CN111880239A - Quantitative evaluation method for cobalt-rich crust resources in Haishan - Google Patents
Quantitative evaluation method for cobalt-rich crust resources in Haishan Download PDFInfo
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Abstract
The invention provides a method for quantitatively evaluating cobalt-rich crusting resources in a sea mountain, belonging to the field of research and development of ocean mineral resources, and comprising the following steps of: s1, dividing the target hill slope into a plurality of adjacent grid cells; s2, according to geological station survey data, taking grid units as objects, and carrying out spatial interpolation calculation to obtain Haishan cobalt-rich crusting resource parameters of each grid unit, wherein the Haishan cobalt-rich crusting resource parameters comprise crusting thickness, crusting water content, crusting wet density, crusting coverage rate, metal concentration and the like; meanwhile, according to regional survey data, extracting the sea and mountain slope mineralization characteristics of each grid unit, wherein the sea and mountain slope mineralization characteristics comprise the incrustation macroscopic coverage rate, the ore-adapted slope ratio, the slope surface area and the like; s3, calculating to obtain the resource quantity of each grid unit; s4, defining key areas and resource quantity thereof according to the sequencing and combination of the grid unit resource quantity, and realizing the quantitative evaluation of the Haishan cobalt-rich crusting resource.
Description
Technical Field
The invention belongs to the field of research and development of ocean mineral resources, and particularly relates to a quantitative evaluation method of cobalt-rich crusting resources in sea mountains.
Background
There are tens of thousands of underwater seas distributed in the world ocean. These seashore surfaces are coated with metal oxide shells, which are rich in a variety of strategic and critical metals, commonly referred to as cobalt rich crusts (cobalt-rich crusts). Under the organization of the international seabed management agency, a plurality of countries in the world serve as pioneer investors to obtain investigation and ore containment rights in relevant mountainous and mountainous regions. On the basis of investigation, the rich mining area and the resource amount are found out, and the future resource interest range is defined. Quantitative evaluation of cobalt-rich crusting resources in the seas and mountains is required to achieve the goals. A comprehensive quantitative evaluation method for cobalt-rich crust resources in the sea and mountains is lacking in systems at home and abroad. The resource evaluation method mainly aims at the resource evaluation method of multi-metal crusting of ocean bottom basins, and comprises the steps of analyzing and estimating multi-metal nodule resources by using a geostatistics method earlier in China, calculating the nodule resource quantity of a Pacific CC area of a developed area of China by using a Kriging method, estimating the nodule abundance and the coverage rate according to a seabed photo by using an image recognition processing technology, calculating the abundance value of the multi-metal nodule by using an artificial neural network technology, estimating the nodule distribution characteristics by using a fractal method, applying a collaborative regionalization theory of mineral resource evaluation and a collaborative Kriging method to the mineral resource evaluation of West areas of the Topacific CC area, dividing a posterior probability interval into favorable sections of ore formation by using an evidence weight method, estimating the multi-metal nodule resource quantity of the Pacific CC area and peripheral areas by using a linear regression method, and the like.
In the aspect of evaluating cobalt-rich crusting resources, a recent region method and a geological block method are combined to comprehensively evaluate and analyze the amount of the crusting resources of the east Pacific ocean mountains; on the basis of researching the mining background and the distribution rule of the cobalt-rich crust, evaluation parameters and an evaluation method are extracted; the fractal theory is applied to research the spatial distribution and the resource amount of the cobalt-rich crust resources respectively; several main methods for evaluating the amount of the cobalt-rich crust resources at present are discussed and analyzed and compared; on the basis of in-depth research and analysis of the information such as the thickness, abundance and the like of cobalt crust in the western Pacific ocean seashore, 8 parameter indexes which are beneficial to delineation of cobalt crust mining areas and resource evaluation are provided. These are all good technologies, but at present there is a lack of comprehensive and systematic technical approaches.
