A survey device for geological mineral prospecting

Abstract

The present application relates to the technical field of geological exploration, and particularly relates to a geological and mineral exploration device, which comprises a rack, a sample collection mechanism, a multi-parameter detection module and a data analysis processing module, the sample collection mechanism is integrated on the top of the rack and is used for obtaining rock and mineral samples from strata; the multi-parameter detection module is integrated on the rack and is used for analyzing element content, fluid properties and microstructure of the samples obtained by the sample collection mechanism; the data analysis processing module is used for receiving and fusing data from the multi-parameter detection module, generating element geochemical maps, mineralization zoning models and the correlation with geological structures. The present application integrates sample collection, multi-parameter detection and intelligent data analysis processing in an integrated device, thereby improving the efficiency and accuracy of field geological exploration.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and more specifically to an exploration device for geological and mineral prospecting. Background Technology

[0002] Geological and mineral exploration originates from the interdisciplinary integration of geology, geophysics, geochemistry, remote sensing science, and modern information technology. Early mineral exploration relied primarily on traditional methods such as manual reconnaissance, heavy mineral analysis, and shallow engineering excavation, which were inefficient, had limited coverage, and could not meet the needs of deep and large-scale exploration. With technological advancements, geophysical methods such as magnetics, electrical resistivity (including induced polarization and electromagnetic methods), gravity, and radiometric measurements have been widely applied, indirectly identifying mineralization anomalies by detecting differences in the physical properties of underground rocks or ore bodies. Simultaneously, geochemical exploration provides mineralization indicators by analyzing anomalies in elemental content in soil, stream sediments, vegetation, or gases; it has high sensitivity but is susceptible to interference from subsequent geological processes.

[0003] While existing technologies for geological and mineral exploration equipment have made significant progress, several shortcomings remain in practical applications. Specifically, some equipment relies on manual sample collection followed by laboratory testing, a cumbersome and time-consuming process that fails to meet the demands of rapid response in exploration. Samples are susceptible to contamination, oxidation, or structural damage during transportation and storage, making it difficult to preserve their original geological information and affecting the accuracy and representativeness of analytical results. Furthermore, some equipment has limited functionality, often only acquiring a single type of parameter and lacking the ability to obtain multi-dimensional information such as elemental combinations, leading to a one-sided understanding of the mineralization process and reduced exploration efficiency.

[0004] Therefore, this invention proposes an exploration device for geological and mineral prospecting. By integrating sample collection, multi-parameter detection, and intelligent data analysis and processing into a single device, it improves the efficiency and accuracy of field geological exploration. It can complete multi-dimensional analysis on-site, from rock and ore collection to elemental content, fluid properties, and microstructure, ensuring the authenticity and timeliness of the data. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides an exploration device for geological and mineral prospecting, which integrates sample collection, multi-parameter detection, and intelligent data analysis and processing into a single device, thereby improving the efficiency and accuracy of field geological exploration.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: an exploration device for geological mineral prospecting, comprising a frame, a sample collection mechanism, a multi-parameter detection module, and a data analysis and processing module, the functions of each component being as follows:

[0007] The sample collection mechanism, integrated on the top of the frame, is used to obtain rock and mineral samples from the strata;

[0008] The multi-parameter detection module, integrated on the rack, is used to analyze the elemental content, fluid properties, and microstructure of samples acquired by the sample acquisition mechanism.

[0009] The data analysis and processing module is used to receive and integrate data from the multi-parameter detection module to generate elemental geochemical maps, mineralization zoning models, and their correlation with geological structures.

[0010] Furthermore, the multi-parameter detection module includes:

[0011] The first analytical chamber, equipped with a built-in spectrometer, is used for on-site quantitative analysis of the elemental content of Ag, Pb, Zn, As, and Sb.

[0012] The second analysis chamber contains built-in microscopic testing components for measuring the temperature and salinity of fluid inclusions.

[0013] The third analysis chamber is equipped with a desktop scanning electron microscope and an energy dispersive spectrometer, used for micro-area morphology observation and composition analysis of ore sections or powder samples.

[0014] Furthermore, the data analysis and processing module is configured to perform the following operations:

[0015] S101: Receive the analysis results from the multi-parameter detection module and bind them with the GNSS coordinates, strata, and structural information of the acquisition point.

[0016] S102. Call the built-in Kriging interpolation algorithm to generate and update the elemental geochemical map of the study area in real time.

[0017] S103. Perform GIS overlay analysis on the elemental geochemical map and the geological structure map to calculate the elemental enrichment statistics within different tectonic units.

[0018] Furthermore, the steps of the Kriging interpolation algorithm are as follows:

[0019] S201. Receive and bind the spatial coordinates and elemental content data of newly collected samples in real time, and update the experimental dataset.

[0020] S202. Based on the updated experimental dataset, recalculate and fit the theoretical variation function model of anisotropy.

[0021] S203. Using the updated theoretical variation function model, the grid of the study area is re-interpolated to generate an updated spatial distribution map of elements.

