Cobalt-rich crust resource distribution circling method and system, electronic equipment and storage medium

Abstract

The invention discloses a cobalt-rich crust resource distribution circling method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring preset geological deposition environment data; wherein the preset geological sedimentary environment data comprises ocean shell age data, sedimentary rate data and preset near-bottom shallow stratum profile data; determining first area data through a preset discrimination model according to the ocean shell age data and the deposition rate data; wherein the preset discrimination model is constructed by a coupling effect of a deposition rate parameter and an ocean shell age parameter; performing terrain constraint correction on the first region data according to a preset terrain constraint parameter to obtain second region data; and according to the second area data and the preset near-bottom shallow stratum profile data, carrying out crusting area analysis to obtain a target cobalt-rich crusting area. According to the embodiment of the invention, the efficiency and accuracy of circling the cobalt-rich crust can be effectively improved. The method can be widely applied to the field of resource development.

Description

Technical Field

[0001] This application relates to the technical field of resource development, and particularly relates to a method, system, electronic device and storage medium for delineating the distribution of cobalt-rich crust resources. Background Art

[0002] Cobalt-rich ferromanganese crusts (referred to as crusts or cobalt-rich crusts for short) occur on the hard rock basement of the entire ocean basin. They are formed on the flanks and summits of seamounts, ridges and plateaus, and are rich in Co, Ti, Mn, Ni, Pt, Zr, Nb, Te, Bi, Mo, W, Th and rare earth elements. The exploration of cobalt-rich crusts requires the use of various technical means, such as geology, geophysics, geochemistry, etc., and various technical methods need to be closely combined. In related technologies, the delineation of cobalt-rich crust development areas is mainly based on the identification of acoustic and optical data, but the corresponding delineation accuracy is low, it is difficult to accurately delineate the cobalt-rich crust development areas, and the work efficiency is not high.

[0003] In summary, the technical problems existing in the related technologies need to be improved. Summary of the Invention

[0004] The main purpose of the embodiments of this application is to propose a method, system, electronic device and storage medium for delineating the distribution of cobalt-rich crust resources, which can effectively improve the efficiency and accuracy of cobalt-rich crust delineation.

[0005] To achieve the above object, on the one hand, an embodiment of this application proposes a method for delineating the distribution of cobalt-rich crust resources, and the method includes the following steps:

[0006] Obtain preset geological sedimentary environment data; wherein, the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic profile data;

[0007] Determine first area data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model; wherein, the preset discrimination model is constructed by the coupling effect of sedimentation rate parameters and oceanic crust age parameters;

[0008] Perform topographic constraint correction on the first area data according to preset topographic constraint parameters to obtain second area data;

[0009] Perform crust area analysis according to the second area data and the preset near-bottom shallow stratigraphic profile data to obtain the target cobalt-rich crust area.

[0010] In some embodiments, before performing the step of determining the first area data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model, it includes:

[0011] Combining and analyzing the deposition rate parameter and the oceanic crust age parameter to determine the coupling effect;

[0012] Generating a model discrimination condition through the coupling effect to construct the preset discrimination model.

[0013] In some embodiments, the determining the first region data according to the oceanic crust age data and the deposition rate data through the preset discrimination model includes:

[0014] Performing a first classification process on the oceanic crust age data through a preset age threshold to obtain oceanic crust classification data;

[0015] Performing a second classification process on the deposition rate data through a preset deposition threshold to obtain deposition classification data;

[0016] Performing region screening on the oceanic crust classification data and the deposition classification data through the preset discrimination model to obtain the first region data.

[0017] In some embodiments, the terrain constraint correction of the first region data according to the preset terrain constraint parameter to obtain the second region data includes:

[0018] Performing buffer analysis on the first region data and the regional terrain data to obtain third region data;

[0019] Performing region screening on the third region data through the preset terrain constraint parameter to obtain the second region data; wherein, the preset terrain constraint parameter includes a regional slope threshold.

[0020] In some embodiments, the crust area analysis of the second region data and the preset near-bottom shallow stratigraphic profile data to obtain the target cobalt-rich crust area includes:

[0021] Determining the longitude and latitude information of the target area according to the second region data;

[0022] Performing regional position constraint on the preset near-bottom shallow stratigraphic profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area.

[0023] In some embodiments, after performing the regional position constraint on the preset near-bottom shallow stratigraphic profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area, the method further includes:

[0024] Extracting preset visualization data from the second region data according to the target cobalt-rich crust area; wherein, the preset visualization data includes longitude data, latitude data, depth data, and amplitude intensity data;

[0025] Perform 3D visualization of the ore body based on the preset visualization data to obtain a 3D image of cobalt-rich crusts.

[0026] In some embodiments, after performing the analysis of the crust area based on the second area data and the preset near-bottom shallow formation profile data to obtain the target cobalt-rich crust area, the method further includes:

[0027] Collect the preset ground truth data corresponding to the target cobalt-rich crust area; wherein, the preset ground truth data includes drilling data and camera data;

[0028] Verify the target cobalt-rich crust area according to the drilling data and the camera data, so as to adjust the parameters of the preset discrimination model through the corresponding verification results.