In summary, the prior art has the following disadvantages: (1) based on observations of the survey data, mean calculations are performed, thus lacking analysis of spatial autocorrelation. The data of the sea and mountain space is not controlled by direction, and the traditional Kriging method is ineffective. (2) Moreover, the resource parameters related in the prior art, such as the cobalt-rich crust coverage rate, are only suitable for local observation and are not suitable for regional calculation analysis. (3) The former resource evaluation is more based on the natural state of cobalt-rich crust, and does not consider the state of exploitation. Not all slopes covering cobalt rich crusts are suitable for mineral production, for example slopes greater than 15 degrees, making underwater machine operations difficult. (4) The method for estimating the hill slope area is rough. (5) Part of the traditional method is based on manual judgment, and the whole flow calculation cannot be realized. Due to the obstruction of a huge thick water layer, the limitation of a survey technology and the high cost of the survey, the method brings great difficulty for people to find out the spatial distribution of deep sea mineral resources and estimate the resource quantity of the deep sea mineral resources. An effective, systematic and comprehensive quantitative evaluation technology for the sea and mountain mineral resources brings great benefits.
Disclosure of Invention
In order to meet the actual requirements of research and development of the cobalt-rich crust resources in the sea and mountains, the invention overcomes the defects in the prior art, and solves the technical problem of a quantitative evaluation method of the cobalt-rich crust resources in the sea and mountains.
In order to solve the technical problems, the invention adopts the technical scheme that: a quantitative evaluation method for cobalt-rich crust resources in Haishan comprises the following steps:
s1, dividing the target sea slope into a plurality of grid units with the same area according to equal length and equal width intervals;
s2, taking geological station survey data as a basis, and calculating by utilizing spatial interpolation to obtain Haishan cobalt-rich crust resource parameters in each grid unit; the sea-mountain cobalt-rich crust resource parameters comprise crust thickness, crust water content, crust wet density, crust coverage rate and metal concentration; meanwhile, based on regional survey data of water depth, taking a grid unit as a target, extracting mineralization characteristics of the cobalt-rich incrustation of the sea mountains, wherein the mineralization characteristics of the cobalt-rich incrustation of the sea mountains comprise incrustation macroscopic coverage rate, ore-adapted slope ratio and slope surface fitting area;
s3, performing combined calculation according to the parameters of the Haishan cobalt-rich crust resource and the parameters in the Haishan slope mineralization characteristic to obtain the resource amount of the grid unit;
s4, sorting the grid cells according to the resource amount of the grid cells; and selecting grid units with larger adjacent resource quantity, and defining the cobalt-rich crust resource key area and giving the resource quantity of the key area.
The grid unit is a square area with the square kilometer of 1-20.
In step S2, the geological site survey data is obtained by sampling survey, and the specific method includes: laying geological sampling points on the sea and mountain oblique waves according to the set distribution density, acquiring rock or ore samples on the sampling points by using various mechanical methods, and then testing and analyzing the geological position samples in a laboratory to obtain geological position data; the regional survey data of water depth refers to data of full coverage or lateral line coverage acquired by using geophysical means.
In step S2, when calculating the haishan cobalt-rich crust resource parameters in each grid cell, a spatial interpolation method is used to interpolate the geological sampling site data to each grid cell.
The spatial interpolation method is a grid moving average method or a kriging method.
In step S2, the calculation formula of the incrustation macro coverage is:
wherein, RcoveriRepresents the incrustation macro coverage rate, N, of the ith grid celliRepresenting the total number of water depth points or water depth grid nodes in the ith grid cell; g represents the slope gradient of a water depth point or a data grid node, gminAnd gmaxRespectively representing a minimum slope suitable for the development of cobalt-rich crusts and a maximum slope suitable for the mining operation of underwater machinery;
the ore-adapted slope ratio is calculated by the formula:
wherein, RslopeiRepresenting the minesuitability slope ratio of the ith grid cell;
the calculation method of the fitting area of the slope surface comprises the following steps: and calculating the three-dimensional space surface area of the minimum grid based on the minimum grid of the multi-beam bathymetric survey gridding data, then accumulating the three-dimensional space surface areas of all the minimum data grids in the grid unit i, and performing fitting calculation to obtain the slope surface fitting area of the grid unit.
