Method and system for determining migration path of ore-forming fluid of non-magmatic epigenetic hydrothermal deposit

Abstract

The application provides a non-magmatic epigenetic hydrothermal deposit ore-forming fluid migration path discrimination method and system, and belongs to the technical field of mineral resources exploration and metallogenic prediction. The method comprises the following steps: obtaining trace element data through structural geochemical sampling; performing multivariate statistical analysis to extract factor combinations reflecting ore-forming element precipitation sequences; drawing structural geochemical factor anomaly maps; measuring fluid inclusion temperature and salinity parameters and drawing contour maps; and comprehensively analyzing structural geochemical anomalies and fluid inclusion gradient characteristics to discriminate ore-forming fluid migration paths. The system comprises data acquisition, processing and analysis, graph generation and path discrimination modules. The application adopts the above non-magmatic epigenetic hydrothermal deposit ore-forming fluid migration path discrimination method and system, solves the problem of path discrimination in the traditional method when deep weak anomaly information extraction and ore-controlling structures are unknown, realizes rapid and accurate ore-forming fluid path identification, and provides reliable technical support for deep concealed ore body exploration.

Description

Technical Field

[0001] This invention relates to the field of mineral resource exploration and mineralization prediction technology, and in particular to a method and system for identifying the migration path of ore-forming fluids in non-magmatic post-thermal deposits. Background Technology

[0002] Deep mineral resource exploration is a crucial link in ensuring energy security and supporting economic and social development. Promoting the expansion of mineral exploration from shallow to deep deposits has become a cutting-edge and strategic issue in global mineral research. Among them, non-magmatic epithermal deposits are an important source of base metal resources, but deep exploration of these deposits has long faced challenges such as low exploration levels and high technical difficulties.

[0003] Studying the migration paths of ore-forming fluids is a core method for effectively predicting the spatial location of concealed ore bodies and clarifying subsequent mineral exploration directions. In traditional ore-forming fluid research, tectonic-fluid coupling analysis, as an important method for revealing mineralization processes and guiding exploration practices, has been widely used and achieved significant results. However, this method is usually time-consuming, costly, and complex, and its effectiveness heavily depends on a clear understanding of the ore-controlling structural framework. For deposits with unclear ore-forming geological bodies, unclear deep extensions of ore-controlling structures, or ambiguous spatial positioning of ore bodies, traditional methods struggle to effectively extract weak deep ore-forming anomalies and reliably determine the activity paths of ore-forming fluids, thus hindering breakthroughs in deep mineral exploration.

[0004] Therefore, there is an urgent need to develop a rapid, effective technical method that can identify deep, weak anomalies in order to accurately determine the activity path of ore-forming fluids and thus provide a basis for the prediction and location of concealed ore bodies. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for identifying the migration path of ore-forming fluids in non-magmatic post-thermal deposits, in order to solve the technical problems of existing technologies that rely on fine structural analysis, such as difficulty in extracting weak and deep anomaly information, long identification cycle, high cost and insufficient accuracy, and to provide rapid and effective technical support for the prediction and exploration of deep concealed ore bodies.

[0006] To achieve the above objectives, this invention provides a method for identifying the migration path of ore-forming fluids in non-magmatic post-thermal deposits, comprising the following steps: Step S1: Perform tectonic-alteration lithofacies mapping and tectonic geochemical mapping on the target deposit to obtain the spatial coordinates and trace element content data of the sampling points; Step S2: Perform multivariate statistical analysis on the trace element content data, extract multiple factors representing different element combinations, and calculate the factor scores corresponding to each sampling point. Step S3: Based on the geochemical properties of the element combinations contained in the factors, determine their geological significance in order to distinguish factor combinations that reflect different precipitation sequences of ore-forming elements. Step S4: Based on the factor scores, determine the lower limit of anomalies and draw a structural geochemical factor anomaly map reflecting different precipitation sequences of ore-forming elements. Step S5: Select representative minerals from the sample in step S1, prepare fluid inclusion thin sections and measure their homogenization temperature and salinity parameters, and correlate the parameters with the spatial coordinates of the sampling points. Step S6: Based on the fluid inclusion parameters, draw a contour map of its spatial distribution to characterize the temperature and salinity gradient changes of the ore-forming fluid. Step S7: Based on the structural geochemical factor anomaly map drawn in step S4, and according to the element precipitation sequence, the migration direction of the ore-forming fluid is preliminarily inferred by connecting the centers of the anomaly regions that reflect different precipitation sequences of ore-forming elements. Step S8: Combining the temperature and salinity gradient change characteristics of the ore-forming fluid obtained in Step S6, the migration direction initially inferred in Step S7 is verified and corrected, and the activity path of the ore-forming fluid is finally determined.

[0007] Preferably, in step S1, sampling is carried out based on large-scale tectonic-alterographic mapping, and the sampling interval is controlled within a preset distance.

[0008] Preferably, the large scale is 1:100 to 1:1000, and the preset distance is 20 meters.