[0029] To achieve the above object, on the other hand, an embodiment of the present application proposes a cobalt-rich crust resource distribution delineation system, the system includes:

[0030] A first module, configured to obtain preset geological sedimentary environment data; wherein, the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow formation profile data;

[0031] A second module, configured to determine first area data through a preset discrimination model according to the oceanic crust age data and the sedimentation rate data; wherein, the preset discrimination model is constructed by the coupling effect of sedimentation rate parameters and oceanic crust age parameters;

[0032] A third module, configured to perform terrain constraint correction on the first area data according to preset terrain constraint parameters to obtain second area data;

[0033] A fourth module, configured to perform crust area analysis according to the second area data and the preset near-bottom shallow formation profile data to obtain the target cobalt-rich crust area.

[0034] To achieve the above object, on the other hand, an embodiment of the present application proposes an electronic device, the electronic device includes:

[0035] At least one processor;

[0036] At least one memory, configured to store at least one program;

[0037] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0038] To achieve the above object, another aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the above method.

[0039] The embodiments of the present application at least include the following beneficial effects: The present application provides a method, a system, an electronic device, and a storage medium for delineating the distribution of cobalt-rich crust resources. The solution obtains preset geological sedimentary environment data, including oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratum profile data. Correspondingly, in the embodiments of the present invention, first area data is determined according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model constructed by the coupling effect of the sedimentation rate parameter and the oceanic crust age parameter, and the first area data is corrected by terrain constraint according to the preset terrain constraint parameter to obtain second area data. Finally, crust area analysis is performed according to the second area data and the preset near-bottom shallow stratum profile data to obtain the target cobalt-rich crust area, realizing the delineation of cobalt-rich crust resources. It is easy to understand that in the embodiments of the present invention, first, the area where cobalt-rich crust can develop is determined within a large range of the horizontal spatial area according to the oceanic crust age data and the sedimentation rate data through the preset discrimination model, that is, the first area data, and the area is limited and constrained by the preset terrain constraint parameter to improve the accuracy of delineating the distribution of cobalt-rich crust resources and obtain the second area data. Furthermore, crust area analysis is performed according to the second area data and the preset near-bottom shallow stratum profile data to determine the depth and shape of the cobalt-rich crust deposit in the vertical space, thereby effectively improving the accuracy of cobalt-rich crust delineation and effectively improving the efficiency of delineating the distribution of cobalt-rich crust resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the steps of the method for delineating the distribution of cobalt-rich crust resources provided by the embodiments of the present invention;

[0041] Figure 2 is a schematic flowchart of processing the preset near-bottom shallow stratum profile data provided by the embodiments of the present invention;

[0042] Figure 3 is a flowchart of the steps of constructing the preset discrimination model provided by the embodiments of the present invention;

[0043] Figure 4 is a flowchart of the steps of determining the first area data according to the oceanic crust age data and the sedimentation rate data through the preset discrimination model provided by the embodiments of the present invention;

[0044] Figure 5 is a flowchart of the steps of correcting the first area data by terrain constraint according to the preset terrain constraint parameter to obtain the second area data provided by the embodiments of the present invention;

[0045] Figure 6It is a schematic diagram of regional terrain data provided by an embodiment of the present invention;

[0046] Figure 7 It is a schematic diagram of the second regional data after removing flat data provided by an embodiment of the present invention;

[0047] Figure 8 It is a flow chart of steps for obtaining a target cobalt-rich crust area by constraining the second regional data according to preset near-bottom shallow stratigraphic profile data provided by an embodiment of the present invention;

[0048] Figure 9 It is a flow chart of steps for visualizing the target cobalt-rich crust area provided by an embodiment of the present invention;

[0049] Figure 10 It is a schematic diagram of the output three-dimensional ore body provided by an embodiment of the present invention;

[0050] Figure 11 It is a flow chart of steps for adjusting parameters of a preset discrimination model provided by an embodiment of the present invention;

[0051] Figure 12 It is a schematic diagram of the overall process architecture for delineating the distribution of cobalt-rich crust resources provided by an embodiment of the present invention;

[0052] Figure 13 It is a schematic diagram of the structure of a cobalt-rich crust resource distribution delineation system provided by an embodiment of the present invention;

[0053] Figure 14 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of the present application. They are only examples of devices and methods that are consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0055] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, as used herein, the words "if", "when" may be interpreted as "when...", "when...", or "in response to determining".

[0056] The terms "at least one", "multiple", "each", "any one", etc. used in this application, at least one includes one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any one refers to any one of the multiple.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0058] Before elaborating on the embodiments of this application in detail, some nouns and terms involved in the embodiments of this application are first explained, and the nouns and terms involved in the embodiments of this application are applicable to the following explanations.

[0059] Cobalt-rich crust: It is a special substance growing on the surface of seabed rocks or rock debris, also known as cobalt crust, ferromanganese crust, manganese crust, cobalt-rich manganese crust, polymetallic crust, cobalt-rich ferromanganese crust, and is a shell-shaped ore deposit rich in metal elements such as manganese, cobalt, platinum, and rare earths on the seabed.

