A method for assessing the thickness of cobalt-rich crusts on seamounts

Abstract

The present invention provides a method for assessing the thickness of seamount cobalt-rich crusts, including: dividing the research area of ​​cobalt-rich crust resources into geological grid units, and assigning crust thickness to the geological grid units; based on the geological sampling information of the sampling station, obtaining the crust thickness of the sampling station within the preset influence range in the adjacent area; using the "distance-slope" spatial interpolation method, the crust thickness within a certain distance range of the sampling station is estimated by the spatial similarity of the crust spatial distribution due to the distance and slope relationship, and the geological grid units outside the adjacent area and within the influence range of the spatial similarity relationship are assigned; using the expected assignment method, the grid units that fail to obtain the crust thickness value after the above assignment are assigned, so as to obtain the crust thickness of the entire study area. The present invention can ensure that the local area is taken into account, highlighting the advantages of the sampling station, and giving a more appropriate thickness assessment of the overall area, which is suitable for the assessment of the thickness of seamount crusts in the case of incomplete investigation.

Description

Technical Field

[0001] The invention belongs to the technical field of cobalt metal resource assessment, and in particular relates to a method for assessing the thickness of seamount cobalt-rich crusts. Background Art

[0002] Seamount cobalt-rich crusts are rich in mineral resources and have attracted worldwide attention in the past two decades. Based on certain exploration work, it is very meaningful to conduct resource assessment of cobalt-rich crusts and calculate the spatial distribution of resources.

[0003] The thickness, wet density, water content, coverage, metal concentration and other indicators of cobalt-rich crust that are related to the quality, grade and reserves of mineral resources are collectively referred to as cobalt-rich crust resource parameters, and the crust thickness is an important parameter for the assessment of cobalt-rich crust resources.

[0004] There have been many studies on the mineralization rules and influencing factors of cobalt-rich crusts. For example, the relationship between crust mineralization and bedrock type is studied: the bedrock of crusts is mainly basalt, volcanic clastic rock, phosphorite, siliceous rock, biogenic limestone, etc. The thickness of crusts on basalt, volcanic rock and phosphorite is greater than that on siliceous rock, and the formation of crusts is closely related to water depth. The average thickness of crusts based on volcanic clastic rock and basalt is the largest, and the surface crusts of loose and brittle rocks such as tuff and phosphorite are thinner, and siliceous rock is not conducive to the growth of crusts. The material forming the crust mainly comes from seawater and is less affected by bedrock. Another example is the relationship between water depth and the thickness of cobalt-rich crusts: the growth of crusts is closely related to the depth of seamounts. From a global perspective, crusts grow in the water depth range of 400-4000m, which is mainly restricted by the minimum oxygen concentration zone (OMZ) and carbonate compensation depth (CCD) of seawater. The crusts grow on the slopes of seamounts between the two. The growth of crusts at different water depths is also different. In general, 80% of the 41mm thick crusts grow in the water depth range of 1000-3000m, of which 55% grow in the water depth range of 1500-2500m. Similarly, half of the crusts of various thicknesses produced in water depths shallower than 2800m are greater than 50mm, and crusts greater than 100mm mainly grow on Pacific seamounts shallower than 2500m.

[0005] At present, the following methods are mainly used to evaluate the crust thickness: a. Most of them use the average crust thickness for estimation. When the statistical grid lacks the survey station, the crust thickness information of the neighboring station is used to estimate the thickness of the adjacent area. b. Research on estimating crust thickness using spatial interpolation methods, such as the "Distance-Slope" Kriging interpolation method, which estimates crust thickness based on the terrain slope and the distance between stations. c. There are also crust thickness estimates based on the crust deposition and mineralization constraint mechanism, or fractal methods, based on terrain characteristics.

[0006] However, the use of a single assessment method has its own advantages and disadvantages, and it is not possible to fully utilize their respective advantages to provide the most appropriate assessment under the current level of exploration and research, resulting in the estimated results not being able to better express the thickness distribution. This increases the uncertainty of regional resource assessment. For example, 1. The most accurate thickness data can be obtained based on station surveys, but because the data is sparse, it is difficult to comprehensively estimate the thickness of the entire area. 2. The interpolation method estimates the crust thickness based on the spatial distribution of station locations, topography, etc., and provides a more comprehensive prediction range, but in the case of insufficient stations, the estimation accuracy is limited, and the farther away from the known information point, the lower the credibility. 3. Using the crust deposition mineralization constraint mechanism and mineralization law, the regional crust thickness is statistically estimated based on terrain characteristics. It is suitable for use in large areas, especially in comparisons of multiple seamounts, but local specificity cannot be reflected. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a method for assessing the thickness of seamount cobalt-rich crust, which is suitable for assessing the thickness of seamount crust without a comprehensive investigation.

[0008] To achieve the above object, the present invention provides a method for assessing the thickness of seamount cobalt-rich crusts, comprising:

[0009] The study area of ​​cobalt-rich crust resources is divided into geological grid units, and based on the geological sampling stations, the study area is divided into several adjacent areas; wherein the adjacent areas include geological grid units and geological sampling stations;

[0010] Based on the geological sampling information of the geological sampling station, obtaining the crust thickness within a preset influence range of the geological sampling station in an adjacent area;

[0011] Based on the crust thickness within the preset influence range of the adjacent area, assigning values ​​to the geological grid cells in the adjacent area to obtain the crust thickness of the geological grid cells in the adjacent area;

[0012] The expected value assignment method is used to assign values ​​to the geological grid cells that have not been assigned values, so as to obtain the crust thickness of the study area.

[0013] Optionally, dividing the study area into a number of adjacent areas comprises:

[0014] Draw a normal line for the line connecting two adjacent points of the geological sampling station, obtain the intersection of the normal line and the boundary of the study area, and the cross nodes between the normal lines, and divide the study area into several adjacent areas based on the normal line, intersection points and cross nodes; among them, if there are cross nodes between the normal lines, remove the part of the normal line beyond the cross node and the intersection of the end of the normal line beyond the cross node and the boundary of the study area.

[0015] Optionally, dividing the study area into a plurality of adjacent areas further comprises:

[0016] Several geological sampling stations with similar preset spatial thresholds are merged into one geological sampling station, and connections are made based on the merged geological sampling station.