[0009] Preferably, in step S1, the sampled rock includes one or more of the following: fractured rock, granulite, siltstone, fault gouge, tectonic breccia, altered rock, and surrounding rock.

[0010] Preferably, in step S2, the multivariate statistical analysis includes R-type cluster analysis and factor analysis; the factor analysis uses the matrix rotation method and determines the number of factors through scree plot, and the cumulative contribution rate of the extracted factors is not less than 70%.

[0011] Preferably, in step S4, the method for determining the lower limit of anomalies is as follows: calculate the average and variance of all factor scores, repeatedly remove data that exceed three times the variance of the average, and use the average of the remaining data plus twice the variance as the lower limit of anomalies.

[0012] Preferably, in step S5, the representative minerals include one or more of sphalerite, calcite, and dolomite.

[0013] The present invention also provides a system for analyzing the path of ore-forming fluids to implement the method described above, comprising: The data acquisition module is used to receive and manage raw data from structural geochemical sampling and fluid inclusion testing, including spatial coordinates of sampling points, trace element content, fluid inclusion homogenization temperature, and salinity. The data processing and analysis module is configured to perform the following operations: Multivariate statistical analysis was performed on the trace element content data to extract factors representing different element combinations and to calculate the factor scores for each sampling point. Calculate the lower limit of anomalies based on factor scores; Process fluid inclusion data to generate spatial distribution data of temperature and salinity; The graphics generation module is configured as follows: Based on factor scores and anomaly lower limits, a structural geochemical factor anomaly map is generated; Based on the spatial distribution data of fluid inclusions, temperature and salinity contour maps are generated. The path determination module is configured as follows: Based on the spatial distribution of anomalous regions in the structural geochemical factor anomaly map, which reflects different precipitation sequences of ore-forming elements, the migration direction of ore-forming fluids can be preliminarily inferred. By combining the gradient change characteristics in the temperature and salinity contour maps, the preliminary inferred migration direction is verified and corrected, and the final ore-forming fluid activity path is output.

[0014] Therefore, the present invention employs the above-mentioned method and system for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits, and the beneficial technical effects are as follows: (1) This invention reduces the complexity of trace element data and merges it into a combination of factors with clear geological significance by constructing geochemical sampling and multivariate statistical analysis, thereby condensing and focusing the scattered and weak elemental anomaly information on specific factor scores; and further, by drawing a structural geochemical factor anomaly map, the weak geochemical signals of deep fluid activity are enhanced and spatially located in a visualized graphical way, overcoming the difficulty in extracting effective mineralization information due to weak deep information and unclear structural patterns in the background technology.

[0015] (2) The core of this invention lies in using the spatial distribution law of factor combinations that reflect different precipitation sequences of ore-forming elements to directly indicate the direction of fluid migration. This method skips the tedious process of clarifying the complex ore-controlling structural pattern in the traditional way. At the same time, this method only requires the systematic measurement of trace elements and the combination of mapping. Compared with the large number of detailed structural observations and tests required by traditional structural-fluid coupling studies, it shortens the research cycle and reduces economic costs.

