Deep prospecting detection system and method based on high-power induced polarization method in wells and between wells

Abstract

The present invention discloses a deep prospecting and detection system and method based on the high-power induced polarization method in wells and between wells, which relates to the field of mining area detection. The system and method include: an upload module for uploading induced polarization data of deep geological bodies in old mining areas and storing the data; a transmission module for receiving the data stored in the upload module, cleaning the data, and forwarding the cleaned data to the summary module after cleaning. The present invention can carry out deep detection work in old mining areas with complex electromagnetic interference, adapt to the mine environment through high-power induced polarization transmission and reception, and have good data acquisition repeatability and high accuracy. Through in-well and between-well induced polarization observations, combined with three-dimensional inversion technology, the deep geological structure and ore body distribution can be clearly presented.

Description

Technical Field

[0001] The present invention relates to the technical field of mining area detection, and in particular to a deep mineral prospecting detection system and method based on a high-power induced polarization method in a well or between wells. Background Art

[0002] Deep prospecting in established mining areas is a highly strategic endeavor. As shallow resources gradually deplete, the search for new treasures at depth becomes inevitable. Advanced geological exploration techniques, such as geophysical and geochemical methods, can precisely locate potential ore bodies at depth. This not only extends the lifespan of established mining areas but also alleviates resource constraints, providing strong support for sustainable economic development.

[0003] The invention patent application with application number 202410605678.6 discloses a method for separating motion noise from a ground-to-air electromagnetic detection system, comprising: constructing a data set, the data set comprising: a motion parameter sequence of the ground-to-air electromagnetic detection system, a magnetic field signal containing motion noise, and a motion noise time series; using the data set to train a preset MNRformer network model to obtain a motion noise separation model; wherein the preset MNRformer network model is obtained based on an improvement of the iTransformer network model; improving the iTransformer network model comprises: introducing an improved multi-attention mechanism layer based on a combination of a self-attention mechanism and a cross-attention mechanism to replace the original iTransformer network model. The multi-attention mechanism layer; using the data set to train the MNRformer network model includes: taking the motion parameter sequence and the magnetic field signal containing motion noise as input, the motion noise time series as output, and defining the magnetic field signal containing motion noise as an internal variable and the motion parameter sequence as an external variable, and training the MNRformer network model. This application solves the problem that "LSTM is difficult to achieve parallel computing and has low processing efficiency when processing large-scale ground-to-air / airborne electromagnetic data. In addition, LSTM only interprets the short-term correlation and change trend of a single time series itself, cannot establish long-term dependence and cannot reveal the correlation between multiple variables, and is difficult to meet the needs of low-frequency motion noise separation in ground-to-air electromagnetic detection systems."

[0004] However, for deep prospecting and detection scenarios in old mining areas, there is strong electromagnetic interference in old mining areas, and conventional ground geophysical methods are difficult to obtain accurate deep geological data, making prospecting work more difficult.

[0005] Therefore, a deep prospecting detection system and method based on high-power induced polarization in wells and between wells was proposed. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a deep prospecting and detection system and method based on the high-power induced polarization method in wells and between wells, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a deep prospecting and detection system based on a high-power induced polarization method in a well or between wells, comprising:

[0009] The upload module is used to upload the IP data of deep geological bodies in old mining areas and store the data; the transmission module is used to receive the data stored in the upload module, clean the data, and forward the cleaned data to the summary module after cleaning; the summary module is used to continuously obtain the cleaned data in the transmission module, summarize the continuously obtained cleaned data, and import them into the inversion module; the inversion module is used to apply the data to generate a geological model and identify potential ore body scale information in the geological model; the storage module is used to receive the identification results of the potential ore body scale information in the inversion module and store the identification results; the visualization module is used to read the ore body scale information stored in the storage module and generate a three-dimensional ore body model based on the ore body scale information;

[0010] Furthermore, when the upload module stores the data, it sets several intervals and stores the data separately based on the several intervals;

[0011] Each interval used to store data corresponds to four spatial coordinates and a height value. The four spatial coordinates corresponding to each interval define a plane, and combined with the height value, define a spatial area in the deep geological body of the old mining area. The data stored in the interval all come from the corresponding spatial area;

[0012] The specification parameters and sizes of the spatial regions corresponding to each interval for storing data are equal.

