Ore-forming fluid main channel inference model based on particle filtering

Abstract

The invention discloses an ore-forming fluid main channel inference model based on particle filtering. The model comprises the following steps: collecting related data of a target ore deposit, establishing an inference database, and defining a state sequence for spatial discrete volume elements; carrying out statistics on known ore-containing body elements in all exploration information to serve as an initial particle swarm of particle filtering, and generating a space channel path of each particle; establishing a probability-velocity coupled ore-forming fluid ion state transfer model according to the particle space position and fluid dynamics; establishing a particle weight observation model based on a particle state transition likelihood function, and dynamically updating the fluid particle weight; spatial sampling is carried out on the particles through a resampling algorithm, weight particles are updated, judgment is carried out through a particle number threshold value, and a maximum posterior probability path is output. The model combines multivariate data constraint and a particle filter algorithm to dynamically estimate the main channel path of the metallogenic fluid in a three-dimensional geological space, and the problems that a traditional method is high in cost and a sampled sample is difficult to obtain are fundamentally solved.

Description

Technical Field

[0001] The present invention relates to an inference model of a main channel of an ore-forming fluid, in particular to an inference model of a main channel of an ore-forming fluid based on particle filtering, and belongs to the technical field of three-dimensional ore-forming research. Background Art

[0002] Ore-forming fluid pathways are key pathways for the migration of metal elements and are closely related to mineralization, enrichment, and sedimentation. They serve as a bridge between "geological processes" and "resource distribution." Inferring the main pathways of ore-forming fluids can reflect the direction of fluid migration in three-dimensional geological space, deepening our understanding of the spatiotemporal structure of mineralization systems. This is of great significance for mineral resource prediction, revealing mineralization dynamics, and subsequent exploration engineering design and disaster prevention. However, current traditional geological methods for revealing fluid pathways are costly and difficult to retrace ancient fluid pathways and further quantify the dynamic behavior of fluids. This results in a low success rate for subsequent mineral exploration and makes it difficult to achieve high-precision predictions of concealed ore bodies. Summary of the Invention

[0003] To address the challenges of the existing technology, the first objective of the present invention is to provide a particle filtering-based model for inferring the main pathways of ore-forming fluids. This model, combined with multivariate data constraints, employs a particle filtering algorithm to dynamically estimate the main pathways of ore-forming fluids in three-dimensional geological space. This fundamentally addresses the high cost and difficulty in obtaining samples associated with traditional methods. Furthermore, by maximally integrating multiple data constraints, the model achieves dynamic, low-cost simulation of the main pathways of ore-forming fluids in three-dimensional geological space.

[0004] In order to achieve the above technical objectives, the present invention provides a particle filtering-based inference model for the main channel of ore-forming fluids, the characteristic values of which are: Step S1: Collect relevant mineralization data of the target ore deposit, establish a database for inferring the main channels of ore-forming fluids, and define a state sequence for spatial discrete elements in a three-dimensional geological space; Step S2: Count all known ore-bearing volume elements in the exploration information in the three-dimensional geological space to use as the initial particle group for particle filtering, and generate the spatial channel path of each particle in the group; Step S3: establishing a probability-velocity coupled ion state transfer model of the ore-forming fluid based on the spatial position of the particles and the fluid dynamics in step S2; Step S4: establishing a particle weight observation model based on the particle state transition likelihood function, and dynamically updating the fluid particle weights according to the stress observation likelihood function; Step S5: perform spatial sampling of particles through the resampling algorithm, and copy high-weight particles and eliminate low-weight particles in the process of updating the fluid particle weights. Then, judge by the particle number threshold, stop the above particle weight update, and output the maximum a posteriori probability path.

[0005] The present invention addresses the technical problems in existing methods for determining the main channels of mineralizing fluids, such as tracer tests based on geological sampling samples and petrographic analysis, which are high cost, low precision, and difficulty in fully revealing the spatial distribution of ancient fluid channels. By adopting a particle filtering algorithm combined with multivariate data constraints, the present invention can not only significantly reduce the cost of inferring the main channels of mineralization and ancient fluid channels, but also significantly improve the speed and accuracy of the inference results.