[0022] S204. Simultaneously calculate and output the Kriging variance distribution map, where areas with variances higher than a preset threshold are marked as areas with insufficient data control. This marking is used to guide the next step of optimization sampling decisions in real time.

[0023] Furthermore, the data analysis and processing module also has a built-in ore-forming information extraction algorithm. Based on element combinations, alteration mineral combinations, and fluid temperature and salinity parameters, the ore-forming information extraction algorithm identifies and delineates hydrothermal activity centers and favorable ore-forming zones, and presents them visually.

[0024] Furthermore, the steps of the mineralization information extraction algorithm are as follows:

[0025] S301. Receive and standardize multiple data streams from the multi-parameter detection module to generate a spatially aligned grid data layer. The grid data layer includes a comprehensive geochemical anomaly index layer, an alteration combination intensity index layer, a tectonic complexity index layer, and a fluid temperature anomaly index layer.

[0026] S302. By using a weighted summation model, the index layers obtained in S301 are merged to generate a comprehensive mineralization favorable zone layer.

[0027] S303. Based on the comprehensive mineralization favorability layer, anomaly thresholds are determined through statistical analysis, mineralization favorable areas of different levels are delineated, and local extreme points are identified as hydrothermal activity centers.

[0028] S304. Generate a spatial distribution map and text report containing favorable mineralization zones and hydrothermal activity centers. The text report includes target area ranking and recommendations for the next steps.

[0029] Furthermore, the sample collection mechanism includes a sampling cylinder rotatably connected to the frame, and the sampling cylinder is equipped with a core-taking component for acquiring samples and a cutting component for cutting samples; the frame is equipped with a drive component for driving the sampling cylinder to perform core drilling and a regulating component for adjusting the feed depth of the sampling cylinder.

[0030] The drive assembly includes a controller and a first drive component. The first drive component is located on the frame, and the controller is electrically connected to the first drive component. The output shaft of the first drive component is coaxially fixedly connected to a drive wheel, and the sampling cylinder is coaxially fixedly connected to a driven wheel. The drive wheel and the driven wheel are in a transmission cooperation. When the core sampling assembly is running, it synchronously drives the cutting assembly to run in order to complete the sampling work.

[0031] Furthermore, the adjustment assembly includes a second drive unit fixedly connected to the top of the frame, the second drive unit being electrically connected to the controller; a lead screw is coaxially fixedly connected to the output shaft of the second drive unit, a nut seat is threaded onto the lead screw, and the nut seat is slidably engaged with the frame; the sampling cylinder is rotatably connected to the bottom of the nut seat, and the first drive unit is fixedly connected to the outer wall of the nut seat.

[0032] Furthermore, the core sampling assembly includes a third driving component fixedly connected to the top wall of the sampling cylinder, and the controller is electrically connected to the third driving component; a rotating shaft is coaxially fixedly connected to the output shaft of the third driving component, and a turntable is coaxially fixedly connected to the end of the rotating shaft away from the third driving component, and spiral strips are symmetrically fixedly connected to the outer wall of the turntable; a T-shaped impact block is slidably fitted to the inner wall of the sampling cylinder, and a locking rod is symmetrically rotatably connected to the inner wall of the impact block, and the locking rods are all in contact with the spiral strips; a tension spring is fixedly connected to the top of the impact block, and the end of the tension spring away from the impact block is fixedly connected to the inner wall of the sampling cylinder.

[0033] Furthermore, the cutting component includes several cavities circumferentially disposed within the side wall of the sampling cylinder. Each cavity has a piston plate slidably fitted on its inner wall, and each piston plate has a cutting block fixedly connected to its outer wall. A sealed space is formed between the sampling cylinder and the impact block, and each cavity is connected to the sealed space. Each cavity has several baffles fixedly connected to the side away from the piston plate.

[0034] The above approach has the following beneficial effects:

[0035] 1. This solution improves the efficiency and accuracy of field geological exploration by integrating sample collection, multi-parameter detection, and intelligent data analysis and processing into a single device. Traditional mineral exploration processes typically require phased completion of sampling, sample delivery, laboratory analysis, and subsequent data integration, resulting in long cycles, high costs, and susceptibility to human error. This device, however, can perform multi-dimensional analysis on-site, from rock and ore collection to elemental content, fluid properties, and microstructure, and instantly generate geochemical maps and mineralization models. This shortens the exploration cycle, reduces contamination or degradation during sample transportation and preservation, and ensures the authenticity and timeliness of the data. Furthermore, the modular design of the device facilitates deployment and movement in complex terrains, enhancing its adaptability to remote or inaccessible mining areas.

[0036] 2. This scheme introduces a Kriging interpolation-based update mechanism and a data control deficiency area identification function to achieve intelligent guidance and adaptive optimization of the exploration process. After each new sample analysis, the device automatically updates the variogram model and redraws the elemental distribution map, making the geochemical anomaly characterization closer to the actual geological conditions. By outputting the Kriging variance distribution map and identifying low control areas, it can prompt operators to supplement sampling at key locations, reducing blind sampling and improving the utilization efficiency of exploration resources. This working mode shifts the mineral exploration process from experience-driven to data-driven, improving the scientific rigor and reliability of target area delineation.

