Method and program for estimating the exposure rate of marine mineral resources

Abstract

To provide a technology which makes it easy to efficiently and highly reliably evaluate marine mineral resources that can exist over the whole sea floor in a wide range.SOLUTION: An estimation method of a marine resource exposure rate includes the steps of: extracting arbitrary range data corresponding to an arbitrary range sea floor S1 from sea floor geometry feature quantity data and backscatter intensity data which can be obtained by scanning a sea floor with underwater acoustic equipment; deriving an actual measurement exposure rate showing a rate of marine mineral resources exposed in a partial range sea floor surface S11 from sea floor imaging data obtained by photographing the partial range sea floor surface S11 of the arbitrary range sea floor S1; and deriving an estimated exposure rate of the marine mineral resources on the arbitrary range sea floor S1. The step of deriving the estimated exposure rate performs a multivariate analysis with the arbitrary range data as an explanatory variable and with the actual measurement exposure rate as an objective variable, and performs operation associating the explanatory variable and the objective variable with each other, so as to derive an estimated exposure rate.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a method and program for estimating the exposure rate of marine mineral resources, and relates to a technique that is effective when evaluating, for example, the distribution status of marine mineral resources. [Background technology]

[0002] Although the vast ocean floor is rich in useful seabed resources, it may take a huge amount of effort and expense to actually identify those resources (for example, narrow down promising areas) and then extract and land them. From this perspective, seabed resources remain largely untapped, making commercialization difficult. Therefore, in order to facilitate the commercialization of seabed resources, various seabed explorations have been conducted, and evaluations of the distribution, type, and amount of those resources are being conducted (for example, Non-Patent Documents 1 to 6).

[0003] Examples of seabed resources include marine mineral resources that exist on the seabed in sedimentary rock structures (such as in the form of crusts) and contain valuable metals (cobalt-rich crusts and associated gravel crusts and nodules).

[0004] One example of a method for evaluating marine mineral resources is a method (hereinafter referred to as simply a scanning data evaluation method) in which seafloor topography feature data (such as seafloor topography map data) and seafloor acoustic property data (such as backscattering intensity data) obtained by scanning the seafloor with underwater acoustic equipment (receiving reflected waves of sound waves emitted on the seafloor) are analyzed and evaluated.

[0005] The seafloor topography feature data and seafloor acoustic property data used in this scanning data evaluation method can be generated relatively easily over a wide area (seafloor area) thanks to recent advances in underwater acoustic equipment. As a result, while the scanning data evaluation method has made it possible to roughly evaluate the distribution of marine mineral resources over a wide area of ​​the seafloor (for example, to identify the locations of seamounts and other areas where marine mineral resources are believed to exist with a high probability), it has been difficult to evaluate with a high degree of confidence the specific distribution of the marine mineral resources (for example, the areas and degree of concentration of marine mineral resources), the type, and the amount of resources.

[0006] Therefore, when specifically evaluating the distribution of marine mineral resources, methods such as analyzing and evaluating samples (drilling cores, etc.) obtained by collecting a portion of the seabed (by drilling, etc.) (hereinafter simply referred to as the sample evaluation method) and analyzing and evaluating seabed imaging data (video data, image data, etc.) obtained by photographing the seabed surface (exposed surface) with an underwater camera (hereinafter simply referred to as the imaging data evaluation method) are used. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Joo, J., Kim, S., Choi, JW, Pak, S., Ko, Y., Son, S., Moon, J. and Kim, J., “Seabed mapping using shipboard multibeam acoustic data for assessing the spatial distribution of ferromanganese crusts on seamounts in the Western Pacific”, Minerals, 2020, Vol.10, No.2,p.155. [Non-patent document 2] Machida , S. , Sato , T. , Yasukawa , K. , Nakamura , K. , Iijima , K. , Nozaki , T. and Kato , Y. , “ Visualization method for the broad distribution of seafloor ferromanganese deposits ,” Marine Georesources & Geotechnology , 2021 , Vol.

