in the multi-field coupling state data where the permeability enhancement coefficient is significantly higher than the threshold value.

[0086] The multi-field coupling simulation refers to a numerical simulation method considering the interaction of multiple physical fields such as stress, permeability, energy, chemical reaction, etc.

[0087] The leaching liquid flow control instruction refers to the leaching agent injection path and flow distribution scheme optimized based on the distribution of the high permeability channel.

[0088] In the embodiments of the present application, first, the region where the permeability enhancement coefficient is significantly higher than the reference value is selected from the multi-field coupling state data, and the spatial clustering algorithm is used to identify the position coordinates of the high permeability channel generated after blasting. Then, based on the position coordinates of the high permeability channel, the optimal flow path of the leaching liquid is constructed by using the path optimization algorithm, and the injection flow is planned combined with the permeability gradient. Finally, the leaching liquid flow control instruction containing the injection point coordinates, pressure gradient and flow parameters is generated by multi-dimensional modeling of pressure, flow and spatial position, and the diffusion path of the leaching agent is accurately controlled.

[0089] Based on a multi-field coupled state dataset, technicians identified areas where the permeability enhancement coefficient was significantly higher than the baseline value. A spatial clustering algorithm was then used to identify the coordinates of the high-permeability channels generated after blasting. Subsequently, a path optimization algorithm was used to construct the optimal flow path for the leaching solution, and the injection rate was planned based on the permeability gradient. Multidimensional modeling was used to generate leaching solution flow control instructions that included injection point coordinates, pressure gradients, and flow parameters. In practical applications, this solution significantly improved leaching solution diffusion efficiency, raised resource recovery rates to a high level, and reduced environmental risks.

[0090] In summary, steps 101 to 105, through multi-source data fusion and dynamic modeling, overcome the scale limitations of traditional physical simulations and achieve precise quantification of the blasting permeability enhancement process in deep sandstone uranium deposits. Multi-field collaborative optimization establishes a nonlinear model of permeability enhancement coefficient and leaching efficiency by correlating vibration energy attenuation characteristics with spatial mapping. Real-time control instructions generate leaching solution flow paths based on the identification of high-permeability channels, significantly improving resource recovery and reducing environmental risks. This technical system provides a full-process dynamic control solution for in-situ leaching of complex deep ore bodies.

[0091] To address the quantitative correlation between fracture evolution and permeability enhancement during blasting of deep sandstone uranium deposits, the solution constructs a spatiotemporal distribution dataset containing the spatial density of fracture events and a vibration energy attenuation feature set. This approach dynamically couples the fracture event density with the vibration energy attenuation rate using a spatially corresponding correlation matrix. This is combined with the vibration energy fluctuation amplitude to generate multi-field coupling state data, enabling a quantitative characterization of the interaction intensity between the blasting energy field and the ore body's permeability field. In some embodiments, the field coupling correlation between the fracture event location coordinates and the vibration energy values ​​described in step 104 to generate the multi-field coupling state data includes:

[0092] 201. Constructing a spatiotemporal distribution data set including the spatial density of the rupture event based on the position coordinates of the rupture event, and analyzing the attenuation law of the vibration energy value over time to generate a vibration energy attenuation feature set representing the vibration energy attenuation rate and the vibration energy fluctuation amplitude;

[0093] In step 201, the fracture event space density refers to the number distribution characteristics of the crack propagation events caused by blasting within a unit three-dimensional space grid. The space-time distribution dataset refers to a dataset containing the density distribution of the fracture events in three-dimensional space and its time sequence characteristics, which is used to analyze the spatial concentration and time dynamics of crack propagation. The attenuation law refers to the phenomenon that the vibration energy value gradually weakens over time during the propagation or evolution process. The vibration energy attenuation feature set refers to the vibration energy attenuation rate and fluctuation amplitude parameters extracted from the elastic wave signal, which reflect the crack propagation intensity and energy dissipation characteristics. The vibration energy attenuation rate refers to the rate at which the vibration energy in the elastic wave signal weakens over time, reflecting the speed of energy dissipation during crack propagation. The vibration energy fluctuation amplitude refers to the difference between the maximum and minimum values of the vibration energy in the elastic wave signal, representing the intensity of energy release during crack propagation.

[0094] In the embodiments of the present application, first, the space coordinates of the fracture initiation points of each blasting event position coordinates are analyzed based on the elastic wave signal inversion algorithm, and a space-time dataset containing time stamps and three-dimensional coordinates of the fracture events is constructed. Then, a space-time clustering algorithm is used to count the fracture event density in each grid cell, forming a space-time distribution dataset reflecting the concentrated distribution characteristics of cracks. At the same time, the time-frequency analysis method is used to extract the attenuation law of the vibration energy value over time, the vibration energy attenuation rate is calculated by the slope, and the vibration energy attenuation feature set is generated by combining the vibration energy fluctuation amplitude analysis.

[0095] 202. According to the spatial position mapping relationship of the space-time distribution dataset and the vibration energy attenuation feature set, a spatial corresponding correlation matrix is constructed;

[0096] In step 202, the spatial position mapping relationship refers to the spatial correspondence relationship between the fracture event density distribution and the energy attenuation parameters in the three-dimensional grid. The spatial corresponding correlation matrix refers to a two-dimensional matrix that correlates the above two groups of data in the spatial dimension through mathematical modeling, which is used to quantify the spatial coupling strength of crack density and energy attenuation characteristics.

[0097] In the embodiments of the present application, first, the space-time distribution dataset and the vibration energy attenuation feature set are aligned by grid cell, and the fracture event density value and the corresponding vibration energy attenuation rate and fluctuation amplitude value of each grid cell are extracted. Then, a mapping table containing spatial position indexes is constructed using a spatial interpolation algorithm, and finally, a spatial corresponding correlation matrix is generated through matrix operation.