Minimum slope g suitable for cobalt-rich crust developmentminThe value of (a) is 4.8 degrees, and the maximum slope g is suitable for the mining operation of underwater machinerymaxIs 15 deg.
In step S4, the calculation formula of the resource amount of the grid cell is:
Oweti＝Areai(km2)×Coveragei×Thicknessi×Densityi×Rcoveri×107；
Osuiti＝Areai(km2)×Coveragei×Thicknessi×Densityi×Rslopei×107；
DOi＝Oweti×(1-Water ratioi)；
DOsuiti＝Osuiti×(1-Water ratioi)；
Metai＝DOi×Concentrationi；
Msuiti＝DOsuiti×Concentrationi；
wherein, OwetiRepresents the wet crusting tonnage of grid cell i, OsuitiRepresenting recoverable wet crusting tonnage of grid cell i, AreaiRepresents the ramp surface area, Coverage, of grid cell iiIndicates the crusting coverage, Thick, of the ith grid celliIndicates the crusting thickness, Density, of the ith grid celliRepresents the ithThe crusting wet density of the grid cells; rcoveriRepresents the crusting macro coverage, Rslope, of the ith grid celliRepresenting the minesuitability slope ratio of the ith grid cell; DOiAnd DOsutitiRespectively representing the tonnage of the dry shell and the tonnage of the recoverable dry shell of the ith grid unitiRepresents the number of metal tons, Msuit, of the ith grid celliRepresenting the tonnage of mined metal, Water ratio, for the ith grid celliIndicates the crust moisture content, Concentration of the ith grid celliRepresenting the metal concentration of the ith grid cell.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, grid unit division is carried out on a target sea slope, grid interpolation calculation is carried out on geological station survey data, sea-mountain cobalt-rich incrustation resource parameters in each grid unit are obtained, the mineralization characteristics of the sea slope of each grid unit are calculated and extracted according to the sea-mountain area survey data, the resource quantity of each grid unit is calculated by combining the sea-mountain cobalt-rich incrustation resource parameters and the mineralization characteristics of each grid unit, finally, a key area is defined according to the sequencing and the spatial combination of the resource quantity of each grid unit, and finally, quantitative evaluation of the sea-mountain cobalt-rich incrustation resources is achieved.
2. In the invention, the sea and mountain slope mineralization characteristics comprise incrustation macroscopic coverage rate, ore-adaptive slope ratio, slope surface area fitting and the like, and are an innovative part of the technology. The invention increases the macroscopic coverage rate, and can eliminate the local area which is not suitable for resource distribution; the tonnage of the wet crusting resources is quantified by adopting the ore-adapting slope rate, a slope area more suitable for future mining is defined, the contradiction that the mineral reserve is large and the mineable reserve is small is avoided, and the quantitative evaluation precision of the crusting resources is improved; the method adopts the minimum grid of the water depth data to calculate the surface area of the slope in a fitting manner, so that the area estimation precision of the slope table is greatly improved, the quantity of resources is determined according to the size of the area of the slope, the surface area of the slope is accurately estimated, and the estimation precision of the resources is improved.
3. According to geological sampling data, the geological station data are inserted into each grid unit by using a spatial interpolation method, so that the investigation cost can be saved, and a grid moving average method and a kriging method can be adopted as a difference method. The kriging method suitable for the sea hill slope space interpolation can solve the space autocorrelation of the sea hill resources.