[0016] (3) This invention does not rely solely on geochemical indicators, but creatively combines and interprets the anomaly information of tectonic geochemical factors with the temperature and salinity gradient changes of fluid inclusions. Two independent chains of evidence (elemental zonation sequence and fluid physicochemical gradient) point to the same fluid migration direction, forming mutual corroboration, thereby improving the reliability of path discrimination results and providing a basis for accurate prediction of concealed ore bodies. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to the present invention; Figure 2 Geological logging map of a typical profile of a deposit in a lead-zinc polymetallic mining area in the northeastern part of a certain region; Figure 3 This is an R-type clustering phylogenetic diagram of a deposit in a lead-zinc polymetallic mining area in the northeastern part of a certain region. Figure 3 (a) in the diagram is the R-type clustering phylogenetic diagram of the A section of deposit No. 1. Figure 3 (b) in the figure is the R-type clustering phylogenetic diagram of the B section of deposit No. 2; Figure 4 This is a gravel map of a deposit in a lead-zinc polymetallic mining area in the northeastern part of a certain region. Figure 4 (a) in the diagram is the crushed stone diagram of section A in deposit No. 1. Figure 4 (b) in the figure is the crushed stone diagram of section B of deposit No. 2; Figure 5 This is a line graph showing the variation of trace element content in a typical profile of the middle section A of deposit No. 1 in a lead-zinc polymetallic mining area in the northeast of a certain region. Figure 6 A line graph showing the variation of trace element content in a typical profile of the middle section B of deposit No. 2 in a lead-zinc polymetallic mining area in the northeast of a certain region; Figure 7 This is a tectonic geochemical anomaly map of the middle section A of deposit No. 1 in a lead-zinc polymetallic mining area in the northeastern part of a certain region. Figure 7 (a) in the figure is the F2 factor anomaly plot. Figure 7 (b) in the figure is the F3 factor anomaly plot. Figure 7 (c) in the figure is the F4 factor anomaly plot; Figure 8 This is a structural geochemical anomaly map of the B section of Deposit No. 2 in a lead-zinc polymetallic mining area in the northeastern part of a certain region. Among them, Figure 8 (a) in the figure is the F2 factor anomaly plot. Figure 8 (b) in the figure is the F3 factor anomaly plot. Figure 8 (c) in the figure is the F4 factor anomaly plot; Figure 9This is a feature map of the NE-trending fault in the No. 2 anomaly superposition zone of Deposit No. 1 in the middle section of the No. 1 section of a lead-zinc polymetallic mining area in the northeastern part of a certain region. Figure 9 (a) in the image is a vectorized image of the NE-direction fracture. Figure 9 (b) in the figure is a sketch of the NE-trending fracture. Figure 9 (c) in the diagram shows the NE-directed fracture Wu's grid projection and stress analysis. Figure 10 This is a statistical diagram of fluid inclusions in a lead-zinc polymetallic deposit in the northeastern part of a certain region. Figure 10 (a) in the figure is a statistical graph of fluid inclusion temperatures. Figure 10 (b) in the figure is a pressure statistics diagram of fluid inclusions. Figure 10 (c) in the figure is a statistical diagram of fluid inclusion density. Figure 10 (d) in the figure is a statistical diagram of the salinity of fluid inclusions; Figure 11 This is a spatial distribution map showing the characteristics of fluid inclusions in section A of deposit No. 1 in a lead-zinc polymetallic ore area in the northeastern part of a certain region. Figure 11 (a) in the image is a map showing the sampling points of the fluid inclusions; Figure 11 (b) in the figure is a contour map of calcite inclusions from Phase II. Figure 11 (c) in the figure shows the variation pattern of calcite inclusions in stage IV. Figure 11 (d) in the figure shows the variation pattern of sphalerite inclusion data; Figure 12 A schematic diagram of the ore-forming fluid activity path in the middle section A of deposit No. 1 in a lead-zinc polymetallic mining area in the northeast of a certain region; Figure 13 This is a schematic diagram of the ore-forming fluid activity path in the middle section of deposit B of a lead-zinc polymetallic mine in the northeastern part of a certain region. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] Example 1 like Figure 1 As shown, this embodiment takes a germanium-rich, lead-zinc-rich polymetallic mining area in the northeastern part of a certain region as an example to illustrate the implementation process of the present invention in detail.

[0021] Step S1: Tectonic-alteration lithofacies mapping and tectonic geochemical sampling.

[0022] like Figure 2For target deposits (such as the middle section of Deposit A ​​of No. 1), large-scale (1:100 to 1:1000) tectonic-alterographic lithofacies mapping and tectonic geochemical geological logging will be carried out. During the mapping process, the characteristics, scale, and spatial location of fault-fold structures, wall rock alteration, and other phenomena will be accurately recorded.

[0023] Based on the mapping, structural geochemical samples were systematically collected at intervals not exceeding 20 meters. Intensive sampling was conducted in areas with well-developed fractures and fissures, strong alteration, and close association with mineralization. If these characteristics were not observed within a 20-meter interval, surrounding rock samples were collected as a supplement, but the amount of surrounding rock collected should be minimized to better reflect the influence of tectonics on ore-forming fluids. The types of samples collected included breccia, granulite, siltstone, fault gouge, tectonic breccia, altered rocks, and some surrounding rocks. Simultaneously, ore specimens from different mineralization stages were collected in mineralized areas. The spatial coordinates and geological properties of all samples were accurately recorded during the mapping process.

[0024] In this embodiment, a total of 276 structural geochemical samples and 23 specimens were collected. Each sample weighed no less than 500g. After drying at room temperature, the samples were described in detail and preliminarily classified. All samples were then ground to 200 mesh (74μm) using a ball mill and subsequently reduced to test samples.

[0025] The trace element content of the samples was determined by a qualified laboratory using inductively coupled plasma mass spectrometry (ICP-MS). A total of 48 trace elements were tested: Ba, Sr, Cu, Zn, Cr, Co, Ni, V, Li, Be, Sc, Ga, Ge, Rb, Y, Zr, Nb, Mo, Ag, Cd, In, Sn, Cs, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Tl, Pb, Bi, Th, U, As, Sb, and Hg. For Pb and Zn, which exceeded the upper limit of ICP-MS determination, volumetric methods were used for re-determination. To ensure data quality, 5% of the samples were treated with cipher samples for quality control, ensuring a data error of <10%.

[0026] Step S2: Construction of geochemical data processing and multivariate statistical analysis.

[0027] The obtained trace element content data were imported into Microsoft Excel for preliminary processing. First, the 16 rare earth elements (Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) were combined into... Standardize the unit of measurement for all data to 10. -6Furthermore, all trace element content data were logarithmically processed to base 10 to eliminate differences in magnitude.