[0013] Furthermore, the IP data of the deep geological bodies in the old mining area uploaded by the upload module during the operation phase include: potential data, resistivity data, polarizability data, electromagnetic background noise in the well, and complex resistivity, density, and magnetic susceptibility of the rock sample;

[0014] When the data is cleaned in the transmission module, the cleaning operations include: deleting duplicate data, discarding incomplete data, denoising, and normalizing;

[0015] Among them, when the transmission module performs the cleaning operation, the incomplete data found will be fed back to the system user. The system user customizes the decision whether to re-upload the complete data corresponding to the incomplete data. After the re-upload operation is performed, the cleaning operation of the data in the transmission module is refreshed and executed synchronously until no data is re-uploaded.

[0016] Furthermore, the transmission module is provided with a retrieval unit and a control unit at the lower level. The retrieval unit is used to retrieve the data stored in the upload module and send the retrieved data to the transmission module. The control unit is used to monitor the operating status of the transmission module and the aggregation module. After the transmission module completes the forwarding operation of the cleaned data and the aggregation module completes the operation based on the received data, the control jumps and returns to the operation stage of the transmission module.

[0017] In the operation phase of the retrieval unit, the interval storing data in the upload module is used as the target of a single operation to retrieve data, and all data in the retrieval interval is sent to the transmission module.

[0018] Furthermore, the aggregation module performs a geoelectric structure tensor-guided anomaly enhancement aggregation operation on the continuously acquired IP data, and the operation includes the following steps:

[0019] a) Based on the IP data points and their 3D spatial coordinates corresponding to each interval collected by the upload module, an initial 3D geoelectric parameter field covering the entire detection area is constructed as a spatial region matrix;

[0020] b) Calculating the initial three-dimensional geoelectric parameter field For each cell c in the grid, calculate the local geoelectric structure tensor in its neighborhood :

[0021] ,

[0022] Where, is in cell c local geoelectric structure tensor; is the local computation window centered on cell c; is the weighting function within the window; is the gradient vector of the initial geoelectric parameter field at point r; Represents the transpose of a vector; the local geoelectric structure tensor Used to characterize the local change direction and anisotropy degree of the parameter field;

[0023] c) Based on the local geoelectric structure tensor The eigenvalue of and the corresponding main eigenvectors , combined with the preset priority geological structure direction vector , define and apply the directional anomaly response function Calculate the enhanced abnormal response value at cell c; where, Expressed as:

[0024] ,

[0025] in, is the enhanced abnormal response value at cell c; is a positive modulation coefficient; To prevent small positive constants with zero denominators; is the original IP parameter value at cell c relative to the background value Deviation;

[0026] d) Generate a three-dimensional geoelectric parameter field after anomaly enhancement and summary , where the value of each cell c is given by is given and passed to the inversion module.

[0027] Furthermore, the inversion module adopts a guided hybrid regularized inversion framework, which is integrated by guiding the regularization process of the RES3DINV inversion software through the model structure generated by the Occam inversion algorithm. The guided hybrid regularized inversion framework includes the following two stages that are executed in sequence:

[0028] Phase 1: Receive the anomaly-enhanced 3D geoelectric parameter field from the aggregation module as observation data and perform a preliminary inversion using the Occam inversion algorithm. The smoothing constraint weights in the model constraint terms of the preliminary inversion are spatially adaptively adjusted based on the geometric distribution of the wellbore and the sensitivity of each model unit to the observation data. This allows the model to have a higher parameter variation gradient in areas with strong data constraints and imposes stronger smoothing constraints in areas with weak data constraints. This generates a preliminary 3D geoelectric parameter model that includes structural features determined by the spatially adaptively adjusted smoothing constraints.

[0029] Phase 2: Use RES3DINV inversion software to perform a detailed inversion of the preliminary three-dimensional geoelectric parameter model and the structural features it contains; wherein, structural information is extracted from the preliminary three-dimensional geoelectric parameter model, and the structural information is used to introduce structural guidance constraints in the regularization process of RES3DINV inversion software. The structural guidance constraints impose stronger smoothing constraints in areas where the preliminary three-dimensional geoelectric parameter model appears smooth, while allowing the model to produce sharper boundaries or more significant abnormal changes in areas where the preliminary three-dimensional geoelectric parameter model shows significant structural changes, ultimately obtaining a detailed three-dimensional geoelectric parameter distribution for identifying the location, shape and extension span of the ore body.