[0006] As a preferred solution, the relevant mineralization data of the target deposit include mineralization distribution, drilling data, geological profiles, and plan views.

[0007] As a preferred solution, the process of defining the state sequence is: in the three-dimensional geological space, the main fluid channel is defined as a series of three-dimensional discrete unit coordinate point sequences, and the three-dimensional discrete unit coordinates of the sequence are expressed as: Formula 1: ; In formula 1, is the coordinate of the discrete unit in three-dimensional space, indicating the fluid at time location.

[0008] As a preferred solution, the process of generating the spatial channel path of each particle is as follows: the initial particle group contains N particles, and the flow channel path of the ore-forming fluid represented by each particle is: Formula 2: ; In formula 2, represents the initial flow path probability of the corresponding particle, Indicates the possible initial flow position of the corresponding particle generated based on prior knowledge, Characterizes the initial fluid flow position weight, which obeys a uniform distribution, that is, .

[0009] As a preferred solution, the process of establishing the probability-velocity coupled ion state transfer model of ore-forming fluids is as follows: Step S3-1: constructing a fluid particle group state transition model based on structural morphological factors and permeability gradient direction factors related to fluid migration; Step S3-2: The spatial position of each particle in the initial particle group in step S2 is regarded as the end point of each channel path, and the current fluid migration position of the initial particle group is measured according to the above fluid particle group state transition model. The fluid migration position at the last moment , you will get it.

[0010] For each of the above particles , there is a three-dimensional geological space fluid migration channel path. Since the fluid migrates from the deep to the shallow part of the geological space, the initial particle group can be regarded as the end point of each channel path. Therefore, the fluid migration path position of the current initial particle group at the previous moment can be predicted by the fluid dynamics model.

[0011] As a preferred solution, the expression of the fluid particle swarm state transition model is: Formula 3: ; The current fluid migration position Compared with the fluid migration position at the previous moment The relationship is: Formula 4: ; In Equation 3 and Equation 4, Indicates the direction of permeability gradient. Its physical meaning indicates that the direction of fluid migration is always from low permeability area to high permeability area. Indicates the factors affecting fluid migration related to structural morphology, which obeys the probability distribution; represents a random disturbance term, which obeys Gaussian distribution; 、 and Respectively represent the weight coefficients of the corresponding constraints; It represents the time interval for the fluid to transfer from the position at the previous moment to the position at the next moment; Represents the noise impact during fluid migration.

[0012] In the present invention, factors affecting fluid migration related to structural morphology include structural surface inclination, undulation, and other characteristics. The physical meaning of this constraint indicates that the fluid migration velocity is closely related to structural morphology. For example, the fluid migration velocity is low in areas with small structural surface inclinations, and high in areas with large inclinations. In addition, the undulation of the structural surface may also affect the fluid flow velocity. 、 and It can be determined according to the geological conditions of the target study area and can be a constant input by the user.

[0013] As a preferred solution, the process of constructing the particle state transfer likelihood function is as follows: combining the tectonic stress characteristic data as the direct observation data of the fluid migration position, constructing the likelihood function , whose expression is: Formula 5: ; In formula 5, Represents the current state observation data, Characterizes the stress value of the corresponding element, is the maximum value of regional stress, is the variance of the likelihood function distribution, Represents a probability normalization constant.

[0014] During the geological evolution process, structures are subjected to stress and produce cracks in the three-dimensional geological space, which directly provide favorable space for fluid migration and affect the spatial location of fluid migration. Therefore, when constructing the particle weight observation model, it is necessary to combine the structural stress characteristic data as the direct observation data of the fluid migration location.

[0015] As a preferred solution, the expression of the particle weight observation model is: Formula 6: ; In formula 6, Represents the particle weight observation value at the corresponding position.