[0037] 3. This scheme integrates geochemical, mineralogy, fluid geochemistry, and tectonic information to construct a multi-source collaborative mineralization information extraction algorithm, effectively improving the identification capability of deep and concealed ore bodies. Unlike single-index discrimination methods, this algorithm comprehensively considers multiple geological factors such as elemental enrichment characteristics, alteration mineral assemblage intensity, fluid inclusion temperature and salinity parameters, and tectonic complexity. It generates a comprehensive mineralization favorability layer through standardization and weighted fusion, and identifies hydrothermal activity centers and high-potential target areas. This multi-parameter coupled analysis is more consistent with the mineralization patterns of hydrothermal deposits. Simultaneously, the results are output in the form of visualized maps and structured text reports, supporting rapid on-site decision-making and providing clear technical basis for subsequent drilling verification, thus promoting the development of geological prospecting towards precision and intelligence.

[0038] 4. This solution integrates core extraction and truncation functions into the same sampling cylinder and achieves synchronous linkage between the two in terms of driving logic, thereby improving the integrity and automation level of rock and mineral sample collection. During core extraction, a third driving component drives the turntable and spiral to rotate, which, through the cooperation of the clamping rod and tension spring, drives the impact block to reciprocate. This impact block not only assists in the impact during core extraction, but its movement also changes the volume of the confined space, thereby generating pressure changes that drive the piston plate to push the truncation block outward, completing the truncation of the obtained rock core; ensuring that the sample is completely extracted, providing a high-quality sample basis for subsequent multi-parameter detection.

[0039] 5. This solution utilizes a drive and adjustment mechanism design to achieve coordinated control of sampling depth, rotation speed, and feed rate, enhancing the device's adaptability and operational stability across different lithological formations. The drive assembly employs a transmission method, featuring a compact structure and smooth transmission. The adjustment assembly, through a second drive component, moves the lead screw and nut seat, allowing the sampling cylinder to smoothly rise and fall vertically along the frame, thus adjusting the feed depth. This mechatronic control mechanism not only improves sampling efficiency but also adjusts parameters according to formation hardness, reducing the risk of drill jamming or damage. It is suitable for sampling operations in complex structural areas or in alternating layers of soft and hard strata, providing reliable technical support for subsequent high-precision exploration and analysis. Attached Figure Description

[0040] Figure 1 This is a system block diagram of the exploration device for geological and mineral prospecting according to the present invention.

[0041] Figure 2 For the present invention Figure 1 A flowchart illustrating the data analysis and processing module.

[0042] Figure 3 For the present invention Figure 2 A flowchart illustrating the Kriging interpolation algorithm.

[0043] Figure 4 For the present invention Figure 2 A flowchart illustrating the algorithm for extracting information from mineralized deposits.

[0044] Figure 5 This is an isometric view of the exploration device for geological and mineral prospecting according to the present invention.

[0045] Figure 6 For the present invention Figure 5 A frontal sectional view of the sampling tube.

[0046] Figure 7 For the present invention Figure 6 A partial cross-sectional view of the sampling tube.

[0047] Figure 8 For the present invention Figure 6 Enlarged view of part A in the middle.

[0048] The reference numerals in the accompanying drawings of the instruction manual include: 1. Frame; 2. Sampling cylinder; 3. First driving component; 4. Driven wheel; 5. Second driving component; 6. Lead screw; 7. Nut seat; 8. Third driving component; 9. Turntable; 10. Spiral strip; 11. Impact block; 12. Clamping rod; 13. Tension spring; 14. Cavity; 15. Piston plate; 16. Cut-off block; 17. Stop block. Detailed Implementation

[0049] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The following detailed description illustrates the specific implementation method:

[0051] The basic implementation examples are as follows: Figures 1-5 As shown: An exploration device for geological and mineral prospecting includes a frame 1, a sample collection mechanism, a multi-parameter detection module, and a data analysis and processing module. The functions of each component are as follows:

[0052] The sample collection mechanism is integrated on the top of the frame 1 and is used to obtain rock and mineral specimens and analyze samples from the strata;

[0053] A multi-parameter detection module is integrated on the rack 1 and is used to analyze the elemental content, fluid properties, and microstructure of samples acquired by the sample collection mechanism. In this embodiment, the multi-parameter detection module also integrates a sensor (such as a short-wave infrared spectrometer-SWIR or a portable XRD) for in-situ identification of altered minerals. Preferably, the multi-parameter detection module includes a first analysis chamber, a second analysis chamber, and a third analysis chamber.

[0054] The first analytical chamber is equipped with a built-in spectrometer (such as laser-induced breakdown spectroscopy (LIBS) or portable X-ray fluorescence spectrometer (XRF) for rapid analysis of major and trace elements). The first analytical chamber is used for on-site quantitative analysis of the elemental content of Ag, Pb, Zn, As, and Sb.