[0017] Optionally, obtaining the crust thickness within a preset influence range in the adjacent area includes:

[0018] Based on the geological sampling information of the geological sampling station, the crust thickness in the preset buffer zone is obtained. Based on the crust thickness in the preset buffer zone, the crust thickness in the preset influence range in the adjacent area is obtained using Thiessen polygons.

[0019] Optionally, assigning values ​​to geological grid cells in the adjacent area includes:

[0020] Based on the "distance-slope" spatial interpolation method, the crust thickness within the preset distance range of the station is estimated through the spatial similarity of the crust spatial distribution due to the distance and slope relationship, and the grids inside and outside the adjacent areas and within the influence range of the spatial similarity relationship are assigned values.

[0021] Optionally, the “distance-slope” spatial interpolation method includes:

[0022] Get the slope of the geological grid cells in the adjacent area;

[0023] The geological sampling stations are spatially superimposed and correlated with the slopes of the geological grid units to obtain the average slope of each geological sampling station;

[0024] Obtain the isobath distance from each geological sampling station to the lower boundary of the study area, use the isobath distance as the coordinate value of the "distance-slope" coordinate system of the geological sampling station, regenerate the spatial layer of the geological sampling station based on the coordinate value, and use the geostatistical analysis method to simulate the crust thickness data of the geological sampling station in the new spatial layer. According to the parameters of the variogram simulation results and the crust investigation experience, carry out spatial interpolation with a preset radius.

[0025] Optionally, using the expected value assignment method, assigning values ​​to the geological grid cells that have not been assigned values, so as to obtain the crust thickness of the study area includes:

[0026] Obtain terrain slope data of the study area, perform unsupervised classification on the terrain slope data, divide the study area into different terrain type boundaries according to the degree of terrain change, classify the micro-topography of the study area based on the boundaries of different terrain types, obtain micro-topography classification areas, and obtain several geological blocks accordingly;

[0027] Based on the micro-topography classification area, combined with the original terrain characteristics and the intuitive form of slope changes, the study area is divided into several preset landform type areas, and several geological blocks related to the seamount crust mineralization period based on the landform type are obtained;

[0028] Based on the spatial distribution of known sampling stations, the mean original crust thickness of the geological sampling stations in the preset geomorphic type area and the mean crust thickness of the geological sampling stations in the micro-topography classification area are obtained. The mean original crust thickness in the preset geomorphic type area and the mean crust thickness in the micro-topography classification area are superimposed to obtain the final crust thickness of the preset geomorphic type area. Based on the crust thickness in the preset geomorphic type area, the areas outside the preset influence range of the geological sampling stations in the adjacent areas are assigned values, and finally the crust thickness value of the geological grid unit in the entire study area is obtained.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] The present invention can achieve better results in the application of seamount crust thickness assessment, can ensure that local areas are taken into account, and can provide a more appropriate assessment on the whole. The present invention is suitable for the assessment of seamount crust thickness without a comprehensive investigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0032] Figure 1 It is a schematic flow chart of a method for assessing the thickness of cobalt-rich crusts on seamounts according to an embodiment of the present invention;

[0033] FIG2 is a schematic diagram of the ME seamount according to an embodiment of the present invention; wherein (a) is a schematic diagram of the geographical location of the study area, (b) is a topographic map of the ME seamount and the spatial distribution of crust sampling stations, and (c) is a schematic diagram of the 3D topography of the study area;

[0034] Figure 3 Schematic diagram of ME seamount terrain analysis according to an embodiment of the present invention; wherein (a) is a schematic diagram of ME seamount terrain unsupervised classification, (b) is a schematic diagram of ME seamount terrain slope analysis, and (c) is a schematic diagram of ME seamount micro-topography classification;

[0035] Figure 4 A schematic diagram of dividing geological units using the geological station adjacent area method according to an embodiment of the present invention;

[0036] Figure 5 Schematic diagram of geological grid unit division according to an embodiment of the present invention; wherein (a) is a regional grid unit, and (b) is an information point contained in each grid unit;

[0037] Figure 6 It is a schematic diagram of a slope mineralized block segment of a segmented statistical unit according to an embodiment of the present invention;

[0038] Figure 7 Schematic diagram of the value assignment method of an embodiment of the present invention; wherein (a) is the value assignment of the mineralized block segment, and (b) is the value assignment of the buffer zone;

[0039] Figure 8 A schematic diagram of geological grid unit division according to an embodiment of the present invention;

[0040] Fig. 9 Schematic diagram of geostatistical unit division using the neighboring area method according to an embodiment of the present invention; wherein (a) is the crust thickness distribution parameter of the 1.5 km buffer zone, and (b) is the crust thickness parameter of the neighboring area of ​​the station obtained by using Thiessen polygons;

[0041] Fig.10 A schematic diagram of grid unit assignment using the neighboring area method according to an embodiment of the present invention;

[0042] Fig.11 A schematic diagram of distance-slope Kriging interpolation according to an embodiment of the present invention;

[0043] Fig.12 A schematic diagram of superimposing and assigning values ​​using a neighboring area and a spatial interpolation method according to an embodiment of the present invention;

[0044] Fig.13 A schematic diagram of micro-topography block segmentation based on slope classification according to an embodiment of the present invention;

[0045] Fig.14 A schematic diagram of dividing geological units using the topographic and geomorphic classification method according to an embodiment of the present invention;

[0046] Fig.15 Schematic diagram of the block method assignment of an embodiment of the present invention; wherein (a) is a schematic diagram of a geological block after superposition of a micro-topographic geological block and a geomorphic type geological block, and (b) is a diagram of assigning a crust thickness value to a geological grid unit based on the expected mean value of the superimposed geological block;

[0047] Fig.16 This is a schematic diagram of the crust thickness results of the geological grid units in the study area obtained by the comprehensive method of an embodiment of the present invention. DETAILED DESCRIPTION

[0048] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0050] Example

[0051] like Figure 1 As shown, this embodiment provides a method for assessing the thickness of cobalt-rich crust on seamounts, namely, a method for assessing the thickness of crust using a comprehensive method of "neighboring area - distance slope interpolation - mathematical expectation", including:

[0052] The study area of ​​cobalt-rich crust resources is divided into geological grid units, and based on the geological sampling stations, the study area is divided into several adjacent areas; wherein the adjacent areas include geological grid units and geological sampling stations;

[0053] Based on the geological sampling information of the geological sampling station, obtaining the crust thickness within a preset influence range of the geological sampling station in an adjacent area;

[0054] Based on the crust thickness within the preset influence range of the adjacent area, assigning values ​​to the geological grid cells in the adjacent area to obtain the crust thickness of the geological grid cells in the adjacent area;

[0055] The expected value assignment method is used to assign values ​​to the geological grid cells that have not been assigned values, so as to obtain the crust thickness of the study area.