Outdoor Tools3

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Direct Environment 5

[0008] While sample evaluation methods may be able to reliably assess the type (chemical composition, density, etc.) and amount (layer thickness, etc.) of marine mineral resources within the sample collection area, assessing the entire seabed over a wide area requires collecting and analyzing a huge number of samples, which is impractical and difficult. For this reason, sample evaluation methods tend to limit the evaluation area to an extremely narrow range.

[0009] While the imaging data evaluation method may be able to evaluate the distribution of marine mineral resources on the seafloor surface within the underwater camera's imaging range, it can be difficult to identify the marine mineral resources if they are obscured by obstructions (e.g., deposits of dead marine organisms). Furthermore, generating the desired seafloor imaging data requires bringing the underwater camera close to the seafloor surface (e.g., close enough to obtain sufficient resolution) to capture the images. Therefore, generating seafloor imaging data covering a wide area of ​​the entire seafloor is inefficient and difficult. This has tended to narrow the evaluation range of the imaging data evaluation method.

[0010] From the above viewpoints, there is a need for a method that can efficiently and reliably evaluate marine mineral resources over a wide area of ​​the seabed.

[0011] The present invention has been made in consideration of the technical challenges described above, and aims to provide a method and program for estimating the exposure rate of marine mineral resources, which makes it easier to efficiently and reliably evaluate marine mineral resources that may exist across a wide area of ​​the seabed. [Means for solving the problem]

[0012] One aspect of the method for estimating the exposure rate of marine mineral resources according to the present invention is characterized by comprising the steps of: extracting arbitrary range data corresponding to an arbitrary range of the seabed from seabed topography feature data and backscattering intensity data that can be obtained by scanning the seabed with underwater acoustic equipment; deriving a measured exposure rate indicating the proportion of marine mineral resources exposed on the seabed surface of a portion of the arbitrary range of the seabed from seabed imaging data obtained by photographing the seabed surface of that portion of the arbitrary range of the seabed with an underwater camera; and performing a multivariate analysis using the arbitrary range data as an explanatory variable and the measured exposure rate as a dependent variable, and performing a calculation to associate the explanatory variable and the dependent variable with each other, thereby deriving an estimated exposure rate of marine mineral resources on the seabed of the arbitrary range.

[0013] The multivariate analysis may be characterized by performing a calculation to associate the explanatory variables and the objective variables with each other based on an objective function of the following equation (1):

[0014]

number

[0015] In addition, in equation (1), F n is arbitrary range data, k n is a coefficient that quantifies the effect of any range of data on the target variable through calculation, and C is a constant term derived from the results of the calculation.

[0016] The response variable may further include data on the subseafloor sedimentary structure of the partial range.

[0017] The marine mineral resource may also be characterized as being a cobalt-rich crust.

[0018] The arbitrary range data may also be characterized in that it includes one or more of the following data: slope gradient, water depth deviation, Laplacian, aboveground opening, underground opening, altitude variance, mean curvature, maximum curvature, ridge-valley degree, relief, and water depth, which are topographical characteristics of the seabed in the arbitrary range.

[0019] One aspect of the program is characterized by causing a computer to execute each step of the method for estimating the exposure rate of marine mineral resources. [Effects of the Invention]

[0020] The present invention can contribute to facilitating efficient and highly reliable evaluation of marine mineral resources across a wide area of ​​the seabed. [Brief explanation of the drawings]

[0021] [Figure 1] An example of image data obtained by simultaneously photographing cobalt-rich crust and non-target objects exposed on a partial area of ​​the seafloor surface. [Figure 2] FIG. 10 is a diagram showing an exposure rate model based on the estimated exposure rate derived in the first embodiment. [Figure 3] FIG. 10 is a diagram showing an exposure rate model based on estimated exposure rates derived in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0022] The method and program for estimating the exposure rate of marine mineral resources according to an embodiment of the present invention are completely different from methods (hereinafter simply referred to as conventional methods) that simply apply one of the scanning data evaluation method, the seafloor imaging data evaluation method, and the sample evaluation method to evaluate marine mineral resources.