[0028] Import the logarithmically processed data into IBM SPSS Statistics software for multivariate statistical analysis: R-type clustering analysis: In the software, the clustering method was set to between-group linkage, and the transformation criterion was set to Z-score. After running the analysis, an R-type clustering phylogenetic diagram was obtained. The R-type clustering phylogenetic diagram for the A section of deposit No. 1 is shown below. Figure 3 (a) shows the R-type clustering phylogenetic diagram of the middle section B of deposit No. 2. Figure 3 (b) In the phylogenetic diagram, preliminary grouping can be performed based on the correlation and affinity between elements.

[0029] Based on the R-type clustering phylogenetic diagram, the samples from section A of deposit No. 1 can be divided into 7 elemental combinations according to their correlation, specifically: ① Zr, Hf, Cs, Th, Nb, Be, Rb, Li, V, Ta, W, Tl, Bi; ② Co, Ni, Sn, Mo, Cr; ③ As, Sb; ④ Ge, In, Cu; ⑤ Zn, Pb, Cd, Hg, Ag; ⑥ U; ⑦ Ba, Ga, Sr, Similarly, the samples from the middle section of deposit B in ore deposit No. 2 can be divided into four elemental combinations, specifically: ①Rb, Th, Zr, Nb, Cs, Tl, Hf, Be, Ga, Li, V, Bi, Ge, Ba, Ta, W, U, , In, Ni, Sn; ②Cu, Cr, Mo, Co, Ag, Hg; ③As, Sb, Zn, Pb, Cd; ④Sr.

[0030] Factor analysis: In the software's factor analysis module, enable the scree plot drawing function. Determine the number of factors based on the inflection point of the scree plot curve (e.g., in this embodiment, the inflection point appears at factor number 5). The scree plot of the middle section of deposit A ​​of No. 1 is shown below. Figure 4 (a) is a diagram of the crushed stone in section B of deposit No. 2. Figure 4 (b) In this case, the matrix rotation method is set to the maximum variance method, and the factor rotation matrix is ​​obtained after running the test.

[0031] When the cumulative contribution rate reached 81.192%, the trace elements in the structural rock samples of the middle section A of deposit No. 1 can be divided into five factor combinations (Table 1). Factor F1 consists of Th, Cs, Nb, Be, Li, Rb, Zr, Hf, V, Ta, W, Bi, Tl, Cr, Ga, and U; Factor F2 consists of Sn, Mo, Cu, Ge, In, Co, Ni, and Zn; Factor F3 consists of Zn, Pb, Hg, Cd, and Ag; Factor F4 consists of As, Sb, and Ag; and Factor F5 consists of Ba, Sr, and ∑REE.

[0032] When the cumulative contribution rate reached 85.721%, the trace elements in the structural rock samples of the middle section B of deposit No. 2 could be divided into five factor combinations (Table 2). Factor F1 consisted of Th, Zr, Rb, Be, Tl, Hf, Ga, Nb, Cs, Li, Bi, Ge, V, W, Ta, Ba, U, In, Composition; F2 factor consists of Ni, Cu, Sn, Cr, Mo, Co, In, Composition: The F3 factor consists of Zn, Pb, Cd, Ag, and Hg; the F4 factor consists of Sb and As; and the F5 factor consists of Sr and Cd.

[0033] Table 1. Maximum variance method factor rotation matrix of section A of deposit No. 1 in a certain lead-zinc polymetallic mining area of ​​a certain region.

[0034] Table 2. Maximum variance method factor rotation matrix of a certain lead-zinc polymetallic mining area in a certain region. (This likely refers to a specific deposit in a certain region, specifically the B section of deposit No. 2.)

[0035] Based on the variation patterns of elements in typical profiles and previous research experience, the geological significance of each factor is inferred as follows: Substitute the trace element data into the typical profiles of the middle section of deposit A ​​in deposit No. 1 and the middle section of deposit B in deposit No. 2, and draw a line graph of trace element variation in the typical profile of the middle section of deposit A ​​in deposit No. 1. Figure 5 ) and a line graph showing the variation of trace elements in a typical profile of the B section of deposit No. 2 ( Figure 6 ).

[0036] Because certain elements with similar geochemical properties exhibit similar geochemical behaviors and migration and enrichment patterns during specific geological processes, the geological significance of each factor can be inferred by comparing the trace element content change line graph with the factor change line graph, and the trace element grouping obtained from R-type cluster analysis with the element combination obtained from factor analysis.

[0037] Step S3: Determine the geological significance of the factors.

[0038] By combining the element groupings obtained from the R-type clustering analysis in step S2 and the element combinations obtained from the factor analysis, and by drawing a line graph of the changes in trace element content in a typical profile for comparison, the geological significance of each factor can be comprehensively inferred.

[0039] For example, in this embodiment: Factor F1 represents the elemental assemblage of the surrounding rocks (Th, Cs, Nb, Be, Li, Rb, Zr, Hf, V, Ta, W, Bi, Tl, Cr, Ga, U); Factor F2 represents the elemental assemblage of high-temperature ore-forming elements (Sn, Mo, Cu, Ge, In, Co, Ni, Zn); Factor F3 represents the elemental assemblage of medium-temperature ore-forming elements (Zn, Pb, Hg, Cd, Ag); Factor F4 represents the elemental assemblage of low-temperature ore-forming elements (As, Sb, Ag); Factor F5 represents the elements related to alteration of carbonate rocks (Ba, Sr, ...). ).