Drawings
FIG. 1 is a flow chart of a quantitative evaluation method of cobalt-rich incrustation resources in Haishan according to the present invention;
fig. 2 is a graphic summary of quantitative evaluation of cobalt-rich crust in the seashore obtained by applying the present invention, wherein (a) represents a spatial interpolation result of cobalt-rich crust thickness, (b) represents a fitting area result of a slope surface area, (c) represents a macroscopic coverage rate of cobalt-rich crust, (d) represents a spatial distribution of cobalt-rich crust geological survey sites and resource parameter value points, (e) represents resource amounts of each grid cell, (f) represents a ranking result of resource amounts of each grid cell, and (g) represents a definition of a key area.
Detailed Description
In order to make the technical scheme and advantages of the invention clearer, the technical scheme of the invention is clearly and completely described below by combining specific examples and accompanying drawings; other applications based on the technology of the present invention and applications implemented by those skilled in the art without authorization belong to the protection scope of the present invention.
As shown in figure 1, the invention provides a quantitative evaluation method of cobalt-rich incrustation resources in Haishan, which comprises the following steps:
and S1, dividing the target hill slope into a plurality of grid units with the same area according to equal length and equal width intervals.
In the embodiment of the present invention, for a target hill slope, a unit is defined by a sub-region with a certain area and shape, for example, a square region of 1, 4, or 20 square kilometers, and generally, the divided unit is divided by a grid, so the unit is also referred to as a grid unit. When grid cells are divided, enough area survey data, such as enough terrain data, is ensured in each cell. If the grid accuracy of the multi-beam terrain survey data is 100m, then the area is 4km2400 water depth data are distributed in the grid cells. In this embodiment, operations such as cobalt-rich crust resource parameter interpolation, slope mineralization feature extraction, ore reserve calculation, and the like, all take each grid cell as an object.
S2, taking the geological station survey data as a basis, taking the grid cells as objects, and carrying out interpolation calculation to obtain the cobalt-rich crust resource parameters of the mountains in each grid cell; the sea-mountain cobalt-rich crust resource parameters comprise crust thickness, crust water content, crust wet density, crust coverage rate, metal concentration and the like; and simultaneously, extracting slope mineralization characteristics of each grid unit according to regional survey data, wherein the hill mineralization characteristics comprise incrustation macroscopic coverage rate, ore-adapted slope ratio, slope surface area and the like.
(1) Geological station survey data and ore resource parameters
Geological sampling points are arranged on the slope of the sea and the mountain according to certain distribution density, and rock or ore samples are obtained on the sampling points by utilizing various mechanical methods. In a laboratory, the geological site data are obtained by testing and analyzing the samples of the geological site. The measured data of the ore sample, such as cobalt-rich crust Density, Thickness, water content, Concentration of certain metal, crust covering rate, etc., are all called ore resource parameters, and the data are characterized by sampling investigation.
In this embodiment, the sampling data of the geological site is a sampling survey method for saving survey cost, and the geological site is not distributed in each grid unit, so that a spatial interpolation method is required, and the geological site data is inserted into each grid unit. Interpolation methods are diverse, such as grid moving averages, Kriging methods, see articles Dewen Du, Chunjun Wang, Xiameng Du, Shijuan Yan, Xiangwen Ren, Xuefa Shi, Hein J. (2017b) Distance-Gradient-Based variance and Kriging to estimate Cobalt chloride-Rich depletion on sources geomology Reviews,2017, (84) 218-227 DOI: 10.1016/j.orgeogore edition.2016.12.028 and articles Dewen Duiging Interpolation for Evaluating the minerals Resources of Cobalt carbide on sources, Minls 2018, 374; doi:10.3390/min 8090374. The kryg method suitable for the spatial interpolation of the hilly slope can be referred to in patent application 201610130598.5, and in this embodiment, the kryg method can be preferably used to realize the interpolation of the hilly cobalt-rich crusting resource parameters of all grid cells. Of course, the invention can also obtain the Haishan cobalt-rich crusting resource parameters of each grid unit by using the traditional spatial interpolation method.