[0060] Cobalt-rich ferromanganese crust (referred to as crust or cobalt-rich crust for short) appears on the hard rock basement of the entire ocean basin. They are formed on the flanks and summits of seamounts, ridges, and plateaus, and are rich in Co, Ti, Mn, Ni, Pt, Zr, Nb, Te, Bi, Mo, W, Th, and rare earth elements. The exploration of cobalt-rich crust requires the use of a variety of technical means, such as geology, geophysics, geochemistry, etc., and various technical methods need to be closely combined. In the related art, the delineation of the cobalt-rich crust development area is mainly based on the identification of acoustic and optical data, but the corresponding delineation accuracy is relatively low, it is difficult to accurately divide the cobalt-rich crust development area, and the work efficiency is not high. For example, the data obtained by the optical method belongs to surface data and can only judge the distribution of the crust on the surface and cannot judge the thickness. And through the method of acoustic thickness measurement, due to the problem that the penetration of high-frequency acoustics is limited, it is still impossible to accurately judge the crust thickness with multiple solutions. In addition, the station sampling method has a series of problems such as unsatisfactory work efficiency and economic efficiency.

[0061] In view of this, an enriched cobalt crust resource distribution delineation method, system, electronic device and storage medium are provided in the embodiments of the present application. This solution obtains preset geological sedimentary environment data, including oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic profile data. Correspondingly, in the embodiments of the present invention, first region data is determined according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model constructed by the coupling effect of the sedimentation rate parameter and the oceanic crust age parameter, and the first region data is corrected by terrain constraint according to the preset terrain constraint parameter to obtain second region data. Finally, the crust region is analyzed according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain the target enriched cobalt crust region, realizing the delineation of the enriched cobalt crust resources, and effectively improving the efficiency and accuracy of the enriched cobalt crust delineation.

[0062] The enriched cobalt crust resource distribution delineation method provided in the embodiments of the present application relates to the technical field of resource development. The enriched cobalt crust resource distribution delineation method provided in the embodiments of the present application can be applied to a terminal, or to a server, or can be software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing the enriched cobalt crust resource distribution delineation method, etc., but is not limited to the above forms.

[0063] The present application can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] Figure 1 This is an optional flowchart of the method for delineating the distribution of cobalt-rich crust resources provided by an embodiment of the present application. Figure 1 The method in may include but is not limited to steps S110 to S140.

[0065] Step S110: Obtain preset geological sedimentary environment data. Among them, the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic profile data.

[0066] Step S120: Determine the first region data through a preset discrimination model according to the oceanic crust age data and the sedimentation rate data. Among them, the preset discrimination model is constructed by the coupling effect of the sedimentation rate parameter and the oceanic crust age parameter.

[0067] Step S130: Perform topographic constraint correction on the first region data according to the preset topographic constraint parameter to obtain the second region data.

[0068] Step S140: Perform cobalt-rich crust region analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain the target cobalt-rich crust region.

[0069] During the working process of this specific embodiment, the embodiment of the present invention first obtains the preset geological sedimentary environment data. Specifically, the preset geological sedimentary environment data in the embodiment of the present invention includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic profile data. Correspondingly, the oceanic crust age data in the embodiment of the present invention refers to the data on the formation time of the oceanic crust. Among them, the embodiment of the present invention obtains the corresponding oceanic crust age data through the global oceanic crust age model (such as global grid data), and the general format is: longitude, latitude, oceanic crust age. In addition, the sedimentation rate data in the embodiment of the present invention refers to the information on the sediment accumulation rate during the formation process of cobalt-rich crusts, that is, the thickness increased by sediments per unit time. Correspondingly, the embodiment of the present invention integrates deep-sea drilling (DSDP / ODP), sediment traps, and geochemical inversion data, or indirectly calculates the sedimentation rate data by using satellite-inverted surface productivity, and the general format is: longitude, latitude, sedimentation rate. At the same time, the preset near-bottom shallow stratigraphic profile data in the embodiment of the present invention refers to the near-bottom shallow stratigraphic profile data, that is, the stratigraphic structure and tectonic information obtained by continuously traversing the shallow part of the seabed using physical means such as sound waves. For example, the embodiment of the present invention collects the preset near-bottom shallow stratigraphic profile data through a near-bottom shallow stratigraphic profiler to obtain information related to the stratigraphic thickness.