[0040] Similarly, analyzing the factors for Deposit B of ore No. 2, the F1 factor in this middle section is the combination factor of surrounding rock elements (Th, Zr, Rb, Be, Tl, Hf, Ga, Nb, Cs, Li, Bi, Ge, V, W, Ta, Ba, U, In, ); F2 factor high-temperature ore-forming element assemblage (Ni, Cu, Sn, Cr, Mo, Co, In, The combination of elements (Zn, Pb, Hg, Cd, Ag) and the F4 factor (Sb, As) is considered. However, considering that the peak value of the F4 factor is not at the strongest mineralization, it is inferred that the F3 factor is a combination of elements of medium temperature mineralization (Zn, Pb, Hg, Cd, Ag); the F4 factor is a combination of elements of low temperature mineralization (Sb, As); and the F5 factor is a combination of elements related to alteration of carbonate rock wall rocks (Sr, Cd).

[0041] Step S4: Determine the lower limit of anomalies and draw a structural geochemical factor anomaly map.

[0042] The outlier limit for each factor score is calculated using traditional statistical methods. Specifically, the mean X and variance S of all valid factor scores are calculated. Outliers higher than X+3S or lower than X-3S are removed multiple times. The mean X' and variance S' are then recalculated. This process is repeated until all data are within the range of X±3S. Finally, X+2S is taken as the outlier limit.

[0043] Based on the calculated lower limit of anomalies (e.g., 0.247 for section A of deposit No. 1), appropriate contour gradients are determined, and F2 (high temperature), F3 (medium temperature), and F4 (low temperature) factors closely related to mineralization are selected. The factor score data are imported into Surfer or ArcGIS contour mapping software to generate a structural geochemical factor anomaly map. The map clearly delineates factor anomaly areas in different temperature ranges and identifies their superposition relationships.

[0044] Application results of section A of deposit No. 1.

[0045] By executing steps S1 to S4 of the method of this invention, a total of 4 F2 (high-temperature ore-forming element combination) factor anomaly zones, 3 F3 (medium-temperature ore-forming element combination) factor anomaly zones, and 4 F4 (low-temperature ore-forming element combination) factor anomaly zones were delineated in this middle section. These anomaly zones spatially form 3 sets of distinct superimposed anomaly zones (see...). Figure 7 , Figure 7 P2 q+m Represents the Qixia Maokou Formation; P2 l Represents the Liangshan Formation; C2 m Represents the Maping Formation; C2 w Represents the Weining Formation; C1 b Represents the Baizuo Formation; C1 d Represents the Datang Formation; D3 zg 3 Represents the third section of the Zaige Formation; D3 zg 2 Represents the second member of the Zaige Formation; D3 zg 1 (Representing a section of the Zaige Formation), with the following specific characteristics: Anomaly superposition zone I: Located on the SW side of the middle section. The spatial distribution of the anomaly zone exhibits obvious zonation: the F2 factor anomaly zone is located on the SW side, the F3 factor anomaly zone is located on its NE side, and the F4 factor anomaly zone is more biased towards the NE side. All three anomaly zones trend NE, highly consistent with the NE-trending fault structure in this region. Pyrite mineralization is visible within this superposition zone, and the Zn content in the sample collected from the center of the F3 factor anomaly zone reaches 2252.50 × 10⁻⁶. -6 The Pb content is 252.00 × 10⁻⁶. -6 It shows significant mineralization anomalies.

[0046] Anomaly Zone II: Located in the central part of the middle section. The anomaly zoning shows the F2 factor anomaly zone located on the SSW side, the F3 factor anomaly zone on its NNE side, and the F4 factor anomaly zone more biased towards the NNE side. The distribution characteristics of the anomaly zone perfectly match the fault with an attitude of NE20°∠68°SE within the area. This fault zone has a maximum width of 7 cm, filled with bluish-gray fault gouge and foliated conglomerate, with abundant sphalerite and galena mineralization. Layered lead-zinc ore bodies are developed on both the hanging wall and footwall of the fault, and these ore bodies are not displaced by the fault. Structural analysis indicates that this fault is left-lateral compressional-shear, with principal compressive stress... The direction is NW-SE, and comprehensive analysis suggests that this fault is an important ore-controlling structure (see [reference]). Figure 9 , Figure 9 In this context, σ1 represents the maximum principal stress, σ2 represents the secondary principal stress, and σ3 represents the minimum principal stress.

[0047] Abnormal superimposed area No. III: Located on the NE side of the middle section. The anomaly zoning in this area shows that the F2 factor anomaly area is located on the SW side, the F3 factor anomaly area is located on its NEE side, and the F4 factor anomaly area is located on the NW side. In the area, there is obvious zoning of pyrite + sphalerite + galena mineralization, occurring in grayish-white medium- to coarse-grained dolomite. The minerals are mainly sphalerite, followed by pyrite, and the least is galena, occurring in patchy, massive and vein-like forms. In the area, several NE-trending faults with similar attitudes (NE35°∠58°SE) are developed. The fault surfaces are gently undulating, with a bandwidth of 3 cm - 6 cm, filled with bluish-gray fault gouge and cataclastic rock. Small-scale ore bodies can be seen near the fault zone.