[0055] The second analysis chamber houses a built-in microscopic testing assembly, which includes a heating and cooling stage, microscopic optical elements, and an image acquisition unit. This chamber is used to measure the homogenization temperature and salinity of fluid inclusions in transparent mineral sections. The image acquisition unit acquires images of the rock's microfractures and pore structure.

[0056] The third analysis chamber is equipped with a desktop scanning electron microscope and an energy dispersive spectrometer, forming a scanning electron microscope-energy dispersive spectrometer. The third analysis chamber is used for micro-area morphology observation and composition analysis of ore sections or powder samples.

[0057] The data analysis and processing module receives and integrates data from the multi-parameter detection module, generating elemental geochemical maps, mineralization zoning models, and visualizing their correlation with geological structures. The module also includes a built-in ore-forming information extraction algorithm. This algorithm identifies and delineates hydrothermal activity centers and favorable mineralization zones based on elemental assemblages (e.g., Ag-As-Sb), alteration mineral assemblages (e.g., sericitization-silicification), and fluid temperature and salinity parameters, and then visualizes these zones on a touchscreen display.

[0058] Specifically, the data analysis and processing module is configured to perform the following operations:

[0059] S101: Receive the analysis results of multiple data streams from the multi-parameter detection module and bind them with the GNSS coordinates, strata and structural information of the acquisition point.

[0060] S102. Call the built-in Kriging interpolation algorithm to generate and update the geochemical map of elements (such as Ag, Pb, Zn, etc.) in the study area in real time.

[0061] S103. Perform GIS overlay analysis on the elemental geochemical map and the geological structure map to calculate the elemental enrichment statistics within different tectonic units.

[0062] Specifically, the steps of the Kriging interpolation algorithm in S102 are as follows:

[0063] S201. Receive and bind the spatial coordinates (coordinates x_i, y_i, z_i, where z_i can be elevation or depth) and elemental content data (e.g., Ag grade v_i) of newly collected samples in real time, and update the experimental dataset {(x_i, y_i, z_i, v_i), i=1,...,n}. Automatically remove outliers that significantly exceed the physical range (e.g., negative content), and perform a normality test on the data. If the data deviates significantly from a normal distribution (common in geological data), automatically perform logarithmic transformation or apply other data transformation methods to meet the basic assumptions of the Kriging algorithm regarding data distribution.

[0064] S202. Based on the updated experimental dataset, recalculate and fit the theoretical variogram model for anisotropy. Specifically, the steps for calculating the experimental variogram are as follows:

[0065] Group the azimuth and distance (lag distance h) between sampling point pairs. For example, set a distance tolerance h ± Δh and an angle tolerance α ± Δα; for a specific lag distance group h in a certain direction, calculate half the mean of the squares of the attribute value differences of all point pairs within the group; the formula is as follows:

[0066] γ(h)=1 / (2N(h))*Σ[v(x_i)-v(x_i+h)]² (1)

[0067] Where N(h) is the number of point pairs in the lag distance group.

[0068] Plotting the calculated γ(h) against the lag distance h yields an experimental variogram, along with a variogram cloud to help identify outlier pairs.

[0069] Specifically, the steps for fitting the theoretical variation function model are as follows:

[0070] Model selection: A selection of common geostatistical models is available, or automatic fitting is possible. For example, the spherical model (most commonly used) represents spatial correlation that gradually weakens over a certain distance (range) and then remains constant. The exponential model shows that correlation decays exponentially with distance. The Gaussian model is suitable for phenomena with very continuous and smooth spatial variations.

[0071] Parameter fitting: Range is the distance at which spatial correlation disappears; points beyond this distance have no spatial correlation. Sill value is the value at which the variogram reaches stationarity, representing the population variance of the data. Nut value is the intercept of the model when the lag distance is 0, representing microscale variability and measurement error.

[0072] Anisotropy analysis: Calculate the experimental variogram in different directions (e.g., 0°, 45°, 90°, 135°) to determine if spatial anisotropy exists (e.g., different correlations along the strike and dip of the ore body). If so, an anisotropy model needs to be fitted.

[0073] S203. Using the updated theoretical variation function model, the grid of the study area is re-interpolated to generate an updated spatial distribution map of elements.

[0074] Specifically, in S203, the fitted variation function model is used to estimate the value of each point (grid node) to be estimated within the study area.

[0075] For each point x0 to be estimated, a system of linear equations is established based on the conditions of unbiasedness and optimality (minimum estimation variance). The coefficients in the system of equations are determined by the covariance between the point to be estimated and the surrounding known sampling points, as well as the covariance between the sampling points (derived from the variation function model).

[0076] To avoid global computation, a search neighborhood (e.g., an ellipse with radius equal to range) is defined for each point to be estimated. Only known points within this neighborhood are used for estimation, improving computational efficiency and conforming to the local stationarity assumption.