[0023] In other words, the method and program for estimating the exposure rate of marine mineral resources according to this embodiment was discovered by noting that there is a sufficient correlation between the seabed topography feature data and backscattering intensity data corresponding to an arbitrary range of the seabed (hereinafter simply referred to as the arbitrary range of the seabed) and the seabed imaging data obtained by photographing a portion of the seabed surface of the arbitrary range of the seabed (hereinafter simply referred to as the partial range of the seabed surface) with an underwater camera, and is capable of estimating the exposure rate (estimated exposure rate) of marine mineral resources that may exist throughout the entire arbitrary range of the seabed.

[0024] Specifically, the method includes the steps of: extracting arbitrary range data corresponding to an arbitrary range of the seabed from bathymetric feature data and backscattering intensity data that can be obtained by scanning the seabed with underwater acoustic equipment (arbitrary range data extraction step); deriving a measured exposure rate indicating the proportion of marine mineral resources exposed on the seabed surface in a partial range of the arbitrary range from seabed imaging data obtained by photographing the seabed surface in the partial range of the arbitrary range of the seabed with an underwater camera (measured exposure rate derivation step); and deriving an estimated exposure rate of marine mineral resources in the arbitrary range of the seabed (estimated exposure rate derivation step). In this estimated exposure rate derivation step, a multivariate analysis is performed using the arbitrary range data as explanatory variables and the measured exposure rate as a response variable, and the estimated exposure rate is derived by performing a calculation to associate the explanatory variables and the response variable with each other.

[0025] According to this embodiment, for example, it is possible to derive with high accuracy an estimated exposure rate of marine mineral resources in an arbitrary area of ​​the seafloor by using seafloor image data of a partial area of ​​the seafloor surface, without generating seafloor imaging data covering the entire area of ​​the seafloor. The estimated exposure rate derived in this way makes it possible to efficiently and reliably evaluate, for example, specific distribution conditions (e.g., areas of concentration of marine mineral resources, degree of concentration, etc.) of marine mineral resources across a wide area of ​​the seafloor.

[0026] The method and program for estimating the exposure rate of marine mineral resources of this embodiment may be configured to derive an estimated exposure rate of marine mineral resources in an arbitrary area of ​​the seafloor using, as explanatory variables, the seafloor topography feature data and backscattering intensity data corresponding to an arbitrary area of ​​the seafloor, and the measured exposure rate of marine mineral resources calculated from seafloor imaging data of a partial area of ​​the seafloor surface, as the objective variable, as described above, and various design modifications are possible. That is, the design can be modified by appropriately applying technical common sense in various fields (e.g., marine exploration, video / image analysis, multivariate analysis, machine learning, etc.) and by appropriately referring to prior art documents, etc. as necessary, and the following examples are given as examples.

[0027] Example The method for estimating the exposure rate of marine mineral resources according to this embodiment derives the estimated exposure rate of marine mineral resources in an arbitrary range of the seabed by appropriately performing the following arbitrary range data extraction process, actual exposure rate derivation process, and estimated exposure rate derivation process, for example, using a computer device equipped with analytical software (image analysis software, multivariate analysis software, etc.).

[0028] <Arbitrary range data extraction process> In the arbitrary range data extraction process, arbitrary range data corresponding to the arbitrary range of the seafloor is extracted from seafloor topography feature data (bathymetry map data, etc.) and backscattering intensity data that can be obtained by scanning the seafloor (e.g., receiving reflected waves of sound waves emitted on the seafloor) using underwater acoustic equipment (e.g., multibeam echo sounder (MBES), sub-bottom profiler (SBP), side scan sonar (SSS), etc.).

[0029] The arbitrary area of ​​the seabed can be set as appropriate and is not particularly limited. For example, the arbitrary area of ​​the seabed can be set by selecting and setting, from a vast area of ​​the seabed (for example, the seabed in the waters surrounding Japan), an area of ​​the seabed that includes a location where marine mineral resources may exist (such as a seamount) and the surrounding area of ​​that location.