[0030] Further:

[0031] The Occam inversion algorithm in stage 1 minimizes the objective function To solve the preliminary three-dimensional geoelectric parameter model, the objective function is expressed as:

[0032] ,

[0033] in, is the observation data; is the three-dimensional geoelectric parameter model to be inverted; is the data fitting term; Based on the model Calculated forward response operator; is the data weighting matrix; is the model constraint; is the a priori reference model; is the spatial weighting operator of the model parameters; is the regularization parameter, which controls the balance between data fitting terms and model constraint terms;

[0034] The structure-guided constraint introduced in the second stage is achieved by adding a structure-guided hybrid regularization term to the objective function of the regularization process of the RES3DINV inversion software. The regularization term is designed as follows:

[0035] ,

[0036] in, The three-dimensional geoelectric parameter model is being optimized for RES3DINV; is a preliminary three-dimensional geoelectric parameter model; the first term is a smooth constraint term for structure preservation, and its spatial weight function and The gradient magnitude at position r Anti-correlation; the second term is the reference model constraint term, and its spatial weight According to Trust level setting for different regions at location r; 、 is the regularization parameter to balance the terms.

[0037] Furthermore, the visualization module is integrated by a computer device with a display function, and the three-dimensional model of the ore body generated by the operation of the visualization module is displayed in real time through the computer device for visual reading by system end users.

[0038] Furthermore, the upload module is interactively connected to the transmission module and the aggregation module through a local area network, the transmission module is interactively connected to the retrieval unit and the control unit through a local area network, the retrieval unit is interactively connected to the upload module through a local area network, the control unit is interactively connected to the aggregation module through a local area network, and the aggregation module is interactively connected to the inversion module, the storage module and the visualization module through a local area network.

[0039] Deep prospecting and detection methods based on high-power IP in wells and between wells include:

[0040] Upload the IP data of deep geological bodies in old mining areas and store the data; retrieve the data from the stored data and clean it, and summarize the data after all the data are cleaned; invert the summarized data based on the Occam inversion algorithm and RES3DINV to obtain the scale information of potential ore bodies in the deep geological bodies in old mining areas; store the scale information of potential ore bodies, and construct a three-dimensional model of the potential ore body based on the scale information of the potential ore body; send the three-dimensional ore body model to a computer device with display function, and display the three-dimensional ore body model on the computer device for user visual reading.

[0041] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0042] 1. The present invention can carry out deep exploration in old mining areas with complex electromagnetic interference. It adapts to the mine environment through high-power induced polarization transmission and reception. The data acquisition has good repeatability and high accuracy. Through in-well and inter-well induced polarization observations, combined with three-dimensional inversion technology, it can clearly present the deep geological structure and ore body distribution.

[0043] 2. The present invention can effectively explore the distribution characteristics of ore bodies around known drill holes, discover deep mineral anomalies, accurately locate potential ore bodies, improve prospecting efficiency, reduce exploration blindness, provide key technical support for the secondary development of resources in old mining areas, and promote the sustainable development of the mining industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0045] Figure 1 This is a schematic diagram of the structure of a deep prospecting and detection system based on the high-power induced polarization method in wells and between wells;

[0046] Figure 2It is a flow chart of the deep prospecting and detection method based on the high-power induced polarization method in wells and between wells. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] The present invention will be further described below with reference to the embodiments.

[0049] Example 1:

[0050] The deep prospecting and detection system based on the high-power IP method in the well and between wells in this embodiment is as follows: Figure 1 Shown, including:

[0051] Upload module 1 is used to upload the IP data of deep geological bodies in old mining areas and store the data;

[0052] When the upload module 1 stores data, it sets several intervals and stores the data separately based on the several intervals;

[0053] Each interval used to store data corresponds to four spatial coordinates and a height value. The four spatial coordinates corresponding to each interval define a plane, and combined with the height value, define a spatial area in the deep geological body of the old mining area. The data stored in the interval all come from the corresponding spatial area;

[0054] Wherein, each interval for storing data corresponds to a spatial region with the same specification parameters and size;

[0055] The IP data of deep geological bodies in the old mining area uploaded during the operation phase of Upload Module 1 include: potential data, resistivity data, polarizability data, electromagnetic background noise in the well, and complex resistivity, density, and magnetic susceptibility of rock samples;

[0056] When data is cleaned in the transmission module 2, the cleaning operations include: deleting duplicate data, discarding incomplete data, denoising, and normalization;

[0057] When the transmission module 2 performs the cleaning operation, it will feedback the incomplete data found to the system end user. The system end user will make a customized decision on whether to re-upload the complete data corresponding to the incomplete data. After the re-upload operation is performed, the cleaning operation of the data in the transmission module 2 will be refreshed and executed synchronously until no data is re-uploaded;