(2) Area survey data: compared with geological station survey data, the regional survey data are full-coverage or side-line coverage data acquired by geophysical means, such as multi-beam side-scan sonar sounding data, gravity data, magnetic data and the like, and have the characteristics of wide coverage area and large data volume. In the present embodiment, the area features that are extracted by the area data and are favorable for mineralization are referred to as mineralization features, such as "macro coverage", "mineable slope ratio", "fitted slope surface area", and the like, which will be mentioned below. These data are characterized by area coverage.
2.1 concept and algorithm of macroscopic coverage rate of cobalt-rich crust and ore-adapting slope ratio in mountain
The slope of the hill slope is too small, for example, the slope is covered by loose sediments when the slope is less than 4.8 degrees, the slope which is greater than a certain slope threshold value is suitable for the growth of the cobalt-rich crust, and the proportion of the slope which is suitable for the growth of the cobalt-rich crust and occupies the area of the grid unit is regarded as the macroscopic coverage rate of the cobalt-rich crust. The slope is too steep, for example 15 °, which is detrimental to mining operations of mining machines in underwater environments. The slope is the slope suitable for mineral distribution in a suitable slope range, such as 4.8-15 °, and the proportion of the slope in the grid cell area is called the mine-suitable slope rate. Assuming a certain grid unit i, distributing a plurality of water depth data, and estimating slope gradients g and g by peripheral water depth combination at each water depth point or water depth grid nodeminAnd gmaxRespectively a minimum slope suitable for the development of cobalt-rich crust and a maximum slope suitable for the mining operation of underwater machinery. Then N isi(gmin<g<gmax) The number of the water depth points or the water depth grid nodes of the ore in the unit i, Ni(gmin<g) The slope in the unit i is largeThe number of water depth points or water depth grid nodes on the minimum slope suitable for cobalt-rich crust development is more than NiIs the total number of water depth points or water depth grid nodes in the unit i. The ore-eligible slope ratio can be calculated using the following equation:
similarly, the macro coverage can be calculated by the following formula:
wherein, RcoveriRepresents the incrustation macro coverage rate, N, of the ith grid celliRepresenting the water depth points or the total number of water depth grid nodes in the ith unit grid; g represents the slope gradient, gminAnd gmaxRespectively representing a minimum slope suitable for the development of cobalt-rich crusts and a maximum slope suitable for the mining operation of underwater machinery; rslope (R slope)iIndicating the minerable slope ratio of the ith grid cell. The two ore-adaptive slope gradient thresholds are variable values. Different mountains may have different values; with the innovation of the investigation technology, a more accurate ore-adapting slope is probably found; as mining technology improves, the slope available for mining may increase, and so on.
2.2 slope surface area fitting concept and algorithm
The slope area is one of important parameters for calculating the ore reserves, and the accurate fitting of the slope area is an important precondition for accurately estimating the ore reserves. The slope area is typically estimated using the quotient of the horizontal projected area of the cell region divided by the cosine of the slope average gradient. In the examples of the present invention, the following methods were used: calculating the three-dimensional space surface Area of the minimum grid based on the minimum grid of the grid data of the multi-beam bathymetric survey, and accumulating all the minimum grid marks in the grid unit i to obtain the Areai。
In this embodiment, the mineral resource parameters of the geological site obtained by geological sampling may be used to perform spatial interpolation on the grid cells and estimate the mineral resource parameters of each grid cell; from regional survey data within the grid cells, the mineralization characteristics (i.e., mineralization slope rate, macro coverage, slope surface area, etc.) of each grid cell can be extracted. Thus, the mineral resource parameters and the mineralization characteristics are fused together by the grid units.
S3, carrying out combined calculation on eight parameters of the cobalt-rich incrustation coverage rate, the density, the metal concentration, the thickness, the water content, the macroscopic coverage rate, the ore-adapting slope rate and the slope fitting surface area of the Haishan to obtain the grid unit resource amount.