[0030] As a specific example, the seafloor of a range including a location showing high sound pressure intensity in the backscattering intensity data, i.e., a location showing acoustic characteristics derived from marine mineral resources, can be selected and set as the arbitrary seafloor range. Examples of marine mineral resources showing such acoustic characteristics include those present on the seafloor in the form of sedimentary rock structures (crusts, etc.) and containing useful metals (cobalt-rich crusts and associated gravel-like crusts and nodules).

[0031] The seabed topography characteristic data may be any data that indicates topography characteristics of an arbitrary range of the seabed, and various forms can be applied, one example of which is a form that includes one or more of the following data: slope gradient, water depth deviation, Laplacian, aboveground opening, underground opening, altitude variance, mean curvature, maximum curvature, ridge-valley degree, relief, and water depth.

[0032] Slope gradient data is data representing the degree of slope in any grid (section) on a bathymetry map based on bathymetry characteristic data (hereinafter simply referred to as a bathymetry map), and is one of the most basic topographical features that express bathymetry, and is used for various types of topography classification and topography interpretation. This slope gradient data can be derived, for example, by setting the center of a calculation grid in which multiple grids are arranged in multiple rows and columns as the center of any grid, and calculating the average value obtained by first-order differentiation of the water depth values ​​within the calculation grid on the bathymetry map. Specific examples of calculation grids include a calculation grid in which nine grids are arranged in three rows and three columns (hereinafter simply referred to as a 3x3 calculation grid), and a calculation grid in which 441 grids are arranged in 21 rows and 21 columns (hereinafter simply referred to as a 21x21 calculation grid), as described below. The depth deviation data is useful for understanding the degree of dispersion (unevenness) of depth values ​​in a bathymetry chart. This depth deviation data can be derived, for example, by calculating the deviation of depth values ​​within a 3x3 calculation grid on a bathymetry chart based on the following equation (2):

[0033]

number

[0034] In addition, in equation (2), X i indicates the value within the 3x3 calculation grid, and M indicates the average water depth value within the 3x3 calculation grid.

[0035] Laplacian data is useful for understanding the degree of change in slope within a bathymetric map, i.e., the distribution of terrain transition points, such as the difference between flat surfaces and steep slopes. This Laplacian data differs from the ridge-valley data described below; for example, a "convex terrain where the periphery is lower than the target point" will have a negative value, and a "concave terrain where the periphery is higher than the target point" will have a positive value. Such Laplacian data can be derived, for example, by second-order differentiation of the water depth values ​​within a 3x3 calculation grid on a bathymetric map in the row (x) and column (y) directions of the grid, as shown in Equation (3) below (by calculating with a Laplacian filter for each 3x3 calculation grid).

[0036]

number

[0037] The ground opening data can be derived by measuring the angles from the zenith to the horizon in eight directions (hereinafter simply referred to as the eight directions), which are a total of four directions: "north, east, west" and "northeast, northwest, southeast, southwest" with an arbitrary point of interest on the bathymetry map as the center, and calculating the average value. This ground opening data will have a small angle when the arbitrary point of interest is located at the bottom of a valley, and will be larger than 90° when it is located at the top of a mountain, etc.

[0038] The underground opening data can be calculated in the same way as the above-mentioned surface opening data, by measuring the smallest angle between the nadir and the ground surface vertically below any point of interest on the bathymetric map in eight directions and averaging these angles. This underground opening data will have a large angle when the point of interest is located at the bottom of a valley, and a small angle when it is located on a ridge or mountaintop.

[0039] Altitude variance data can be defined as the standard deviation of the depth data contained within a unit area of ​​the bathymetry chart, and is similar to the relief data described below. A larger value of this altitude variance data indicates the development of deeper valleys, while a smaller value indicates the area has fewer valleys and flatter terrain. This altitude variance data can be derived in the same way as slope gradient data, for example, by calculating the variance of depth values ​​within a 3x3 calculation grid on the bathymetry chart using the following equation (4).

[0040]

number

[0041] In addition, in equation (4), X i indicates the value within the 3x3 calculation grid, and M indicates the average water depth value within the 3x3 calculation grid.

