Ocean polymetallic crusting mineralization prediction method

Abstract

The invention provides an ocean polymetallic crusting mineralization prediction method. The method comprises the following steps: establishing a mineral-geochemical mapping model reflecting an occurrence mechanism of mineralization elements; constructing a multi-factor coupling metallogenic mechanism judgment model for comprehensively representing three types of control factors, namely a hydrodynamic condition, a seawater geochemical condition and a microbial action; the method comprises the following steps: establishing quantitative evaluation indexes corresponding to the biogeochemical process of each stage according to three microscopic growth stages of multi-metal crusting from colloid formation, metal adsorption to precipitation mineralization, and forming a microscopic mineralization stage evaluation system; coupling a mineral-geochemical mapping model, a multi-factor coupling metallogenic mechanism judgment model and a microscopic metallogenic stage evaluation system, and establishing a bidirectional simulation model; and inputting known crusting quality parameters, inverting the bidirectional simulation model and outputting deduced macroscopic geological environment characteristics, and screening a sea and mountain area with similar environment characteristics in a target sea area according to the macroscopic geological environment characteristics so as to delineate a favorable prospective area of the multi-metal crusting.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marine mineral exploration, and particularly relates to a method for predicting the mineralization of oceanic polymetallic crust. BACKGROUND

[0002] Oceanic polymetallic crust, especially cobalt-rich crust, is a key deep-sea mineral resource rich in cobalt, nickel, copper, rare earth elements and other strategic metals. Its formation is the result of the comprehensive action of physical, chemical and biological processes in complex marine environments over a long geological period. Its spatial distribution and resource potential are controlled by multiple factors and multiple scales, including macro-tectonic background, paleo-ocean environment, seamount topography, bottom water dynamics, microscopic mineral growth mechanism and microbial activity, and the mineralization regularity is extremely complex.

[0003] At present, the resource potential evaluation and prospective area prediction of polymetallic crust mainly rely on geological analogy, single-factor statistical analysis or simple multivariate statistical model. These traditional methods have obvious limitations: first, they mainly focus on the correlation analysis of single or a few static environmental factors such as water depth, latitude and topography with crust element content, and fail to systematically integrate key control factors such as water dynamics, seawater geochemistry and microbial action, and fail to depict the dynamic coupling relationship and causal chain between these factors. For example, the intensity change of Antarctic bottom water not only directly affects the growth rate of the crust, but also indirectly restricts the mineralization of microorganisms by changing the redox interface and material supply flux. This synergistic effect is difficult to reflect in existing models. Second, the existing models lack depth in depicting the mineralization mechanism, and mainly stay at the statistical correlation level of macro-environment and resource parameters, without effectively integrating the microscopic mineralization sequence from colloid formation, metal adsorption to final rock formation and its quantitative evaluation index, resulting in unclear model mechanism and limited extrapolation prediction capability. This greatly restricts the exploration efficiency of unknown and concealed resources in the vast deep sea.

[0004] Therefore, there is an urgent need for an improved method for predicting the mineralization of oceanic polymetallic crust. SUMMARY

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present application provides a method for predicting the mineralization of oceanic polymetallic crust, which solves the problem that the prediction of oceanic polymetallic crust mainly relies on one-way, static and fails to deeply integrate multi-scale mineralization mechanism.

[0006] In order to achieve the above-mentioned purpose, the main technical scheme adopted by the present application includes:

[0007] The present application provides a method for predicting the mineralization of oceanic polymetallic crust, comprising the following steps:

[0008] A polymetallic nodule sample of a target area is obtained, a manganese-iron oxide mineral phase in the sample is identified through mineral phase analysis, and a mineral-geochemical mapping model reflecting the ore-forming element occurrence mechanism is established accordingly;

[0009] A multi-factor coupled ore-forming mechanism judgment model is constructed, which comprehensively represents three types of control factors, i.e., hydrodynamic conditions, seawater geochemical conditions and microbial action, and parameterizes the synergistic and causal correlations among the three types of control factors.