[0058] The transmission module 2 is used to receive the data stored in the upload module 1, clean the data, and forward the cleaned data to the aggregation module 3 after the cleaning is completed;

[0059] The transmission module 2 is provided with a retrieval unit 21 and a control unit 22 at the lower level. The retrieval unit 21 is used to retrieve the data stored in the upload module 1 and send the retrieved data to the transmission module 2. The control unit 22 is used to monitor the operating status of the transmission module 2 and the aggregation module 3. After the transmission module 2 completes the forwarding operation of the cleaned data and the aggregation module 3 completes the operation based on the received data, the control jumps and returns to the operation stage of the transmission module 2.

[0060] Among them, during the operation phase of the retrieval unit 21, the interval storing data in the upload module 1 is used as the target for a single operation to retrieve data, and all data in the retrieval interval is sent to the transmission module 2;

[0061] The summarizing module 3 is used to continuously obtain the data that has been cleaned in the transmission module 2, summarize the continuously obtained cleaned data, and import the data into the inversion module 4;

[0062] The logic of the aggregation operation for continuously acquired data in aggregation module 3 is as follows:

[0063] Based on the spatial regions corresponding to each interval in the upload module 1, a spatial region matrix is constructed, data belonging to each spatial region is placed based on the spatial region matrix, and the data in the spatial region matrix is enhanced and summarized using geoelectric structure tensor analysis and directional response function;

[0064] After the summary is completed, the inversion module 4 is jumped to run.

[0065] Specifically, after receiving the cleaned data from the transmission module 2, the aggregation module 3 first constructs a spatial region matrix and executes an anomaly enhancement aggregation method guided by the geoelectric structure tensor. The specific steps are as follows:

[0066] 1. Construction and preliminary integration of the spatial region matrix: The spatial region matrix is a structured expression of the spatial regions corresponding to each interval in the upload module (1). It is not just a simple division of the physical space, but a data structure with data organization and analysis functions. The construction method of the spatial region matrix is as follows:

[0067] First, a 3D grid structure is constructed based on the spatial region defined by the four spatial coordinates and height values corresponding to each interval. The resolution of this grid can be dynamically determined based on exploration requirements and data density to ensure that geological variations are fully represented without causing redundant calculations. Each grid cell corresponds to a 3D voxel, which is used to store the geoelectric parameters at that location.

[0068] The formal expression is: for the spatial region corresponding to the kth interval, construct the spatial region matrix , where the matrix elements Indicates position in space If some cells do not have data points directly falling into them, they are temporarily marked as empty or preliminary interpolation is performed using adjacent data. Through this process, an initial three-dimensional geoelectric parameter field covering the entire detection area is formed. This parameter field is a spatialized data set, where each cell c has a preliminary IP parameter value .

[0069] 2. Calculation of local geoelectric structure tensor: In order to capture the local structural characteristics of the electrical parameters of the underground medium, the initial geoelectric parameter field For each cell c in the grid, calculate the local geoelectric structure tensor in its neighborhood This tensor can describe the direction of change and degree of anisotropy of the parameter field in the local area. For three-dimensional data, the structure tensor can be obtained by the outer product of the gradient in the local window. The inner weighted average is:

[0070] ,

[0071] Where, is cell c (center coordinates are ) Local geoelectric structure tensor. is the local computation window centered at cell c. is the weighting function within the window. is the gradient vector of the initial geoelectric parameter field at point r. Represents the transpose of a vector. The eigenvalue of and the corresponding eigenvector The changing characteristics of the local geoelectric parameter field are revealed: Indicates the direction of the largest parameter change, Characterizes the intensity of change in this direction.

[0072] 3. Definition of directional anomaly response function: Based on geological prior information, set one or more priority geological structure direction vectors Then, define a directional anomaly response function , used to enhance Geoelectric anomalies with consistent direction and significant structure:

[0073] ,

[0074] in, is the enhanced abnormal response value at cell c. is a positive modulation coefficient used to control the strength of structural information enhancement. It is an indicator of the linearity or planarity of the structure. is a small positive constant that prevents the denominator from being zero. It represents the square of the degree of alignment between the main change direction of the parameter field and the priority geological structure direction. The value ranges from [0,1], and the higher the alignment, the larger the value. is the original IP parameter value at cell c relative to the background value The deviation represents the amplitude of the basic abnormal signal.