[0042] Mean curvature data is used as an index of the roughness of a bathymetric map, and can be defined as the average of the maximum and minimum curvature values ​​for all geodesic lines (the curves with the shortest distance between two points on a surface) that pass through a given point on any curved surface. This mean curvature data will take a negative value if the surface is convex, and a positive value if the surface is concave. The more undulating the surface, the more the value will deviate from 0. This mean curvature data can be derived, for example, by calculating the curvature within a 21 x 21 calculation grid on a bathymetric map using the following equation (5):

[0043]

number

[0044] The maximum curvature data can be derived in the same way as the mean curvature data described above, for example, by calculating the curvature in each of the four directions in the 21 × 21 calculation grid and determining the curvature with the largest absolute value as the maximum curvature. As with the mean curvature data, the maximum curvature data will be negative if the surface is convex and positive if it is concave, and will deviate from 0 as the surface becomes more undulating.

[0045] Ridge-valley degree data can be derived using the following concept. First, at a certain search distance on the bathymetric map, the maximum value of the angle of the line connecting the point of interest and a point on the cross-section of that bathymetric map is the angle between the zenith and the upper tangent to the topographic cross-section (=above-ground opening). The minimum value is the angle between the nadir and the lower tangent to the topographic cross-section (=underground opening). In other words, ridge-valley degree data can be derived based on the relationship between the above-ground opening (corresponding to the angle of the upper tangent) and the underground opening (corresponding to the angle of the lower tangent) using the following equation (6).

[0046]

number

[0047] Ridge-valley degree data takes positive values ​​on ridges and negative values ​​in valleys, and a value of 0 in flat plains with little relief or wide, flat slopes. It also takes small values ​​on mountain spurs and high values ​​on terrace cliff edges. By using this type of ridge-valley degree data, it is possible to represent microtopography on a bathymetry map according to the search distance, and to represent large-scale topography by smoothing the microtopography.

[0048] Relief data can be defined as the difference in elevation (depth difference) between the deepest and shallowest water depths within the target area on a bathymetric map. Generally, it reflects the ruggedness of the seabed topography, and the greater the relief, the greater the degree of relief and the deeper the valleys. On the other hand, a small amount of relief indicates a flat area with little ridges or valleys. When deriving such relief data, for example, the difference between the deepest and shallowest water depths within a 3x3 calculation grid can be used as the relief, and it can be derived based on the following equation (7).

[0049]

number

[0050] The water depth data indicates the distance between any point on the bathymetry map and the water surface (ocean surface), and can be easily derived from the bathymetry characteristic data.

[0051] The data such as the slope gradient, water depth deviation, Laplacian, above-ground opening, underground opening, vertical dispersion, mean curvature, maximum curvature, ridge-valley degree, relief, and water depth shown above can be selected and applied as appropriate, and are not particularly limited, but preferably selected according to the desired range of seabed.

[0052] <Process for deriving actual exposure rate> In the process of deriving the actual exposure rate, the actual exposure rate, which indicates the proportion of exposed marine mineral resources on the surface of the seafloor in a certain area that is part of the seafloor in the process of extracting arbitrary area data, is derived from seafloor imaging data (video data or image data) obtained by photographing the surface of the seafloor in that area with an underwater camera (for example, a towed deep-sea camera (FDC), an underwater drone camera (ROV), or an underwater camera mounted on an autonomous underwater vehicle (AUV)).

[0053] The partial seabed surface area can be set appropriately within any range of the seabed, and is not particularly limited. For example, if there is a location in the arbitrary range of the seabed where the backscattering intensity data shows high sound pressure intensity (i.e., a seabed area including a location showing acoustic characteristics derived from marine mineral resources), the partial seabed surface area can be selected and set to include the location showing high sound pressure intensity.