[0010] According to three micro-growth stages of the polymetallic nodule, i.e., colloid formation, metal adsorption and precipitation mineralization, a quantitative evaluation index corresponding to the biological and geochemical process of each stage is established, and a micro-ore-forming stage evaluation system is formed.

[0011] The mineral-geochemical mapping model, the multi-factor coupled ore-forming mechanism judgment model and the micro-ore-forming stage evaluation system are coupled to establish a bidirectional simulation model; the bidirectional simulation model can perform reverse simulation, which includes inputting known nodule quality parameters, and outputting inferred ore-forming environmental factor parameters and macro-geological environment characteristics matched therewith, the nodule quality parameters include nodule thickness, cobalt content, total rare earth element content and mineral composition ratio, and the macro-geological environment characteristics include tectonic background, paleogeographic location, seamount slope and bedrock exposure degree.

[0012] According to the macro-geological environment characteristics output by the reverse simulation, a seamount area with similar environmental characteristics in the target sea area is selected to delineate a favorable prospective area of the polymetallic nodule.

[0013] Optionally, the manganese-iron oxide mineral phase includes at least one manganese oxide in hydroxymangite, calcio-mangite and glaucocobaltite, and at least one iron oxide in hexagonal akageneite, goethite and hematite.

[0014] Optionally, the mineral-geochemical mapping model includes the oxidation-reduction conversion relationship between Mn 4+ and Mn 2+ , the oxidation-reduction conversion relationship between Fe 3+ and Fe 2+ , and the adsorption and lattice substitution mechanism of cobalt, nickel, copper and rare earth elements in the manganese-iron oxide mineral phase.

[0015] Optionally, in the multi-factor coupled ore-forming mechanism judgment model: the hydrodynamic conditions are represented by the parameters of the intensity of the Antarctic bottom water flow, the direction of the bottom flow and the sediment removal capacity; the seawater geochemical conditions are represented by the parameters of the oxidation-reduction potential, the dissolved oxygen content and the concentration of key metal ions; and the microbial action is represented by the parameters of the amount of microbial extracellular polymeric substance and the catalytic activity of the biological membrane to manganese oxidation.

[0016] Optionally, in the micro metallogenic stage evaluation system, the quantitative evaluation indexes corresponding to the three micro growth stages include: in the colloid formation stage, the colloid oxidation rate and microbial catalytic oxidation activity are used as evaluation indexes; in the metal adsorption stage, the vacancy density, the proportion of cobalt ions replaced by oxidation, and the chelation abundance of metal ions by microbial extracellular polymers are used as evaluation indexes; in the precipitation mineralization stage, the development degree of colloid aggregates, the continuity of mineralization lamella, and the self-catalytic nucleation strength are used as evaluation indexes. 4+

[0017] Optionally, the bidirectional simulation model can also perform forward simulation, and the forward simulation comprises: inputting ore-forming environmental factor parameters including the intensity of Antarctic bottom water flow, the minimum oxygen-containing zone depth, and paleo-productivity, and outputting predicted multi-metal crust quality parameters.

[0018] Optionally, the macro-geological environmental characteristics further include: the formation age of the seamount, the paleo-plate tectonic background of the location, the variation history of the carbonate compensation depth, and the influence intensity of the Antarctic bottom water.

[0019] Optionally, the seamount regions with similar environmental characteristics in the target sea area are screened, comprising: based on a global seamount topographic database, marine hydrological data and paleo-ocean reconstruction data, extracting geological environmental characteristics of each candidate region in the target sea area, the geological environmental characteristics including seamount slope, bedrock exposure ratio, bottom water flow rate and paleo-geographic location parameters; performing multi-parameter similarity matching and spatial clustering analysis on the geological environmental characteristics of each candidate region and the macro-geological environmental characteristics output by the reverse simulation, and delineating a favorable prospective area.

