A method and system for monitoring an in-situ leaching process of ion-type rare earth

Abstract

The application provides an ion type rare earth in-situ leaching process monitoring method and system, and belongs to the technical field of in-situ leaching process monitoring. The method comprises the following steps: constructing a real background resistivity field and a first level set function and a second level set function based on electrical method monitoring data before and after liquid injection; dividing an underground ore body into partitions, and constructing a resistivity model in combination with the real background resistivity field and resistivity parameters of each partition; performing electrical method forward calculation based on the resistivity model, driving the first level set function and the second level set function to iteratively update and update the resistivity model until a preset convergence condition is met according to the difference between the forward calculation result and second electrical method monitoring data; and extracting the zero contour line of the updated first level set function and the second level set function as the tracking results of the wetting front and the solute front respectively. The application solves the problem that the method in the prior art cannot directly and stably obtain the positions and dynamic advancing processes of the wetting front and the solute front based on electrical method monitoring data.

Description

Technical Field

[0001] This invention relates to the field of in-situ leaching process monitoring technology, and in particular to a method and system for monitoring ion-type rare earth in-situ leaching processes. Background Technology

[0002] Ion-adsorption rare earth elements are important strategic mineral resources in my country. In-situ leaching is the main mining technology for this type of deposit, and key processes such as injection displacement, ion exchange, and solute transport all occur within the underground ore body. Due to the deep burial of the ore body and the complex geological conditions, these processes are difficult to observe directly, resulting in the leaching operation remaining in a "black box" state for a long time. This poses significant challenges to optimizing injection schemes, improving leaching rates, and controlling environmental risks.

[0003] High-density electrical resistivity tomography (HIP) is highly sensitive to changes in groundwater content, pore fluid ion concentration, and dielectric properties, and has been attempted for use in monitoring in-situ leaching processes. Current techniques typically collect multiple HIP data points before, during, or after leaching, and then use conventional resistivity inversion methods to obtain underground resistivity distribution maps or resistivity change maps, thereby inferring the diffusion range of the leaching solution.

[0004] However, resistivity is a comprehensive response to various factors such as ore body water content, pore fluid ion concentration, and ore body structure, and is not equivalent to wetting front or solute front. The inversion results are affected by smoothing constraints, and the anomalous boundaries usually have a gradual shape, making it difficult to accurately determine the precise location of the front. The weathered crust ore body itself has obvious heterogeneity, and the differences in natural electrical properties and the changes in electrical properties caused by fluid injection are superimposed, making it difficult to distinguish the respective contributions of the two from the inversion results. More importantly, the existing methods lack stable front discrimination criteria, and field personnel need to rely on manually setting thresholds or relying on experience to delineate the front from the resistivity map. This is not only highly subjective and has poor repeatability, but also makes it difficult to achieve stable tracking of the front advancement process between multiple monitoring data. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a method and system for monitoring the in-situ leaching process of ion-type rare earth minerals, aiming to solve the problem in the prior art of lacking a method for monitoring the in-situ leaching process of ion-type rare earth minerals that can directly and stably obtain the position and dynamic advancement process of wetting front and solute front based on electrical monitoring data.

[0006] A method for monitoring the in-situ leaching process of ion-type rare earth minerals according to an embodiment of the present invention, the method comprising: A true background resistivity field is constructed based on the first electrical resistivity monitoring data before injection, and a first level set function for characterizing the boundary of the injected liquid's influence and a second level set function for characterizing the boundary of the effective solute's interaction are constructed based on the second electrical resistivity monitoring data during injection. Based on the first level set function and the second level set function, the underground ore body is divided into a background zone, a pre-mining water zone, and a leaching zone, and a resistivity model is constructed by combining the real background resistivity field and the resistivity parameters of each zone. Based on the resistivity model, forward electrical modeling is performed. According to the difference between the forward modeling result and the second electrical monitoring data, the first level set function and the second level set function are driven to iteratively update, and the resistivity model is updated according to the updated function until the preset convergence condition is met. The zero contour lines of the updated first and second level set functions are extracted respectively as the tracking results of wetting front and solute front to monitor the leaching process.

[0007] In addition, the method for monitoring the in-situ leaching process of ion-type rare earth minerals according to the above embodiments of the present invention may also have the following additional technical features: Furthermore, the steps of constructing a true background resistivity field based on the first electrical resistivity monitoring data before injection, and constructing a first level set function to characterize the boundary of the injected liquid's influence and a second level set function to characterize the boundary of the effective solute interaction based on the second electrical resistivity monitoring data during injection, include: The first electrical resistivity monitoring data collected before injection is obtained. A calculation grid is established based on the topography of the mining area, electrode positions and survey line layout. The first electrical resistivity monitoring data is used as a constraint to perform inversion on the calculation grid to obtain the true background resistivity field. The second electrical resistivity monitoring data collected during injection is obtained, the resistivity inversion is performed on the second electrical resistivity monitoring data to obtain the initial resistivity distribution, and the resistivity anomaly core region is extracted from the initial resistivity distribution. Based on at least one of the following: the actual background resistivity field, resistivity parameters of each zone, injection hole location, field experience, or previous tracking results, the locations of the initial wetting front and initial solute front are determined based on the core area of ​​resistivity anomaly. The initial wetting front and the initial solute front are respectively constructed as symbolic distance functions to obtain the first level set function and the second level set function.