[0054] As a specific example, a portion of the seafloor surface of an area including a location on the seafloor of an arbitrary area where marine mineral resources may exist (such as a seamount) and the surrounding area of ​​that location may be selected and set as the partial seafloor surface. For example, in the case of an arbitrary seafloor area S1 (in Figure 2, the arbitrary seafloor area S1 has a rectangular projection plane when viewed vertically from the seamount M1 side) including a portion of a seamount M1 (in Figure 2, a portion of the flat-top M11 of the seamount M1 and a portion of the slope M12 on the outer periphery of the flat-top M11) as shown in Figure 2 described below, the seafloor surface of a linear area (the area indicated by the symbol S11) extending along the arbitrary seafloor area S1 in the direction between the flat-top M11 and the slope M12 may be set as the partial seafloor surface.

[0055] To obtain seabed imaging data of a portion of the seabed surface, for example, an underwater camera can be used to take images while maintaining a depth at a predetermined distance from the portion of the seabed surface and moving along the portion of the seabed surface.

[0056] Various methods can be applied to derive the measured exposure rate, which indicates the proportion of marine mineral resources exposed on the surface of the seafloor in a certain area, using the seafloor imaging data described above.

[0057] For example, if the image data extracted from seafloor imaging data is data obtained by simultaneously photographing marine mineral resources exposed on a portion of the seafloor surface and non-marine mineral resources (for example, simple exposed rocks or gravel; hereinafter referred to as non-target objects) (for example, image data such as that shown in Figure 1 below), the content relating to the marine mineral resources and non-target objects will be able to be output with different image characteristics (physical properties such as color, brightness, and shading).

[0058] In other words, in the image data extracted as described above, first, the image feature parts due to marine mineral resources are identified, and then the proportion of the identified image feature parts (occupancy rate in the image data) is derived, and the derived proportion can be regarded as the measured exposure rate of marine mineral resources. The identification of the image feature parts and the derivation of the proportion as described above can be easily achieved by appropriately performing image processing on the image data.

[0059] Figure 1 shows an example of image data obtained by simultaneously capturing images of cobalt-rich crust exposed on a portion of the seafloor surface and non-target objects. In Figure 1, the relatively dark areas (depicted in the shape of a crust) represent the cobalt-rich crust exposed on a portion of the seafloor surface, while the relatively light areas represent non-target objects. Therefore, by appropriately calculating the proportion of the relatively dark areas in Figure 1, the actual exposure rate of the cobalt-rich crust can be obtained.

[0060] <Estimated exposure rate derivation process> In the estimated exposure rate deriving step, a multivariate analysis is performed using the arbitrary range data (submarine topography feature data and backscattering intensity data) obtained in the arbitrary range data extraction step as explanatory variables and the actual exposure rate obtained in the actual exposure rate deriving step as a response variable, and a calculation is performed to associate the explanatory variables with the response variable. Through this calculation, an estimated exposure rate of marine mineral resources on the seafloor in the arbitrary range is derived.

[0061] The multivariate analysis can be applied in various ways as long as it can associate the arbitrary range data, which is the explanatory variable, with the measured exposure rate, which is the objective variable. One example is performing a calculation to associate the explanatory variables and the objective variable with each other based on the objective function of the following equation (1):

[0062]

number

[0063] In addition, in equation (1), F n is arbitrary range data, k n is a coefficient that quantifies the effect of arbitrary range data on the target variable through calculation, and C is a constant term derived from the results of the calculation.

[0064] Coefficient k in equation (1) n The constant term C can be derived by estimating it so that the sum of squares of the residuals, which is the difference between the actual values ​​and the predicted values ​​in equation (1), which is the objective function, is minimized.Specific examples include estimation by applying a generalized linear model, partial least squares regression, neural network regression, etc.

[0065] The generalized linear model can assume a distribution other than normal distribution as the distribution of the difference between the actual and predicted values ​​of the objective function, and in a more extended form, the coefficient k n and the constant term C can be estimated.

[0066] Partial least squares is a method developed to prevent multicollinearity when explanatory variables are correlated and to be applicable even when the sample size is small. It reduces the number of explanatory variables to a smaller set of uncorrelated components and performs least squares regression on the set of components instead of the original data, thereby reducing the coefficient k n and the constant term C can be estimated.