[0016] Since the above-mentioned calculated particle swarm includes all ore-bearing elements in the three-dimensional geological space, the fluid channel paths represented by them may cover the entire three-dimensional geological space, making it difficult to observe the distribution pattern of the main channel position of the fluid. Therefore, it is necessary to resample the particle swarm and discard particles with lower influence weights in order to observe the overall characteristic trend of fluid migration in space.

[0017] As a preferred solution, before spatially sampling the particles, the number of valid particles needs to be calculated. The process is as follows: Formula 7: ; In formula 7, is the number of particles in the initial particle group, that is, the number of all ore-bearing elements in three-dimensional space.

[0018] As a preferred solution, the process of updating the fluid particle weight is as follows: after retaining the high-weight particles, the above fluid path inference process needs to be repeated to infer the next particle path, and the weight is reset to: Formula 8: ; The judgment process of high-weight particles and low-weight particles is as follows: set the threshold ,when When , high-weight particles are retained according to the weight distribution rule.

[0019] above It is a threshold set by the user. When the value is large, the obtained fluid main channel distribution will highlight the local path characteristics. When the value is set small, the output main channel highlights the overall characteristics of the main channel of the ore-forming fluid.

[0020] As a preferred solution, the output process of the maximum a posteriori probability path is: select the particle with the largest weight for output, and connect the high-weight particles in the order of state transition inference to form the main channel path for the flow of mineralizing fluid in three-dimensional geological space.

[0021] Compared with the prior art, the beneficial technical effects of the technical solution of the present invention are: 1) The model for inferring the main pathways of ore-forming fluids provided by the present invention combines multivariate data constraints, such as structural morphology, permeability gradient direction, and stress data, with a particle filtering algorithm to infer the main pathways of fluid migration in three-dimensional geological space. This method avoids the high cost of inference based on traditional methods using tracers and petrographic fluid pathway analysis based on geological sampling, while also addressing the limitation of traditional methods in tracing ancient fluid pathways.

[0022] 2) The technical solution provided by the present invention, based on the fusion of multivariate data constraints in the model, achieves dynamic, low-cost, high-precision inference of the main channels of mineralizing fluids in three-dimensional geological space, especially accurate prediction of the spatiotemporal structure of the mineralization system, providing a reliable model basis for subsequent exploration projects and mineral resource prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The spatial distribution characteristics of the fluid main channel of the inference model provided in Example 1 of the present invention; Figure 2 Statistical characteristics of the main fluid channel of the inference model provided in Example 1 of the present invention; in, Figure 2 (a) is the relationship between the volume element through which the main fluid channel passes and the normal direction of its corresponding fracture surface model. Figure 2 (b) is the cumulative frequency relationship diagram between the volume element through which the main fluid channel passes and the normal direction of its corresponding fracture surface model. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is further described in detail below with reference to specific embodiments and accompanying drawings. To facilitate understanding of the present invention, the present invention will be described in more comprehensive and detailed form below with reference to the accompanying drawings and preferred embodiments. It should be noted that the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Example 1

[0026] This embodiment provides a particle filtering-based inference model for the main channel of ore-forming fluids, and the modeling process is as follows: Step S1: collect and organize relevant mineralization data of the target ore deposit, such as mineralization distribution, drilling data, geological profiles, plan views, etc., to form a database for inferring the main channels of ore-forming fluids, and implement state space modeling in three-dimensional geological space to define state sequences for spatial discrete elements; Collect and organize relevant mineralization data of the target deposit, such as mineralization distribution, drilling data, geological profiles, and plan views, to form a database for inferring the main channels of ore-forming fluids. In addition, clarify the coordinates of the three-dimensional geological space discrete units and establish the fluid flow state space. That is, in the three-dimensional geological space, define the main fluid channel as a series of three-dimensional discrete unit coordinate points. This sequence can be expressed as a series of three-dimensional discrete unit coordinates: Formula 1: ; In formula 1, is the coordinate of the discrete unit in three-dimensional space, indicating the fluid at time location.