[0048] Application results of the B middle section of Deposit No. 2.

[0049] In the same middle section, 4 F2 factor anomaly areas, 3 F3 factor anomaly areas and 3 F4 factor anomaly areas were also outlined, forming 3 sets of abnormal superimposed areas (see Figure 8 , Figure 8 in P2 q+m represents the Qixia Maokou Formation; P2 l represents the Liangshan Formation; C2 m represents the Maping Formation; C2 w represents the Weining Formation; C1 b represents the Baizuo Formation; C1 d represents the Datang Formation; D3 zg 3 represents the third member of the Zaige Formation; D3 zg 2 represents the second member of the Zaige Formation; D3 zg 1 represents the first member of the Zaige Formation). Although there are varying degrees of factor anomalies in most of the adits in this middle section, no exposed ore bodies have been found yet. The specific characteristics are as follows: Abnormal superimposed area No. I: Restricted by the adit layout, the spatial relationship of the anomaly area shows that the F2 factor anomaly area is located on the NWW side of the F3 factor anomaly area, and the F3 factor anomaly area is located on the NWW side of the F4 factor anomaly area. Based on this, it is preliminarily inferred that the ore-forming fluid migrates from west to east. The anomaly trend is mainly NE. The F3 factor anomaly area is located within the in-shaped structure composed of NE-trending faults and NW-trending faults. The NE-trending fault surface is not obvious, with a bandwidth of 2 - 10 cm. There are grayish-green schistosity and chloritization near the fault surface; the NW-trending fault surface is straight, about 1 m wide. The zone is filled with calcite-enveloped residual breccia of altered limestone, with an attitude of NW30°∠81°SW.

[0050] Anomaly superposition zone II: Limonite mineralization of a certain scale is visible within the zone. The anomaly zoning is clear: the F2 factor anomaly zone is located on the SW side, the F3 factor anomaly zone on its NE side, and the F4 factor anomaly zone is more biased towards the NE side. Two NW-trending faults are developed within the superposition zone; the lithology of both the hanging wall and footwall is beige-grayish-white coarse-grained dolomite.

[0051] Anomaly superposition zone III: Located on the NE side of the middle segment. Anomaly zoning is as follows: the F2 factor anomaly zone is located on the SW side, the F3 factor anomaly zone is located on the NEE side, and the F4 factor anomaly zone is located on the SEE side. Both the F3 and F4 factor anomaly zones are located within the "I"-shaped structural zone formed by the NE-trending fault and the NW-trending fault.

[0052] Step S5: Preparation of fluid inclusion samples and determination of parameters.

[0053] The mineralized samples collected in step S1 from the middle section of deposit A ​​of deposit No. 1 and the middle section of deposit B of deposit No. 2 were systematically sorted and classified. After cleaning, observation and description, representative minerals with complete crystal forms (including sphalerite, calcite and dolomite) were selected, cut and finely polished to prepare double-sided polished fluid inclusion thin sections with a thickness of 0.1 mm–0.3 mm. A total of 23 inclusion sections were made, ensuring that the original fluid inclusions were not damaged by subsequent hot pressing.

[0054] Homogenization temperature and freezing point temperature of fluid inclusions in each thin section were determined using a fluid inclusion hot stage. Based on the freezing point temperature data, the equivalent NaCl salinity of the fluid inclusions was calculated according to the Bodnar formula. Furthermore, using Flincor software, combined with the homogenization temperature and salinity data, the density and pressure values ​​of the corresponding inclusions were calculated. All the measured and calculated parameters (homogenization temperature, salinity, density, and pressure) were systematically correlated with the spatial coordinates of the sampling points to establish a complete fluid inclusion dataset. Finally, a table of petrographic and thermometric data of fluid inclusions from a lead-zinc polymetallic deposit in northeastern China was compiled (see Table 3).

[0055] Table 3. Petrographic and thermometric data of fluid inclusions in a lead-zinc polymetallic deposit in a certain region.

[0056] Step S6: Fluid inclusion data analysis and contour plotting.

[0057] The fluid inclusion data obtained in step S5 were systematically organized and statistically analyzed, and a statistical diagram of fluid inclusions in a lead-zinc polymetallic deposit in northeastern Yunnan was drawn (see [link]). Figure 10Analysis shows that as mineralization continues, the homogenization temperature, pressure, and salinity of fluid inclusions in calcite generally show a significant decreasing trend, while the density shows an increasing trend. This evolutionary sequence reveals the complete law of the evolution of ore-forming fluids from medium-high temperature, high salinity, high pressure, and medium-low density to medium-low temperature, low salinity, low pressure, and high density.