[0008] Furthermore, the steps of dividing the underground ore body into a background zone, a pre-mineral water zone, and a leaching zone based on the first and second level set functions, and constructing a resistivity model by combining the actual background resistivity field and the resistivity parameters of each zone, include: The regions where the first level set function is less than zero and the second level set function is less than zero are divided into background regions, the regions where the first level set function is greater than zero and the second level set function is less than zero are divided into pre-mine water regions, and the regions where the first level set function is greater than zero and the second level set function is greater than zero are divided into leaching regions. The background regions are characterized by the real background resistivity field, the pre-mine water regions are characterized by the pre-mine water resistivity, and the leaching regions are characterized by the leaching region resistivity. A smooth transition function is used to transform the first level set function and the second level set function respectively to obtain continuous partition weights corresponding to the wetting front and the solute front; The resistivity model is constructed based on the real background resistivity field, the resistivity of the pre-mine water area, the resistivity of the leaching area, and the continuous partition weights.

[0009] Furthermore, the resistivity model update step includes: Electrical forward modeling is performed based on the current resistivity model to obtain forward modeling response data; The forward response data is compared with the second electrical resistivity monitoring data to construct an objective function, which is used to quantify the difference between the two. Based on the gradient information of the objective function with respect to the first level set function and the second level set function, update the first level set function and the second level set function respectively; The background area, the pre-mineral water area, and the leaching area are redefined based on the updated first and second level set functions, and the resistivity model is reconstructed. If the objective function does not meet the preset convergence condition, return to the step of performing forward modeling based on the current resistivity model until the objective function meets the preset convergence condition and outputs the updated first level set function, second level set function and resistivity model.

[0010] Furthermore, the steps of extracting the zero contour lines of the updated first and second level set functions as tracking results of wetting and solute fronts to monitor the leaching process include: The extent of the pre-mineral water zone and the extent of the leaching zone are determined based on the location of the wetting front and the location of the solute front. Calculate at least one quantitative indicator of the morphology, propulsion distance, influence range, and propulsion speed of the wetting front and the solute front; Based on the tracking results of multi-period monitoring data, the dynamic changes of the wetting front and the solute front over time are obtained; Output at least one of the position, shape, propulsion distance, influence range, propulsion speed, and dynamic changes of the wetting front and the solute front, and form a visualization or quantitative indicator.

[0011] Furthermore, the process of iteratively updating the first level set function and the second level set function and updating the resistivity model according to the updated function until the preset convergence condition is met also includes a stabilization process, which includes at least one of the following: Every preset number of iterations, the first level set function and the second level set function are reinitialized to maintain their signed distance function properties. Step size control is used to limit the update magnitude of the first level set function and the second level set function in each iteration; A narrowband update strategy is adopted, and the first and second level set functions are updated only in the region near the front.

[0012] Furthermore, the method for constructing the objective function includes: Calculate the difference between the forward response data and the second electrical resistivity monitoring data, and construct a data error term; Calculate the frontal interface lengths corresponding to the first and second level set functions, and construct an interface length regularization term; The objective function is constructed based on the data error term and the interface length regularization term.

[0013] Another object of the present invention is to provide a monitoring system for the in-situ leaching process of ion-type rare earth minerals, for implementing the above-mentioned monitoring method for the in-situ leaching process of ion-type rare earth minerals, the system comprising: The resistivity field determination module is used to construct a real background resistivity field based on the first electrical resistivity monitoring data before injection, and to construct a first level set function to characterize the boundary of the injected liquid's influence and a second level set function to characterize the boundary of the effective solute's interaction based on the second electrical resistivity monitoring data during injection. The resistivity model construction module is used to divide the underground ore body into a background zone, a pre-mining water zone, and a leaching zone based on the first level set function and the second level set function, and to construct a resistivity model by combining the real background resistivity field and the resistivity parameters of each zone. The function update module is used to perform forward modeling calculations based on the resistivity model. According to the difference between the forward modeling calculation results and the second electrical monitoring data, the first level set function and the second level set function are driven to iteratively update and the resistivity model is updated according to the updated function until the preset convergence condition is met. The result determination module is used to extract the zero contour lines of the updated first and second level set functions, respectively, as the tracking results of the wetting front and solute front to monitor the leaching process.

[0014] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for monitoring the in-situ leaching process of ion-type rare earth minerals.

[0015] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for monitoring the ion-type rare earth in-situ leaching process.

[0016] This invention employs a dual-level set function to characterize the front and uses resistivity as an intermediate physical quantity connecting the front and electrical resistivity observation data. This departs from the existing approach of using resistivity as the direct inversion object, allowing the difference between forward modeling results and measured data to be used to directly drive front iteration updates. This establishes a direct mapping relationship between electrical resistivity response anomalies and front positions, avoiding the uncertainty of manual interpretation from resistivity maps. Furthermore, since the actual background resistivity field before injection is introduced as the electrical reference for the background area during the construction and iteration of the resistivity model, the natural electrical heterogeneity of the ore body and the electrical changes caused by injection can be effectively separated during the iteration process, preventing the misjudgment of natural electrical anomalies as front advance signals. Based on this, through repeated iterations, the front gradually approximates the actual underground state, ultimately outputting the front position directly from the zero contour line of the level set function, thereby achieving automatic tracking of wetting and solute fronts. Therefore, this invention solves the problem in the prior art of lacking a method for monitoring the in-situ leaching process of ion-type rare earth minerals that can directly and stably obtain the position and dynamic advancement process of wetting front and solute front based on electrical resistivity monitoring data. Attached Figure Description

[0017] Figure 1 This is a flowchart of the monitoring method for the in-situ leaching process of ion-type rare earth minerals in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the ion-type rare earth in-situ leaching process monitoring system in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention; Figure 4 This is a semi-synthetic resistivity profile with the true value of a double ellipse in the first embodiment of the present invention; Figure 5 This is the result of conventional high-density electrical resistivity tomography (EDT) inversion in the first embodiment of the present invention; Figure 6The results of tracking the wetting front and solute front obtained by the method of the present invention in the first embodiment of the present invention; The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0018] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 Please see Figure 1 The figure shows a method for monitoring the in-situ leaching process of ion-type rare earth minerals in the first embodiment of the present invention, which specifically includes S01-S04.