[0020] Optionally, the geological environmental characteristics of each candidate region and the macro-geological environmental characteristics output by the reverse simulation are subjected to multi-parameter similarity matching and spatial clustering analysis to delineate a favorable prospective area, comprising: determining the weight coefficients of each parameter in the macro-geological environmental characteristics, the weight coefficients being preset based on historical mineralization data statistics or expert experience; calculating the quantitative similarity between the geological environmental characteristics of each candidate region in the target sea area and the macro-geological environmental characteristics output by the reverse simulation according to the weight coefficients; screening the candidate regions with a quantitative similarity higher than a threshold value to form a preliminary prospective area set according to a preset similarity threshold value; and performing spatial clustering analysis on all candidate regions in the preliminary prospective area set, and merging the candidate regions that are spatially continuous and similar in characteristics into the same ore-forming prospective area based on the proximity of spatial positions and the similarity of geological environmental characteristics, so as to delineate the final favorable prospective area.

[0021] The beneficial effects of the present application are:

[0022] ​The present application realizes the whole-chain, mechanism and quantitative characterization of the oceanic polymetallic crust ore-forming system from the element occurrence microscopic mechanism, the dynamic correlation of the multi-sphere control factors to the macroscopic geological background constraint by constructing a two-way simulation model coupling the mineral-geochemical mapping model, the multi-factor coupled ore-forming mechanism judgment model and the microscopic ore-forming stage evaluation system, and significantly improves the accuracy and exploration efficiency of resource prediction. In particular, the present application innovatively introduces an evaluation index system based on three microscopic growth stages, so that the model can finely capture the biogeochemical details of the ore-forming process: in the colloid formation stage, by quantifying the oxidation rate and microbial catalytic activity and other indicators, the dynamic process of the conversion of Mn²⁺ / Fe²⁺ to δ-MnO2 / Fe(OH)3 colloid is accurately described; in the metal adsorption stage, by using the water hydroxymanganese ore lattice vacancy density, Co²⁺ oxidation substitution ratio and microbial EPS chelation abundance and other indicators, the selective enrichment mechanism of key metals (such as Co, Ni, REE) is directly related, and the limitation of the traditional model in describing the surface adsorption-oxidation substitution coupling effect is overcome; in the precipitation mineralization stage, by evaluating the development degree of colloid aggregate, the continuity of mineralization lamella and the self-catalytic nucleation intensity, the integrity of colloid precipitation-solidification crust is effectively simulated, and the omission of diagenetic reconstruction factors is avoided. The integration of these microscopic stage indicators not only enhances the transparency of the model to the ore-forming mechanism, but also realizes the diagnostic prediction of "from cause to reason" through the two-way simulation function (forward from environment to predict resources, and backward from resources to infer environment), so as to quickly delineate the favorable prospecting area in the vast deep sea environment, and provide a new technical approach for ocean mineral resource exploration. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The coupling relationship diagram of the mineral-geochemical mapping model, the multi-factor coupled ore-forming mechanism judgment model and the microscopic ore-forming stage evaluation system according to the specific embodiment. DETAILED DESCRIPTION

[0024] In order to better explain the present application, so as to be understood, the present application is described in detail below by specific embodiments in combination with the drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer, more thorough understanding of the present application and to convey the complete scope of the present application to those skilled in the art.

[0025] The present application provides an oceanic polymetallic crust ore-forming prediction method, comprising the following steps:

[0026] S1, obtain a polymetallic crust sample of a target area, identify manganese-iron oxide mineral phases therein through mineral phase analysis, and establish a mineral-geochemical mapping model reflecting ore-forming element occurrence mechanism based on the mineral phase analysis results.

[0027] Specifically, the target area is the Magellan seamount area in the northwest Pacific Ocean, and the polymetallic crust sample is a cobalt-rich crust ore.

[0028] Specifically, the manganese-iron oxide mineral phases are identified through mineralogical analysis methods such as X-ray diffraction and scanning electron microscopy, or geological record queries.

[0029] Specifically, the mineral phase analysis includes quantitative proportion analysis of layered manganese oxides (such as birnessite, todorokite), tunnel-like manganese oxides (such as cryptomelane), and hexagonal ferrihydrite, goethite, akageneite, hematite, and other iron oxides. The main mineral phases of cobalt-rich crust are poorly crystallized nanoscale birnessite (δ-MnO2) and amorphous or weakly crystalline oxyhydroxide iron (such as hexagonal ferrihydrite, δ-FeOOH), which often coexist in nanoscale intercalated structures.