[0058] Based on this, and according to the precise spatial coordinates of each sampling point and the corresponding fluid inclusion parameters (temperature, salinity), a spatial contour map of the fluid inclusion parameters was drawn using professional plotting software (see [link]). Figure 11 , Figure 11 P2 q+m Represents the Qixia Maokou Formation; P2 l Represents the Liangshan Formation; C2 m Represents the Maping Formation; C2 w Represents the Weining Formation; C1 b Represents the Baizuo Formation; C1 d Represents the Datang Formation; D3 zg 3 Represents the third section of the Zaige Formation; D3 zg 2 Represents the second member of the Zaige Formation; D3 zg 1 (Representing a section of the Zaige Formation). These contour maps clearly characterize the temperature and salinity gradient variations of ore-forming fluids in three-dimensional space, providing reliable graphical evidence for intuitive analysis of the migration direction and evolution of ore-forming fluids.

[0059] Step S7: Based on geochemical anomalies, make a preliminary inference about the migration direction of ore-forming fluids.

[0060] Based on the structural geochemical factor anomaly map drawn in step S4, the anomaly superposition zones are analyzed according to the element geochemical behavior pattern of high-temperature elements precipitating first and low-temperature elements precipitating later. By systematically connecting the centers of the high-temperature ore-forming element combination factor (F2), the medium-temperature ore-forming element combination factor (F3), and the low-temperature ore-forming element combination factor (F4) anomaly zones within each superposition zone, the spatial relationship of the element precipitation sequence is established, and the migration direction of ore-forming fluids is preliminarily inferred.

[0061] Using this method, schematic diagrams of ore-forming fluid activity paths were obtained for the middle section of Deposit A ​​of Deposit No. 1 and the middle section of Deposit B of Deposit No. 2 in a lead-zinc polymetallic mining area in northeastern Yunnan (see [references]). Figure 12 and Figure 13 , Figure 12 and Figure 13 P2 q+m Represents the Qixia Maokou Formation; P2 l Represents the Liangshan Formation; C2 mRepresents the Maping Formation; C2 w Represents the Weining Formation; C1 b Represents the Baizuo Formation; C1 d Represents the Datang Formation; D3 zg 3 Represents the third section of the Zaige Formation; D3 zg 2 Represents the second member of the Zaige Formation; D3 zg 1 (Representing a section of the Zaige Formation). Analysis results show: The three superimposed anomalous zones in the A section of Deposit No. 1 all exhibit a consistent spatial distribution pattern: the high-temperature ore-forming element combination factor (F2) anomalous zone is concentrated on the SW side, the low-temperature ore-forming element combination factor (F4) anomalous zone is biased towards the NE side, and the medium-temperature ore-forming element combination factor (F3) anomalous zone lies between the two. This systematic spatial zoning characteristic clearly indicates that the ore-forming fluids in this section generally migrated from the SW side to the NE side.

[0062] The B section of Deposit No. 2 exhibits a similar pattern of elemental anomalies as Deposit No. 1, further confirming that the ore-forming fluids in this mining area have a unified SW-NE migration trend. Simultaneously, the orientation of the anomaly zones within each section is highly consistent with the NE-trending fault structures within the area, indicating that the NE-trending fault system is the main channel controlling the migration of ore-forming fluids.

[0063] Step S8: Verify and correct the fluid migration path by combining fluid inclusion characteristics.

[0064] To verify the fluid migration direction inferred from geochemical anomalies, a comprehensive analysis was conducted, combining the temperature and salinity gradient variation characteristics of fluid inclusions obtained in step S6. According to the principles of fluid geochemistry, within the same mineralization stage, the homogenization temperature, salinity, and pressure of fluids typically exhibit a systematic decreasing trend along the migration direction.

[0065] Taking the A section of Deposit No. 1 as an example for detailed verification: From sampling point HYK-36 to HDK-16 (SW→NE), the calcite fluid inclusion parameters of mineralization stage II show that the average salinity decreased from 10.95wt% NaCl to 8.55wt% NaCl, the average homogenization temperature decreased from 276.28℃ to 243.20℃, and the average pressure decreased from 412.98 bar to 365.34 bar. All parameters show a clear gradient decrease. The variation law of the fluid physicochemical parameters of this system fully confirms the migration direction of the ore-forming fluid from SW to NE.

[0066] Fluid inclusion analysis in the B section of Deposit No. 2 yielded the same conclusion. Through the mutual corroboration of two independent chains of evidence—geochemical anomalies and fluid inclusion parameters—the overall activity path of ore-forming fluids in the study area was determined to be SW to NE. This comprehensive judgment significantly improved the accuracy and reliability of ore-forming fluid path identification.