[0077] Solving the Kriging equations yields a set of Kriging weights λ_i used for estimation. The value v*(x0) of the point to be estimated, x0, is calculated by a weighted average of the surrounding known points, as shown in the following formula:

[0078] v*(x0)=Σλ_i*v(x_i)(2)

[0079] At the same time, the algorithm will provide the kriging variance σ for each estimate. 2 _k(x0) is a measure of valuation uncertainty; regions with large variance indicate insufficient data control and are target areas that require further encrypted sampling.

[0080] S204. Simultaneously calculate and output the Kriging variance distribution map, where areas with variances higher than a preset threshold are marked as areas with insufficient data control. This marking is used to guide the next step of optimization sampling decisions in real time.

[0081] Specifically, in S204, the grid diagram is generated: after traversing all grid nodes to complete the estimation, the predicted value grid and the kriging variance grid for that element are generated.

[0082] Visualization rendering: The GIS engine in the data processing module renders the predicted value grid as a contour map or a color gradient map, intuitively displaying the spatial distribution pattern of elements (such as high-value areas, low-value areas, and zonal characteristics). Kriging variance plots are usually overlaid with semi-transparent layers or independent layers to indicate the reliability of the prediction.

[0083] Anomaly delineation: Based on user-defined thresholds (e.g., mean plus 2 standard deviations), the system automatically delineates geochemical anomalies on the elemental distribution map and performs spatial overlay analysis with the geological structure map, directly serving the prediction of mineral exploration target areas.

[0084] The preferred steps of the mineralization information extraction algorithm are as follows:

[0085] S301. Receive and standardize multiple data streams from the multi-parameter detection module to generate a spatially aligned grid data layer. The grid data layer includes a comprehensive geochemical anomaly index layer, an alteration combination intensity index layer, a tectonic complexity index layer, and a fluid temperature anomaly index layer.

[0086] Specifically, the geochemical data comes from the spectrometer built into the first analysis chamber, including gridded content data of elements such as Ag, Pb, Zn, As, and Sb (generated by Kriging interpolation).

[0087] Alteration mineral data are derived from field identification results of spectral mineral analysis sensors or XRD, and are input in the form of intensity indices or distribution maps of different alteration types (such as silicification, sericitization, and chloritization).

[0088] Fluid inclusion data were obtained from a microthermometry unit, including statistics (such as median and range) of homogenization temperature (Th) and salinity (wt% NaCl eq.).

[0089] The structural data comes from the structural-alteration identification module or pre-loaded geological maps, including gridded data of structurally derived variables such as fault line density, fault intersection density, and structural complexity index.

[0090] Unify the spatial reference system of all input data to the same coordinate system and grid of the same resolution; normalize each variable (such as Min-Max normalization or Z-score standardization) to eliminate the influence of dimensions and make all data within a comparable numerical range.

[0091] S302. By using a weighted summation model, the index layers obtained in S301 are merged to generate a comprehensive mineralization favorable zone layer.

[0092] Specifically, the fusion steps are as follows:

[0093] Calculate the Integrated Geochemical Anomaly Index (IGAI): Using standardized elemental content data, principal component analysis or factor analysis is employed to extract principal factor scores representing ore-forming element combinations (such as Ag-As-Sb factors, Pb-Zn factors). Alternatively, the cumulative or product index of element combinations can be calculated, for example: (Ag*As*Sb)^(1 / 3), highlighting areas of multi-element co-enrichment. The calculated integrated anomaly index is then used to generate new gridded data.

[0094] The Altering Assemblage Index (AAI) is calculated by assigning higher weights to alteration assemblages closely related to mineralization (such as silicification + sericitization) based on geological models. The intensity indices of each alteration type are weighted and summed to generate an alteration assemblage intensity grid.

[0095] Constructing a Multi-Source Information Fusion Layer (MIFL): The following layers are fused using a linear weighted synthesis method or a fuzzy logic method: Integrated Geochemical Anomaly Index (IGAI) layer, Alteration Assemblage Index (AAI) layer, Tectonic Complexity Index layer, and Fluid Temperature Anomaly layer (e.g., high-temperature regions with Th>250°C). The linear weighting formula is as follows:

[0096] MIFL = w1 * IGAI + w2 * AAI + w3 * structural index + w4 * fluid temperature index (3)

[0097] The weights w1, w2, w3, and w4 are determined based on known ore deposit models or expert knowledge in the study area, or obtained through machine learning training.

[0098] S303. Based on the comprehensive mineralization favorability layer, anomaly thresholds are determined through statistical analysis, mineralization favorable areas of different levels are delineated, and local extreme points are identified as hydrothermal activity centers.

[0099] Specifically, the steps for extracting mineralization information and delineating target areas are as follows:

[0100] Anomaly threshold determination and classification: Statistical analysis (such as cumulative frequency method) is performed on the fused MIFL values ​​to determine the background value and the lower limit of anomalies; MIFL values ​​are usually divided into three levels (background area, weak anomaly area and strong anomaly area, with the strong anomaly area directly corresponding to the mineralization favorable area).