[0021] S01, a real background resistivity field is constructed based on the first electrical resistivity monitoring data before injection, and a first level set function for characterizing the boundary of the injected liquid influence and a second level set function for characterizing the boundary of the effective solute interaction are constructed based on the second electrical resistivity monitoring data during injection.

[0022] In practical implementation, by constructing a real background resistivity field, an electrical benchmark can be provided for subsequent front tracking when it is not affected by the injection fluid. This effectively distinguishes between the heterogeneity of the natural strata and the electrical changes caused by the injection fluid, avoiding misjudging the original electrical differences as abnormal migration of the leaching fluid. By constructing two level set functions, the boundary of the injected fluid influence and the boundary of the effective solute action can be characterized in an implicit mathematical description, without the need to explicitly track every point on the interface. This facilitates the handling of the deformation, expansion and topological changes of the front in the complex underground medium.

[0023] Specifically, the first electrical resistivity monitoring data collected before injection is acquired, and a computational grid is established based on the mine topography, electrode positions, and survey line layout. The first electrical resistivity monitoring data is used as a constraint to perform inversion on the computational grid to obtain the true background resistivity field. The second electrical resistivity monitoring data collected during injection is acquired, and resistivity inversion is performed on the second electrical resistivity monitoring data to obtain the initial resistivity distribution. The resistivity anomaly core area is extracted from the initial resistivity distribution. Combining the true background resistivity field, resistivity parameters of each zone, injection hole positions, field experience, or at least one of the previous tracking results, the positions of the initial wetting front and the initial solute front are determined based on the resistivity anomaly core area. The initial wetting front and the initial solute front are respectively constructed as signed distance functions to obtain the first level set function and the second level set function.

[0024] In practical implementation, inversion is performed on the computational grid using the first electrical resistivity monitoring data as a constraint. This fully preserves the electrical differences in the ore body under its original state caused by lithology, pore structure, natural water-bearing state, and heterogeneity, ensuring that the background resistivity field truly reflects the underground electrical structure before fluid injection. Resistivity inversion and extraction of the anomaly core area from the second electrical resistivity monitoring data can quickly pinpoint the approximate range of electrical changes caused by fluid injection, providing a reasonable initial guess for the level set function. Determining the initial front location by combining at least one of the following: the true background resistivity field, zone resistivity parameters, injection hole location, field experience, or previous tracking results. This effectively reduces the impact of deep inversion artifacts and anomaly boundary diffusion on the initial value, improving the convergence speed and stability of subsequent iterative tracking. Constructing the initial front as a signed distance function, where the absolute value represents the distance from the point to the front and the sign indicates the inside or outside, facilitates the direct extraction of the front location through zero contour lines.

[0025] S02, based on the first level set function and the second level set function, the underground ore body is divided into a background zone, a pre-mining water zone and a leaching zone, and a resistivity model is constructed by combining the real background resistivity field and the resistivity parameters of each zone.

[0026] In practical implementation, the ore body is naturally divided into three regions with clear technological significance by combining two level set functions. This makes the resistivity model no longer a general description of the underground medium, but a zonal expression corresponding to the three states of unaffected, water-affected, and solute-affected during in-situ leaching. By constructing a resistivity model in combination with the real background resistivity field, the natural electrical differences and the electrical changes caused by injection can be distinguished spatially, thereby improving the reliability of front identification.

[0027] Specifically, the regions where both the first and second level set functions are less than zero are classified as background regions, the regions where both the first and second level set functions are greater than zero are classified as pre-mine water regions, and the regions where both the first and second level set functions are greater than zero are classified as leaching regions. The background regions are characterized by the true background resistivity field, the pre-mine water regions by the pre-mine water resistivity, and the leaching regions by the leaching region resistivity. A smooth transition function is used to transform the first and second level set functions respectively, obtaining continuous partition weights corresponding to the wetting front and the solute front. The resistivity model is constructed based on the true background resistivity field, the pre-mine water resistivity, the leaching region resistivity, and the continuous partition weights.

[0028] In practical implementation, the positive and negative values ​​of the level set function are used as the partitioning criteria, which can mathematically and strictly define the spatial range of the background area, the pre-mineral water area, and the leaching area. The positions of the wetting front and the solute front correspond to the zero contour lines of the first and second level set functions, respectively. In addition, a smooth transition function is used for transformation, which can simulate the characteristics of the frontal transition zone caused by hydrodynamic dispersion, molecular diffusion, ion exchange, and ore body heterogeneity during the actual leaching process. This avoids discontinuous abrupt changes in the resistivity model at the front, thereby improving the numerical stability of forward modeling and iterative updates. Then, a resistivity model is constructed based on the real background resistivity field, the resistivity of the pre-mineral water area, the resistivity of the leaching area, and the continuous partition weights. This gives the electrical parameters of each region in the model a clear physical meaning and establishes a physical connection with the high-density electrical resistivity observation response that can be forward modeled.