[0027] Step S2: Count all ore-bearing volume elements in the three-dimensional geological space and use them as the initial particle group for particle filtering. The spatial position of each particle is generated and stored separately to generate the spatial channel path of each particle. In the main channel inference process, each particle group represents a possible fluid migration channel path, which can be characterized by coupling the ore-forming fluid state transfer model with the particle weight observation model. Based on the fluid main channel inference database, all ore-bearing elements and their spatial position coordinates in the three-dimensional geological space are counted and used as the initial particle group of the particle filter algorithm. The number of particles included in the initial particle group is , each particle can represent a flow path of the ore-forming fluid, which can be expressed as: Formula 2: ; In formula 2, represents the initial flow path probability of the corresponding particle, Indicates the possible initial flow position of the corresponding particle generated based on prior knowledge, Characterizes the initial fluid flow position weight, which obeys a uniform distribution, that is, .

[0028] Step S3: Based on the spatial position of the particle group and in combination with the fluid dynamics model, a probability-velocity coupled ore-forming fluid ion state transfer model is established. This model mainly considers the influence of factors such as structural morphology and permeability gradient direction on the fluid migration velocity. Therefore, in addition to the spatial position term related to the fluid transfer state, the state transfer model also includes a velocity model term to consider the factors affecting fluid migration. The state transfer probability is integrated into the state transfer velocity model to infer the possible position of the fluid particles in space at the previous moment. For each of the above particles , there is a three-dimensional geological space fluid migration path. Since the fluid migrates from the deep to the shallow part of the geological space, the initial particle group can be regarded as the end point of each path. Therefore, the fluid dynamics model can be used to predict the fluid migration path position of the current initial particle group at the previous moment and the current fluid migration position. Compared with the fluid migration position at the previous moment The relationship can be expressed as: Formula 3: ; in, represents the fluid particle group state transfer velocity model, It represents the time interval for the fluid to transfer from the position at the previous moment to the position at the next moment. In the inference process, it is assumed that the time interval is small to fully simulate the spatial position change process of fluid migration, so it can be regarded as a unit time interval. Represents the noise effect during fluid migration, thereby simulating the uncertainty of fluid state transfer in three-dimensional geological space; For the fluid particle group state transfer velocity model, considering the structural morphology factors and permeability gradient direction factors that are closely related to fluid migration, it can be expressed as: Formula 4: ; In formula 4, Indicates the direction of permeability gradient. Its physical meaning indicates that the direction of fluid migration is always from low permeability area to high permeability area. Represents factors related to structural morphology that affect fluid migration. These factors include structural surface inclination, undulation, and other characteristics. Specifically, they are expressed in the form of probability distribution. The physical meaning of this constraint indicates that the fluid migration velocity is closely related to structural morphology. For example, the fluid migration velocity is low in areas with small structural surface inclinations, and high in areas with large inclinations. Furthermore, the undulation of the structural surface may also affect the fluid flow velocity. represents the random disturbance term, which takes into account the random factors in the fluid migration process and obeys the Gaussian distribution; 、 、 They represent the weight coefficients of the corresponding constraint items, which need to be determined according to the geological conditions of the target study area and can be constants input by the user; Permeability gradient direction It is usually related to geological prior knowledge, and its value is closely related to the sampling of the corresponding area by predecessors. Factors affecting fluid migration related to structural morphology It usually includes characteristic factors such as structural morphology. However, in the implementation process, due to the nonlinear relationship between structural morphology and fluid flow velocity, it is difficult to directly give the nonlinear analytical expression of the two. Therefore, it is assumed that the fluid flow velocity is in a very small time interval. The values before and after remain unchanged, so the velocity value can be given by the Fokker-Planck equation, that is: Formula 5: ; In formula 5, , representing a smaller time interval, and Indicates the current time and The spatial position of the fluid at the moment, represents the diffusion coefficient associated with random fluctuations; The probability distribution of fluid flow position in the above formula can be expressed by the fluid transfer probability, that is: Formula 6: ; In formula 6, Indicates a point The location of the field point; Subsequently, the factors affecting fluid migration related to structural morphology can be used to describe