[0070] Next, in the embodiment of the present invention, the first area data is determined according to the oceanic crust age data and the sediment quantity data through a preset discrimination model. Specifically, in the embodiment of the present invention, the preset discrimination model is used to screen the oceanic crust areas that are suitable for the long-term enrichment of metal oxides and are easy to form crusts. Correspondingly, the preset discrimination model in the embodiment of the present invention is constructed through the coupling efficiency of the sedimentation rate parameter and the oceanic crust age parameter. It is easy to understand that when different sedimentation rates and oceanic crust ages are coupled, there are different effects on the enrichment of metal oxides and the formation of crusts. Therefore, the preset discrimination model is constructed through the coupling effect between the sedimentation rate parameter and the oceanic crust age parameter in the embodiment of the present invention, and then the corresponding oceanic crust age data and sedimentation rate data are input into the preset discrimination model for analysis and discrimination to obtain the first area data suitable for the formation of cobalt-rich crusts. Further, in the embodiment of the present invention, the first area data is corrected by terrain constraints according to the preset terrain constraint parameters to obtain the second area data. Specifically, the preset terrain constraint parameters in the embodiment of the present invention refer to the constraint parameters determined according to the terrain where the crusts occur. Correspondingly, in the embodiment of the present invention, the range of the first area data is limited, that is, terrain constraint correction, through the preset terrain constraint parameters determined in advance, so as to obtain the second area data. Finally, in the embodiment of the present invention, the crust area is analyzed according to the second area data and the preset near-bottom shallow stratigraphic profile data to obtain the target cobalt-rich crust area. Specifically, in the embodiment of the present invention, the range of the preset near-bottom shallow stratigraphic profile data is constrained by the determined second area data, so as to combine the horizontal space to determine the regional range and the vertical space regional range to determine the target cobalt-rich crust area to be traced, effectively improving the efficiency and accuracy of the delineation of cobalt-rich crusts.

[0071] It should be noted that, as Figure 2 shown, in the process of obtaining the preset near-bottom shallow stratigraphic profile data in the embodiment of the present invention, it mainly includes steps such as original SEGY data decoding, data filtering, cross-section generation, sea bottom line extraction, and layer boundary extraction. Among them, in the embodiment of the present invention, according to the frequency domain filtering step of shallow profile data processing, the influence of formation random noise, low-frequency and high-frequency interference is effectively removed, and a grayscale cross-section is made based on the filtered result. Then, the sea bottom line of the data is extracted, which mainly includes structural enhancement filtering of the amplitude image, amplitude layer boundary extraction after enhancement filtering, calculation of the local phase map, phase image enhancement processing, and layer boundary extraction based on the local phase map. Finally, due to the characteristics that the phase layer boundary is fine but greatly affected by non-stratigraphic echoes, and the amplitude layer boundary cannot reflect the fine / weak layer boundary but can reflect the layer boundary trend, in the embodiment of the present invention, based on the complementarity of the two, the combined amplitude and phase layer boundary is comprehensively obtained, and finally the processed near-bottom shallow stratigraphic profile image with layer boundary information is output.

[0072] Referring to Figure 3, in some examples of the present invention, before determining the first region data according to the oceanic crust age data and sedimentation rate data through a preset discrimination model, the cobalt-rich crust resource distribution delineation method provided by the embodiments of the present invention further includes but is not limited to the following steps:

[0073] Step S210: Combine and analyze the sedimentation rate parameter and the oceanic crust age parameter to determine the coupling effect.

[0074] Step S220: Generate model discrimination conditions through the coupling effect to construct a preset discrimination model.

[0075] In this specific embodiment, the embodiments of the present invention combine and analyze the sedimentation rate parameter and the oceanic crust age parameter to determine the corresponding coupling effect, and then generate model discrimination conditions through the coupling effect, thereby constructing a preset discrimination model. Specifically, the embodiments of the present invention first divide the oceanic crust age parameter into different age segments according to the oceanic crust age division threshold, and different age segments correspond to different types of oceanic crust. For example, the embodiments of the present invention divide the oceanic crust with an oceanic crust age parameter in the age segment less than 10 Ma into young oceanic crust. This oceanic crust area is close to the mid-ocean ridge, mainly composed of fresh basalt, with rugged terrain (such as seamounts, fracture zones), but with frequent hydrothermal activities, which may inhibit the growth of crusts. In addition, the embodiments of the present invention divide the oceanic crust with an oceanic crust age parameter in the age segment of 10 Ma to 80 Ma into middle-aged and old oceanic crust. At this time, the basalt is gradually covered by deep-sea sediments, but the hard substrate may still be exposed in the low sedimentation rate area, providing an ideal interface for the growth of crusts. At the same time, the embodiments of the present invention divide the oceanic crust with an oceanic crust age parameter in the age segment greater than 80 Ma into ancient oceanic crust. At this time, the sediment layer is thick. If the sedimentation rate is high, the substrate will be buried and it is difficult to form crusts. However, in the low sedimentation rate area (such as the CC area in the South Pacific), crusts may still be retained. Correspondingly, the embodiments of the present invention divide the sedimentation rate parameter into different sedimentation segments according to the corresponding sedimentation rate threshold. For example, when the sedimentation rate is less than 1 cm / kyr, it is divided into low sedimentation rate. At this time, the sediment input is small, and the substrate is long-term exposed to the seawater-sediment interface, and the crust can grow continuously (such as seamounts in the equatorial Pacific). In addition, when the sedimentation rate is greater than 3 cm / kyr, it is divided into high sedimentation rate. At this time, the rapid sedimentation buries the substrate and hinders the formation of crusts (such as marginal sea basins or high productivity areas).