[0098] 203. The fracture event space density and the vibration energy attenuation rate are dynamically coupled through the spatial corresponding correlation matrix to obtain a permeability enhancement coefficient of the blasting energy field to the ore body permeability field;

[0099] In step 203, the permeability enhancement coefficient is a quantitative parameter that characterizes the degree to which the blasting energy field alters the ore body's permeability field, reflecting the proportion of permeability increase caused by fracture expansion. The blasting energy field refers to the three-dimensional distribution of energy in the ore body under blasting, and is determined by charge density, blasting design, and rock mass response. The ore body permeability field refers to the spatial distribution of the ore body's natural or altered permeability of leachate, and is determined by fracture density, connectivity, and pore structure.

[0100] In this example, the spatial density of rupture events and the corresponding vibration energy decay rate for each grid cell are first extracted using a spatial correlation matrix. Subsequently, a weighted regression algorithm is used to establish a dynamic coupling relationship model between the two, with the spatial density of rupture events serving as the independent variable and the vibration energy decay rate as the dependent variable. By adjusting the weight coefficients using a time decay factor, the process of blasting energy altering the permeability of the ore body over time is simulated. Finally, the output of the regression model is normalized to generate a permeability enhancement coefficient that reflects the intensity of the blasting energy field's alteration of the ore body's permeability field.

[0101] 204. Superimpose and correlate the permeability enhancement coefficient and the vibration energy fluctuation amplitude to generate multi-field coupling state data representing the interaction strength between the blasting energy field and the ore body permeability field.

[0102] In step 204, superposition correlation involves integrating the permeability enhancement coefficient and the energy fluctuation amplitude parameter through a mathematical model to generate multi-field coupling state data representing the interaction strength between the blasting energy field and the ore body permeability field. The ore body permeability field refers to the spatial distribution characteristics of the natural or modified leaching liquid permeability of the ore body, which is determined by the fracture density, connectivity, and pore structure.

[0103] In the examples of this application, the permeability enhancement coefficient and the vibration energy fluctuation amplitude are first aligned according to the grid cells, and the interaction strength between the two is calculated using a nonlinear superposition model. Subsequently, the superposition weight is adjusted by introducing a spatial gradient correction factor, so that the permeability enhancement coefficient and the vibration energy fluctuation amplitude form a dynamic correlation in the spatial dimension. Finally, using three-dimensional grid cells as data carriers, the fracture density, energy dissipation characteristics, and permeability enhancement parameters are integrated to generate multi-field coupling state data representing the interaction strength between the blasting energy field and the ore body's permeability field.

[0104] Here's a specific example:

[0105] During the actual mining of a deep sandstone uranium mine, technicians used elastic wave signal inversion technology to obtain spatial positioning data for multiple fracture events and construct a spatiotemporal dataset containing the distribution characteristics of fracture density. By simultaneously analyzing the attenuation curve of the vibration energy over time, the shorter-term mesoscale characteristics of the energy attenuation were calculated, and the significant range of vibration amplitude fluctuations was identified. The fracture density distribution and the energy attenuation rate were dynamically coupled using a spatial correlation matrix to generate a permeability enhancement coefficient that characterizes the degree to which the blasting energy alters the permeability of the ore body. Further integration of the vibration energy fluctuation amplitude information yielded multi-field coupled state data that synthesized the interaction between the stress field, energy field, and permeability field, allowing for the precise identification of the distribution areas of high-permeability channels. Based on this data, the leaching solution injection path was planned, and the injection flow and pressure parameters were optimized, ultimately significantly improving the leaching solution diffusion efficiency, achieving a high level of uranium resource recovery, and effectively reducing the environmental impact of the mining process.

[0106] In summary, steps 201 to 204 break through the scale limitations of traditional physical simulations by constructing a quantitative correlation model between fracture evolution and energy dissipation, enabling precise control of the blasting permeability enhancement process in deep sandstone uranium mines. By integrating the spatiotemporal distribution characteristics of fracture expansion events with dynamic energy attenuation data, the intrinsic correlation mechanism between fracture network evolution and energy dissipation is revealed. Based on spatial mapping relationships and a dynamic coupling algorithm, the interaction between the blasting energy field and the ore body's permeability field is converted into a quantifiable permeability enhancement coefficient, providing a scientific basis for the permeability transformation intensity. Furthermore, combined with energy fluctuation amplitude information, multi-physics field interaction state data is generated to guide the optimized design of the leaching solution flow path. By integrating data from the entire process of fracture monitoring, energy analysis, and leaching control, a closed-loop control system from blasting permeability enhancement to resource recovery is achieved. This not only significantly improves leaching efficiency and resource recovery rate, but also effectively reduces the environmental impact of the mining process, providing a systematic solution for the efficient development of deep ore bodies under complex geological conditions.

[0107] To address the quantitative correlation between the energy field and the permeability field during blasting permeability enhancement in deep sandstone uranium mines, the solution is based on the distribution intervals of the fracture event density and the vibration energy attenuation rate at each associated location in the spatial corresponding correlation matrix. Through superposition and combination, the permeability influencing parameters are calculated and spatially integrated to generate a permeability enhancement coefficient for the blasting energy field on the ore body's permeability field, thereby enhancing the local characterization capability of the dynamic correlation between energy and permeability fields. In some embodiments, the method described in step 203 dynamically couples the spatial density of the fracture event with the vibration energy attenuation rate through the spatial corresponding correlation matrix to obtain the permeability enhancement coefficient for the ore body's permeability field, including:

[0108] 301. Calculate a spatial density distribution interval based on the spatial density of the rupture events at each associated position point in the spatially corresponding association matrix, and simultaneously calculate an energy attenuation distribution interval of the vibration energy attenuation rate corresponding to the associated position point;

[0109] In step 301, the spatial density distribution range refers to the statistical range of the spatial density of rupture events within each grid cell in the correlation matrix, such as high-density, medium-density, and low-density regions. An associated location point refers to a discrete point with a clear physical or logical association within a specific space or dataset. The energy decay distribution range refers to the statistical range of the vibration energy decay rate within the corresponding grid cell, such as the fast decay region, medium decay region, and slow decay region.