The site samples were taken, 5 mineral parameters assigned to each grid cell by spatial interpolation, and 3 mineralization signatures extracted from the regional survey data in the grid cell. Each grid unit corresponds to the 8 quantitative indexes, each index corresponds to one map layer (see figure 2) and the indexes are multiplied to obtain the resource amount of each unit, so that the purpose of quantitative evaluation of resources is achieved.
For grid cell i, the tonnage calculation formula of wet crusting is as follows:
Oweti＝Areai(km2)×Coveragei×Thicknessi×Densityi×Rcoveri×107； (3)
the formula for calculating the tonnage of the recoverable wet crusting resource is as follows:
Osuiti＝Areai(km2)×Coveragei×Thicknessi×Densityi×Rslopei×107；(4)
the tonnage of the dry shell is calculated by the formula:
DOi＝Oweti×(1-Waterratio)； (5)
tonnage of dry crust taken:
DOsuiti＝Osuiti×1-Waterratio)； (6)
the tonnage of a certain metal is calculated by the formula:
Metai＝DOi×Concentration； (7)
the tonnage of a certain metal can be adopted:
Msuiti＝DOsuiti×Concentration； (8)
wherein, OwetiRepresents the wet crusting tonnage of grid cell i, OsuitiRepresenting recoverable wet crusting tonnage of grid cell i, AreaiRepresents the ramp surface area, Coverage, of grid cell iiIndicates the crusting coverage, Thick, of the ith grid celliIndicates the crusting thickness, Density, of the ith grid celliRepresents the crusting wet density of the ith grid cell; rcoveriRepresents the crusting macro coverage, Rslope, of the ith grid celliRepresenting the minesuitability slope ratio of the ith grid cell; DOiAnd DOsutitiRespectively representing the tonnage of the dry shell and the tonnage of the recoverable dry shell of the ith grid unitiRepresents the number of metal tons, Msuit, of the ith grid celliRepresenting the recoverable metal tonnage of the ith grid cell, WatersatioiIndicates the crust moisture content, Concentration of the ith grid celliRepresenting the metal concentration of the ith grid cell.
And S4, defining the key area and the resource amount according to the grid resource amount.
All grid cells are sorted according to the amount of recoverable resources or their market total value, and the mineral-rich cells are spatially combined to delineate the target area of mineral production, referring to the relevant spatial adjacency principles of the international subsea authority, see fig. 2, and references Dewen Du, Xiangwen Ren, Shijuan Yan, xfa Shi, Yonggang Liu, gaoween he, (2017a) An Integrated Method for the Quantitative Evaluation of mineral resources, 2017(84), 174-184, DOI: 10.1016/j.oregory Reviews, 2017.011.011.011.b.