the transfer probability in the form of probability distribution. For example, for the structural surface inclination, its probability distribution can be expressed as: Formula 7: ; In formula 7, is the probability distribution variance, is the maximum inclination value of the structural surface, represents the inclination value of the corresponding voxel, is a normalization constant. This probability distribution expression ensures that when the corresponding element inclination angle is larger, the fluid transfer probability is greater and the fluid flow velocity will be greater; Step S4: Establish a particle state transfer likelihood function to represent the particle weight observation model. Here, the stress characteristic data related to the structure is considered and expressed in the form of a probability likelihood function. The fluid particle weight is dynamically updated according to the stress observation likelihood function. During the geological evolution process, the structure generates cracks in the three-dimensional geological space under the action of stress, which directly provides favorable space for fluid migration and affects the spatial position of fluid migration. Therefore, when constructing the particle weight observation model, it is necessary to combine the structural stress characteristic data as the direct observation data of the fluid migration position, and construct the likelihood function here. , which represents the difference between the fluid position and the observed stress data during the inference process of the fluid main channel, can be expressed as: Formula 8: ; in, represents the current state (the position of the fluid path inference) observation data, Characterizes the stress value of the corresponding element, is the maximum value of regional stress, is the variance of the likelihood function distribution, represents a probability normalization constant; The difference between the current state and the observed stress data represented by the likelihood function can be further constructed into a particle weight observation model: Formula 9: ; Equation 9 indicates that the particle weight observation is a recursive function. Each time the fluid state position is dynamically updated, the particle weight needs to be dynamically updated to solve the particle weight observation at the next moment. During the inference process, the stress of discrete elements may be difficult to represent using quantitative numerical representations, but the changes in tensile and compressive stress of the structure during geological activities can be deduced from the overall morphological characteristics of the structural surface. Therefore, in the specific implementation process, the present invention uses the coupling of indicators of two geological elements, strike and dip, to represent the stress value of discrete elements. Indicators of strike expansion and dip expansion are provided to characterize the stress changes of the elements. The two indicators are solved by the third-order derivatives of the local implicit function of the structural surface. Finally, the stress of the discrete elements can be represented as the coupling form of strike expansion and dip expansion indicators, that is, ; Step S5: perform spatial sampling of particles using a resampling algorithm, and copy high-weight particles according to the particle weight update process, while discarding low-weight particles. After each sampling, the particle weight needs to be dynamically updated and reset. Since the above-mentioned calculated particle swarm includes all ore-bearing elements in the three-dimensional geological space, the fluid channel paths represented by them may cover the entire three-dimensional geological space, making it difficult to observe the distribution pattern of the main channel position of the fluid. Therefore, it is necessary to resample the particle swarm and discard particles with lower influence weights in order to observe the overall characteristic trend of fluid migration in space.

[0029] Before resampling, calculate the number of effective particles, that is: Formula 10: ; In formula 10, is the number of particles in the initial particle group, that is, the number of all ore-bearing body elements in the three-dimensional space, Represents the particle weight observation value at the corresponding position. Then it is judged according to the particle number threshold. When , high-weight particles are retained according to the weight distribution rule, and low-weight particles are discarded. The threshold value set by the user. When the value is large, the main channel distribution of the fluid obtained will highlight the local path characteristics. When the value is set small, the output main channel will highlight the overall characteristics of the main channel of the ore-forming fluid. After retaining the high-weight particles, repeat the above fluid path inference process to infer the next particle path and reset the weight to: Formula 11: ; In this way, the real-time update of the particle weight observation model can be achieved; Step S6: stop updating the particle weights by judging the particle number threshold, and output the maximum a posteriori probability path as the main channel of the ore-forming fluid in the three-dimensional geological space; After completing the fluid path inference process of all particles, i.e., the resampling process, the maximum a posteriori probability path is output. That is, the particle with the largest weight is selected for output, and the high-weight particles are connected in the order of state transition inference to form the main channel path of the ore-forming fluid flowing in the three-dimensional geological space.