[0110] In this embodiment, the spatial density of rupture events at each associated location is extracted based on the spatial correlation matrix. A cluster analysis algorithm is then used to group the spatial density, dividing it into high-density, medium-density, and low-density intervals to form a spatial density distribution interval. Simultaneously, data on the vibration energy decay rate at the corresponding associated locations is extracted and, using a statistical quantile method, the vibration energy decay rate is divided into rapid decay, moderate decay, and slow decay intervals to generate an energy decay distribution interval.

[0111] 302. Superimpose and combine the spatial density distribution interval and the energy attenuation distribution interval, calculate the permeability influence parameters of each associated position point based on the superposition and combination result, spatially integrate each permeability influence parameter, and generate a permeability enhancement coefficient of the blasting energy field on the ore body permeability field.

[0112] In step 302, the permeability impact parameter is a quantitative indicator calculated by superimposing the spatial density distribution interval and the energy attenuation distribution interval, combined with a weight coefficient. It is used to characterize the local transformation intensity of the ore body permeability caused by blasting energy. The permeability enhancement coefficient is a global parameter generated by spatially integrating the permeability impact parameters of all grid cells. It reflects the overall enhancement effect of the blasting energy field on the ore body's permeability field.

[0113] In this example, the spatial density distribution interval and the energy attenuation distribution interval are first superimposed on a grid-by-grid basis, and a weighted regression model is used to calculate the permeability impact parameter for each associated location. The weight coefficient is set based on the physical correlation between fracture density and energy attenuation rate, and the parameter combination relationship is optimized using a nonlinear fitting algorithm. Subsequently, a spatial interpolation algorithm is used to integrate the permeability impact parameters of each grid cell into a three-dimensional grid framework, and a normalized process is used to generate a continuously distributed permeability enhancement coefficient.

[0114] Here's a specific example:

[0115] During the actual mining of a deep sandstone uranium mine, technicians used a spatial correlation matrix to identify a continuous region within a specific spatial range with dense cracks and a rapid vibration energy decay rate, such as a short energy dissipation time. By superimposing and analyzing the crack density characteristics and energy decay properties of this region, they calculated parameter values ​​significantly higher than conventional levels, reflecting the local enhancement of the ore body's permeability by blasting energy. Further integrating the parameters of all spatial units generated an overall permeability enhancement coefficient, indicating that the blasting energy field had a high degree of effect on the ore body's permeability field. Based on this result, the leaching solution injection strategy was optimized, significantly improving leaching efficiency and achieving a high level of uranium resource recovery.

[0116] In summary, steps 301 to 302 dynamically quantify the impact of blasting energy on ore permeability by constructing a spatial distribution relationship between fracture density and energy attenuation, combining it with weighted regression and spatial interpolation algorithms, and generate a global permeability enhancement coefficient. This method accurately identifies high-permeability channel regions, optimizes leachate flow paths, significantly improves leaching efficiency and uranium resource recovery, and simultaneously reduces environmental risks during blasting permeability enhancement. This method provides systematic technical support for the efficient mining of deep sandstone uranium deposits under complex geological conditions.

[0117] To address the problem of identifying the spatial gradient effects of the energy field and permeability field during blasting permeability enhancement in deep sandstone uranium deposits, the solution traverses the three-dimensional spatial grids in the blasting energy-permeability coupling map, calculates the energy gradient change rate between adjacent grids, and screens areas exceeding a threshold, marking them as signal-targeted acquisition areas, thereby accurately locating the spatial locations where the energy gradient changes dramatically. In some embodiments, step 103, marking areas in the blasting energy-permeability coupling map where the energy gradient change rate exceeds a preset threshold as signal-targeted acquisition areas, includes:

[0118] 401. Traverse each three-dimensional space grid in the blasting energy penetration coupling map, and calculate the energy gradient change rate between the current three-dimensional space grid and the adjacent three-dimensional space grid based on the spacing and energy intensity values ​​between the three-dimensional space grids;

[0119] In step 401, the three-dimensional spatial grid refers to the division of the ore body into regular or irregular cubic or hexahedral cells, used to store spatial parameter data. Spacing refers to the geometric distance between adjacent grid cells along a certain direction or spatial diagonal in the three-dimensional coordinate system. The energy intensity value refers to the physical quantity stored in the three-dimensional spatial grid cell, which is used to characterize the energy density or diffusion intensity of the blasting energy field at that location. The energy gradient change rate refers to the ratio of the difference in blasting energy intensity between adjacent grid cells to the distance, reflecting the diffusion rate and directionality of the energy field in space.

[0120] In this embodiment, all three-dimensional grids in the blast energy penetration coupling map are first traversed one by one, and the spacing and energy intensity values ​​between the current grid and its adjacent grids (e.g., vertically, horizontally, and vertically) are extracted. Subsequently, a difference algorithm is used to calculate the energy intensity difference between adjacent three-dimensional grids. This difference is then combined with the spacing and a gradient algorithm is used to generate the energy gradient change rate for the current grid.