[0067] Neural network regression is a method of constructing a network in which each explanatory variable is input and the objective function is output, connected with random weighting. By repeatedly changing the weighting so that the difference between the actual and predicted values ​​of the objective function becomes smaller, it is possible to obtain the optimal relationship between the explanatory variables and the objective function. The coefficient k obtained in this way is n Although the basis for the constant term C may be weaker than other methods, by performing learning using a complex network configuration (deep learning, etc.), it is possible to obtain the desired coefficient k with higher accuracy than other methods. n and a constant term C may be obtained.

[0068] When multivariate analysis is performed based on a generalized linear model or neural network regression, the dimensions of explanatory variables may be reduced in advance by principal component analysis.

[0069] An exposure rate model can also be created based on the estimated exposure rate derived in the estimated exposure rate derivation step. This exposure rate model can be used as appropriate, for example, by outputting it via an output display of a computer device so that a user of the exposure rate model can visually recognize it (for example, by visually classifying the estimated exposure rates by the degree of shading, as shown in Figures 2 and 3 described below).

[0070] Example 1 In this Example 1, one seamount (designated M1 in Figure 2) was selected from among the various seamounts existing on the seafloor in the waters surrounding Japan that contain cobalt-rich crust, and a portion of the flat-top M11 of that seamount M1 and a portion of the sloping portion M12 on the outer periphery of the flat-top M11 were set as the arbitrary seafloor area S1. Then, by appropriately performing the arbitrary area data extraction step, the measured exposure rate deriving step, and the estimated exposure rate deriving step described above, an exposure rate model was obtained in which the estimated exposure rates were divided by the degree of shading, as shown in Figure 2.

[0071] In the exposure rate model of Figure 2, the measured exposure rates obtained in the measured exposure rate derivation step are superimposed and depicted. The multiple broken lines L running vertically in the right-hand side of the figure indicate contour lines in the arbitrary range of seabed S1 and depict the shape of the slope M12. The region S11 in the middle of the figure, where multiple circles are arranged horizontally, indicates a partial range of the seabed surface set in the arbitrary range of seabed S1, and the degree of shading within each circle indicates the measured exposure rate obtained in the measured exposure rate derivation step.

[0072] 2, the estimated exposure ratio is relatively high, particularly at the inclined portion M12, and tends to decrease as one moves away from the inclined portion M12 toward the flat top portion M11. The measured exposure ratio is also relatively high, particularly at the inclined portion M12, and tends to decrease as one moves away from the inclined portion M12 toward the flat top portion M11.

[0073] Therefore, in the exposure rate model shown in Figure 2, there is sufficient correlation between the estimated exposure rate and the measured exposure rate, and it was found that the distribution status of marine mineral resources in an arbitrary range of the seabed S1 (e.g., areas of marine mineral resource concentration, degree of concentration, etc.) can be evaluated with a high degree of reliability.

[0074] <Example 2> In this Example 2, one of the various seamounts on the seafloor around Japan where cobalt-rich crust exists was selected (the seamount designated by symbol M2 in Figure 3 was selected), and the flat-top M21 of the seamount M2 (the flat-top depicted in a roughly elliptical shape in the center of Figure 3) and a part of the slope M22 on the outer periphery of the flat-top M21 were set as the arbitrary range seafloor S2. Then, by appropriately performing the arbitrary range data extraction step, the measured exposure rate derivation step, and the estimated exposure rate derivation step described above, an exposure rate model was obtained in which the estimated exposure rates were divided by the degree of shading, as shown in Figure 3.

[0075] In addition, the hatched areas in FIG. 3 are areas where the depiction of the estimated exposure rate has been intentionally omitted (no data) for the sake of convenience.

[0076] Figure 3 shows that there are areas in the center of the flat top M21 of seamount M2 where the estimated exposure rate is relatively low, but there are many areas in the surrounding area (for example, on the side of the slope M22) where the rate is relatively high.