According to the method, grid unit division is carried out on a target sea slope, geological station survey data is subjected to gridding processing to obtain sea cobalt-rich crust resource parameters in each grid unit, slope mineralization characteristic parameters of each grid unit are obtained by combining sea area survey data, the resource amount of each grid unit is calculated by combining the sea cobalt-rich crust resource parameters and the slope mineralization characteristic parameters of each grid unit, a key area is defined, and quantitative evaluation of the sea cobalt-rich crust resources is realized; according to the invention, the sea and mountain slope mineralization characteristics of each grid unit comprise incrustation macroscopic coverage rate, ore-adapted slope ratio and slope surface area, besides the traditional coverage rate, the invention increases the macroscopic coverage rate, avoids the defect that the coverage rate can only be locally observed and can not be calculated in an area, quantifies the tonnage of wet incrustation resources by adopting the ore-adapted slope ratio, and improves the quantitative evaluation precision of incrustation resources; in addition, the invention adopts a fitting calculation method of the minimum survey data grid slope area accumulation to calculate the slope surface area of the grid unit, and has the advantage of high precision.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (8)
1. A quantitative evaluation method for cobalt-rich crust resources in Haishan is characterized by comprising the following steps:
s1, dividing the target sea slope into a plurality of grid units with the same area according to equal length and equal width intervals;
s2, taking geological station survey data as a basis, and calculating by utilizing spatial interpolation to obtain Haishan cobalt-rich crust resource parameters in each grid unit; the sea-mountain cobalt-rich crust resource parameters comprise crust thickness, crust water content, crust wet density, crust coverage rate and metal concentration; meanwhile, based on regional survey data of water depth, taking a grid unit as a target, extracting mineralization characteristics of the cobalt-rich incrustation of the sea mountains, wherein the mineralization characteristics of the cobalt-rich incrustation of the sea mountains comprise incrustation macroscopic coverage rate, ore-adapted slope ratio and slope surface fitting area;
s3, performing combined calculation according to the parameters of the Haishan cobalt-rich crust resource and the parameters in the Haishan slope mineralization characteristic to obtain the resource amount of the grid unit;
s4, sorting the grid cells according to the resource amount of the grid cells; and selecting grid units with larger adjacent resource quantity, and defining the cobalt-rich crust resource key area and giving the resource quantity of the key area.
2. The method for quantitatively evaluating the cobalt-rich incrustation resources in the mountains and the sea according to claim 1, wherein the grid unit is a square area with a square kilometer of 1-20 square kilometers.
3. The method for quantitatively evaluating the cobalt-rich incrustation resources in the mountains as claimed in claim 1, wherein in the step S2, the geological site survey data is obtained by sampling survey, and the specific method comprises: laying geological sampling points on the sea and mountain oblique waves according to the set distribution density, acquiring rock or ore samples on the sampling points by using various mechanical methods, and then testing and analyzing the geological position samples in a laboratory to obtain geological position data;
the regional survey data of water depth refers to data of full coverage or lateral line coverage acquired by using geophysical means.
4. The method as claimed in claim 1, wherein in step S2, when calculating the Haishan cobalt-rich crust resource parameters in each grid cell, a spatial interpolation method is used to interpolate geological sampling site data to each grid cell.
5. The method for quantitatively evaluating the Haishan cobalt-rich crusting resource as claimed in claim 4, wherein the spatial interpolation method is a grid moving average method or a Kriging method.
6. The method for quantitatively evaluating the cobalt-rich incrustation resources in Haishan according to claim 1, wherein in the step S2, the calculation formula of the macroscopic coverage rate of incrustation is as follows:
wherein, RcoveriRepresents the incrustation macro coverage rate, N, of the ith grid celliRepresenting the total number of water depth points or water depth grid nodes in the ith grid cell; g represents the slope gradient of a water depth point or a data grid node, gminAnd gmaxRespectively representing a minimum slope suitable for the development of cobalt-rich crusts and a maximum slope suitable for the mining operation of underwater machinery;
the ore-adapted slope ratio is calculated by the formula:
wherein, RslopeiRepresenting the minesuitability slope ratio of the ith grid cell;
the calculation method of the fitting area of the slope surface comprises the following steps: and calculating the three-dimensional space surface area of the minimum grid based on the minimum grid of the multi-beam bathymetric survey gridding data, then accumulating the three-dimensional space surface areas of all the minimum data grids in the grid unit i, and performing fitting calculation to obtain the slope surface fitting area of the grid unit.
7. The method for quantitatively evaluating the cobalt-rich incrustation resources in the sea and mountains as claimed in claim 6, wherein the minimum slope g suitable for the development of the cobalt-rich incrustation isminThe value of (a) is 4.8 degrees, and the maximum slope g is suitable for the mining operation of underwater machinerymaxIs 15 deg.