[0029] As an example, and not a limitation, in some alternative embodiments, a smoothed Heaviside function is preferred to construct a resistivity model with a continuous transition, rather than a completely abrupt step assignment model. This is because, during in-situ leaching of ion-adsorption rare earth minerals, the propulsion of the injected liquid and effective solute migration are both affected by hydrodynamic dispersion, molecular diffusion, ion exchange, and adsorption / desorption. The wetting front and solute front are typically not physically sharp abrupt interfaces, but rather transitional bands of a certain width. Therefore, using a smoothed Heaviside function not only conforms to the physical characteristics of leaching liquid transport and solute migration, but also helps avoid discontinuous abrupt changes in the resistivity model at the front, thereby improving the stability of forward modeling and level set iterative updates.

[0030] set up For the reason The resulting smoothed Heaviside function, For the reason The resulting smoothed Heaviside function, where, For moist fronts, If the solute front is present, then the resistivity of the underground ore body can be expressed as:

[0031] In the formula, The resistivity of the ore body is determined by both the wetting front and the solute front. This represents the actual background resistivity field. Resistivity of the water zone in front of the mine; The resistivity of the leaching zone; This is the smoothed Heaviside function corresponding to the moist front. Let be the smoothed Heaviside function corresponding to the solute front. and The value ranges from 0 to 1, representing the continuous weight of the corresponding location belonging to the injection influence zone or the solute action zone, respectively. When a certain location in the ore body... and When all are much less than 0, The resistivity of the ore body is close to 0, and is mainly determined by the true background resistivity. Decision; when Much greater than 0 and When much less than 0, Close to 1 and The resistivity of the ore body is close to 0, and its main characteristic is the resistivity of the pre-mineral water area. ;when and When all are much greater than 0, and All values ​​are close to 1, and the resistivity of the ore body is mainly reflected in the resistivity of the leaching area. .

[0032] Through the above description, this invention unifies wetting fronts, solute fronts, and three types of leaching zones with clear field significance into a single model. Resistivity is no longer the final interpretation target, but rather an intermediate physical quantity connecting the state of the subsurface front with high-density electrical resistivity tomography (EDS) observation data. Subsequently, high-density EDS forward modeling and data error feedback can drive... and The update enables the joint tracking of the wetting front and the solute front.

[0033] Furthermore, in a preferred embodiment, the smoothed Heaviside function can take the form of tightly supported sinusoidal smoothing:

[0034] In the formula: The width parameter of the frontal transition zone controls the smoothing range near wetting or solute fronts. This form only smooths within a narrow band near the front, maintaining clear zoning characteristics in the background area, pre-mineral water area, and leaching area far from the front. Depending on specific implementation needs, other monotonic smoothing forms such as hyperbolic tangent can also be used for the smoothing Heaviside function, as long as a continuous transition from 0 to 1 can be achieved and the resistivity model near the front remains differentiable with respect to the level set function.

[0035] S03, perform forward modeling based on the resistivity model, and drive the first level set function and the second level set function to iteratively update according to the difference between the forward modeling result and the second electrical monitoring data, and update the resistivity model according to the updated function until the preset convergence condition is met.

[0036] In practical implementation, a quantitative correspondence between the current frontal state and electrical observation data is established through electrical forward modeling. This allows the location of the invisible front to be constrained by the observable electrical response. Furthermore, the difference between the forward modeling results and the measured monitoring data drives the update of the level set function, forming a data-driven closed-loop optimization mechanism. This enables the frontal location to gradually evolve towards the direction that best matches the measured data, without relying on manual thresholds or empirical delineation. Moreover, by using preset conditions to constrain the iterative updates, the tracking results can be ensured to stabilize within the allowable range of data errors, avoiding overfitting or insufficient iteration.

[0037] Specifically, forward modeling is performed based on the current resistivity model to obtain forward modeling response data; the forward modeling response data is compared with the second electrical resistivity monitoring data to construct an objective function to quantify the difference between the two; the first and second level set functions are updated according to the gradient information of the objective function with respect to the first and second level set functions, respectively; the background area, the pre-mineral water area, and the leaching area are re-divided according to the updated first and second level set functions, and the resistivity model is reconstructed; if the objective function does not meet the preset convergence condition, the process returns to the step of performing forward modeling based on the current resistivity model until the objective function meets the preset convergence condition and outputs the updated first level set function, second level set function, and resistivity model.

[0038] In practical implementation, forward modeling based on the current resistivity model can obtain the theoretical response that the electrical resistivity observation system should have under the current frontal state. The forward modeling response data is compared with the second electrical resistivity monitoring data to construct an objective function, which can quantify the deviation between the current front and the actual underground state in a numerical manner. In addition, the gradient information of the two level set functions is updated according to the objective function, and the chain rule can be used to backpropagate the data error into the frontal movement direction, so that the two fronts can be adjusted independently in the direction of reducing data error. Then, the region is re-divided and the resistivity model is reconstructed to provide an updated physical parameter field for the next round of forward modeling. By cyclically executing until the convergence condition is met, the wetting front and solute front gradually approach the actual position.