[0056] Furthermore, the study area is divided into several adjacent areas including:

[0057] Draw a normal line for the line connecting two adjacent points of the geological sampling station, obtain the intersection of the normal line and the boundary of the study area, and the cross nodes between the normal lines, and divide the study area into several adjacent areas based on the normal line, intersection points and cross nodes; among them, if there are cross nodes between the normal lines, remove the part of the normal line beyond the cross node and the intersection of the end of the normal line beyond the cross node and the boundary of the study area.

[0058] Furthermore, dividing the study area into a number of adjacent areas also includes:

[0059] Several geological sampling stations with similar preset spatial thresholds are merged into one geological sampling station, and connections are made based on the merged geological sampling station.

[0060] Further, obtaining the crust thickness within a preset influence range in the adjacent area includes:

[0061] Based on the geological sampling information of the geological sampling station, the crust thickness in the preset buffer zone is obtained. Based on the crust thickness in the preset buffer zone, the crust thickness in the preset influence range in the adjacent area is obtained using Thiessen polygons.

[0062] Further, assigning values ​​to the geological grid cells in the adjacent area includes:

[0063] Based on the "distance-slope" spatial interpolation method, the crust thickness within the preset distance range of the station is estimated through the spatial similarity of the crust spatial distribution due to the distance and slope relationship, and the grids inside and outside the adjacent areas and within the influence range of the spatial similarity relationship are assigned values.

[0064] Furthermore, the “distance-slope” spatial interpolation method includes:

[0065] Get the slope of the geological grid cells in the adjacent area;

[0066] The geological sampling stations are spatially superimposed and correlated with the slopes of the geological grid units to obtain the average slope of each geological sampling station;

[0067] Obtain the isobath distance from each geological sampling station to the lower boundary of the study area, use the isobath distance as the coordinate value of the "distance-slope" coordinate system of the geological sampling station, regenerate the spatial layer of the geological sampling station based on the coordinate value, and use the geostatistical analysis method to simulate the crust thickness data of the geological sampling station in the new spatial layer. According to the parameters of the variogram simulation results and the crust investigation experience, carry out spatial interpolation with a preset radius.

[0068] Furthermore, the expected value assignment method is used to assign values ​​to the geological grid cells that have not been assigned values, so as to obtain the crust thickness of the study area, including:

[0069] Obtain terrain slope data of the study area, perform unsupervised classification on the terrain slope data, divide the study area into different terrain type boundaries according to the degree of terrain change, classify the micro-topography of the study area based on the boundaries of different terrain types, obtain micro-topography classification areas, and obtain several geological blocks accordingly;

[0070] Based on the micro-topography classification area, combined with the original terrain characteristics and the intuitive form of slope changes, the study area is divided into several preset landform type areas, and several geological blocks related to the seamount crust mineralization period based on the landform type are obtained;

[0071] Based on the spatial distribution of known sampling stations, the mean original crust thickness of the geological sampling stations in the preset geomorphic type area and the mean crust thickness of the geological sampling stations in the micro-topography classification area are obtained. The mean original crust thickness in the preset geomorphic type area and the mean crust thickness in the micro-topography classification area are superimposed to obtain the final crust thickness of the preset geomorphic type area. Based on the crust thickness in the preset geomorphic type area, the areas outside the preset influence range of the geological sampling stations in the adjacent areas are assigned values, and finally the crust thickness value of the geological grid unit in the entire study area is obtained.

[0072] Seamount cobalt-rich crusts are rich in cobalt metal resources and have attracted widespread attention worldwide. The thickness of the crust is an important parameter for the assessment of the amount of cobalt-rich crust resources in seamounts. The assessment of crust thickness faces two problems. First, due to the high cost, the most effective geological station sampling distribution for seamount exploration is sparse, which is not enough to estimate the crust thickness distribution in the region; second, the use of a single assessment method has its own advantages and disadvantages, and it cannot highlight the advantages of the area with collected data, that is, it can ensure that the local area is taken into account, and give a more appropriate assessment on the whole, resulting in the estimated results cannot more fully reflect the advantages of local data and take into account the calculation of the overall thickness distribution. Therefore, in this embodiment, under the ArcGIS technical environment, a crust thickness assessment plan is formulated based on the station survey thickness data and terrain data. First, the adjacent area method is used to calculate the crust thickness within the influence range of the station. (This method) combines the station buffer radius and the Thiessen polygon method to jointly give an estimate of the crust thickness within 1.5km of the survey station. Second, the crust thickness of the study area is calculated using the "Slope-Distance" Kriging interpolation method [1] to give the crust thickness within the optimal effective radius area, thus making up for the missing part of the previous (first step) estimation result. Third, the geological blocks are divided by terrain classification method, and the crust thickness of the remaining unassigned areas is estimated using the mathematical expectation method. Thus, the overall distribution of crust thickness in the study area is obtained. This embodiment achieves better results in the application of ME seamount crust thickness assessment work. This method is suitable for the assessment of seamount crust thickness under incomplete investigation conditions.

[0073] The following takes ME seamount as an example to explain the specific implementation steps of this method in detail:

[0074] ME Seamount is located in the Magellan Seamount Chain in the western Pacific Ocean. It is a flat-topped seamount with a roughly square distribution. As shown in Figure 2. ME Seamount is one of the earliest seamounts in my country to carry out exploration of cobalt-rich crusts. Since the 1990s, eight voyages of crust surveys have been carried out, including DY95-9, DY95-10, DY105-11, DY105-15, DY105-16A, DY105-17B, DY 115-18, and DY 115-19. There are a total of 109 crust survey stations, including 59 geological trawl surveys, 40 deep-sea shallow drilling surveys, and 10 other sampling methods. In addition to geological sampling surveys, topographic and video exploration work has also been carried out.