[0101] Hydrothermal activity center identification: Search for local extrema within the MIFL strong anomaly region. Verification is performed using elemental zoning characteristics. Around these extrema, check for typical hydrothermal zoning patterns such as a decreasing As / Sb ratio and an increasing Pb / Zn ratio from the inside out (calculated by calling the elemental ratio grid). Extrema conforming to the zoning pattern are identified and marked as suspected hydrothermal activity centers.

[0102] 3D Target Area Modeling and Uncertainty Assessment: Favorable areas delineated on the surface are coupled with geophysical and borehole data to delineate three-dimensional mineral exploration target areas. A mineralization probability score is calculated for each target area, based on the average value, size, and geological environment fit of the MIFL (Mineralization Flow Facility) within the target area. Uncertainty is also assessed, primarily based on data density (kriging variance) and the degree of consistency between different information sources.

[0103] S304. Generate a spatial distribution map and text report containing favorable mineralization zones and hydrothermal activity centers. The text report includes target area ranking and recommendations for the next steps.

[0104] Specifically, the results output and visualization are as follows:

[0105] Automatic generation of outcome maps: The GIS system is driven to generate a series of outcome maps: a color gradient map based on MIFL values ​​for mineralization favorability prediction, with superimposed hierarchical anomaly boundaries; a hydrothermal center and target area distribution map that clearly marks the identified hydrothermal centers and delineated mineral exploration target areas (suggested exploration areas) at all levels; and a multi-source information contribution map that shows the contribution weight of each variable (geochemistry, alteration, tectonics) to the final prediction results, enhancing the interpretability of the results.

[0106] Generate prediction report: The algorithm automatically generates a structured text report, which includes: the coordinates and intensity of the identified hydrothermal center, the range and mineralization probability ranking of the delineated target area, and recommendations for the next steps (such as conducting encrypted sampling or deep verification of a certain target area).

[0107] In another embodiment, as shown in the appendix Figures 5-8 As shown, the sample collection mechanism includes a sampling cylinder 2 rotatably connected to the frame 1. The sampling cylinder 2 contains a core-taking assembly for obtaining samples and a cutting assembly for cutting samples. The frame 1 is equipped with a drive assembly for driving the sampling cylinder 2 to perform core drilling and a regulating assembly for adjusting the feed depth of the sampling cylinder 2. In this embodiment, the frame 1 includes a base plate and vertical rods, with the vertical rods symmetrically bolted to the top of the base plate.

[0108] The drive assembly includes a controller and a first drive component 3. In this embodiment, the controller is a PLC controller and the first drive component 3 is an engine. The first drive component 3 is located on the frame 1. The controller is electrically connected to the first drive component 3. The output shaft of the first drive component 3 is coaxially keyed to a drive wheel. The sampling cylinder 2 is coaxially keyed to a driven wheel 4. The drive wheel and the driven wheel 4 are driven by a transmission belt. When the core sampling assembly is running, it synchronously drives the cutting assembly to run in order to complete the sampling work.

[0109] The adjustment assembly includes a second drive component 5 bolted to the top of the frame 1. In this embodiment, the second drive component 5 is a stepper motor and is electrically connected to the controller. The output shaft of the second drive component 5 is coaxially connected to a lead screw 6 via a coupling. A nut seat 7 is threaded onto the lead screw 6 and slides with the vertical rod. The sampling cylinder 2 is rotatably connected to the bottom of the nut seat 7, and the first drive component 3 is bolted to the outer wall of the nut seat 7.

[0110] Combination Figure 6As shown, the core sampling assembly includes a third driving component 8 embedded and fixed to the inner top wall of the sampling cylinder 2. In this embodiment, the third driving component 8 is a servo motor, and the controller is electrically connected to the third driving component 8. The output shaft of the third driving component 8 is coaxially connected to a rotating shaft via a coupling. The end of the rotating shaft away from the third driving component 8 is coaxially keyed to a turntable 9. The outer wall of the turntable 9 is symmetrically and integrally formed with spiral strips 10. The inner wall of the sampling cylinder 2 is slidably fitted with a T-shaped impact block 11. The inner wall of the impact block 11 is symmetrically and rotatably connected with a locking rod 12 (such as...). Figure 7 As shown), the lever 12 is in contact with the spiral bar 10; the top of the impact block 11 is fixedly connected to the tension spring 13 by screws, and the end of the tension spring 13 away from the impact block 11 is fixedly connected to the inner wall of the sampling cylinder 2 by screws.

[0111] Combination Figure 8 As shown, the cutting assembly includes several cavities 14 circumferentially disposed within the sidewall of the sampling cylinder 2. Piston plates 15 are slidably fitted onto the inner walls of each cavity 14, and cutting blocks 16 are integrally formed on the outer walls of each piston plate 15. A sealed space is formed between the sampling cylinder 2 and the impact block 11, and each cavity 14 communicates with this sealed space. Several stop blocks 17 are screwed to the side of each cavity 14 away from the piston plate 15. In this embodiment, the stop blocks 17 are used to limit the movement of the piston plate 15, ensuring it always moves within the cavity 14.