[0039] Furthermore, the method for constructing the objective function includes: calculating the difference between the forward response data and the second electrical resistivity tomography (ERT) monitoring data, and constructing a data error term; calculating the frontal interface lengths corresponding to the first level set function and the second level set function, and constructing an interface length regularization term; and constructing the objective function based on the data error term and the interface length regularization term.

[0040] In practical implementation, constructing a data error term can directly measure the degree of agreement between forward modeling prediction and actual monitoring, providing data constraints for the update of the level set function; constructing an interface length regularization term can suppress unreasonable small-scale oscillations, broken interfaces, or isolated small patches on the front during the iteration process, making the tracking results more consistent with the physical reality that underground leaching fronts usually advance continuously; combining the data error term and the interface length regularization term together to form the objective function can maintain the rationality of the front morphology while ensuring the accuracy of data fitting, avoiding excessive pursuit of data agreement that leads to front geometric distortion.

[0041] As an example, and not a limitation, in some alternative embodiments, the objective function may include a data error term and an interface length regularization term:

[0042] In the formula, The error term for high-density electrical resistivity tomography (EDT) observation data is expressed as follows; For regularization weights; and These represent the interface lengths of the wetting front and the solute front, respectively. Interface length regularization is used to suppress unreasonable small-scale oscillations, broken interfaces, or isolated small patches on the front, making the tracking results more consistent with the actual situation that underground leaching fronts usually have continuous advancing characteristics.

[0043]

[0044] In the formula: W The weight matrix for the observation data; dFor observational data; F For the orthogonal operator, In actual calculations, the base-10 logarithm of the observed data and forward response can be taken, and a data error term can be constructed in the logarithmic space to improve the numerical stability under different orders of magnitude of data.

[0045] In the formula: Let be the weight matrix of the observation data in logarithmic space.

[0046]

[0047]

[0048] In the formula: For smoothing the Dirac delta function, it is usually defined as the derivative of the smoothing Heaviside function, and its expression is shown above; for The magnitude of the gradient vector; For the computational domain.

[0049] Furthermore, the update direction of the level set function is calculated based on the chain rule. Using molecular layout notation, the derivative of the scalar function with respect to the column vector is written as a row vector, and its transpose is taken as the descent direction in column vector form during level set updates. To update the two fronts based on data errors, this invention uses the chain rule to calculate the change direction of the objective function with respect to the level set function of the wetting front and the solute front:

[0050] The relevant partial derivatives in the chain rule are calculated as follows: The calculation formula is shown below:

[0051]

[0052]

[0053]

[0054]

[0055] Based on the gradient information above, the two level set functions can be updated using gradient descent:

[0056] in, k For the number of iterations, and These are the update step sizes for the wetting front and the solute front, respectively. The update step size can be a fixed value, or it can be determined through line search, adaptive step size, or maximum update amplitude limitation to ensure that the objective function decreases after each iteration and to avoid excessive frontal movement. Through the above updates, the wetting front and the solute front move in the direction that reduces the objective function.

[0057] In addition, the process of driving the first level set function and the second level set function to iteratively update and updating the resistivity model according to the updated function until the preset convergence condition is met also includes a stabilization process, which includes at least one of the following: re-initializing the first level set function and the second level set function every preset number of iterations to maintain their sign distance function properties; using step size control to limit the update magnitude of the first level set function and the second level set function in each iteration; and using a narrowband update strategy to update the first level set function and the second level set function only in the region near the front.

[0058] In practical implementation, reinitializing the level set function can correct the degradation of the signed distance function property during iteration, ensuring the stability of the gradient magnitude of the level set function and thus guaranteeing the accuracy of frontal normal and curvature calculations. Step size control limits the update amplitude of the level set function in each iteration, preventing the front from exceeding its optimal position or becoming numerically unstable due to excessively large step sizes. Furthermore, a narrow-band update strategy allows calculations to be performed only in the vicinity of the front, significantly reducing the computational load in each iteration and improving tracking efficiency, while keeping the level set function far from the front unaffected by irrelevant perturbations. Additionally, distance regularization constraints can be used to prevent unreasonable oscillations, fragmentation, or numerical degradation of the front.

[0059] S04. The zero contour lines of the updated first and second level set functions are extracted respectively as the tracking results of wetting front and solute front to monitor the leaching process.

[0060] In practical implementation, the location and shape of wetting front and solute front can be directly obtained by extracting the zero contour lines, transforming abstract mathematical functions into spatial boundaries with clear on-site significance. This elevates the results of electrical resistivity monitoring from the resistivity imaging level to the process front tracking level. By outputting the tracking results of the two fronts separately, the diffusion range of the injected liquid and the effective solute action range can be reflected simultaneously, providing a direct basis for judging whether the leaching liquid has reached the target ore body, evaluating the leaching effect, and identifying abnormal diffusion risks.

[0061] Specifically, based on the location of the wetting front and the location of the solute front, the range of the pre-mineral water zone and the leaching zone are determined; at least one quantitative indicator among the morphology, advancing distance, influence range, and advancing speed of the wetting front and the solute front is calculated; based on the tracking results of multi-period monitoring data, the dynamic change process of the wetting front and the solute front over time is obtained; the location, morphology, advancing distance, influence range, advancing speed, and at least one of the dynamic change process of the wetting front and the solute front are output and visualized as a chart or quantitative indicator.