[0075] The geological sampling locations of the ME seamount crust are basically around the edge of the mountain top and the ridge. The spacing between the stations is uneven, ranging from 1-2 kilometers to 10 kilometers, as shown in Figure 2, where Figure 2 (a). Geographic location of the ME seamount; Figure 2 (b). Topography and distribution of geological sampling stations of the ME seamount; Figure 2 (c). 3D topographic map of the ME seamount. For the calculation of crust thickness, shallow drilling and TV grab samples have clear station coordinates and unique crust thickness, which are better determined. Due to the long towing distance and large number of samples, the geographic coordinates of the towing station are taken from the middle position between the start and end of the towing of the equipment, and the crust thickness is taken from the median thickness of the obtained samples. The crust thickness obtained by the station sampling method is relatively accurate, but its spatial distribution density is far from meeting the requirements for calculating the distribution of crust resources.

[0076] The ME seamount obtains topographic data through shipborne multi-beam surveys, and the accuracy of topographic post-processing data is 230m, which is much higher than the accuracy of geological sampling surveys, such as Figure 2(b) and 2(c) .

[0077] The growth pattern and thickness of cobalt-rich crusts are related to topography. Large-scale topography and micro-topography are closely related to the distribution of crusts. Micro-topography refers to relatively small-scale landforms, which are also the smallest landform units. Micro-topography is a sub-topography developed on small-scale landform units. Micro-topography depends on the slope of the terrain. By calculating the slope of the seamount terrain, you can clearly see the rope-like accumulation at the ridge, and the sites where the crusts grow better are generally located in the gentle and stable micro-topography in the area with large relative slope changes. Figure 3 ,in, Figure 3 (a) is a schematic diagram of the unsupervised classification of ME seamount terrain. Figure 3 (b) is a schematic diagram of the ME seamount terrain slope analysis. Figure 3 (c) is a schematic diagram of the ME seamount microtopography classification.

[0078] The growth of cobalt-rich crusts is restricted by the physical and chemical environment of seawater. When the water depth at the edge of the seamount summit is greater than the water depth of the minimum oxygen content, its isobath can be used as the upper boundary of the ore body. When it is less than the water depth of the minimum oxygen content, the isobath of the minimum oxygen content can be regarded as the upper boundary of the ore body. The water depth at the edge of the summit of the ME seamount is greater than the water depth of the minimum oxygen content, so the upper limit of the ore body is the control range of the crust sampling station. The lower boundary can be the carbonate compensation interface isobath. It can also be divided by terrain classification. Here, the unsupervised classification method of Arcmap10.0 is used for analysis. The result divides the seamount terrain into two categories. The boundary line of the classification result is the 3500m isobath, such as Figure 3 Therefore, the lower boundary of the crust thickness estimation involved in this embodiment is set to 3500m, which is consistent with the carbonate compensation interface depth (about 3500m) of the seamount.

[0079] In ArcGIS 10.0, based on the above two types of data, the buffer zone of geological sites is calculated, and the sites are used to evaluate the crust thickness within a certain range; the Thiessen polygon method is used to estimate the influence range of geological sites; the geological grid units are divided, the terrain slope and site distance are calculated, and the crust thickness is evaluated using the "distance-slope" method; and the landform types are divided according to the terrain data, and the average crust thickness is calculated based on the landform type. This is feasible in terms of data and technology.

[0080] 1. Methods

[0081] 1.1 Division of geostatistical units

[0082] The statistical unit is a subspace domain of the cobalt-rich crust evaluation area. A cobalt-rich crust evaluation area contains several statistical units, and its area is much smaller than the evaluation area. If A represents the cobalt-rich crust evaluation area, a represents the statistical unit, and n represents the number of units. Then they have the following relationship: A = {ai}, i = 1, 2, ..., n.

[0083] The statistical unit is the smallest unit of statistical evaluation, which can be assigned a data set consisting of geological characteristics, mineral parameters, resource quantities, etc. The geological characteristics and mineral parameters of the same unit are considered to be homogenized. Units are considered to be different, that is, the spatial anisotropy of geological characteristics and resource parameters is reflected between units.

[0084] The division of statistical units is a basic work of mineral resource evaluation. The results of mineral resource evaluation are based on units and expressed through units. The rationality of unit division directly affects the quality of mineral resource evaluation results. There are two commonly used methods, namely geological body unit and grid unit division methods.

[0085] The method of dividing geological units is to use geological conditions and prospecting signs that have a clear control effect on a certain mineral resource to delineate statistical units at all levels. This method has two key features. The first is that it is based on clear ore-controlling characteristics; the second is that the ore-controlling conditions and prospecting signs of different scales or scales are not the same. For example, if the central and western Pacific Ocean is used as the evaluation area for cobalt-rich crust resources, the seamount landform can be used as an indicator for dividing the geological units for the evaluation of cobalt-rich crust resources. At this time, the seamounts within the central and western Pacific Ocean are geological units; when the seabed resource evaluation area is a certain seamount, the geological conditions that control the growth of cobalt-rich crusts, such as slope gradient and water depth, are the basis for dividing geological units. This geological unit is essentially to divide the seamount slope blocks with similar ore-controlling conditions and adjacent spaces into one geological unit; when the water depth and slope of the seamount slope are similar, the areas adjacent to each geological station are divided into different geological units. The evaluation of cobalt-rich crust resources at different scales has different geological units and different geological indicators.

[0086] Grid unit division refers to dividing the evaluation area into several units with regular grids, using the grids as statistical units. Grid units are suitable for spatial data analysis techniques, such as spatial interpolation of various mineral parameters, spatial extraction of geological features, etc.

[0087] Of course, the three-scale division methods can also be used simultaneously in the resource evaluation work of the previous scale. For example, all three methods can be used at a large scale, while the topographic and geomorphic classification method and the survey station adjacent area method can be simultaneously applied to the resource evaluation work at the seamount scale.