[0121] 402. Filter the three-dimensional space grids whose energy gradient change rate exceeds a preset threshold in the blasting energy penetration coupling map, and mark the filtered three-dimensional space grids as signal target acquisition areas.

[0122] In step 402, the preset threshold refers to a critical value set based on historical data or engineering experience, used to distinguish between significant and common energy gradient changes. The signal target acquisition area is a high-gradient region formed by screening grid cells whose energy gradient change rate exceeds the preset threshold. This region represents potential leachate flow channels where the blast energy field and the permeation field interact strongly.

[0123] In this embodiment, a comparison algorithm is first used to traverse all grid cells based on the energy gradient change rate of the three-dimensional grid. The energy gradient change rate is extracted and compared with a preset threshold. This grid-by-grid comparison selects three-dimensional grids whose energy gradient change rate exceeds the preset threshold, forming a preliminary candidate region. Subsequently, a cluster analysis algorithm is used to determine the spatial proximity of the selected three-dimensional grids, merging adjacent high-gradient grids to eliminate interference from isolated points and generating a continuous signal target acquisition area.

[0124] Here's a specific example:

[0125] During actual mining operations at a deep sandstone uranium mine, technicians constructed a blast energy penetration coupling map encompassing a specific spatial range. A grid-by-grid analysis revealed that the rate of change of energy gradients in a certain continuous region was significantly higher than in surrounding areas, with gradient values ​​exceeding typical levels. This high-gradient region was further identified and designated as a targeted signal acquisition area. After optimizing the leaching solution injection strategy based on this region, leaching efficiency was significantly improved, achieving a high level of uranium resource recovery.

[0126] In summary, steps 401 to 402 dynamically quantify the spatial gradient characteristics of the blasting energy field, accurately locate the high permeability potential area combined with threshold screening technology, and finally realize the targeted optimization of the leaching path. Based on the difference algorithm, the energy gradient change rate of the three-dimensional space grid is calculated, and the area where the energy diffusion rate significantly increases is identified; by pre-setting the threshold, the high gradient grid is screened out, and the signal target collection area is delineated to provide accurate target points for the injection of leaching liquid; combined with the whole-process data closed-loop regulation, the energy field analysis is seamlessly connected to the permeability evaluation, not only improving the leaching efficiency and resource recovery rate, but also effectively reducing the environmental risk in the process of blasting and permeability enhancement, providing efficient and controllable technical support for the development of deep ore bodies under complex geological conditions.

[0127] To solve the problem of spatial coupling of energy field and permeability field in the process of blasting and permeability enhancement of deep sandstone uranium mine, the scheme divides the three-dimensional space grid based on the coordinates of the blasting drill hole, maps the charge density data and the in-situ permeability data of the ore body to the grid and weights the fusion to generate an energy-permeability fusion value set, and constructs a three-dimensional energy-permeability coupling graph containing the spatial coupling relationship of the blasting energy field and the permeability field. In some embodiments, the superposition of the charge density data and the in-situ permeability data of the ore body to the three-dimensional space grid corresponding to the spatial coordinates of the blasting drill hole in step 102 generates a blasting energy-permeability coupling graph, which includes:

[0128] 501. Divide the three-dimensional ore body space into a three-dimensional space grid based on the spatial coordinates of the blasting drill hole, and map the charge density data and the in-situ permeability data of the ore body into the corresponding three-dimensional space grid to generate a grid attribute data set;

[0129] In step 501, the three-dimensional space grid refers to a discrete structure obtained by dividing the ore body space, which is used to carry spatial attribute data. The charge density data refers to the ratio of explosive mass to drill hole volume in each drill hole, which reflects the distribution characteristics of blasting energy. The in-situ permeability data of the ore body refers to the natural permeability parameter of the ore body obtained by field test or numerical simulation. The grid attribute data set refers to a comprehensive data set generated by mapping the charge density and the in-situ permeability data of the ore body to the three-dimensional space grid, which contains the spatial coordinates, charge density value and permeability value of each grid element.

[0130] In the embodiments of the present application, first, based on the spatial coordinates of the blasting drill hole, the three-dimensional ore body space is divided into regular or irregular three-dimensional space grids using a spatial interpolation algorithm, ensuring that the grid boundary is aligned with the geological structure of the ore body. Then, the charge density data of each grid element is calculated using the drill hole charge amount and the drill hole volume, and the in-situ permeability data of the corresponding grid element is obtained through field permeability test data or numerical simulation results. Finally, the charge density data and the in-situ permeability data of the ore body are respectively assigned to the corresponding three-dimensional grid elements by a spatial mapping algorithm to generate a grid attribute data set.

[0131] 502. Calculate the energy intensity value of the three-dimensional space grid based on the charge density data in the grid attribute data set, and perform weighted superposition on the energy intensity value and the in-situ permeability data of the ore body according to a preset weight ratio to generate an energy permeability fusion value set;

[0132] In step 502, the energy intensity value represents the distribution intensity of the blasting energy within the spatial grid, calculated from charge density data. It reflects the concentration of the blasting energy. The preset weight ratio refers to a parameter set based on engineering experience or physical correlations, used to balance the contributions of energy intensity and permeability in the coupled analysis. The energy-permeability fusion value set is a comprehensive set of parameters generated by weighted superposition of energy intensity and permeability data, representing the interaction strength between blasting energy and ore body permeability.

[0133] In this embodiment, the energy intensity value for each three-dimensional grid is first calculated using a blasting energy propagation model based on a grid attribute dataset. The blasting energy propagation model inputs include data such as charge density, medium elastic modulus, and blasting distance. Subsequently, a weighted superposition algorithm is used to linearly combine the energy intensity values ​​with the in-situ permeability data of the ore body according to preset weight ratios to generate a set of fused energy-permeability values.