[0062] In practical implementation, the range of the pre-mine water zone and the leaching zone are determined based on the location of the wetting front and solute front. This transforms the underground leaching state into an intuitive spatial zoning result. Furthermore, by calculating quantitative indicators such as morphology, advancement distance, influence range, and advancement speed, the front tracking results are upgraded from qualitative descriptions to quantifiable engineering parameters. This facilitates correlation analysis with process parameters such as injection intensity and injection duration. Based on multi-period monitoring data, the dynamic change process can be obtained, revealing the patterns of advancement, expansion, or shift of the wetting front and solute front over time. This provides a data foundation for time-series tracking and trend prediction. Finally, visual maps or quantitative indicators are output, presenting the complex underground leaching process in an intuitive form. This allows on-site personnel to directly use it for adjusting injection plans, evaluating leaching effects, and managing environmental risks.

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below in conjunction with specific scenarios. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0064] This embodiment is used to verify the joint tracking capability of the present invention for wetting fronts and solute fronts under the conditions of real mine background electrical structure and real high-density electrical resistivity tomography (EDT) observation system. This embodiment uses real pre-injection high-density EDT monitoring data collected by the Schlenbel apparatus as background data. Artificially defined wetting and leaching zones are embedded in the real background resistivity field to form semi-synthetic monitoring data with known true values ​​of the two fronts. The method of the present invention is then used to track wetting and solute fronts in this semi-synthetic monitoring data, and the tracking results are compared with the known true values.

[0065] First, high-density electrical resistivity monitoring data collected before injection in the ion-adsorption rare earth in-situ leaching area were selected as background data. A first computational grid was established based on the site topography, electrode coordinates, survey line layout, and observation equipment. The background data was then subjected to conventional resistivity inversion using the open-source inversion library pyGIMLi to obtain the true background resistivity field. This background resistivity field preserves the electrical differences in the actual ore body caused by lithology, pore structure, natural water-bearing state, and heterogeneity.

[0066] Then, artificial wetting zones and artificial leaching zones are set up on the first computational grid. The artificial wetting zone simulates the area already affected by the injected fluid or pre-ore water, while the artificial leaching zone simulates the area where effective solutes have arrived and leaching has occurred. To approximate the actual morphology of underground leaching fluid diffusion and solute migration, the artificial wetting zone and artificial leaching zone are set as nested elliptical regions, with the artificial leaching zone located inside or primarily within the artificial wetting zone. The boundary of the artificial wetting zone is used as the true value of the wetting front, and the boundary of the artificial leaching zone is used as the true value of the solute front.

[0067] Based on the real background resistivity field, resistivity substitution is performed on the artificially wetted area and the artificially leached area to construct a semi-synthetic true resistivity model. Specifically, the region within the artificially wetted area but outside the artificially leached area is assigned the resistivity of the pre-ore water region, the region within the artificially leached area is assigned the resistivity of the leaching area, and other regions retain the real background resistivity. This yields a semi-synthetic true-value model encompassing the real background area, the pre-ore water region, and the leaching area, as shown below. Figure 4 As shown.

[0068] Based on the aforementioned semi-synthetic true value model, high-density electrical resistivity tomography (EDS) forward modeling is performed on the first computational grid to generate semi-synthetic monitoring data. This semi-synthetic monitoring data possesses the same terrain conditions, electrode layout, and observation equipment as the actual field, and the positions of its wetting front and solute front are known. Therefore, it can be used to verify whether the method of this invention can recover the double front from high-density EDS data. Detailed parameters for semi-synthetic data generation and conventional high-density EDS inversion are shown in Table 1.

[0069] Table 1. Semi-synthetic data generation and parameters obtained from conventional high-density electrical resistivity tomography (EDS) inversion.

[0070] To avoid overly idealized results from directly tracking the front on the same grid used to generate the data, this embodiment further establishes a second computational grid as the front tracking grid. The second computational grid uses the same terrain, electrode locations, and observation system, but excludes the geometric boundaries of the artificially wetted and artificially leached areas. The background resistivity field is then re-inverted on the second computational grid using real background data from the same period. As attached Figure 5 As shown, a wetted front level set function is constructed on the second computational grid. and solute front level set function By separating the data generation grid and the front tracking grid, the influence of preset grid boundaries on the tracking results can be reduced, making the verification process closer to actual unknown frontal conditions.

[0071] Subsequently, the semi-synthetic monitoring data was used as the data to be interpreted, and calculations were performed according to the joint tracking steps of wetting front and solute front described in this invention. First, conventional resistivity inversion was performed on the semi-synthetic monitoring data to obtain the initial resistivity distribution. From the conventional inversion results, anomaly core regions with significant resistivity anomalies and good spatial continuity were extracted to construct the initial wetting front and the initial solute front, and then the initial level set function was constructed. and Then according to and A smoothed two-level-set three-part resistivity model was constructed, and high-density electrical resistivity forward modeling was performed to calculate the values ​​from the observed data. These values ​​were then compared with semi-synthetic monitoring data to form the objective function. The level sets, smoothing Heaviside function, objective function, and related parameters for iterative control during the front tracking process are detailed in Table 2.

[0072] Table 2. Parameters for joint tracking of wetting front and solute front

[0073] During the iteration process, the objective function pairs are calculated using the high-density electrical resistivity matrix and the chain rule, respectively. and The update direction is adjusted so that the wetting front and solute front move in the direction that reduces data error. In this embodiment, in addition to the data error term, the objective function also includes an interface length regularization term for the wetting front and solute front to suppress unreasonable small-scale oscillations and isolated small patches generated by the front during the iteration process. After each update, the resistivity model is reconstructed and forward modeling is performed. The iteration is repeated until the data error meets the preset requirements, or the positional changes of the wetting front and solute front are less than the preset threshold in several consecutive iterations. During the iteration process, step size control and horizontal weight initialization are used to improve computational stability.