[0088] The working area for the evaluation of seabed solid mineral resources (such as survey areas, exploration areas, resource evaluation task areas, etc., collectively referred to as seabed evaluation areas) is vast. A smaller area, such as a seamount, covers an area of ​​more than thousands of square kilometers, and a larger area, such as the Pacific CC area of ​​polymetallic nodules, covers an area of ​​millions of square kilometers. The seabed evaluation area is at least equivalent to dozens of 1:100,000 geological maps of land. The first thing to do in the large-scale (1:10,000, 1:50,000, 1:100,000) mineral resource evaluation work on land is to divide the statistical units, and the seabed evaluation needs to divide the statistical units even more. The division of geological statistical units for cobalt-rich crust resources can include the following three methods:

[0089] 1.1.1 Topographic and geomorphic classification method (geological block method)

[0090] The type of slope landform and water depth control the distribution of cobalt-rich crusts. Seamount slopes are divided according to the slope. Gentle slopes are generally covered by loose sediments without cobalt-rich crusts, larger slopes are generally covered by cobalt-rich crusts, steep slopes may have landslides but are not covered by cobalt-rich crusts, and the thickness, coverage rate and grade of cobalt-rich crusts vary with the water depth. Adjacent areas with similar distribution of cobalt-rich crust resources are divided according to water depth and topography. The boundaries of the ore body can be divided according to the water depth classification. The seamount slope landform types can be distinguished according to the slope classification, that is, the seamount slope micro-topography can be divided. In order to explore its original distribution law, the water depth classification in this embodiment adopts an unsupervised classification method. The slope calculation adopts the third-order difference method.

[0091] 1.1.2 Neighborhood Area Method

[0092] The neighboring area method is used to divide the entire research target area into geological statistical units. The sampling point is taken as the center and polygonal blocks are divided. Each block surrounds one sampling point. These polygons together constitute the entire mining area. The mineral parameters of each sampling point are regarded as the mineral parameters of the corresponding polygonal block.

[0093] like Figure 4 First, merge several geological stations that are very close in space into one station, for example, station D is formed by merging three geological sampling points; second, connect two adjacent points of the geological station and draw a normal line (perpendicular bisector), for example, the perpendicular bisector ah of AB intersects with the boundaries Boundary1 and Boundary2 at points a and h respectively, and the perpendicular bisector BC intersects with the boundaries at points b and i; or the perpendicular bisectors intersect each other to form nodes, such as n, which circle the areas adjacent to the corresponding geological stations with the geological stations as the center, for example, abih circles the blocks adjacent to station B, lnfgm circles the area adjacent to geological station G Block6, and so on, and finally obtain geological units such as Block1, Block2, ..., Block7. These divided blocks are the blocks adjacent to the geological stations. This type of geological unit is automatically assigned the geological characteristics and mineral parameters of the geological stations it contains or the average values ​​of multiple geological stations.

[0094] 1.1.3 Grid method for dividing geological units

[0095] The size of the grid unit is determined based on the data accuracy and evaluation accuracy requirements of the evaluation area. It is necessary to ensure that the statistical results of the grid unit meet the evaluation accuracy requirements of the area at a specific stage, and to ensure that the number of information points contained in each grid unit meets the statistical requirements and has statistical significance. Figure 5 , the evaluation area is divided into several 1km grid units, such as Figure 5 (a), each grid cell contains 10×10 information points, such as Figure 5(b) The regional grid cells are divided according to data accuracy and evaluation calculation needs.

[0096] 1.2 Geostatistical unit assignment method

[0097] Assigning resource parameter values ​​to each statistical unit Uniti (i = 1, 2, ..., N) is the premise for estimating the resource volume of the statistical unit. Crust thickness is the most important parameter in the evaluation of cobalt-rich crust resources. The following introduces the method of assigning crust thickness to geological statistical units.

[0098] 1.2.1 Mineralized block assignment method

[0099] First, divide the grid into certain shapes and specifications to generate N statistical unit layers (see Figure 5 and Figure 6 ), and then use prior knowledge, survey data and terrain characteristics to divide the seamount slope into non-mineralized slopes (landslide bodies or loose sediment cover areas) and mineralized slopes of stages I, II and III (see Figure 6 ) to generate a slope mineralization type zoning layer. These mineralization zonings have a certain control effect on the cobalt-rich crust resource parameters. For example, the 2-3 layers of cobalt-rich crusts developed in the I and II phase mineralization slopes are generally thicker than the 1 layer of crusts developed in the III phase mineralization slope. The cobalt-rich crusts of different phases of mineralization have different densities and mineral contents, so their specific gravity, water content, and metal density are also different. Among them, Figure 6 To divide the slope mineralization block segment of the statistical unit: First, the statistical units Unit1, Unit2 and any unit Unit i ; Secondly. Slope mineralization zoning and geological survey stations. The left side is the Phase III mineralized slope, where two survey stations No. 1 and No. 2 are distributed; the upper right part is the Phase I mineralized slope, where survey station No. 3 is distributed; the lower right part is the Phase II mineralized slope, where there is no survey station; the middle part is the landslide body (non-mineralized slope); Thirdly. Superposition calculation of statistical units with mineralization zoning, geological stations and other layers. The Phase III mineralized slope is divided into two blocks using the Thiessen polygon method based on two of the geological survey stations, and each block has a survey station. Finally. The mineralized blocks of the statistical unit. Unit 1, except for a non-mineralized block, has two blocks controlled by geological survey stations. 1,1 and Block 1,2 , both belong to the III phase mineralized slope; Unit 2, in addition to a non-mineralized block, has two blocks, one of which is the block controlled by survey station No.3 2,1 , belonging to the Phase I mineralized slope; another block II2,1, without survey station control, belongs to the Phase II mineralized slope. i(i=1,2,..,N) represents any unit Uniti among the N statistical units.

[0100] Then, within the mineralized zone, based on the geological survey station, the Thiessen polygon segmentation method is used, or the buffer zone method representing the influence range of the geological station is used to divide the mineralized block controlled by each geological station.