[0075] 4. Generate enhanced summary data: Finally, the summary data field output by the summary module 3 for use by the inversion module 4 , where the value of each cell c is given by Given. That is: , such aggregated data Not only does it integrate the measurement information of all intervals, but it also enhances the geoelectric anomaly signals related to the target geological structure and mineralization through the application of structural tensor analysis and directional response function, while possibly suppressing some random noise and non-target responses.

[0076] Inversion module 4 is used to apply data to generate a geological model and identify potential ore body size information in the geological model;

[0077] During the inversion module 4 operation phase, an initial geoelectric model containing structural information is established through a preliminary inversion that takes into account wellbore geometry and data sensitivity, combined with the geological structure, physical properties of the ore deposit, and existing geological data of the study area. The structural information of this initial geoelectric model is used to guide the regularization process of the multi-source IP 3D inversion software. The processed IP data is input, and based on iterative calculations, the model parameters are repeatedly adjusted to achieve the best fit between the model response and the measured data. The three-dimensional spatial distribution of the underground resistivity and polarizability is obtained, and the location, shape, and extension span of the ore body are identified in the model.

[0078] Among them, the geoelectric model construction stage is based on the geological model, and the potential ore body scale information is the location, shape and extension span of the ore body;

[0079] Inversion module 4 achieves integration by guiding the regularization process of RES3DINV inversion software through the model structure generated by the Occam inversion algorithm;

[0080] Among them, RES3DINV is the inversion software.

[0081] Specifically, in order to more effectively utilize high-power IP data in wells and between wells and adapt to the complex geological conditions of old mining areas, the present invention adopts a guided hybrid regularized inversion framework in the inversion module 4. The content of this framework is as follows:

[0082] Phase 1: Occam-type preliminary inversion based on wellbore geometry and data sensitivity constraints

[0083] This stage aims to use the Occam-like algorithm to perform an analysis on the enhanced summary data output by the summary module 3. Preliminary inversion generates a background / reference model that is relatively smooth in structure but fits the main data features well and reflects the wellbore geometry constraints and data sensitivity distribution .

[0084] Its objective function can be expressed as:

[0085] ,

[0086] ,

[0087] ,

[0088] in, is the three-dimensional geoelectric parameter model to be inverted; is the data fitting term; is the aggregated observation data The actual data vector after sorting for inversion; According to the model Calculated forward response operator; It is a data weighting matrix, whose weights can be determined according to the signal-to-noise ratio of the original data and the contribution of each data point to the structure enhancement calculated in the GST-AEA process; is the regularization parameter, which controls the balance between data fitting terms and model constraint terms; is the model constraint term (regularization term); It is an a priori reference model, which can be a homogeneous half-space model or an initial model based on geological knowledge of a large region; is the spatial weighted sum (or difference) operator of the model parameters, which is improved by the present invention: The weight coefficients of will be spatially adaptively adjusted according to the geometric distribution of the wellbore and the sensitivity of each model unit to the wellbore and inter-well observation data (which can be obtained from the Jacobian matrix or its approximation). Specifically, in areas with high sensitivity to wellbore and inter-well data, Relatively weak smoothing constraints are imposed (allowing the model to have a higher parameter change gradient); while in deep or marginal areas far away from the wellbore and with weaker data constraints, strong smoothing constraints are imposed to ensure the stability and geological rationality of the model. In addition, Directional smoothing can also be introduced, for example allowing greater parameter continuity in directions parallel to major geological structures (such as bedding planes, faults), while allowing more significant parameter jumps in the vertical direction.

[0089] Phase 2: Based on Structure-guided RES3DINV fine inversion

[0090] This stage uses 3D inversion software for iterative inversion. Its core is to use the model generated in stage 1 to guide the regularization process to better recover deep and complex ore bodies while maintaining model stability.

[0091] The inversion of RES3DINV software usually also optimizes an objective function including data fitting term and model regularization term. Improvements are made to form structure-guided hybrid regularization, as follows:

[0092] ,

[0093] in, It is the three-dimensional geoelectric parameter model that RES3DINV is optimizing.

[0094] The first term is a structure-preserving smoothness constraint: It is the L2 norm of the model parameter gradient, which promotes model smoothness; is the spatial weight function, which is related to The extracted structural information is anti-correlated with The gradient magnitude at position r Inversely correlated. That is, Display as smooth ( smaller) areas, Larger, encouraging Also remains smooth; while Significant structural changes were shown ( larger, possibly indicating geological boundaries or the edges of anomalies), Smaller, allowed Producing sharper boundaries or more significant abnormal changes in these areas, thereby better recovering local details.