[0076] Accordingly, in the embodiments of the present invention, the deposition rates and oceanic crust ages of the divided stages are combined to determine the corresponding coupling effects. For example, when middle-aged and old oceanic crusts are combined with a low deposition rate, the base exposure time is long and the crust thickness is large (such as the seamount groups in the central Pacific Ocean), which is the best combination for hosting cobalt-rich crusts. In addition, when ancient oceanic crusts and a low deposition rate are combined, ancient crusts may be preserved, but due to the deep burial of the basement, there are fewer new crusts. Similarly, when young oceanic crusts and a low deposition rate are combined, the topography is suitable, but hydrothermal activities or volcanic debris may interfere with the crust composition. Then, in the embodiments of the present invention, corresponding parameter screening thresholds, that is, the discrimination conditions for cobalt-rich crusts (model discrimination conditions), are determined according to the corresponding coupling effects. For example, the oceanic crust age is between 30 and 70 Ma and the deposition rate is less than 1 cm / kyr, so as to construct a preset discrimination model. Among them, the parameter screening thresholds in the embodiments of the present invention need to be calibrated according to the region. For example, in the CC area, it can be relaxed to less than 2 cm / kyr.

[0077] Referring Figure 4 , in some embodiments of the present invention, the first regional data is determined through a preset discrimination model according to the oceanic crust age data and the deposition rate data, including but not limited to the following steps:

[0078] Step S310: Perform a first classification process on the oceanic crust age data through a preset age threshold to obtain oceanic crust classification data.

[0079] Step S320: Perform a second classification process on the deposition rate data through a preset deposition threshold to obtain deposition classification data.

[0080] Step S330: Perform regional screening through the preset discrimination model according to the oceanic crust classification data and the deposition classification data to obtain the first regional data.

[0081] In this specific embodiment, the embodiment of the present invention first performs a first classification process on the oceanic crust age data through a preset age threshold to obtain oceanic crust classification data, and performs a second classification process on the sedimentation rate data through a preset sedimentation threshold to obtain sedimentation classification data. Specifically, the preset age threshold in the embodiment of the present invention corresponds to the division threshold of the oceanic crust age parameter in the process of constructing the preset discrimination model. The embodiment of the present invention classifies the oceanic crust regions corresponding to each oceanic crust age data through the preset age threshold to determine the oceanic crust type corresponding to each oceanic crust region, that is, to obtain the corresponding oceanic crust classification data. For example, when the oceanic crust age data corresponding to an oceanic crust region is less than 10 Ma, it is determined as a young oceanic crust; when the oceanic crust age data corresponding to an oceanic crust region is between 10 Ma and 80 Ma, it is determined as a middle-aged and old oceanic crust; and when the oceanic crust age data corresponding to an oceanic crust region is greater than 80 Ma, it is determined as an ancient oceanic crust. Similarly, the preset sedimentation threshold in the embodiment of the present invention also corresponds to the division threshold of the sedimentation rate parameter in the process of constructing the preset discrimination model. The corresponding preset sedimentation threshold is used to divide the oceanic crust data corresponding to each sedimentation rate data to determine the sedimentation type corresponding to each oceanic crust region, that is, the sedimentation classification data. For example, when the sedimentation rate data corresponding to an oceanic crust region is less than 1 cm / kyr, the sedimentation type corresponding to this oceanic crust region is a low sedimentation rate. And when the sedimentation rate data corresponding to an oceanic crust region is greater than 3 cm / kyr, the sedimentation type corresponding to this oceanic crust region is a high sedimentation rate.

[0082] Furthermore, the embodiment of the present invention performs regional screening on the oceanic crust classification data and the sedimentation classification data through a preset discrimination model to obtain first regional data. Specifically, the embodiment of the present invention inputs the classification results of the oceanic crust age data and the sedimentation rate data into the preset discrimination model for analysis and screening to screen out regions that meet the occurrence conditions of cobalt-rich crusts and determine the first regional data. Exemplarily, in the GIS, the embodiment of the present invention overlays the layers corresponding to the oceanic crust age data and the sedimentation rate data, and extracts the oceanic crust regions that meet the best coupling effect according to the oceanic crust classification data and the sedimentation classification data corresponding to each oceanic crust region, that is, the combination of middle-aged and old oceanic crusts and low sedimentation rates, and screens out oceanic crust regions that simultaneously meet the oceanic crust age: 30–70 Ma and sedimentation rate: <1 cm / kyr to obtain the first regional data.

[0083] Referring to Figure 5 , in some embodiments of the present invention, terrain constraint correction is performed on the first regional data according to preset terrain constraint parameters to obtain second regional data, including but not limited to the following steps:

[0084] Step S410: Perform buffer analysis on the first regional data and the regional terrain data to obtain third regional data.

[0085] Step S420: Perform regional screening on the third-region data using preset terrain constraint parameters to obtain the second-region data. The preset terrain constraint parameters include a regional slope threshold.