[0074] After the calculation is complete, extract the final result. The zero contour lines are used as the results of moist front tracking to extract the final... The zero contour lines are used as the results of solute front tracing, as shown in the attached figure. Figure 6 As shown in the attached diagram. Figure 5 and attached Figure 6 It is evident that conventional high-density electrical resistivity inversion results can reflect the approximate range of underground resistivity anomalies, but the anomaly boundaries exhibit gradual changes, making it difficult to directly identify wetting fronts and solute fronts. Furthermore, in the deep region, there are inversion artifacts inconsistent with the actual model. The method of this invention can directly output the location and morphology of wetting fronts and solute fronts, and the tracking results show good consistency with the true values ​​of artificially set double elliptical fronts, indicating that this invention can effectively convert high-density electrical resistivity monitoring data into tracking results of wetting fronts and solute fronts.

[0075] In summary, this invention, by employing a dual-level set function to characterize the front and using resistivity as an intermediate physical quantity connecting the front and electrical resistivity observation data, changes the existing approach of using resistivity as the direct inversion object. This allows the difference between the forward modeling results and the measured data to be used to directly drive the iterative update of the front, thereby establishing a direct mapping relationship between electrical resistivity response anomalies and front positions, avoiding the uncertainty of manual interpretation from resistivity maps. Furthermore, since the real background resistivity field before injection is introduced as the electrical reference of the background area into the construction and iterative update process of the resistivity model, the natural electrical heterogeneity of the ore body and the electrical changes caused by injection can be effectively separated during the iteration process, avoiding the misjudgment of natural electrical anomalies as front advancement signals. Based on this, through repeated iterations, the front gradually approximates the actual underground state, and finally the front position is directly output from the zero contour line of the level set function, thereby achieving automatic tracking of wetting fronts and solute fronts. Therefore, this invention solves the problem in the prior art of lacking a method for monitoring the in-situ leaching process of ion-type rare earth minerals that can directly and stably obtain the position and dynamic advancement process of wetting front and solute front based on electrical resistivity monitoring data.

[0076] Example 2 Please see Figure 2 The diagram shows a structural block diagram of the ion-type rare earth in-situ leaching process monitoring system proposed in the second embodiment of the present invention. The ion-type rare earth in-situ leaching process monitoring system 200 includes: a resistivity field determination module 21, a resistivity model construction module 22, a function update module 23, and a result determination module 24, wherein: The resistivity field determination module 21 is used to construct a real background resistivity field based on the first electrical resistivity monitoring data before injection, and to construct a first level set function to characterize the boundary of the injected liquid influence and a second level set function to characterize the boundary of the effective solute interaction based on the second electrical resistivity monitoring data during injection. The resistivity model construction module 22 is used to divide the underground ore body into a background zone, a pre-mining water zone and a leaching zone based on the first level set function and the second level set function, and to construct a resistivity model by combining the real background resistivity field and the resistivity parameters of each zone. Function update module 23 is used to perform forward modeling calculations based on the resistivity model, and drive the first level set function and the second level set function to iteratively update according to the difference between the forward modeling calculation results and the second electrical monitoring data, and update the resistivity model according to the updated functions until the preset convergence condition is met. The result determination module 24 is used to extract the zero contour lines of the updated first level set function and the second level set function, respectively, as the tracking results of the wetting front and the solute front to monitor the leaching process.

[0077] Example 3 In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device according to the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the above-described method for monitoring the in-situ leaching process of ion-type rare earth minerals.

[0078] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0079] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0080] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0081] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring the in-situ leaching process of ion-type rare earth minerals.

[0082] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0083] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0084] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0086] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for monitoring the in-situ leaching process of ion-type rare earth minerals, characterized in that, The method includes: A true background resistivity field is constructed based on the first electrical resistivity monitoring data before injection, and a first level set function for characterizing the boundary of the injected liquid's influence and a second level set function for characterizing the boundary of the effective solute's interaction are constructed based on the second electrical resistivity monitoring data during injection. Based on the first level set function and the second level set function, the underground ore body is divided into a background zone, a pre-mining water zone, and a leaching zone, and a resistivity model is constructed by combining the real background resistivity field and the resistivity parameters of each zone. Based on the resistivity model, forward electrical modeling is performed. According to the difference between the forward modeling result and the second electrical monitoring data, the first level set function and the second level set function are driven to iteratively update, and the resistivity model is updated according to the updated function until the preset convergence condition is met. The zero contour lines of the updated first and second level set functions are extracted respectively as the tracking results of wetting front and solute front to monitor the leaching process.

2. The method for monitoring the in-situ leaching process of ion-type rare earth minerals according to claim 1, characterized in that, The steps of constructing a true background resistivity field based on the first electrical resistivity monitoring data before injection, and constructing a first level set function to characterize the boundary of the injected liquid's influence and a second level set function to characterize the boundary of the effective solute interaction based on the second electrical resistivity monitoring data during injection, include: The first electrical resistivity monitoring data collected before injection is obtained. A calculation grid is established based on the topography of the mining area, electrode positions and survey line layout. The first electrical resistivity monitoring data is used as a constraint to perform inversion on the calculation grid to obtain the true background resistivity field. The second electrical resistivity monitoring data collected during injection is obtained, the resistivity inversion is performed on the second electrical resistivity monitoring data to obtain the initial resistivity distribution, and the resistivity anomaly core region is extracted from the initial resistivity distribution. Based on at least one of the following: the actual background resistivity field, resistivity parameters of each zone, injection hole location, field experience, or previous tracking results, the locations of the initial wetting front and initial solute front are determined based on the core area of ​​resistivity anomaly. The initial wetting front and the initial solute front are respectively constructed as symbolic distance functions to obtain the first level set function and the second level set function.