[0134] 503. Construct a blasting energy penetration coupling map based on the energy penetration fusion value set, wherein the blasting energy penetration coupling map includes a spatial coupling relationship between the blasting energy field and the ore body penetration field.

[0135] In step 503, the blasting energy penetration coupling map is a three-dimensional visualization model generated by integrating the energy penetration fusion value set, which intuitively displays the spatial correlation characteristics of the blasting energy field and the ore body penetration field. The spatial coupling relationship refers to the interaction pattern of the energy field and the penetration field in the spatial distribution, including the distribution patterns of energy-enhanced penetration areas, energy-inhibited penetration areas, and neutral areas.

[0136] In the embodiment of the present application, first, a three-dimensional space modeling algorithm is used to interpolate and grid the data based on the energy penetration fusion value set, and the discrete fusion value distribution is converted into a continuous three-dimensional space model. Subsequently, different fusion value intervals are mapped to the color gradient of the three-dimensional grid unit through color mapping technology to generate a visualization map, which intuitively reflects the spatial correlation characteristics of the blasting energy field and the ore body penetration field. On this basis, a spatial clustering analysis algorithm is used to identify continuous areas with fusion values ​​significantly higher than the surrounding areas, and high fusion value areas are extracted as key areas for blasting energy enhanced penetration. Finally, combining geological modeling with interactive visualization tools, the spatial coupling relationship between the blasting energy field and the ore body penetration field is presented in the form of a blasting energy penetration coupling map, which supports engineering personnel in quantitative evaluation and decision optimization of the ore body permeability transformation effect.

[0137] Here's a specific example:

[0138] During the actual mining of a deep sandstone uranium mine, technicians divided a specific spatial range into a three-dimensional grid based on the spatial coordinates of the blasting borehole. By mapping charge density and permeability data onto the grid, they calculated that the energy penetration fusion value of a certain continuous area was significantly higher than that of the surrounding area. Further construction of a blasting energy-penetration coupling map identified this area as a key region for enhanced penetration by blasting energy. After optimizing blasting parameters based on this analysis, the permeability of the ore body was significantly improved, and the leaching efficiency reached a high level.

[0139] In summary, steps 501 to 503 achieve dynamic correlation modeling of the blasting energy field and the orebody's permeability field by constructing a three-dimensional grid model and performing multi-parameter fusion analysis. The spatial grid is divided based on borehole coordinates and mapped to charge density and permeability data, unifying the spatial scale and data structure. A weighted overlay algorithm quantifies the interaction between energy intensity and permeability to generate a set of fused values. Three-dimensional modeling and visualization techniques are combined to generate a coupled map, precisely locating key areas for energy-enhanced permeability. This technical system not only improves the correlation accuracy between blasting parameters and permeability assessment, but also optimizes the leachate injection path through a closed-loop data process, significantly improving resource recovery efficiency and reducing environmental risks. This provides practical technical support for the efficient development of deep orebodies under complex geological conditions.

[0140] To address the spatial distribution discreteness issue in deep sandstone uranium blasting energy field modeling, the solution maps charge density data to three-dimensional grid coordinate positions to generate a distribution map. Based on a preset energy conversion ratio, the charge density is converted into energy intensity values, achieving a quantitative conversion of charge parameters to energy field intensity and providing basic data support for subsequent coupled analysis. In some embodiments, the calculation of the energy intensity value of the three-dimensional grid based on the charge density data in the grid attribute dataset in step 502 includes:

[0141] 601. Mapping the charge density data in the grid attribute data set to corresponding coordinate positions of a three-dimensional space grid divided based on the blasting borehole spatial coordinates to obtain a position charge density distribution map;

[0142] In step 601, charge density data refers to the ratio of explosive mass to volume in the borehole, reflecting the spatial distribution characteristics of the blasting energy. The spatial coordinates of the blasting borehole refer to a set of coordinate parameters that accurately locate the blasting borehole in three-dimensional space. These typically include the borehole mouth coordinates and the hole bottom coordinates, and are used to describe the geometric position and orientation of the borehole within the ore body or rock formation. A three-dimensional spatial grid is a regular cubic unit divided based on the borehole coordinates and is used to carry spatial attribute data. The positional charge density distribution map is a continuous spatial model generated by mapping the charge density data to the grid using spatial mapping technology.

[0143] In the present embodiment, a spatial interpolation algorithm is first used to regularize the ore body space into a grid, ensuring that the grid cells are aligned with the borehole coordinates. Subsequently, the charge density data from the grid attribute dataset is extracted, and the charge density data corresponding to each borehole is matched to the corresponding coordinate position of the three-dimensional spatial grid using spatial mapping technology. For grid cells that are not directly measured, an interpolation algorithm is used to supplement the charge density values ​​to eliminate data discreteness. Finally, the charge density values ​​of all grid cells are integrated into a continuously distributed position charge density distribution map.

[0144] 602. Convert the charge density value corresponding to each coordinate position in the position charge density distribution map into an energy intensity value according to a preset energy conversion ratio.

[0145] In step 602, the energy conversion ratio refers to a parameter set according to the blasting physics model, which is used to convert the charge density into an energy intensity value. The energy intensity value is a parameter generated by a mathematical calculation model that represents the actual intensity of the blasting energy in the medium.

[0146] In this embodiment, the charge density values ​​for each three-dimensional grid cell in the charge density distribution map are first extracted as input data. Subsequently, a mathematical calculation model performs a weighted calculation on the charge density values ​​for each grid cell based on a preset energy conversion ratio, taking into account parameters such as the elastic modulus of the medium, blasting distance, and energy attenuation characteristics, to generate a corresponding energy intensity value.