[0086] In this specific embodiment, the embodiment of the present invention first performs buffer analysis on the first-region data and the regional terrain data to obtain the third-region data, and then performs regional screening on the third-region data using preset terrain constraint parameters to obtain the second-region data. Specifically, the regional terrain data in the embodiment of the present invention refers to Digital Terrain Model (DTM) data, including data such as precision, latitude, and depth. Among them, since the crust is mainly stored in terrains such as seamounts and guyots (water depth of 2000 to 3000 meters), it is necessary to superimpose the regional terrain data to further limit the scope. Accordingly, the embodiment of the present invention superimposes the first-region data obtained by regional screening on the regional terrain data and performs buffer analysis to obtain data within a preset range (such as within the range of 2 - 3 kilometers) of water depth, that is, the third-region data, as Figure 6 shown. Among them, Figure 6 is the terrain data DTM corresponding to the first-region data (the color shade represents the water depth), and the black frame-lined areas A and B are the data ranges of the first-region data. In addition, the preset terrain constraint parameters in the embodiment of the present invention include a regional slope threshold, that is, a terrain slope threshold. Since it is difficult for the crust to be stored in the deep-sea plain terrain, the embodiment of the present invention further determines the cobalt-rich crust area by removing the plain areas in the third-region data. For example, the embodiment of the present invention removes the areas with a slope less than 10 degrees in the third-region data, so as to obtain an area that meets the terrain conditions for the storage of the crust, that is, the second-region data, as Figure 7 shown. Among them, Figure 7 The high-thickness development areas are the colored parts within the ranges circled by areas A and B in the figure, that is, the obtained second-region data, including data such as longitude, latitude, water depth, slope, sedimentation rate, and oceanic crust age.

[0087] Referring to Figure 8 , in some embodiments of the present invention, crust area analysis is performed on the second-region data and preset near-bottom shallow stratigraphic profile data to obtain the target cobalt-rich crust area, including but not limited to the following steps:

[0088] Step S510: Determine the longitude and latitude information of the target area according to the second-region data.

[0089] Step S520: Perform regional position constraint on the preset near-bottom shallow stratigraphic profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area.

[0090] In this specific embodiment, the embodiment of the present invention first determines the longitude and latitude information of the target area according to the second area data, and then performs regional position constraint on the preset near-bottom shallow stratum profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area. Specifically, the longitude and latitude information of the target area in the embodiment of the present invention refers to the longitude and latitude data of the target cobalt-rich crust area, that is, the longitude and latitude data corresponding to the second area data. Correspondingly, the embodiment of the present invention extracts the corresponding longitude and latitude data, that is, the longitude and latitude information of the target area, from the second area data to perform regional restriction, that is, regional position constraint, on the near-bottom shallow stratum profile image corresponding to the preset near-bottom shallow stratum profile data, so as to delineate the target cobalt-rich crust area in the near-bottom shallow stratum profile image.

[0091] Referring to Figure 9 , in some embodiments of the present invention, after performing regional position constraint on the preset near-bottom shallow stratum profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area, the cobalt-rich crust resource distribution delineation method provided by the embodiment of the present invention further includes but is not limited to the following steps:

[0092] Step S610: Extract preset visualization data from the second area data according to the target cobalt-rich crust area. The preset visualization data includes longitude data, latitude data, depth data, and amplitude intensity data.

[0093] Step S620: Perform three-dimensional ore body visualization according to the preset visualization data to obtain a three-dimensional cobalt-rich crust image.

[0094] In this specific embodiment, the embodiment of the present invention first extracts preset visualization data from the second area data according to the target cobalt-rich crust area. Specifically, the preset visualization data in the embodiment of the present invention includes the longitude data, latitude data, depth data, and amplitude intensity data corresponding to the target cobalt-rich crust area. Among them, the embodiment of the present invention uses the longitude data, latitude data, and depth data as the three-dimensional data for visualization, and associates the amplitude intensity data with the color depth of the visual display to perform three-dimensional ore body visualization, that is, perform three-dimensional visualization on the delineated target cobalt-rich crust area to generate a three-dimensional cobalt-rich crust image, so as to quickly and accurately identify the high-thickness development area of the cobalt-rich crust, as Figure 10 shown. Among them, the coordinate axes X, Y, and Z in the figure correspond to the longitude data, latitude data, and depth data respectively.

[0095] Referring to Figure 11 , in some embodiments of the present invention, after performing crust area analysis according to the second area data and the preset near-bottom shallow stratum profile data to obtain the target cobalt-rich crust area, the cobalt-rich crust resource delineation method provided by the embodiment of the present invention further includes but is not limited to the following steps:

[0096] Step 710: Collect the preset ground truth data corresponding to the target cobalt-rich crust area. The preset ground truth data includes drilling data and camera data.

[0097] Step 720: Verify the target cobalt-rich crust area according to the drilling data and camera data, so as to adjust the parameters of the preset discrimination model according to the corresponding verification results.

[0098] In this specific embodiment, the embodiment of the present invention first collects the preset ground truth data corresponding to the target cobalt-rich crust area to verify the target cobalt-rich crust area through the collected preset ground truth data, so as to adjust the parameters of the preset discrimination model according to the verification results. Specifically, the preset ground truth data in the embodiment of the present invention refers to the data obtained by near-bottom observation or sampling, such as drilling data and camera data. Correspondingly, after determining the target cobalt-rich crust area, the embodiment of the present invention drills and acquires images of this area to analyze the collected drilling data and camera data, and determine whether the circled target cobalt-rich crust area is accurate, so as to obtain the corresponding verification results. Correspondingly, when it is determined that the delineation error of the target cobalt-rich crust area is less than the preset error threshold, the verification result passes. On the contrary, when it is determined that the delineation error of the target cobalt-rich crust area is greater than the preset error threshold, it means that the verification fails, that is, the current delineation error of the cobalt-rich crust resource distribution is too large. At this time, the embodiment of the present invention adjusts the corresponding discrimination conditions and parameters in the preset discrimination model according to the verification results, so as to effectively improve the accuracy of the delineation of the cobalt-rich crust resource distribution.