[0112] The specific implementation process is as follows: When the controller is started, the first drive component 3 (engine) drives the sampling cylinder 2 to rotate as a whole through the drive wheel, transmission belt, and driven wheel 4, realizing the drilling function. At the same time, the second drive component 5 (stepper motor) drives the lead screw 6 to rotate, causing the nut seat 7 to move smoothly down along the vertical rod, thereby controlling the rotating sampling cylinder 2 to feed into the formation at a constant pressure, completing the drilling and core sampling. In this embodiment, the vertical rod can provide guidance for the nut seat 7, so that the nut seat 7 maintains a linear movement trajectory.

[0113] When drilling reaches the predetermined depth, the controller commands the third drive unit 8 (servo motor) to start; its output shaft drives the turntable 9 and the spiral strip 10 on the turntable to rotate. The spiral strip 10 contacts the symmetrically arranged locking rods 12 inside the impact block 11. Utilizing the special shape design of the spiral strip 10, the locking rods 12 are displaced on the spiral strip 10, thereby driving the impact block 11 to move upward. When the impact block 11 moves upward, it compresses the tension spring 13 to store energy; furthermore, the upward movement of the impact block 11 compresses the sealed space above it, compressing the air above. When the locking rod 12 passes the top of the spiral strip 10, it falls below the spiral strip 10. Utilizing the gravity of the impact block 11, the compressed air, and the energy stored in the tension spring 13, the rotational motion is converted into a linear downward thrust of the impact block 11. When the impact block 11 thrusts downward, the sealed space formed between it and the inner wall of the sampling cylinder 2 is compressed, generating transient high pressure.

[0114] In existing technologies, core samples are typically cut by hammering after drilling, which may result in uneven cuts or damage to the core sample. This high pressure is transmitted through connecting pipes to the circumferentially distributed cavities 14, pushing the piston plate 15 to move outwards synchronously. This causes the cutting block 16 on the piston plate 15 to extend radially from the side wall of the sampling cylinder 2. The combined action of the impact block 11 and the cutting block 16 neatly cuts the core sample at the bottom of the borehole. Furthermore, the extended cutting block 16 confines the sample, allowing it to be retrieved by the upward movement of the sampling cylinder 2 after cutting.

[0115] After core sampling is completed, the third drive component 8 rotates again, and the spiral bar 10 drives the impact block 11 to overcome the tension of the tension spring 13 and return to its original position via the locking rod 12. The volume of the sealed space increases, creating negative pressure, which causes each piston plate 15 to drive the cutting block 16 back into the cavity 14, completing one sampling. The entire process is controlled by a programmed controller, and the actions of each component are precisely coordinated, realizing automated linkage from rotary drilling and depth control to dynamic impact cutting.

[0116] In this embodiment, the impact block 11 is driven downward by the action of the spiral strip 10 and the tension spring 13. The pressure change in the confined space triggers the cutting component to extend the cutting block 16 synchronously to complete the core cutting. The fracture is flat and the core is intact. It reduces manual intervention, controls the sampling depth and cutting timing, ensures the integrity of the core, and improves the efficiency and quality of geological exploration sample collection.

[0117] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An exploration device for geological and mineral prospecting, comprising a frame (1), characterized in that, Also includes: The sample collection mechanism, integrated on the top of the frame (1), is used to obtain rock and mineral specimens from the strata; The multi-parameter detection module is integrated on the rack (1) and is used to analyze the elemental content, fluid properties and microstructure of the sample acquired by the sample acquisition mechanism; The data analysis and processing module is used to receive and integrate data from the multi-parameter detection module to generate elemental geochemical maps, mineralization zoning models, and their correlation with geological structures.

2. The exploration apparatus for geological and mineral prospecting according to claim 1, characterized in that, The multi-parameter detection module includes: The first analytical chamber is equipped with a built-in spectrometer for on-site quantitative analysis of the elemental content of Ag, Pb, Zn, As, and Sb. The second analysis chamber, equipped with a built-in microscopic testing component, is used to measure the temperature and salinity of fluid inclusions. The third analysis chamber is equipped with a desktop scanning electron microscope and an energy dispersive spectrometer, used for micro-area morphology observation and composition analysis of ore sections or powder samples.

3. The exploration apparatus for geological and mineral prospecting according to claim 2, characterized in that, The data analysis and processing module is configured to perform the following operations: S101. Receive the analysis results from the multi-parameter detection module and bind them with the GNSS coordinates, strata and structural information of the acquisition point; S102. Call the built-in Kriging interpolation algorithm to generate and update the elemental geochemical map of the study area in real time; S103. Perform GIS overlay analysis on the elemental geochemical map and the geological structure map to calculate the elemental enrichment statistics within different tectonic units.