[0077] Therefore, it was found that the exposure rate model shown in Figure 3, like the exposure rate model shown in Figure 2, can evaluate with high reliability the distribution status of marine mineral resources in an arbitrary range of the seabed S2 (e.g., areas of marine mineral resource concentration, degree of concentration, etc.).

[0078] Although the present invention has been described in detail above only with respect to the specific examples, it will be apparent to those skilled in the art that various modifications are possible within the scope of the technical concept of the present invention, and it is natural that such modifications fall within the scope of the claims.

[0079] For example, in the step of deriving the estimated exposure rate described in the embodiment, multivariate analysis is performed using only the measured exposure rate obtained in the step of deriving the measured exposure rate as the objective variable, but the objective variable may also be various other data. One example is data including sedimentary structure data estimated from results obtained by observing a portion of the subseafloor below the seafloor surface using a sub-bottom profiler (SBP). This makes it possible to evaluate with high reliability the thickness of sediments such as sand that cover marine mineral resources.

[0080] Furthermore, in the method for estimating an estimated exposure rate shown in the embodiments, the arbitrary range data extraction process, the actual exposure rate derivation process, and the estimated exposure rate derivation process can be made into a program that causes a computer to execute the process. The program can be, for example, made to be capable of being distributed via a network, or stored on a recording medium or the like and distributed. [Explanation of symbols]

[0081] S1, S2… any range of seabed S11…A range of seabed surfaces M1, M2…Haishan M11, M21… Flat top M12, M22… Inclined section L… contour lines

Claims

1. extracting arbitrary range data corresponding to an arbitrary range of the seabed from the seabed topography feature data and backscattering intensity data that can be obtained by scanning the seabed with an underwater acoustic device; a step of deriving a measured exposure rate, which indicates the proportion of marine mineral resources exposed on the seabed surface of a portion of the arbitrary area of ​​the seabed, from seabed image data obtained by photographing the seabed surface of the portion of the arbitrary area of ​​the seabed with an underwater camera; a step of performing a multivariate analysis using the arbitrary range data as explanatory variables and the measured exposure rate as a response variable, and performing a calculation to associate the explanatory variables and the response variable with each other, thereby deriving an estimated exposure rate of marine mineral resources on the seabed of the arbitrary range; A method for estimating the exposure rate of marine mineral resources, comprising:

2. The method for estimating the exposure rate of marine mineral resources according to claim 1, characterized in that the multivariate analysis performs a calculation to associate the explanatory variables and the objective variables with each other based on the objective function of the following equation (1). [Equation 9] In addition, in formula (1), F n is arbitrary range data, k n is a coefficient that quantifies the effect of arbitrary range data on the target variable through calculation, and C is a constant term derived from the results of the calculation.

3. 3. The method for estimating the exposure rate of marine mineral resources according to claim 1, wherein the dependent variable further includes data on the sedimentary structure below the seafloor in the partial area.

4. 3. The method for estimating the exposure rate of marine mineral resources according to claim 1, wherein the marine mineral resources are cobalt-rich crusts.

5. A method for estimating the exposure rate of marine mineral resources as described in claim 1 or 2, characterized in that the arbitrary range data includes one or more of the following data: slope gradient, water depth deviation, Laplacian, aboveground opening, underground opening, altitude dispersion, mean curvature, maximum curvature, ridge-valley degree, relief, and water depth, which are topographical characteristics of the seabed in the arbitrary range.

6. 3. A program for causing a computer to execute each step of the method for estimating the exposure rate of marine mineral resources according to claim 1 or 2.

7. 7. The program according to claim 6, wherein the response variables further include data on the subseafloor sedimentary structure of the partial range.

8. 7. The program according to claim 6, wherein the marine mineral resource is cobalt-rich crust.

9. The program described in claim 6, characterized in that the arbitrary range data includes one or more of the following data, which are topographical characteristics of the seabed in the arbitrary range: slope gradient, water depth deviation, Laplacian, aboveground opening, underground opening, altitude variance, mean curvature, maximum curvature, ridge-valley degree, relief, and water depth.

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