[0099] It should be noted that in some embodiments of the present invention, the distribution of cobalt-rich crust resources in known mining areas can be predicted, and the prediction results can be compared and verified with the known mining area data to verify the accuracy of the model. For example, in the Central Pacific Seamounts, the oceanic crust age is about 50 - 60 Ma, the sedimentation rate is < 0.5 cm / kyr, the crust coverage rate is > 70%, and the average thickness is 5 - 8 cm. In the Central Indian Ocean Basin, the oceanic crust age is > 80 Ma, but the sedimentation rate is low (< 1 cm / kyr), and thin crusts (2 - 3 cm) are still found, indicating that ancient oceanic crust can be locally mineralized under low sedimentation conditions. Further, the embodiment of the present invention adjusts the model parameters according to the verification results to achieve the calibration and optimization of the model.

[0100] Next, in combination with the specific scenario of delineating the distribution of cobalt-rich crust resources, the solution of the embodiment of the present invention will be introduced and described in detail:

[0101] Exemplarily, as Figure 12 shown, Figure 12Schematic diagram of the overall process framework for delineating the distribution of cobalt-rich crust resources provided by the embodiments of the present invention. Specifically, in the embodiments of the present invention, oceanic crust age data, sedimentation rate data, and near-bottom shallow stratigraphic profile data are first obtained, and data discrimination is performed through a preset discrimination model. Among them, in the embodiments of the present invention, it is first determined whether the oceanic crust age data is within the threshold to screen out oceanic crust areas suitable for the occurrence of high-thickness crusts. For example, in the embodiments of the present invention, middle-aged and old oceanic crust (30 Ma - 70 Ma) areas are screened out. In these areas, the basement rocks are stable and the degree of differentiation is moderate, which is suitable for the long-term enrichment of metal oxides and is easy to form cobalt-rich crusts. Then, on the basis of the screened oceanic crust areas, the embodiments of the present invention further screen out oceanic crust areas within the sedimentation rate threshold. Correspondingly, the preset discrimination model in the embodiments of the present invention is constructed based on the coupling effect between the sedimentation rate and the oceanic crust age. Through the preset discrimination model, oceanic crust areas that meet the corresponding parameter thresholds can be screened out, such as the combination of middle-aged and old oceanic crust and low sedimentation rate (30 - 70 Ma, sedimentation rate: < 1 cm / kyr), to obtain first-region data, including data such as longitude, latitude, sedimentation rate, and oceanic crust age. Then, the embodiments of the present invention perform spatial overlay of the digital terrain model (DTM) data and the first-region data obtained by screening to obtain areas with a water depth within the range of 2 - 3 kilometers, and exclude areas with a slope less than 10 degrees, thereby obtaining second-region data to screen out topographic areas suitable for the occurrence of crusts. Further, the embodiments of the present invention perform layer boundary processing on the near-bottom shallow stratigraphic profile data through the second-region data. Among them, in the embodiments of the present invention, the near-bottom shallow stratigraphic profile data is constrained according to the longitude and latitude data of the second-region data, so as to determine the target cobalt-rich crust area. At the same time, the embodiments of the present invention extract the corresponding position data in the near-bottom shallow stratigraphic profile data according to the longitude and latitude data of the second-region data, including depth, amplitude intensity, longitude, and latitude data, for three-dimensional visualization. Correspondingly, the embodiments of the present invention perform ground truth verification through known mining area data, drilling data, and camera data to determine whether the current delineation of the cobalt-rich crust resource distribution is accurate. When it is determined that the error of the verification result of the ground truth verification is less than the corresponding error threshold, the three-dimensional ore body data obtained by delineation is output.

[0102] It is easy to understand that in the embodiments of the present invention, the areas where cobalt-rich crusts can develop are first determined in a large horizontal space through oceanic crust age data and sedimentation rate data, and then the depth and shape of the cobalt-rich crust deposits are determined in the vertical space to realize the delineation of the cobalt-rich crust resource distribution. Among them, since seismic waves belong to the category of stress waves, the near-bottom shallow profile data can finely distinguish the growth boundaries of bedrock and crusts that are difficult to distinguish in traditional data, significantly improving the scientific accuracy and data precision of the delineation of cobalt-rich crust development areas. The comparison between the solution of the present application and other methods is shown in Table 1 below:

[0103] Table 1

[0104]

[0105] Please refer to Figure 13 , the embodiment of the present application further provides a cobalt-rich crust resource distribution delineation system, which can implement the above-mentioned cobalt-rich crust resource distribution delineation method. The system includes:

[0106] The first module 810 is used to obtain preset geological sedimentary environment data. Among them, the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic profile data.