[0147] Here's a specific example:

[0148] During the actual mining of a deep sandstone uranium deposit, technicians created a three-dimensional grid within a specific spatial range based on the borehole coordinates. Using a spatial interpolation algorithm, they mapped the charge density data for each borehole onto the grid. Subsequently, they calculated the energy intensity of each grid cell based on the energy conversion ratio and discovered that the energy intensity of a certain continuous area was significantly higher than that of the surrounding area. Blasting parameters were optimized based on this high-energy area, enhancing the effect of crack expansion in the ore body and ultimately raising leaching efficiency to a high level.

[0149] In summary, steps 601 to 602, through spatial interpolation and an energy conversion model, convert the borehole charge density into a continuous energy intensity distribution map, addressing the data discretization issue in modeling the blasting energy field of deep ore bodies. Technicians first construct a three-dimensional grid based on the borehole coordinates and use an interpolation algorithm to generate a global charge density distribution. The energy intensity of each grid is calculated based on the medium's characteristic parameters, accurately locating high-energy areas. In practical applications, optimizing blasting parameters significantly improves crack expansion in the target area and enhances leaching efficiency. This technology achieves closed-loop optimization from charge data to energy field assessment, providing efficient decision-making support for ore body reconstruction under complex geological conditions.

[0150] To address the multi-field coupling control problem in optimizing the flow path of leachate in deep sandstone uranium deposits, the solution analyzes the permeability distribution characteristics in the multi-field coupling state data, screens the interconnected fracture network to define the location of high-permeability channels, and calculates the gradient flow and duration control parameters of the leachate based on the spatial distribution density of the permeability. This generates multi-field coupling simulation flow control instructions that take into account both high-permeability channels and low-permeability areas. In some embodiments, step 105 involves identifying the location coordinates of the high-permeability channels generated after blasting based on the multi-field coupling state data and generating the leachate flow control instructions based on the location coordinates for the multi-field coupling simulation, including:

[0151] 701. Analyze the permeability distribution characteristics of the multi-field coupling state data to generate a permeability distribution map containing permeability values, and define the spatial regions corresponding to the permeability values ​​exceeding a preset threshold in the permeability distribution map as high permeability channel candidate regions;

[0152] In step 701, the permeability distribution map is a three-dimensional visualization model generated by analyzing multi-field coupled state data, which intuitively displays the spatial distribution of the ore body's permeability. High-permeability channel candidate areas are areas with permeability values ​​exceeding a preset threshold, representing potential channels for preferential flow of leachate.

[0153] In this example, a data parsing algorithm is first used to extract the permeability values ​​of each grid cell in the ore body based on multi-field coupled state data. A continuous permeability distribution map is then generated using spatial interpolation techniques. Subsequently, a threshold screening technique is used to identify spatial regions in the permeability distribution map with permeability values ​​above a preset threshold as areas of high permeability potential.

[0154] 702. Perform spatial connectivity analysis on the candidate high permeability channel area to screen out spatial regions containing a through-going fracture network, and mark the screened out spatial regions as the location coordinates of the high permeability channel;

[0155] In step 702, spatial connectivity analysis involves evaluating the coherence of the fracture network within the candidate high-permeability channel region to identify the presence of interpenetrating fracture channels. A interpenetrating fracture network, defined as a collection of fractures forming a continuous flow path in three-dimensional space, is a key structure for efficient leachate infiltration. Position coordinates, a set of three-dimensional coordinates that mark the spatial location of the high-permeability channel, are used to guide subsequent leachate injection path design.

[0156] In the examples of this application, first, a spatial clustering algorithm is used to determine the proximity of grid cells within candidate areas of high permeability channels, identifying continuously distributed high permeability regions. Subsequently, a maximum flow algorithm is used to assess the connectivity of the fracture network and screen out spatial regions containing penetrating fractures. Finally, the positions of the screened spatial regions are converted into three-dimensional coordinates using a coordinate marking technique to form the location coordinates of the high permeability channels.

[0157] 703. Calculate a gradient flow control parameter of the leaching liquid in the high permeability channel based on the spatial distribution density of the position coordinates, and calculate a time control parameter of the leaching liquid in the low permeability region based on the permeability value of the low permeability region in the permeability distribution map;

[0158] In step 703, the leachate refers to a chemical solution composed of a leachant and water in a specific ratio, used to leach valuable metals from the ore body. The gradient flow control parameter refers to a flow adjustment coefficient calculated based on the permeability gradient within the high-permeability channel and is used to control the flow rate of the leachate in the high-permeability region. Low-permeability regions refer to areas of geological media with extremely low permeability, typically characterized by low permeability values. The duration control parameter refers to the leachate residence time parameter calculated based on the permeability values ​​of the low-permeability regions and is used to optimize the permeability efficiency of the low-permeability regions.

[0159] In the present embodiment, a spatial density analysis algorithm is first used to calculate the distribution density of the coordinates of the high-permeability channel, identifying dense areas within the high-permeability channel where the leachate preferentially flows. Subsequently, a permeability gradient algorithm is used, combined with the permeability values ​​of the high-permeability channel, to calculate the gradient flow control parameters for the leachate within the channel. Simultaneously, an inversion calculation is performed to determine the duration control parameters for the leachate remaining in the low-permeability region within the permeability distribution map.

[0160] 704. Correspondingly associate the gradient flow control parameter with the duration control parameter to generate an immersion liquid flow control instruction for multi-field coupling simulation.