[0095] The second term is the reference model constraint term: Penalize the current model Compared with the reference model obtained in stage 1 The difference between the spatial weight According to The trust level of different areas at position r is set.

[0096] 、 is a hyperparameter that balances the two regularization terms and the data fitting term.

[0097] In addition, during the RES3DINV inversion process, the grid division can also be based on Data weighting should also take into account the characteristics of high-power IP in-well and inter-well data, such as assigning higher weight to data with high sensitivity and high signal-to-noise ratio at depth.

[0098] Through this two-stage, structure-guided hybrid regularized inversion framework, inversion module 4 can make more effective use of high-power IP data in and between wells, overcome the complex geological conditions and electromagnetic interference in old mining areas, and generate geological models that can not only better fit the observed data, but also clearly and accurately reflect the spatial location, occurrence, and scale information of deep potential ore bodies.

[0099] The storage module 5 is used to receive the identification result of the potential ore body scale information in the inversion module 4 and store the identification result;

[0100] The visualization module 6 is used to read the ore body scale information stored in the storage module and generate a three-dimensional model of the ore body based on the ore body scale information;

[0101] The visualization module 6 is integrated with a computer device having a display function. The three-dimensional model of the ore body generated by the operation of the visualization module 6 is displayed in real time through the computer device for visual reading by the system end user;

[0102] The upload module 1 is interactively connected to the transmission module 2 and the aggregation module 3 through the local area network. The transmission module 2 is interactively connected to the retrieval unit 21 and the control unit 22 through the local area network. The retrieval unit 21 is interactively connected to the upload module 1 through the local area network. The control unit 22 is interactively connected to the aggregation module 3 through the local area network. The aggregation module 3 is interactively connected to the inversion module 4, the storage module 5 and the visualization module 6 through the local area network.

[0103] In this embodiment, the upload module 1 runs to upload the induced polarization data of the deep geological body in the old mining area and stores the data. The transmission module 2 runs in the back-end to receive the data stored in the upload module 1, cleans the data, and forwards the cleaned data to the summary module 3 after cleaning. The retrieval unit 21 synchronously retrieves the data stored in the upload module 1 and sends the retrieved data to the transmission module 2. The control unit 22 monitors the operation status of the transmission module 2 and the summary module 3 in real time. After the transmission module 2 completes the forwarding operation of the cleaned data and the summary module 3 ends its operation based on the received data, the control jumps and returns to the operation stage of the transmission module 2. The summary module 3 then continuously obtains the cleaned data in the transmission module 2, summarizes the continuously obtained cleaned data, and imports it into the inversion module 4. The inversion module 4 simultaneously uses the data to generate a geological model and identifies potential ore body scale information in the geological model. Finally, the storage module 5 receives the identification result of the potential ore body scale information in the inversion module 4 and stores the identification result. The visualization module 6 reads the ore body scale information stored in the storage module and generates a three-dimensional ore body model based on the ore body scale information.

[0104] Through the operation of the system in the above embodiment, technical support is provided for deep mineral exploration in old mining areas, so that the deep mineral resources in old mining areas can be further developed.

[0105] Example 2:

[0106] In terms of specific implementation, based on Example 1, this example refers to Figure 2 The deep prospecting and detection system based on the high-power IP method in the well and between wells in Example 1 is further described in detail:

[0107] Deep prospecting and detection methods based on high-power IP in wells and between wells include:

[0108] Step 1: Upload the IP data of the deep geological body in the old mining area and store the data;

[0109] Step 2: Retrieve data from the stored data and clean it. After all the data has been cleaned, summarize the data.

[0110] Step 3: Invert the summarized data based on the Occam inversion algorithm and RES3DINV to obtain the scale information of potential ore bodies in the deep geological bodies of the old mining area;

[0111] Step 4: Store the potential ore body scale information and construct a potential ore body three-dimensional model based on the potential ore body scale information;

[0112] Step 5: Send the ore body three-dimensional model to a computer device with a display function, and display the ore body three-dimensional model on the computer device for visual reading by the user.