4. The exploration apparatus for geological and mineral prospecting according to claim 3, characterized in that, The steps of the Kriging interpolation algorithm are as follows: S201. Receive and bind the spatial coordinates and elemental content data of newly collected samples in real time, and update the experimental dataset; S202. Based on the updated experimental dataset, recalculate and fit the theoretical variation function model of anisotropy; S203. Using the updated theoretical variogram model, the grid of the study area is re-interpolated to generate an updated spatial distribution map of elements. S204. Simultaneously calculate and output the Kriging variance distribution map, where areas with variances higher than a preset threshold are marked as areas with insufficient data control. This marking is used to guide the next step of optimization sampling decisions in real time.

5. The exploration apparatus for geological and mineral prospecting according to claim 4, characterized in that, The data analysis and processing module also has a built-in ore-forming information extraction algorithm. Based on element combinations, alteration mineral combinations, and fluid temperature and salinity parameters, the ore-forming information extraction algorithm identifies and delineates hydrothermal activity centers and favorable ore-forming zones, and presents them visually.

6. The exploration apparatus for geological and mineral prospecting according to claim 5, characterized in that, The steps of the mineralization information extraction algorithm are as follows: S301. Receive and standardize multiple data streams from the multi-parameter detection module to generate a spatially aligned grid data layer. The grid data layer includes a comprehensive geochemical anomaly index layer, an alteration combination intensity index layer, a tectonic complexity index layer, and a fluid temperature anomaly index layer. S302. By using a weighted summation model, the index layers obtained in S301 are merged to generate a comprehensive mineralization favorable zone layer. S303. Based on the comprehensive mineralization favorability layer, anomaly thresholds are determined through statistical analysis, mineralization favorable areas of different levels are delineated, and local extreme points within them are identified as hydrothermal activity centers. S304. Generate a spatial distribution map and text report containing favorable mineralization zones and hydrothermal activity centers. The text report includes target area ranking and recommendations for the next steps.

7. The exploration apparatus for geological and mineral prospecting according to claim 6, characterized in that, The sample collection mechanism includes a sampling cylinder (2) rotatably connected to the frame (1). The sampling cylinder (2) is equipped with a core-taking component for obtaining samples and a cutting component for cutting samples. The frame (1) is equipped with a drive component for driving the sampling cylinder (2) to perform core drilling and an adjustment component for adjusting the feed depth of the sampling cylinder (2). The drive assembly includes a controller and a first drive component (3). The first drive component (3) is located on the frame (1). The controller is electrically connected to the first drive component (3). The output shaft of the first drive component (3) is coaxially fixedly connected to a drive wheel. The sampling cylinder (2) is coaxially fixedly connected to a driven wheel (4). The drive wheel and the driven wheel (4) are in a transmission cooperation. When the core sampling assembly is running, it synchronously drives the cutting assembly to run in order to complete the sampling work.

8. The exploration apparatus for geological and mineral prospecting according to claim 7, characterized in that, The adjustment assembly includes a second drive unit (5) fixedly connected to the top of the frame (1), the second drive unit (5) being electrically connected to the controller; the output shaft of the second drive unit (5) is coaxially fixedly connected to a lead screw (6), the lead screw (6) is threaded with a nut seat (7), the nut seat (7) is slidably connected to the frame (1); the sampling cylinder (2) is rotatably connected to the bottom of the nut seat (7), and the first drive unit (3) is fixedly connected to the outer wall of the nut seat (7).

9. The exploration apparatus for geological and mineral prospecting according to claim 8, characterized in that, The core sampling assembly includes a third drive unit (8) fixedly connected to the inner top wall of the sampling cylinder (2), and the controller is electrically connected to the third drive unit (8); the output shaft of the third drive unit (8) is coaxially fixedly connected to a rotating shaft, and the end of the rotating shaft away from the third drive unit (8) is coaxially fixedly connected to a turntable (9), and the outer wall of the turntable (9) is symmetrically fixedly connected to a spiral strip (10); the inner wall of the sampling cylinder (2) is slidably fitted with a T-shaped impact block (11), and the inner wall of the impact block (11) is symmetrically rotatably connected to a locking rod (12), and the locking rod (12) is in contact with the spiral strip (10); the top of the impact block (11) is fixedly connected to a tension spring (13), and the end of the tension spring (13) away from the impact block (11) is fixedly connected to the inner wall of the sampling cylinder (2).

10. The exploration apparatus for geological and mineral prospecting according to claim 9, characterized in that, The cutting component includes several cavities (14) arranged circumferentially in the side wall of the sampling cylinder (2). The inner wall of each cavity (14) is slidably fitted with a piston plate (15), and the outer wall of each piston plate (15) is fixedly connected with a cutting block (16). A sealed space is formed between the sampling cylinder (2) and the impact block (11). Each cavity (14) is connected to the sealed space. Several blocks (17) are fixedly connected to the side of each cavity (14) away from the piston plate (15).

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