[0107] The second module 820 is used to determine the first area data through a preset discrimination model according to the oceanic crust age data and the sedimentation rate data. Among them, the preset discrimination model is constructed by the coupling effect of sedimentation rate parameters and oceanic crust age parameters.

[0108] The third module 830 is used to perform terrain constraint correction on the first area data according to the preset terrain constraint parameters to obtain the second area data.

[0109] The fourth module 840 is used to perform crust area analysis according to the second area data and the preset near-bottom shallow stratigraphic profile data to obtain the target cobalt-rich crust area.

[0110] It can be understood that the content in the above method embodiments is applicable to the system embodiments of the present application. The functions specifically implemented in the system embodiments of the present application are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0111] The embodiment of the present application further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned cobalt-rich crust resource distribution delineation method. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0112] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present application. The functions specifically implemented in the device embodiments of the present application are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0113] Please refer to Figure 14 , Figure 14 schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0114] The processor 910 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0115] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 920 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 920 and are called by the processor 910 to execute the method for delineating the distribution of cobalt-rich crust resources in the embodiments of the present application.

[0116] The input / output interface 930 is used to implement information input and output.

[0117] The communication interface 940 is used to implement communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0118] The bus 950 transmits information between various components of the device (such as the processor 910, the memory 920, the input / output interface 930, and the communication interface 940).

[0119] Among them, the processor 910, the memory 920, the input / output interface 930, and the communication interface 940 achieve communication connections with each other inside the device through the bus 950.

[0120] The embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for delineating the distribution of cobalt-rich crust resources is implemented.

[0121] It can be understood that the content in the above method embodiments is applicable to the embodiments of this storage medium. The functions specifically implemented by the embodiments of this storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0122] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0124] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0125] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0127] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0128] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or its similar expressions refer to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0129] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0130] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0132] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0133] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall fall within the scope of the rights of the embodiments of this application.

Claims

1. A method for delineating the distribution of cobalt-rich crust resources, characterized in that, The method includes the following steps: Obtain preset geological sedimentary environment data; wherein, the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic profile data; Determine first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model; wherein, the preset discrimination model is constructed by the coupling effect of sedimentation rate parameters and oceanic crust age parameters; Perform terrain constraint correction on the first region data according to preset terrain constraint parameters to obtain second region data; Perform crust area analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust area.

2. The method according to claim 1, wherein Before performing the step of determining the first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model, it includes: Perform combined analysis on the sedimentation rate parameters and the oceanic crust age parameters to determine the coupling effect; Generate model discrimination conditions through the coupling effect to construct the preset discrimination model.

3. The method according to claim 1, wherein The step of determining the first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model includes: Perform first classification processing on the oceanic crust age data through a preset age threshold to obtain oceanic crust classification data; Perform second classification processing on the sedimentation rate data through a preset sedimentation threshold to obtain sedimentation classification data; Perform region screening on the oceanic crust classification data and the sedimentation classification data through the preset discrimination model to obtain the first region data.

4. The method according to claim 1, characterized in that, The step of performing terrain constraint correction on the first region data according to preset terrain constraint parameters to obtain second region data includes: Perform buffer analysis on the first region data and regional terrain data to obtain third region data; Perform region screening on the third region data through the preset terrain constraint parameters to obtain the second region data; wherein, the preset terrain constraint parameters include a regional slope threshold.

5. The method according to claim 1, wherein The step of performing crust area analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust area includes: Determine the longitude and latitude information of the target area according to the second region data; Perform regional position constraint on the preset near-bottom shallow stratigraphic profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area.

6. The method according to claim 5, wherein After performing the step of performing regional position constraint on the preset near-bottom shallow stratigraphic profile data through the longitude and latitude information of the target area to obtain the target cobalt-rich crust area, the method further includes: Extract preset visualization data from the second region data according to the target cobalt-rich crust area; wherein, the preset visualization data includes longitude data, latitude data, depth data, and amplitude intensity data; Perform three-dimensional ore body visualization according to the preset visualization data to obtain a three-dimensional image of cobalt-rich crust.

7. The method according to claim 1, characterized in that, After performing the step of performing crust area analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust area, the method further includes: Collect the preset ground truth data corresponding to the target cobalt-rich crust area; wherein, the preset ground truth data includes drilling data and camera data; Verify the target cobalt-rich crust area according to the drilling data and the camera data, so as to adjust the parameters of the preset discrimination model through the corresponding verification results.

8. A cobalt-rich crust resource distribution delineation system, characterized in that, The system includes: A first module, configured to obtain preset geological deposition environment data; wherein, the preset geological deposition environment data includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic profile data; A second module, configured to determine first area data through a preset discrimination model according to the oceanic crust age data and the sedimentation rate data; wherein, the preset discrimination model is constructed by the coupling effect of sedimentation rate parameters and oceanic crust age parameters; A third module, configured to perform terrain constraint correction on the first area data according to preset terrain constraint parameters to obtain second area data; A fourth module, configured to perform crust area analysis according to the second area data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust area.

9. An electronic device, characterized in that, Includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1 to 7.

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