[0101] Secondly, the above layers are divided into several mineralized blocks according to the slope mineralization zoning and geological station control range (see Figure 6 ). For example, any statistical unit Unit i Can be divided into M i Mineralized blocks controlled by geological sites i , j (j=1,…,M i ), and K i Phase I mineralized slope mineralized block I i (i=1,2,…,K i )、L i Phase II Mineralized Slope Mineralized Block II i (i=1,2,…,L i ), or J i Phase III mineralized slope mineralized block III i (i=1,2,…,Ji). These blocks are either controlled by geological survey stations or mineralization stages. The statistical unit is divided into several mineralized blocks. Each mineralized block is given cobalt-rich crust resource parameters and mineralization characteristics according to the geological station or slope mineralization stage. For ease of expression, all blocks can be expressed as Block. i,j (j=1,…,M i ) to indicate.

[0102] The statistical unit layer is spatially overlaid with the mineral resource layers such as geological sites and slope mineralization zones to divide the statistical unit into several mineralized blocks. The resource parameters or mineralization characteristic values ​​of these blocks are assigned by the geological sites or slope mineralization characteristics they contain. The resource reserves of each sub-block of the statistical unit are calculated separately, and then the sum is obtained to obtain the resource reserves of the statistical unit. Of course, due to data sparseness, in many cases a statistical unit contains only one sub-block, or even multiple statistical units in one sub-block.

[0103] 1.2.2 Spatial interpolation method

[0104] The spatial interpolation assignment method is to use the thickness data of geological survey stations as information points, and perform spatial interpolation on each statistical unit to obtain a unique cobalt-rich crust thickness parameter. This spatial interpolation method can be the inverse distance weighted average method, moving window average, or geostatistical method. Since traditional geostatistical methods are not suitable for the interpolation application of seamount spatial data, Du Dewen et al. proposed a seamount geostatistical method, that is, the use of the "distance-slope" variogram Kriging method to estimate the crust thickness of ME seamounts. The variogram simulation is to find out the autocorrelation of the spatial distribution of crusts through statistical methods, and can borrow information points in adjacent areas with similar distribution characteristics for experiments or simulations. The Kriging estimation of crust thickness can only use this variogram and the information points in this area to perform Kriging equation operations to achieve the purpose of interpolation. This also shows that geological station information is the most effective way to ultimately improve the understanding of crust thickness distribution. Statistical methods can only use existing conditions to obtain an approximate understanding of the spatial distribution of objects, and the degree of approximation depends on the layout and density of sample sampling stations. After evaluating the crust thickness based on geological station information and the “distance-slope” Kriging method, each statistical unit Unit i (i=1,2,…,N) is assigned a value to obtain the unique solution for the thickness of the cobalt-rich crust in the statistical unit.

[0105] 1.2.3 Expected Value Assignment Method

[0106] During the seamount geological survey stage, several cobalt-rich crust station survey data were obtained. The crust thickness was estimated by calculating the mean value of the station crust resource parameters within the geological unit, that is, the expected value. This expected value was used to assign a value to each statistical unit. This method is called the "expected value assignment method". This assignment method is simple and easy to use. It is suitable for cobalt-rich crust resource parameters that do not vary much in space, but not for parameters that vary greatly in space.

[0107] The expected value estimation of resource parameters for the entire seamount is to statistically analyze the resource parameters of all geological survey stations of one or more adjacent seamounts to obtain the mean of each parameter, and then estimate the expected value of resource parameters for the entire seamount (referred to as "seamount expected value") based on this. The seamount expected value is used to assign values ​​to statistical units Uniti (i=1,2,…,N), which is suitable for assigning values ​​to resource parameters with smaller deviations for the entire seamount.

[0108] The expected value of the mineralized slopes of the same period is estimated, that is, a seamount slope is divided into several slopes of different mineralization periods. The expected value of resource parameters obtained by the resource parameter statistics of the slope stations of the same mineralization period is called the expected value of resource parameters of the mineralized slopes of the same period (referred to as "same mineralization expected value"). The same mineralization expected value is used to assign values ​​to the blocks Blocki,j (j=1,…,Mi; i=1,2,…,N) of the statistical unit. Compared with the geological survey stations of the whole seamount, the resource parameters of the slope stations of the same mineralization period have smaller deviations, which is more suitable for assigning values ​​to the statistical units of the slopes of the same mineralization period.

[0109] 1.2.4 Joint Assignment Method

[0110] The joint assignment method combines the above methods to assign values. When there are many stations, the information points of the slopes in the same mineralization period are used to perform spatial interpolation on the statistical units of the slopes. When the stations are sparse, the Thiessen polygon block method can be used for assignment, for example Figure 7 (a) When there is no geological station control, the expected value of the slope of the mineralization period is used for assignment; when the geological station is isolated, the buffer zone is used to assign values ​​to the slopes around it, and the other slope ranges are assigned values ​​using the expected value of the mineralization period, see Figure 7 (b) as shown.

[0111] 2. Application

[0112] The thickness of cobalt-rich crust is controlled by many factors such as slope type and water depth, and the spatial distribution range can reach 1-30cm. According to the accuracy requirements of resource evaluation targets, different assignment methods are selected: a. The adjacent area method assigns values ​​to adjacent areas based on the crust thickness of the measuring station; b. The spatial interpolation assignment method assigns crust thickness values ​​to statistical units (Thicknessi, i = 1, 2, ..., N); c. The mineralized block assignment method assigns values ​​to each block of the statistical unit (Thicknessij, j = 1, 2, ... Mi) i = 1, 2, ..., N). Here, the comprehensive assignment method is used to obtain the crust thickness value of the statistical unit.

[0113] 2.1 Geological grid unit division

[0114] In order to facilitate resource evaluation, the geological grid units are divided first. The accuracy of the ME seamount terrain data is 230m×230m, which can be converted into 250m×250m accuracy data. As the smallest data unit, in order to ensure that each geological unit has sufficient statistical data, the study area is divided into 1km×1km grid units, each unit contains 25 statistical data points. The grid division of the geological units in the study area is implemented using the FishNet function of ArcGIS. The 3500m boundary of the unsupervised classification of terrain is the CCD depth of the area, which is also the lower limit of the water depth of the sampling station (with crust samples), that is, the ME seamount is covered by crust growth at shallower than 3500 meters. Therefore, the 3500-meter isobath is used as the lower boundary of the resource evaluation area to divide the geological units in the study area. A total of 3706 geological unit grids are divided, each of which contains 25 data points. Figure 8 .