[0067] Example 2 A system for analyzing the path of ore-forming fluids to implement the method described above includes: The data acquisition module is used to receive and manage raw data from structural geochemical sampling and fluid inclusion testing, including spatial coordinates of sampling points, trace element content, fluid inclusion homogenization temperature, and salinity. The data processing and analysis module is configured to perform the following operations: Multivariate statistical analysis was performed on the trace element content data to extract factors representing different element combinations and to calculate the factor scores for each sampling point. Calculate the lower limit of anomalies based on factor scores; Process fluid inclusion data to generate spatial distribution data of temperature and salinity; The graphics generation module is configured as follows: Based on factor scores and anomaly lower limits, a structural geochemical factor anomaly map is generated; Based on the spatial distribution data of fluid inclusions, temperature and salinity contour maps are generated. The path determination module is configured as follows: Based on the spatial distribution of anomalous regions in the structural geochemical factor anomaly map, which reflects different precipitation sequences of ore-forming elements, the migration direction of ore-forming fluids can be preliminarily inferred. By combining the gradient change characteristics in the temperature and salinity contour maps, the preliminary inferred migration direction is verified and corrected, and the final ore-forming fluid activity path is output.

[0068] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0069] Therefore, the present invention employs the above-mentioned method and system for identifying the migration path of ore-forming fluids in non-magmatic post-thermal deposits, which can quickly, economically, and effectively extract information on weak mineralization anomalies in deep deposits, accurately identify the migration path of ore-forming fluids, and improve the accuracy of prediction and exploration efficiency of deep concealed ore bodies.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits, characterized in that, Includes the following steps: Step S1: Perform tectonic-alteration lithofacies mapping and tectonic geochemical mapping on the target deposit to obtain the spatial coordinates and trace element content data of the sampling points; Step S2: Perform multivariate statistical analysis on the trace element content data, extract multiple factors representing different element combinations, and calculate the factor scores corresponding to each sampling point. Step S3: Based on the geochemical properties of the element combinations contained in the factors, determine their geological significance in order to distinguish factor combinations that reflect different precipitation sequences of ore-forming elements. Step S4: Based on the factor scores, determine the lower limit of anomalies and draw a structural geochemical factor anomaly map reflecting different precipitation sequences of ore-forming elements. Step S5: Select representative minerals from the sample in step S1, prepare fluid inclusion thin sections and measure their homogenization temperature and salinity parameters, and correlate the parameters with the spatial coordinates of the sampling points. Step S6: Based on the fluid inclusion parameters, draw a contour map of its spatial distribution to characterize the temperature and salinity gradient changes of the ore-forming fluid. Step S7: Based on the structural geochemical factor anomaly map drawn in step S4, and according to the element precipitation sequence, the migration direction of the ore-forming fluid is preliminarily inferred by connecting the centers of the anomaly regions that reflect different precipitation sequences of ore-forming elements. Step S8: Combining the temperature and salinity gradient change characteristics of the ore-forming fluid obtained in Step S6, the migration direction initially inferred in Step S7 is verified and corrected, and the activity path of the ore-forming fluid is finally determined.

2. The method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to claim 1, characterized in that, In step S1, sampling is carried out based on large-scale tectonic-alterographic mapping, and the sampling interval is controlled within a preset distance.

3. The method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to claim 2, characterized in that, Large scale is 1:100 to 1:1000, with a preset distance of 20 meters.

4. The method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to claim 1, characterized in that, In step S1, the sampled rock includes one or more of the following: breccia, granulite, siltstone, fault gouge, tectonic breccia, altered rock, and surrounding rock.

5. The method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to claim 1, characterized in that, In step S2, the multivariate statistical analysis includes R-type cluster analysis and factor analysis; Factor analysis employed a matrix rotation method and determined the number of factors using a scree plot. The cumulative contribution rate of the extracted factors was no less than 70%.

6. The method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to claim 1, characterized in that, In step S4, the method for determining the lower limit of abnormality is as follows: calculate the mean and variance of all factor scores, repeatedly remove data that exceed three times the variance of the mean, and use the mean of the final remaining data plus twice the variance as the lower limit of abnormality.

7. The method for determining the migration path of ore-forming fluids in non-magmatic post-thermal deposits according to claim 1, characterized in that, In step S5, representative minerals include one or more of sphalerite, calcite, and dolomite.

8. A system for analyzing the path of ore-forming fluids for implementing the method as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to receive and manage raw data from structural geochemical sampling and fluid inclusion testing, including spatial coordinates of sampling points, trace element content, fluid inclusion homogenization temperature, and salinity. The data processing and analysis module is configured to perform the following operations: Multivariate statistical analysis was performed on the trace element content data to extract factors representing different element combinations and to calculate the factor scores for each sampling point. Calculate the lower limit of anomalies based on factor scores; Process fluid inclusion data to generate spatial distribution data of temperature and salinity; The graphics generation module is configured as follows: Based on factor scores and anomaly lower limits, a structural geochemical factor anomaly map is generated; Based on the spatial distribution data of fluid inclusions, temperature and salinity contour maps are generated. The path determination module is configured as follows: Based on the spatial distribution of anomalous regions in the structural geochemical factor anomaly map, which reflects different precipitation sequences of ore-forming elements, the migration direction of ore-forming fluids can be preliminarily inferred. By combining the gradient change characteristics in the temperature and salinity contour maps, the preliminary inferred migration direction is verified and corrected, and the final ore-forming fluid activity path is output.

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