[0030] The mineral-geochemical mapping model includes the oxidation-reduction conversion relationship between Mn 4+ and Mn 2+ , the oxidation-reduction conversion relationship between Fe 3+ and Fe 2+ , and the adsorption and lattice substitution mechanism of key metal elements such as cobalt, nickel, copper, zinc, and rare earth elements (such as Ce, REEs) in manganese-iron oxide mineral phases. Studies have shown that elements such as Co, Ce, Ni, Cu, Zn, and Tl are usually enriched in manganese phase minerals, among which Co 2+ can be oxidized and replaced as Co 3+ and enter the MnO6 octahedral void, elements such as Te, As, and Sb are mainly present in iron phase minerals, and elements such as Pb, Pt, Mo, W, and rare earth elements (except Ce) are distributed in both manganese and iron phases. The model also takes into account the specific occurrence position of trace elements in the manganese oxide crystal structure, including isomorphism replacement of Mn 4+ / Mn 3+ in the octahedral layer and co-hexagonal with surrounding MnO6 octahedra, co-dual-angle at the edge of the mineral layer, or co-triangle above the Mn void in the octahedral layer.

[0031] S2, construct a multi-factor coupled ore-forming mechanism judgment model that comprehensively represents the three types of control factors of hydrodynamic conditions, seawater geochemical conditions, and microbial action, and parameterizes the synergistic and causal relationships between the three types of control factors.

[0032] The formation of deep-sea polymetallic nodules is the result of the dynamic coupling of multiple factors, including hydrodynamic conditions, seawater geochemistry, and microbial action. This step aims to construct a parameterized decision model that characterizes these factors and their synergistic and causal relationships.

[0033] Specifically, the multi-factor coupled mineralization mechanism decision model parameterizes the following factors:

[0034] Hydrodynamic conditions: represented by parameters such as the intensity of Antarctic Bottom Water (AABW) flow, bottom current direction, and sediment removal capacity, with AABW input intensity ≥ 30 Sv and bottom current activity affecting sediment removal and bedrock exposure.

[0035] Seawater geochemical conditions: represented by redox environment (e.g., redox potential), dissolved oxygen content (e.g., the minimum oxygen zone OMZ depth located at 2000-4000 meters water layer), key metal ion concentrations (e.g., Mn 2+ , Fe 2+ , Co 2+ , Ni 2+ , etc.), and element migration and adsorption capacity. The element migration and adsorption capacity can be represented by pH value and phosphate concentration. For example, the redox interface controls the oxidation and precipitation of Mn²⁺ / Fe²⁺, the concentrations of Co, Ni, Mo, and Zn in cobalt-rich crusts are usually higher in areas with lower seawater oxygen concentration, and phosphatization can cause Co, Ni depletion and REEs and Pt enrichment.

[0036] Microbial action: represented by parameters such as microbial extracellular polymeric substance (EPS) production, biological membrane catalytic activity for manganese oxidation (microorganisms can increase Mn 2+ oxidation rate by more than 10 3 times), and microbial-driven metal chelation and enrichment efficiency.

[0037] This model does not simply list the above factors, but parameterizes the synergistic and causal chains among them. For example, the strength of Antarctic Bottom Water (hydrodynamic) directly affects the interface depth and dissolved oxygen content of the oxygen minimum zone OMZ (geochemistry), which in turn regulates microbial community activity and Mn / Fe oxidation rate (microbial action), ultimately determining the composition and properties of the initial colloid (δ-MnO2 / Fe(OH)3). The model uses weight or coupling index methods to quantitatively evaluate the contribution of each factor to nodule enrichment.

[0038] S3, according to the three micro-growth stages of polymetallic nodules from colloid formation, metal adsorption to precipitation mineralization, establish quantitative evaluation indexes corresponding to the biogeochemical processes of each stage, form a micro-mineralization stage evaluation system.