[0115] 2.2 Division of neighboring areas and assignment of statistical units

[0116] The division of geological units in the vicinity of the ME seamount was first carried out based on the geological sampling stations, the station influence range was evaluated, and the buffer zone was established by setting the buffer zone radius to 1.5 km using the ArcGIS bufur method, and the preliminary blocks were divided, such as Fig. 9 (a). Secondly, the lower boundary of the ore body in the study area is used as the boundary to generate the geological units controlled by the station in the whole area using the Thiessen polygon (ArcToolbox-Analysis Tools-Proximity-Create Thiessen polygons) (1.2.1) method, that is, to generate the Blocks mentioned in the previous article (1.1.2). Fig. 9 (b).

[0117] The crust thickness information points of ME seamounts are scarce. According to the actual density of crust geological sampling survey, in order to make full use of geological sampling information, the crust thickness distribution parameters of the 1.5 km buffer zone are first obtained according to the station location, such as Fig. 9 (a), and then use Thiessen polygons to obtain the crust thickness parameters of the area adjacent to the station, such as Fig. 9 (b) The adjacent area controlled by the former station buffer is mainly used, and the boundary between adjacent stations is delineated by the latter Thiessen polygon ( Fig. 9 (b)) is limited as an auxiliary, and the 1km grid unit is assigned a value. The assignment result is as follows Fig.10 .

[0118] 2.3 Spatial interpolation assignment

[0119] In order to realize the interpolation assignment of "distance-slope" Kriging method, the terrain data is first processed to make its range consistent with the coverage of the divided geological unit (1km×1km). Secondly, in ArcMap10.0, the slope of each point of the 250m precision terrain data is calculated using the Slope method, and then the average slope of 25 points in each geological unit grid is calculated as the slope of the geological unit. The crust survey station site is spatially superimposed and associated with the geological statistical unit to calculate the average slope of each station site (1km×1km range). The Near method of ArcToolbox-Analysis Tools is used to calculate the distance from each station site to the lower boundary 3500m isobath. This distance and slope are used as the coordinate values ​​of the "distance-slope" coordinate system of the station site, and all station sites are regenerated in ArcMap. The spatial layer "Slope-Distance.shp" is generated. The Kriging method in Geostatistical Analyst is used to simulate the variogram of the crust thickness data for the station points in the new layer ("Slope-Distance.shp"). Fig.11 The QQ histogram shows that except for the top of the mountain, the crust thickness at other locations is close to normal distribution. Based on the simulation result parameters of the variogram and the crust survey experience, spatial interpolation is carried out with a radius of 2 km, such as Fig.11 Based on this, the statistical units that have not been assigned values ​​after step 4.2 are assigned values. The results are as follows Fig.12 .

[0120] 2.4 Block division and statistical unit assignment

[0121] To divide the ME seamount geological blocks, the slope method is first used to calculate the terrain slope, and then the Iso Cluster Unsupervised Classification method is used to perform unsupervised classification on the slope data, such as Figure 3 c. Different terrain types are divided according to the degree of terrain change, vectorized, and the lower boundary is defined by covering the regional boundary (3500m isobath) with crust geological units, such as Fig.13 The cut-off values ​​of the unsupervised classification of slope are 4.8, 15, 27, and 42, respectively, and the geological blocks based on micro-topography classification are divided accordingly. The mean thickness of the crust sampling stations contained in each classification area, i.e., the block, is 5, 4.3, 2.8, 2.0, and 1.5, respectively. The geological blocks are assigned values ​​based on this, such as Fig.13 .

[0122] According to the micro-topography classification results ( Fig.13), combined with the original terrain characteristics and the intuitive form of slope changes, the seamount is divided into a gentle summit area, a gentle slope area at the junction of the summit and the slope, a ridge area with various micro-topography, a long straight slope area and a "spoon-shaped" collapse area, and the geological blocks based on the landform type are divided accordingly, such as Fig.14 .

[0123] This type of slope can be identified by slope classification. It often appears as a "spoon shape" in the terrain. In this embodiment, such an area is called a "collapse zone". The collapse zone of a spoon-shaped landslide is a landslide body in the geological history period. The thickness of the crust on the exposed bedrock is 0, and its slope is generally greater than 15°. Moreover, when the slope is greater than 15°, it is not conducive to mining work with current mining technology and is an unfavorable mining terrain. It should be excluded from the circled mining area. In the calculation, the focus should be on estimating the proportion of unfavorable terrain in each exploration unit in detail.

[0124] Slopes covered by cobalt-rich crusts are called "mineralized seamount slopes" (abbreviated as "mineralized slopes"). This type of slope has two major characteristics: one is that it is not a gentle slope, and the other is that there has been no slope reconstruction since the middle and late Quaternary period. The amount of resources distributed on the mineralized slopes is the amount of resources that exist naturally. In terms of topography, it appears as a "gentle slope on the top of the mountain", "long straight slope" or a relatively stable "ridge area". Slopes that are not covered by sediments and are suitable for mining are considered to be favorable slope areas. The proportion of these slope areas in the grid cells is an important parameter for estimating the amount of mineable ore or metal reserves.

[0125] The above-mentioned geological blocks based on micro-topography classification and the geological blocks based on landform types are spatially superimposed, and new and more detailed geological blocks are divided after superposition, such as Fig.15 (a).

[0126] The mean thickness of the crust sites included in the five landform types was calculated, and the thickness parameters were 3.03, 3.94, 4.50, 3.72, and 3.22, respectively. The crust thickness of each block was finally obtained by averaging the geological blocks obtained by terrain slope classification. The crust thickness of the block obtained in this way was assigned to the spatial superposition of the above geological grid units, and the results are as follows: Fig.15 (b).

[0127] Based on the grid cell assignment results obtained in (2.3), the crust thickness values ​​of the previously unassigned grid cells are supplemented with the geological grid cell assignment results in 15(b), and finally the crust thickness assignment of the entire regional geological grid cells is completed. Fig.16 .