[0161] In step 704, the leaching liquid flow control instruction of the multi-field coupling simulation refers to a dynamic control scheme generated by associating the gradient flow control parameter with the duration control parameter according to the spatial position, which is used to guide the real-time adjustment of the leaching liquid injection system.

[0162] In the embodiments of the present application, first, the gradient flow control parameters and the duration control parameters are matched according to the spatial position to construct a parameter mapping relationship. The gradient parameters are bound to the position coordinates of the high permeability channel through a spatial association algorithm to ensure the flow control accuracy of the leaching liquid in the high permeability area. Subsequently, the duration parameters are matched with the spatial coordinates of the low permeability area, and the residence time distribution is optimized through time series modeling technology. Finally, a control algorithm is used to convert the two types of parameters into specific leaching liquid injection flow and time instructions, generating leaching liquid flow control instructions for multi-field coupling simulation.

[0163] Here's a specific example:

[0164] During the actual mining of a deep sandstone uranium mine, technicians, through multi-field coupled state data analysis, discovered that a specific area had significantly higher permeability than surrounding areas, defining it as a candidate for a high-permeability channel. Further spatial connectivity assessment of the candidate area identified a continuous network of fractures within it, forming a continuous flow channel, and annotated its spatial location using three-dimensional coordinates. Subsequently, the gradient flow control parameter for the leachate flow was calculated based on the permeability characteristics of the channel. Simultaneously, the leachate residence time control parameter was derived based on the permeability of the low-permeability area. Finally, the two parameters were spatially correlated to generate dynamically adjusted leachate flow control instructions. By optimizing the injection strategy, the leachate diffused rapidly within the high-permeability channel, while the residence time in the low-permeability area was extended to improve infiltration efficiency. After practical application, the uranium resource recovery rate of the ore body was significantly improved, and the leaching cycle was significantly shortened, achieving both improved resource utilization efficiency and economic efficiency.

[0165] In summary, steps 701 to 704 achieve precise optimization of deep orebody leaching paths through multi-field coupled data analysis and spatial connectivity assessment. First, based on permeability distribution characteristics and threshold screening, areas of high permeability potential are located. Second, spatial clustering and maximum flow algorithms are combined to verify the connectivity of the fracture network and ensure the continuity of the flow channels. Finally, through a closed-loop process from data analysis to control instructions, the leaching solution injection strategy is dynamically adjusted, significantly improving leaching efficiency and reducing resource waste. This technical system provides a systematic solution for efficient leaching under complex geological conditions.

[0166] Figure 2 The present invention provides a schematic diagram of a multi-field coupling simulation system for blasting-in-situ leaching of sandstone uranium ore. Figure 2 As shown, the system includes:

[0167] Acquisition module 21, acquires the spatial coordinates of the blasting drill hole, charge density data and in-situ permeability data of the ore body in the deep sandstone uranium mine;

[0168] A superposition module 22 superimposes the charge density data and the in-situ permeability data of the ore body onto a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map;

[0169] a marking module 23 for marking an area in the blasting energy penetration coupling map where the energy gradient change rate exceeds a preset threshold as a signal target acquisition area, and synchronously acquiring elastic wave signals in the signal target acquisition area;

[0170] a correlation module 24 for extracting the rupture event position coordinates and the vibration energy value from the elastic wave signal, and performing field coupling correlation between the rupture event position coordinates and the vibration energy value to generate multi-field coupling state data;

[0171] The generation module 25 identifies the position coordinates of the high permeability channel generated after the blasting according to the multi-field coupling state data, and generates the leaching liquid flow control instructions of the multi-field coupling simulation based on the position coordinates.

[0172] Figure 2 The multi-field coupling simulation system for sandstone uranium blasting and in-situ leaching can be used to perform Figure 1 The implementation principles and technical effects of the multi-field coupled simulation method for blasting-enhanced in-situ leaching of sandstone uranium deposits described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the multi-field coupled simulation system for blasting-enhanced in-situ leaching of sandstone uranium deposits in the aforementioned embodiment has been described in detail in the corresponding embodiments of the method and will not be further elaborated here.

[0173] In one possible design, Figure 2 The multi-field coupling simulation system for blasting-in-situ leaching of sandstone uranium ore in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0174] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0175] The processing component 32 is used for the above Figure 1 The embodiment provides a multi-field coupling simulation method for blasting-enhanced in-situ leaching mining of sandstone uranium ore.

[0176] The processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0177] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0178] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0179] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0180] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0181] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0182] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a multi-field coupling simulation method for blasting-enhanced in-situ leaching mining of sandstone uranium ore.

[0183] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0184] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0185] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-field coupling simulation method for blasting-in-situ leaching of sandstone uranium ore, characterized in that: include: Obtain blasting borehole spatial coordinates, charge density data, and in-situ permeability data for deep sandstone uranium deposits; Superimposing the charge density data and the in-situ permeability data of the ore body onto a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map; Marking the area where the energy gradient change rate in the blasting energy penetration coupling map exceeds a preset threshold as a signal target acquisition area, and synchronously acquiring elastic wave signals in the signal target acquisition area; extracting the rupture event position coordinates and the vibration energy value from the elastic wave signal, performing field coupling correlation on the rupture event position coordinates and the vibration energy value to generate multi-field coupling state data; The position coordinates of the high permeability channel generated after the blasting are identified according to the multi-field coupling state data, and based on the position coordinates, the leaching liquid flow control instructions of the multi-field coupling simulation are generated.