[0039] Specifically, the three micro-stages and corresponding evaluation indexes include:

[0040] Phase 1 (Colloid Formation Phase): At the junction of the oxygen-rich bottom water and the anoxic minimum layer (OMZ), Mn 2+ with Fe 2 + Oxidation produces δ-MnO2 and Fe(OH)3 colloids. Evaluation indicators include dissolved Mn in seawater. 2+ / Fe 2+ The oxidation rate of conversion to colloids and the catalytic activity of microbial extracellular enzymes (such as polycopper oxidases) in this oxidation process.

[0041] Phase Two (Metal Adsorption Phase): Mn-Fe colloids adsorb Co through surface coordination and oxidative substitution reactions. 2+ Ni 2+ Cu 2+ Metal ions, including Co 2+ Oxidized to Co 3+ It then enters the octahedral vacancies of MnO6. Evaluation metrics include the Mn content in the hydrous manganese lattice. 4+ The density of vacancies (for Co) 3+ Plasma substitution provides sites), Co 2+ Surface oxidation and substitution occur to generate low-spin Co. 3+ The proportion of microbial EPS and the specific chelation abundance of metal ions.

[0042] Stage Three (Precipitation and Mineralization Stage): Mn-Fe colloids accumulate, dehydrate, and cement on the bedrock surface to form a layered cobalt-rich crust, influenced by Mn... 4+ / Fe 3+ Redox cycle control. Evaluation indicators include the development degree of nanoscale spherical manganese oxide aggregates, the continuity of biofilm mineralization laminae, the intensity of autocatalytic nucleation, and the intensity of alteration by later diagenetic processes such as phosphate formation.

[0043] S4. Coupled mineral-geochemical mapping model, multi-factor coupled mineralization mechanism determination model, and micro-mineralization stage evaluation system; establish a two-way simulation model. For example... Figure 1 As shown.

[0044] The two-way simulation model can perform both forward and backward simulations:

[0045] Forward simulation: Input a set of given mineralization environmental factors (such as Antarctic bottom water flow intensity, minimum oxygen zone depth, paleoproductivity, water depth, latitude, key ion concentration, microbial activity index, etc.), and the model can simulate, calculate and output predicted crust quality parameters based on the inherent coupling mechanism, including crust thickness, cobalt content, Ni content, total rare earth element content, Pt content and mineral composition ratio, etc.

[0046] Backward simulation: input known crustal parameters (e.g. crustal thickness, Co content, total REE content, mineral composition ratio), the model can be inversely inferred and output the inferred optimal combination of ore-forming environmental factors (e.g. dissolved oxygen content, key ion concentration) necessary to form such crust, and further deduce the matching macro-geological environment characteristics. Macro-geological environment characteristics include tectonic setting (e.g. seamount formation age, paleo-plate position), paleo-geographic position (paleo-latitude), seamount topographic features (e.g. slope between 15°-30°, bedrock exposure area ratio ≥70%), paleo-ocean environment (carbonate compensation depth CCD evolution history, Antarctic Bottom Water AABW influence intensity), and key water chemistry interface (e.g. minimum oxygen minimum zone OMZ depth). This function realizes the breakthrough from "known ore body geochemical fingerprint" to "unknown regional ore-forming environment diagnosis".

[0047] S5, based on the macro-geological environment characteristics output by the backward simulation, screening seamount regions with similar environmental characteristics in the target sea area to delineate favorable polymetallic crust prospecting areas.

[0048] Specifically, screening seamount regions with similar environmental characteristics in the target sea area includes: based on global seamount topographic database, marine hydrological data and paleo-ocean reconstruction data, extracting the geological environment characteristics of each candidate region in the target sea area. These geological environment characteristics should cover the macro-geological environment characteristics output by the backward simulation, such as slope, bedrock exposure rate, paleo-geographic position, calculated bottom water flow intensity, paleo-CCD depth, etc. Perform multi-parameter similarity matching of the geological environment characteristics of each candidate region with the macro-geological environment characteristics output by the backward simulation. Specifically, different characteristic parameters can be assigned a weight coefficient based on historical ore-forming data statistics or expert experience, and the comprehensive similarity score of each candidate region is calculated. Set a reasonable similarity threshold, and select regions with scores higher than the threshold to form a preliminary prospecting area set.