[0030] In order to verify the performance of the above-mentioned ore-forming fluid main channel inference model, the present invention takes a certain mining area as an example to simulate the spatial distribution position, that is, the distribution characteristics of its deep fluid main channel.

[0031] In this process, due to the structural control characteristics of the mining area, the formation of the ore body in the mining area is closely related to the structure. Therefore, in the process of building the database, the structural surface data of the fault zone were collected for model construction and its morphological characteristic indicators were extracted. In addition, the permeability gradient direction data were collected, and the particle filter algorithm was used to initialize the particle group of the ore-bearing body elements in the database. Then, the fluid particle state transfer model and the particle weight observation model were constructed to infer the spatial channel path position of the fluid migration, and the main channel of the fluid migration was extracted through the resampling algorithm. According to the above model, the spatial distribution of the main fluid channel in the mining area is as follows: Figure 1 As shown in the figure, the statistical relationship between the path of ore-forming fluid migration along the main channel to the shallow part and the depth is as follows: Figure 2 shown.

[0032] Figure 1 The distribution characteristics of the main fluid channel of the inferred model in three-dimensional space are characterized. As can be seen from the figure, the method of the present invention can effectively combine the morphological characteristics of the fracture surface and the observed mineralization distribution, infer the reliable position of the main fluid channel through the particle filtering algorithm, clearly visualize the flow characteristics of the fluid in three-dimensional space, and can more clearly observe the branch channels of the fluid in space, thereby further inferring the size of the fluid channel flux. Figure 2 The statistical characteristics of the main fluid channel of the inference model provided by this embodiment are shown, wherein: Figure 2 (a) is the relationship between the volume element through which the main fluid channel passes and the normal direction of its corresponding fracture surface model, which shows the influence of the fracture surface morphology on fluid migration. Figure 2 (b) is the cumulative frequency relationship between the volume element through which the main fluid channel passes and the normal direction of its corresponding fracture surface model, which shows that the main fluid channel inferred by this method is beneficial to subsequent prospecting applications.

[0033] From the above description, it can be seen that the mineralizing fluid main channel inference model provided by the present invention effectively overcomes the limitations of traditional methods of inferring main channels, such as high cost and difficulty in inferring main fluid channels. It can not only integrate the constraints of multivariate data, but also realize dynamic and low-cost simulation of fluid main channels, ensuring high-precision inference of fluid main channels.

[0034] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A particle filter-based inference model for the main channel of ore-forming fluids, whose characteristic values are: Step S1: Collect relevant mineralization data of the target ore deposit, establish a database for inferring the main channels of ore-forming fluids, and define a state sequence for spatial discrete elements in a three-dimensional geological space; Step S2: Count all known ore-bearing volume elements in the exploration information in the three-dimensional geological space to use as the initial particle group for particle filtering, and generate the spatial channel path of each particle in the group; Step S3: establishing a probability-velocity coupled ion state transfer model of the ore-forming fluid based on the spatial position of the particles and the fluid dynamics in step S2; Step S4: establishing a particle weight observation model based on the particle state transition likelihood function, and dynamically updating the fluid particle weights according to the stress observation likelihood function; Step S5: perform spatial sampling of particles through the resampling algorithm, and copy high-weight particles and eliminate low-weight particles in the process of updating the fluid particle weights. Then, judge by the particle number threshold, stop the above particle weight update, and output the maximum a posteriori probability path.