2. The method according to claim 1, characterized in that Performing field coupling correlation on the rupture event position coordinates and the vibration energy value to generate multi-field coupling state data, including: Constructing a spatiotemporal distribution dataset containing the spatial density of the rupture events based on the location coordinates of the rupture events, and analyzing the attenuation law of the vibration energy value over time to generate a vibration energy attenuation feature set that characterizes the vibration energy attenuation rate and the vibration energy fluctuation amplitude; Constructing a spatial correspondence matrix according to the spatial position mapping relationship between the spatiotemporal distribution data set and the vibration energy attenuation feature set; Dynamically coupling the spatial density of the rupture events with the vibration energy attenuation rate through the spatial corresponding correlation matrix to obtain a penetration enhancement coefficient of the blasting energy field on the ore body penetration field; The permeability enhancement coefficient is superimposed and correlated with the vibration energy fluctuation amplitude to generate multi-field coupling state data representing the interaction intensity between the blasting energy field and the ore body permeability field.

3. The method according to claim 2, characterized in that The spatial density of the rupture events and the vibration energy attenuation rate are dynamically coupled through the spatial corresponding correlation matrix to obtain the penetration enhancement coefficient of the blasting energy field on the ore body penetration field, including: Calculating a spatial density distribution interval based on the spatial density of rupture events at each associated position point in the spatial corresponding association matrix, and simultaneously calculating an energy attenuation distribution interval of the vibration energy attenuation rate corresponding to the associated position point; The spatial density distribution interval and the energy attenuation distribution interval are superimposed and combined, and the penetration influence parameters of each associated position point are calculated based on the superimposed and combined results. The penetration influence parameters are spatially integrated to generate the penetration enhancement coefficient of the blasting energy field on the ore body penetration field.

4. The method according to claim 1, wherein Marking the area where the energy gradient change rate in the blasting energy penetration coupling map exceeds a preset threshold as a signal target acquisition area, including: Traversing each three-dimensional space grid in the blasting energy penetration coupling map, and calculating the energy gradient change rate between the current three-dimensional space grid and the adjacent three-dimensional space grid according to the spacing and energy intensity values ​​between the three-dimensional space grids; The three-dimensional space grids whose energy gradient change rate exceeds a preset threshold are screened in the blasting energy penetration coupling map, and the screened three-dimensional space grids are marked as signal target acquisition areas.

5. The method according to claim 1, wherein The charge density data and the in-situ permeability data of the ore body are superimposed on a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map, including: Dividing the three-dimensional ore body space into three-dimensional spatial grids based on the blasting borehole spatial coordinates, and mapping the charge density data and the ore body in-situ permeability data into corresponding three-dimensional spatial grids to generate a grid attribute data set; Calculating the energy intensity value of the three-dimensional space grid according to the charge density data in the grid attribute data set, and weightedly superimposing the energy intensity value and the in-situ permeability data of the ore body according to a preset weight ratio to generate an energy permeability fusion value set; A blasting energy penetration coupling map is constructed based on the energy penetration fusion value set, and the blasting energy penetration coupling map includes a spatial coupling relationship between the blasting energy field and the ore body penetration field.

6. The method according to claim 5, characterized in that Calculating the energy intensity value of the three-dimensional space grid according to the charge density data in the grid attribute data set includes: Mapping the charge density data in the grid attribute data set to corresponding coordinate positions of a three-dimensional space grid divided based on the blasting borehole spatial coordinates to obtain a position charge density distribution map; The charge density value corresponding to each coordinate position in the position charge density distribution diagram is converted into an energy intensity value according to a preset energy conversion ratio.

7. The method according to claim 1, characterized in that Identifying the position coordinates of the high permeability channel generated after the blasting according to the multi-field coupling state data, and generating a leaching liquid flow control instruction for the multi-field coupling simulation based on the position coordinates, including: Analyzing the permeability distribution characteristics of the multi-field coupling state data to generate a permeability distribution map containing permeability values, and defining the spatial regions corresponding to the permeability values ​​exceeding a preset threshold in the permeability distribution map as high permeability channel candidate regions; Performing spatial connectivity analysis on the candidate high permeability channel area, screening spatial regions containing a through-going fracture network, and marking the screened spatial regions as the location coordinates of the high permeability channel; Calculating a gradient flow control parameter of the leaching liquid in the high permeability channel according to the spatial distribution density of the position coordinates, and calculating a time control parameter of the leaching liquid in the low permeability region based on the permeability value of the low permeability region in the permeability distribution map; The gradient flow control parameter is correspondingly associated with the duration control parameter to generate an immersion liquid flow control instruction for multi-field coupling simulation.

8. A multi-field coupling simulation system for blasting-in-situ leaching of sandstone uranium ore, characterized by: include: Acquisition module, which obtains the spatial coordinates of blasting drill holes, charge density data and in-situ permeability data of ore bodies in deep sandstone uranium mines; a superposition module, superimposing the charge density data and the in-situ permeability data of the ore body onto a three-dimensional spatial grid corresponding to the spatial coordinates of the blasting borehole to generate a blasting energy-permeability coupling map; a marking module for marking an area in the blasting energy penetration coupling map where the energy gradient change rate exceeds a preset threshold as a signal target acquisition area, and synchronously acquiring elastic wave signals in the signal target acquisition area; a correlation module, extracting the rupture event position coordinates and the vibration energy value from the elastic wave signal, performing field coupling correlation on the rupture event position coordinates and the vibration energy value, and generating multi-field coupling state data; A generation module identifies the position coordinates of the high permeability channel generated after the blasting according to the multi-field coupling state data, and generates a leaching liquid flow control instruction for the multi-field coupling simulation based on the position coordinates.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-field coupling simulation method for blasting-enhanced in-situ leaching mining of sandstone uranium ore as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a multi-field coupling simulation method for blasting-enhanced in-situ leaching mining of sandstone uranium mines according to any one of claims 1 to 7 is implemented.

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