[0113] In summary, the system and method in the above embodiments can carry out deep exploration in old mining areas with complex electromagnetic interference, adapt to the mine environment through high-power induced polarization transmission and reception, and have good data acquisition repeatability and high accuracy. Through in-well and inter-well induced polarization observations, combined with three-dimensional inversion technology, the deep geological structure and ore body distribution can be clearly presented. At the same time, for the expansion and deepening of old mining areas, it can effectively explore the distribution characteristics of ore bodies around known drill holes, discover deep mineral anomalies, accurately locate potential ore bodies, improve prospecting efficiency, reduce exploration blindness, provide key technical support for the secondary development of old mining area resources, and contribute to the sustainable development of the mining industry.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A deep prospecting and detection system based on high-power IP in wells and between wells, characterized by: include: Upload module (1), used to upload induced polarization data of deep geological bodies in old mining areas and store the data; The transmission module (2) is used to receive the data stored in the upload module (1), clean the data, and after the cleaning is completed, forward the cleaned data to the aggregation module (3); A summary module (3) is used to continuously obtain the data that has been cleaned in the transmission module (2), summarize the continuously obtained cleaned data, and import the data into the inversion module (4); Inversion module (4), used to generate a geological model using the data and identify potential ore body size information in the geological model; A storage module (5) is used to receive the identification result of the potential ore body scale information in the inversion module (4) and store the identification result; A visualization module (6) is used to read the ore body scale information stored in the storage module and generate a three-dimensional ore body model based on the ore body scale information; The summarization module (3) performs an anomaly enhancement summarization operation guided by a geoelectric structure tensor on the continuously acquired IP data, and the operation includes the following steps: a) Based on the IP data points and their three-dimensional spatial coordinates corresponding to each interval collected by the upload module (1), an initial three-dimensional geoelectric parameter field covering the entire detection area is constructed. as a spatial region matrix; b) Calculating the initial three-dimensional geoelectric parameter field For each cell c in the grid, calculate the local geoelectric structure tensor in its neighborhood : ， Where, is in cell c local geoelectric structure tensor; is the local computation window centered on cell c; is the weighting function within the window; is the gradient vector of the initial geoelectric parameter field at point r; Represents the transpose of a vector; the local geoelectric structure tensor Used to characterize the local change direction and anisotropy degree of the parameter field; c) Based on the local geoelectric structure tensor The eigenvalue of and the corresponding main eigenvectors , combined with the preset priority geological structure direction vector , define and apply the directional anomaly response function Calculate the enhanced abnormal response value at cell c; where, Expressed as: ， in, is the enhanced abnormal response value at cell c; is a positive modulation coefficient; To prevent small positive constants with zero denominators; is the original IP parameter value at cell c relative to the background value Deviation; d) Generate a three-dimensional geoelectric parameter field after anomaly enhancement and summary , where the value of each cell c is given by is given and passed to the inversion module (4).

2. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 1 is characterized in that: When storing data, the upload module (1) sets a number of intervals and stores the data in a differentiated manner based on the number of intervals; Each interval used to store data corresponds to four spatial coordinates and a height value. The four spatial coordinates corresponding to each interval define a plane, and combined with the height value, define a spatial area in the deep geological body of the old mining area. The data stored in the interval all come from the corresponding spatial area; The specification parameters and sizes of the spatial regions corresponding to each interval for storing data are equal.

3. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 1 is characterized in that: The induced polarization data of the deep geological body in the old mining area uploaded by the upload module (1) during the operation phase include: potential data, resistivity data, polarizability data, electromagnetic background noise in the well, and complex resistivity, density and magnetic susceptibility of the rock sample; When the transmission module (2) cleans the data, the cleaning operations include: deleting duplicate data, discarding incomplete data, denoising and normalizing; When the transmission module (2) performs a cleaning operation, the incomplete data found is fed back to the system end user, and the system end user makes a self-defined decision on whether to re-upload the complete data corresponding to the incomplete data. After the re-upload operation is performed, the cleaning operation for the data in the transmission module (2) is refreshed and executed synchronously until no data is re-uploaded.

4. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 1 is characterized in that: The transmission module (2) is provided with a retrieval unit (21) and a control unit (22) at the lower level. The retrieval unit (21) is used to retrieve the data stored in the upload module (1) and send the retrieved data to the transmission module (2). The control unit (22) is used to monitor the operating status of the transmission module (2) and the aggregation module (3). After the transmission module (2) completes the forwarding operation of the cleaned data and the aggregation module (3) completes the operation based on the received data, the control jump is controlled to return to the operation stage of the transmission module (2). In the operation phase of the retrieval unit (21), the interval storing data in the upload module (1) is used as the target for single-run data retrieval, and all data in the retrieval interval is sent to the transmission module (2).

5. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 1 is characterized in that: The inversion module (4) adopts a guided hybrid regularized inversion framework, and realizes integration by guiding the regularization process of the RES3DINV inversion software through the model structure generated by the Occam inversion algorithm; the guided hybrid regularized inversion framework includes the following two stages executed in sequence: Phase 1: receiving the three-dimensional geoelectric parameter field after anomaly enhancement and aggregation from the aggregation module (3) as observation data, and performing preliminary inversion using the Occam inversion algorithm; wherein, the smoothing constraint weight in the model constraint item of the preliminary inversion is spatially adaptively adjusted according to the geometric distribution of the wellbore and the sensitivity of each model unit to the observation data, so as to allow the model to have a higher parameter change gradient in the area with strong data constraints and impose a strong smoothing constraint in the area with weak data constraints, thereby generating a preliminary three-dimensional geoelectric parameter model containing structural features determined by the spatially adaptively adjusted smoothing constraint; Phase 2: Use RES3DINV inversion software to perform a detailed inversion of the preliminary three-dimensional geoelectric parameter model and the structural features it contains; wherein, structural information is extracted from the preliminary three-dimensional geoelectric parameter model, and the structural information is used to introduce structural guidance constraints in the regularization process of RES3DINV inversion software. The structural guidance constraints impose stronger smoothing constraints in areas where the preliminary three-dimensional geoelectric parameter model appears smooth, while allowing the model to produce sharper boundaries or more significant abnormal changes in areas where the preliminary three-dimensional geoelectric parameter model shows significant structural changes, ultimately obtaining a detailed three-dimensional geoelectric parameter distribution for identifying the location, shape and extension span of the ore body.

6. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 5 is characterized by: The Occam inversion algorithm in stage 1 minimizes the objective function To solve the preliminary three-dimensional geoelectric parameter model, the objective function is expressed as: ， in, is the observation data; is the three-dimensional geoelectric parameter model to be inverted; is the data fitting term; Based on the model Calculated forward response operator; is the data weighting matrix; is the model constraint; is the a priori reference model; is the spatial weighting operator of the model parameters; is the regularization parameter, which controls the balance between data fitting terms and model constraint terms; The structure-guided constraint introduced in the second stage is achieved by adding a structure-guided hybrid regularization term to the objective function of the regularization process of the RES3DINV inversion software. The regularization term is designed as follows: ， in, The three-dimensional geoelectric parameter model is being optimized for RES3DINV; is a preliminary three-dimensional geoelectric parameter model; the first term is a smooth constraint term for structure preservation, and its spatial weight function and The gradient magnitude at position r Anti-correlation; the second term is the reference model constraint term, and its spatial weight According to Trust level setting for different regions at location r; 、 is the regularization parameter to balance the terms.

7. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 1 is characterized in that: The visualization module (6) is integrated by a computer device with a display function, and the three-dimensional model of the ore body generated by the operation of the visualization module (6) is displayed in real time through the computer device for visual reading by a system end user.

8. The deep prospecting and detection system based on the high-power IP method in wells and between wells according to claim 1 is characterized in that: The upload module (1) is interactively connected to the transmission module (2) and the aggregation module (3) via a local area network; the transmission module (2) is interactively connected to a retrieval unit (21) and a control unit (22) via a local area network; the retrieval unit (21) is interactively connected to the upload module (1) via a local area network; the control unit (22) is interactively connected to the aggregation module (3) via a local area network; and the aggregation module (3) is interactively connected to the inversion module (4), the storage module (5) and the visualization module (6) via a local area network.

9. A deep prospecting and detection method based on high-power induced polarization in wells and between wells, the method being an implementation method of a deep prospecting and detection system based on high-power induced polarization in wells and between wells as claimed in any one of claims 1 to 8, characterized in that: include: Step 1: Upload the IP data of the deep geological body in the old mining area and store the data; Step 2: Retrieve data from the stored data and clean it. After all the data has been cleaned, summarize the data. Step 3: Invert the summarized data based on the Occam inversion algorithm and RES3DINV to obtain the scale information of potential ore bodies in the deep geological bodies of the old mining area; Step 4: Store the potential ore body scale information and construct a potential ore body three-dimensional model based on the potential ore body scale information; Step 5: Send the ore body three-dimensional model to a computer device with a display function, and display the ore body three-dimensional model on the computer device for visual reading by the user.

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