2. The particle filter-based inference model for the main channel of ore-forming fluids according to claim 1, wherein the characteristic value is: the relevant mineralization data of the target ore deposit includes mineralization distribution, drilling data, geological profiles, and plan views; the process of defining the state sequence is: in three-dimensional geological space, the main channel of the fluid is defined as a series of three-dimensional discrete unit coordinate point sequences, and the three-dimensional discrete unit coordinates of the sequence are expressed as: Formula 1: ; In formula 1, is the coordinate of the discrete unit in three-dimensional space, indicating the fluid at time location.

3. According to the particle filtering-based ore-forming fluid main channel inference model of claim 1, its characteristic value is: the spatial channel path of each particle is generated in the following process: the initial particle group contains N particles, and the flow channel path of the ore-forming fluid represented by each particle is: Formula 2: ; In formula 2, represents the initial flow path probability of the corresponding particle, Indicates the possible initial flow position of the corresponding particle generated based on prior knowledge, Characterizes the initial fluid flow position weight, which obeys a uniform distribution, that is, .

4. The particle filtering-based ore-forming fluid main channel inference model according to claim 1, wherein the characteristic value is: the establishment process of the probability-velocity coupled ore-forming fluid ion state transfer model is: Step S3-1: constructing a fluid particle group state transition model based on structural morphological factors and permeability gradient direction factors related to fluid migration; Step S3-2: The spatial position of each particle in the initial particle group in step S2 is regarded as the end point of each channel path, and the current fluid migration position of the initial particle group is measured according to the above fluid particle group state transition model. The fluid migration position at the last moment , you will get it.

5. The particle filtering-based ore-forming fluid main channel inference model according to claim 4, wherein the characteristic value is: the expression of the fluid particle group state transition model is: Formula 3: ; The current fluid migration position Compared with the fluid migration position at the previous moment The relationship is: Formula 4: ; In Equation 3 and Equation 4, Indicates the direction of permeability gradient, and its physical meaning indicates that the direction of fluid migration is always from low permeability area to high permeability area; Indicates the factors affecting fluid migration related to structural morphology, which obeys the probability distribution; represents a random disturbance term, which obeys Gaussian distribution; 、 and Respectively represent the weight coefficients of the corresponding constraints; It represents the time interval for the fluid to transfer from the position at the previous moment to the position at the next moment; Represents the influence of noise during fluid migration.

6. The particle filter-based ore-forming fluid main channel inference model according to claim 1, wherein the characteristic value is: the particle state transfer likelihood function is constructed by combining the tectonic stress characteristic data as the direct observation data of the fluid migration position to construct the likelihood function , whose expression is: Formula 5: ; In formula 5, Represents the current state observation data, Characterizes the stress value of the corresponding element, is the maximum value of regional stress, is the variance of the likelihood function distribution, Represents a probability normalization constant.

7. A particle filtering-based ore-forming fluid main channel inference model according to claim 1, wherein the characteristic value is: the expression of the particle weight observation model is: Formula 6: ; In formula 6, Represents the particle weight observation value at the corresponding position.

8. The particle filtering-based ore-forming fluid main channel inference model according to claim 1, wherein the characteristic value is: before spatially sampling the particles, the number of effective particles needs to be calculated, and the process is: Formula 7: ; In formula 7, is the number of particles in the initial particle group, that is, the number of all ore-bearing elements in three-dimensional space.

9. A particle filtering-based ore-forming fluid main channel inference model according to claim 8, wherein the characteristic value is: the process of updating the fluid particle weight is: after the high-weight particles are retained, the above-mentioned fluid path inference process needs to be repeated to infer the next particle path, and the weight is reset to: Formula 8: ; The judgment process of high-weight particles and low-weight particles is as follows: set the threshold ,when When , high-weight particles are retained according to the weight distribution rule.

10. According to claim 1, a particle filtering-based main channel inference model for mineralizing fluids, the characteristic value of which is that the output process of the maximum a posteriori probability path is: selecting the particles with the largest weight for output, and connecting the high-weight particles in the order of state transition inference, thereby forming the main channel path of the mineralizing fluid flowing in the three-dimensional geological space.

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