3. The method for monitoring the in-situ leaching process of ion-type rare earth minerals according to claim 1, characterized in that, The steps of dividing the underground ore body into a background zone, a pre-mineral water zone, and a leaching zone based on the first and second level set functions, and constructing a resistivity model by combining the actual background resistivity field and the resistivity parameters of each zone, include: The regions where the first level set function is less than zero and the second level set function is less than zero are divided into background regions, the regions where the first level set function is greater than zero and the second level set function is less than zero are divided into pre-mine water regions, and the regions where the first level set function is greater than zero and the second level set function is greater than zero are divided into leaching regions. The background regions are characterized by the real background resistivity field, the pre-mine water regions are characterized by the pre-mine water resistivity, and the leaching regions are characterized by the leaching region resistivity. A smooth transition function is used to transform the first level set function and the second level set function respectively to obtain continuous partition weights corresponding to the wetting front and the solute front; The resistivity model is constructed based on the real background resistivity field, the resistivity of the pre-mine water area, the resistivity of the leaching area, and the continuous partition weights.

4. The method for monitoring the in-situ leaching process of ion-type rare earth minerals according to claim 1, characterized in that, The steps for updating the resistivity model include: Electrical forward modeling is performed based on the current resistivity model to obtain forward modeling response data; The forward response data is compared with the second electrical resistivity monitoring data to construct an objective function, which is used to quantify the difference between the two. Based on the gradient information of the objective function with respect to the first level set function and the second level set function, update the first level set function and the second level set function respectively; The background area, the pre-mineral water area, and the leaching area are redefined based on the updated first and second level set functions, and the resistivity model is reconstructed. If the objective function does not meet the preset convergence condition, return to the step of performing forward modeling based on the current resistivity model until the objective function meets the preset convergence condition and outputs the updated first level set function, second level set function and resistivity model.

5. The method for monitoring the in-situ leaching process of ion-type rare earth minerals according to claim 1, characterized in that, The steps for extracting the zero contour lines of the updated first and second level set functions, respectively, as tracking results of wetting and solute fronts to monitor the leaching process include: The extent of the pre-mineral water zone and the extent of the leaching zone are determined based on the location of the wetting front and the location of the solute front. Calculate at least one quantitative indicator of the morphology, propulsion distance, influence range, and propulsion speed of the wetting front and the solute front; Based on the tracking results of multi-period monitoring data, the dynamic changes of the wetting front and the solute front over time are obtained; Output at least one of the position, shape, propulsion distance, influence range, propulsion speed, and dynamic changes of the wetting front and the solute front, and form a visualization or quantitative indicator.

6. The method for monitoring the in-situ leaching process of ion-adsorption rare earth minerals according to claim 4, characterized in that, The process of iteratively updating the first level set function and the second level set function and updating the resistivity model according to the updated function until a preset convergence condition is met also includes a stabilization process, which includes at least one of the following: Every preset number of iterations, the first level set function and the second level set function are reinitialized to maintain their signed distance function properties. Step size control is used to limit the update magnitude of the first level set function and the second level set function in each iteration; A narrowband update strategy is adopted, and the first and second level set functions are updated only in the region near the front.

7. The method for monitoring the in-situ leaching process of ion-type rare earth minerals according to claim 4, characterized in that, The method for constructing the objective function includes: Calculate the difference between the forward response data and the second electrical resistivity monitoring data, and construct a data error term; Calculate the frontal interface lengths corresponding to the first and second level set functions, and construct an interface length regularization term; The objective function is constructed based on the data error term and the interface length regularization term.

8. A monitoring system for in-situ leaching of rare earth minerals using ion-type ore, characterized in that, The system for implementing the monitoring method for ion-type rare earth in-situ leaching processes according to any one of claims 1 to 7, the system comprising: The resistivity field determination module is used to construct a real background resistivity field based on the first electrical resistivity monitoring data before injection, and to construct a first level set function to characterize the boundary of the injected liquid's influence and a second level set function to characterize the boundary of the effective solute's interaction based on the second electrical resistivity monitoring data during injection. The resistivity model construction module is used to divide the underground ore body into a background zone, a pre-mining water zone, and a leaching zone based on the first level set function and the second level set function, and to construct a resistivity model by combining the real background resistivity field and the resistivity parameters of each zone. The function update module is used to perform forward modeling calculations based on the resistivity model, and drive the first level set function and the second level set function to iteratively update according to the difference between the forward modeling calculation results and the second electrical monitoring data, and update the resistivity model according to the updated function until the preset convergence condition is met. The result determination module is used to extract the zero contour lines of the updated first and second level set functions, respectively, as the tracking results of the wetting front and solute front to monitor the leaching process.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the monitoring method for ion-type rare earth in-situ leaching process as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for monitoring the in-situ leaching process of ion-type rare earth minerals as described in any one of claims 1 to 7.

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