8. The method for quantitatively evaluating the cobalt-rich incrustation resource in the mountains as claimed in claim 1, wherein in the step S4, the calculation formula of the resource amount of the grid cell is as follows:
Oweti＝Areai(km2)×Coveragei×Thicknessi×Densityi×Rcoveri×107；
Osuiti＝Areai(km2)×Coveragei×Thicknessi×Densityi×Rslopei×107；
DOi＝Oweti×(1-Water ratioi)；
DOsuiti＝Osuiti×(1-Water ratioi)；
Metai＝DOi×Concentrationi；
Msuiti＝DOsuiti×Concentrationi；
wherein, OwetiRepresents the wet crusting tonnage of grid cell i, OsuitiRepresenting recoverable wet crusting tonnage of grid cell i, AreaiRepresents the ramp surface area, Coverage, of grid cell iiIndicates the crusting coverage, Thick, of the ith grid celliIndicates the crusting thickness, Density, of the ith grid celliRepresents the crusting wet density of the ith grid cell; rcoveriRepresents the crusting macro coverage, Rslope, of the ith grid celliRepresenting the minesuitability slope ratio of the ith grid cell; DOiAnd DOsutitiRespectively representing the tonnage of the dry shell and the tonnage of the recoverable dry shell of the ith grid unitiRepresents the number of metal tons, Msuit, of the ith grid celliRepresenting the tonnage of mined metal, Water ratio, for the ith grid celliIndicates the crust moisture content, Concentration of the ith grid celliRepresenting the metal concentration of the ith grid cell.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112945153A (en) * | 2021-02-08 | 2021-06-11 | 国家深海基地管理中心 | Cobalt-rich crust thickness measuring method based on multi-beam receiving technology |
| CN113780898A (en) * | 2021-11-04 | 2021-12-10 | 名客(山东)智能制造有限公司 | Mechanical operation evaluation method based on Internet of things |
| CN114782211A (en) * | 2022-05-16 | 2022-07-22 | 广州海洋地质调查局 | Method and system for acquiring information of sea and mountain distribution range |
| CN118521048A (en) * | 2024-07-24 | 2024-08-20 | 自然资源部第一海洋研究所 | Deep sea polymetallic nodule ore body coiling method based on coverage rate data |
-
2020
- 2020-08-31 CN CN202010891682.5A patent/CN111880239A/en active Pending
Non-Patent Citations (2)
| Title |
|---|
| DEWEN DU等: "An Integrated Method for the Quantitative Evaluation of Mineral Resources of Cobalt-rich Crusts on Seamounts", 《ORE GEOLOGY REVIEWS》 * |
| 张训华: "海洋地质调查技术", 海洋出版社 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112945153A (en) * | 2021-02-08 | 2021-06-11 | 国家深海基地管理中心 | Cobalt-rich crust thickness measuring method based on multi-beam receiving technology |
| CN112945153B (en) * | 2021-02-08 | 2022-07-29 | 国家深海基地管理中心 | Cobalt-rich crust thickness measuring method based on multi-beam receiving technology |
| CN113780898A (en) * | 2021-11-04 | 2021-12-10 | 名客(山东)智能制造有限公司 | Mechanical operation evaluation method based on Internet of things |
| CN113780898B (en) * | 2021-11-04 | 2024-01-16 | 深圳市拓艾维信息技术有限公司 | Mechanical operation evaluation method based on Internet of things |
| CN114782211A (en) * | 2022-05-16 | 2022-07-22 | 广州海洋地质调查局 | Method and system for acquiring information of sea and mountain distribution range |
| CN114782211B (en) * | 2022-05-16 | 2023-04-28 | 广州海洋地质调查局 | Sea mountain distribution range information acquisition method and system |
| CN118521048A (en) * | 2024-07-24 | 2024-08-20 | 自然资源部第一海洋研究所 | Deep sea polymetallic nodule ore body coiling method based on coverage rate data |
| CN118521048B (en) * | 2024-07-24 | 2024-10-18 | 自然资源部第一海洋研究所 | Deep sea polymetallic nodule ore body coiling method based on coverage rate data |
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