[0128] from Fig.16The results of the assignment show that the crust thickness has a clustering characteristic near the survey station, which is related to the assignment method. It changes regularly with the changes in the topography of the seamount, and has a ring-shaped distribution feature. The thickness distribution in the collapse area and the long straight slope area with a large area is obviously different from that in other parts. This is consistent with the survey and research situation.

[0129] Different from the previous research methods, this embodiment is based on the ME seamount geological sampling data and fine terrain data for detailed analysis, and the seamount mineralization period is divided, and the geological unit is divided accordingly. At the same time, the crust thickness evaluation scheme is comprehensively formulated through the crust station thickness buffer, the adjacent area of ​​the station thickness, the expected value of the crust thickness of the geological block, and the spatial interpolation statistics. First, the station buffer method is used to calculate the crust thickness of the 1.5km area around the station, and then the Thiessen polygon method is used to evaluate the crust thickness covering the entire mineralized area according to the station position. The combination of the two gives the best crust thickness within the range of 1.5km, and the thickness value is assigned to the crust evaluation grid unit. Secondly, based on ArcGIS, the "Slope-Distance" Kriging interpolation method is used to calculate the crust thickness of the mineralized area, and the crust thickness in the optimal effective radius area is given to make up for the missing part of the previous grid unit estimation result. Finally, the mathematical expectation method is used to calculate the average crust thickness of the geological sampling station in the unit for the geological unit that divides the mineralization period, and the remaining unassigned grid units are assigned. The "neighboring area - slope distance spatial interpolation - geological block" method proposed in this example is applied to the evaluation of the thickness of the ME seamount crust and achieves good results, indicating that this method is suitable for the evaluation of the thickness of the seamount crust without a comprehensive survey.

[0130] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for assessing the thickness of seamount cobalt-rich crusts, characterized in that: include: The study area of ​​cobalt-rich crust resources is divided into geological grid units, and based on the geological sampling stations, the study area is divided into several adjacent areas; wherein the adjacent areas include geological grid units and geological sampling stations; Based on the geological sampling information of the geological sampling station, obtaining the crust thickness of the geological sampling station within a preset influence range of the adjacent area; Based on the crust thickness within the preset influence range of the adjacent area, assigning values ​​to the geological grid cells in the adjacent area to obtain the crust thickness of the geological grid cells in the adjacent area; The expected value assignment method is used to assign values ​​to the geological grid cells that have not been assigned values, so as to obtain the crust thickness of the study area.

2. The method for assessing the thickness of cobalt-rich crusts on seamounts according to claim 1, characterized in that: The study area is divided into several adjacent areas including: Draw a normal line for the line connecting two adjacent points of the geological sampling station, obtain the intersection of the normal line and the boundary of the study area, and the cross nodes between the normal lines, and divide the study area into several adjacent areas based on the normal line, intersection points and cross nodes; among them, if there are cross nodes between the normal lines, remove the part of the normal line beyond the cross node and the intersection of the end of the normal line beyond the cross node and the boundary of the study area.

3. The method for assessing the thickness of cobalt-rich crusts on seamounts according to claim 2, characterized in that: The division of the study area into several adjacent areas also includes: Several geological sampling stations with similar preset spatial thresholds are merged into one geological sampling station, and connections are made based on the merged geological sampling station.

4. The method for assessing the thickness of cobalt-rich crusts on seamounts according to claim 1, characterized in that: Obtaining the crust thickness within a preset influence range in the adjacent area includes: Based on the geological sampling information of the geological sampling station, the crust thickness of the preset buffer zone is obtained. Based on the crust thickness of the preset buffer zone, the crust thickness within the preset influence range in the adjacent area is obtained by combining Thiessen polygons with the preset buffer zone.

5. The method for assessing the thickness of cobalt-rich crusts on seamounts according to claim 1, characterized in that: Assigning values ​​to the geological grid cells in the adjacent area includes: Based on the "distance-slope" spatial interpolation method, the crust thickness within the preset distance range of the station is estimated through the spatial similarity of the crust spatial distribution caused by the distance and slope relationship, and the grids inside and outside the adjacent areas and within the influence range of the spatial similarity relationship are assigned values.

6. The method for assessing the thickness of cobalt-rich crusts on seamounts according to claim 5, characterized in that: The "distance-slope" spatial interpolation method includes: Get the slope of the geological grid cells in the adjacent area; The geological sampling stations are spatially superimposed and correlated with the slopes of the geological grid units to obtain the average slope of each geological sampling station; The isobath distance from each geological sampling station to the lower boundary of the study area was obtained, and the isobath distance was used as the coordinate value of the "distance-slope" coordinate system of the geological sampling station. The geological sampling station was regenerated into a spatial layer based on the coordinate value. The variogram of the crust thickness data of the geological sampling station in the new spatial layer was simulated using the geostatistical analysis method. According to the parameters of the variogram simulation results and the crust investigation experience, spatial interpolation was carried out with a preset radius.

7. The method for assessing the thickness of cobalt-rich crusts on seamounts according to claim 5, characterized in that: Using the expected value assignment method, the geological grid cells that have not been assigned values ​​are assigned values ​​to obtain the crust thickness of the study area, including: Obtain terrain slope data of the study area, perform unsupervised classification on the terrain slope data, divide the study area into different terrain type boundaries according to the degree of terrain change, classify the micro-topography of the study area based on the boundaries of different terrain types, obtain micro-topography classification areas, and obtain several geological blocks accordingly; Based on the micro-topography classification area, combined with the original terrain characteristics and the intuitive form of slope changes, the study area is divided into several preset landform type areas, and several geological blocks related to the seamount crust mineralization period based on the landform type are obtained; Based on the spatial distribution of known sampling stations, the mean original crust thickness of the geological sampling stations in the preset geomorphic type area and the mean crust thickness of the geological sampling stations in the micro-topography classification area are obtained. The mean original crust thickness in the preset geomorphic type area and the mean crust thickness in the micro-topography classification area are superimposed to obtain the final crust thickness of the preset geomorphic type area. Based on the crust thickness in the preset geomorphic type area, the areas outside the preset influence range of the geological sampling stations in the adjacent areas are assigned values, and finally the crust thickness value of the geological grid unit in the entire study area is obtained.

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