[0049] Then, spatial clustering analysis is performed on the preliminary prospecting area set. Based on the proximity of spatial location and the similarity of geological environment characteristics, candidate regions that are spatially continuous and have similar genetic environment are merged to delineate the final favorable ore-forming prospecting area with clear spatial boundaries and geological basis, providing direct and reliable target areas for subsequent exploration deployment.

[0050] The present application realizes the whole-chain, mechanism and quantitative characterization of the oceanic polymetallic crust ore-forming system from the element occurrence microscopic mechanism, the dynamic correlation of the multi-sphere control factors to the macroscopic geological background constraint by constructing a bidirectional simulation model coupling the mineral-geochemical mapping model, the multi-factor coupled ore-forming mechanism judgment model and the microscopic ore-forming stage evaluation system, and significantly improves the accuracy and exploration efficiency of resource prediction. In particular, the present application innovatively introduces an evaluation index system based on three microscopic growth stages, so that the model can finely capture the biogeochemical details of the ore-forming process: in the colloid formation stage, by quantifying the oxidation rate and microbial catalytic activity and other indicators, the dynamic process of the conversion of Mn²⁺ / Fe²⁺ to δ-MnO2 / Fe(OH)3 colloid is accurately described; in the metal adsorption stage, by using the water hydroxymanganese ore lattice vacancy density, Co²⁺ oxidation substitution ratio and microbial EPS chelation abundance and other indicators, the selective enrichment mechanism of key metals (such as Co, Ni, REE) is directly related, overcoming the limitation of the traditional model in describing the surface adsorption-oxidation substitution coupling effect; in the precipitation mineralization stage, by evaluating the development degree of colloid aggregate, the continuity of mineralization lamella and the self-catalytic nucleation intensity, the integrity of colloid precipitation-solidification crust is effectively simulated, avoiding the omission of diagenetic modification factors. The integration of these microscopic stage indicators not only enhances the transparency of the model to the ore-forming mechanism, but also realizes the diagnostic prediction of "from cause to effect" through the bidirectional simulation function (forward from environment to predict resources, and backward from resources to infer environment), so as to quickly delineate the favorable prospecting area in the vast deep sea environment, and provide a new technical approach for ocean mineral resource exploration.

[0051] In the description of the present application, it should be understood that the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0052] In the present application, unless otherwise specifically defined and limited, the terms "mounting", "connection", "connection", "fixing" and other terms should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrated; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0053] In the present application, unless otherwise explicitly specified and limited, a first feature is "on" or "under" a second feature can mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature is "over", "above" and "on top of" a second feature can mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is horizontally higher than the second feature. A first feature is "under", "below" and "underneath" a second feature can mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is horizontally lower than the second feature.

[0054] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "an embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are contained in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples, without contradiction.

[0055] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and the person skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

Claims

1. A method for predicting the mineralization of polymetallic crusts in the ocean, characterized in that, Includes the following steps: Obtain polymetallic crust samples from the target area, identify manganese iron oxide mineral phases through mineral phase analysis, and establish a mineral-geochemical mapping model that reflects the occurrence mechanism of ore-forming elements. A multi-factor coupled mineralization mechanism determination model is constructed to comprehensively characterize three types of controlling factors: hydrodynamic conditions, marine geochemical conditions, and microbial activity, and to parameterize the synergistic and causal relationships among these three types of controlling factors. Based on the three microscopic growth stages of polymetallic crusts—colloidal formation, metal adsorption, and precipitation mineralization—quantitative evaluation indicators corresponding to the biogeochemical processes of each stage are established to form an evaluation system for microscopic mineralization stages. A coupled mineral-geochemical mapping model, a multi-factor coupled mineralization mechanism determination model, and a micro-mineralization stage evaluation system were established, and a two-way simulation model was constructed. Among them, the two-way simulation model can perform inverse simulation, which includes: inputting known crust quality parameters, inverting and outputting inferred mineralization environmental factor parameters and matching macro-geological environmental characteristics. The crust quality parameters include crust thickness, cobalt content, total rare earth element content, and mineral composition ratio. The macro-geological environmental characteristics include tectonic setting, paleogeographic location, seamount slope, and bedrock exposure degree. Based on the macroscopic geological environment characteristics output by the reverse simulation, seamount areas with similar environmental characteristics are screened within the target sea area to delineate favorable prospective areas for polymetallic crusts.

2. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, The manganese iron oxide mineral phase includes at least one manganese oxide from hydrous manganese ore, hydrous manganese ore, and calcium manganese ore, and at least one iron oxide from hexagonal lepidocrocite, goethite, and hematite.

3. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, Mineral-geochemical mapping models include Mn in manganese-iron oxide mineral phases. 4+ With Mn 2+ The redox transformation relationship between them, Fe 3+ with Fe 2+ The redox transformation relationships between them, and the adsorption and lattice substitution mechanisms of cobalt, nickel, copper, and rare earth elements in manganese iron oxide mineral phases.

4. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, In the multi-factor coupled mineralization mechanism determination model: hydrodynamic conditions are characterized by parameters such as the intensity of Antarctic bottom water flow, bottom flow direction, and ability to remove sediments; marine geochemical conditions are characterized by parameters such as redox potential, dissolved oxygen content, and concentration of key metal ions; and microbial activity is characterized by parameters such as the amount of extracellular polymers generated by microorganisms and the catalytic activity of biofilms on manganese oxidation.

5. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, In the evaluation system for the micro-mineralization stage, the quantitative evaluation indicators corresponding to the three micro-growth stages include: in the colloid formation stage, the colloidal oxidation rate and microbial catalytic oxidation activity are used as evaluation indicators; in the metal adsorption stage, the Mn content in the manganese oxide lattice is used as an evaluation indicator. 4+ Vacancy density, the proportion of cobalt ions oxidized and replaced, and the chelation abundance of metal ions by microbial extracellular polymers were used as evaluation indicators; in the precipitation mineralization stage, the degree of development of colloidal aggregates, the continuity of mineralization laminae, and the intensity of autocatalytic nucleation were used as evaluation indicators.

6. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, The two-way simulation model can also perform forward simulations, which include: inputting mineralization environmental factors such as Antarctic bottom water flow intensity, minimum oxygen zone depth, and paleoproductivity, and outputting predicted polymetallic crust quality parameters.

7. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, Macro-geological environmental characteristics also include: the formation age of the seamount, the ancient plate tectonic background of its location, the history of changes in carbonate compensation depth, and the intensity of the influence of Antarctic bottom water.

8. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 1, characterized in that, Seamount areas with similar environmental characteristics were selected within the target sea area, including: Based on the global seamount topography database, marine hydrological data, and paleoocean reconstruction data, the geological environmental characteristics of each candidate area within the target sea area are extracted. These characteristics include seamount slope, bedrock exposure ratio, bottom water flow velocity, and paleogeographical location parameters. The geological environmental characteristics of each candidate area are then matched with the macroscopic geological environmental characteristics output by the reverse simulation using multi-parameter similarity matching and spatial clustering analysis to delineate favorable prospective areas.

9. The method for predicting the mineralization of polymetallic crusts in the ocean according to claim 8, characterized in that, The geological environmental characteristics of each candidate region are compared with the macroscopic geological environmental characteristics output by the reverse simulation using multi-parameter similarity matching and spatial clustering analysis to delineate favorable prospective areas, including: Determine the weighting coefficients of each parameter in the macro-geological environment characteristics. The weighting coefficients are preset based on historical mineralization data statistics or expert experience. Based on the weighting coefficients, the quantitative similarity between the geological environmental characteristics of each candidate area within the target sea area and the macroscopic geological environmental characteristics output by the reverse simulation is calculated. Based on a preset similarity threshold, candidate regions with quantitative similarity higher than the threshold are selected to form a preliminary set of prospective regions. Spatial clustering analysis was performed on all candidate areas in the preliminary prospective area set. Based on the proximity of spatial location and the similarity of geological environment characteristics, candidate areas that are spatially continuous and have similar characteristics were merged into the same mineralization prospective area to delineate the final favorable prospective areas.

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