A method for judging the nature of seamounts by using the correlation between seafloor topography and gravity and magnetic anomalies

Abstract

The present application belongs to the technical field of marine geophysical exploration, and particularly relates to a method for judging the nature of seamounts by using the correlation between seafloor topography and gravity-magnetic anomalies, comprising: obtaining multi-source geophysical data of seamounts in a target sea basin and uniformly processing the coordinates, extracting multiple topographic features such as seamount top surface depth, bottom area and slope, and simultaneously completing gravity and magnetic anomaly correction; using Pearson correlation coefficient to carry out full-feature correlation calculation, and setting a threshold to screen strong correlation feature combinations of topography and gravity-magnetic anomalies, constructing a seamount comprehensive judgment index system based on the strong correlation feature combinations, and respectively establishing judgment index combinations of seamounts with roots and seamounts without roots; and judging the types of seamounts with roots and seamounts without roots of unknown seamounts by calculating the weighted distance between the comprehensive index of the seamount to be identified and different types of judgment index combinations. The present application fully excavates the physical correlation between shallow topography and deep gravity-magnetic field, and takes into account the seamount identification accuracy and engineering operability.

Description

Technical Field

[0001] This invention belongs to the field of marine geophysical exploration technology, specifically relating to a method for determining the nature of seamounts by utilizing the correlation between seabed topography and gravity and magnetic anomalies. Background Technology

[0002] In marine geology and geodynamics research, seamounts, as products of submarine magmatism, record the entire process of oceanic crust formation, magmatic activity, and basin evolution through their morphological characteristics and deep structures. Due to differences in their formation mechanisms, seamount properties and topographic features, seamounts within basins can be classified into rooted and rootless seamounts according to isostatic theory. Seamounts exhibiting strong compensation, with a concave Moho discontinuity and relatively thickened crust, can be considered to have a root structure. Seamounts with weak or near-uncompensated compensation, whose load is mainly supported by the elastic bending of the lithosphere with a certain strength, do not exhibit a clear root structure. These two types of seamounts show different characteristics in different types of gravity and magnetic anomaly maps. The topographic features of seamounts are closely related to their deep structures; by analyzing the relationship between seafloor topography and physical anomaly field characteristics, the nature of seamounts can be determined.

[0003] Currently, the classification of seamounts mainly relies on methods such as seafloor topography mapping, gravity anomalies, and seismic profile analysis. Existing methods can be summarized into two categories: (1) Analysis method based on gravity and magnetic physical field inversion: This method establishes a three-dimensional density and magnetic body model by fitting spatial gravity anomalies, Bouguer gravity anomalies, and magnetic anomalies to infer the internal structure and compensation mechanism of seamounts. However, this type of analysis method is highly dependent on high-density observation data. Although it can theoretically reveal the deep root structure of seamounts, in practical applications, the dense distribution of seamounts or the sparse observation points in deep-sea basins result in insufficient data, which directly leads to the inversion model being unable to fully fit the real physical field. The contributions of shallow and deep structures are difficult to separate, and the results of seamount property determination are discontinuous, unstable, and difficult to form a unified standard. The inversion process is highly dependent on the initial model and prior assumptions. When the prior conditions deviate from the actual structure, the reliability of the determination results will be significantly reduced.

[0004] (2) Method based on seismic imaging and lithospheric bending tensile model analysis: This method obtains the velocity structure of seamounts and the underlying crust and upper mantle through wide-angle seismic refraction and reflection profiles, and then combines this with elastic bending model analysis of the lithospheric response to determine the deep compensator or root structure. However, deploying a large number of OBS instruments in the deep-sea environment is costly and labor-intensive, making it difficult to achieve large-scale coverage of the sea basin. When data is sparse or the profile spacing is large, velocity tomography and reflection profiles cannot continuously depict the structure of seamounts and the underlying lithosphere, making the determination of root structure limited by local data and introducing uncertainty. This method is highly dependent on the accuracy of seafloor topography, gravity load, and stratigraphic data. If data is missing or the error is large, it will directly affect the accuracy of elastic bending calculation and deep structure inference.

[0005] In summary, existing methods have three limitations in determining seamount properties: First, it is highly dependent on the density of observation data; in areas with sparse or missing data, it is difficult to guarantee the accuracy of judgment. Second, the failure to fully integrate the physical relationship between topographic features and various anomalies resulted in an unreliable comprehensive assessment of shallow and deep structures. Third, the methods are computationally complex or rely on model assumptions, making it difficult to balance accuracy, stability, and operability when applied on a large scale in ocean basins. Summary of the Invention

[0006] This invention overcomes the above-mentioned defects and provides a method for determining the nature of seamounts by utilizing the correlation between seabed topography and gravity and magnetic anomalies. It solves the problems of the prior art, such as high dependence on observation data density, ignoring the physical correlation between topographic features and multiple types of anomalies, fixed model assumptions, complex calculations, and poor practicality of large-scale operations. It improves the accuracy, stability, and operability of seamount nature identification.

[0007] To achieve the above objectives, the present invention provides a method for determining the nature of seamounts by utilizing the correlation between seabed topography and gravity / magnetic anomalies, comprising the following steps: S1. Acquire gravity anomaly data, magnetic anomaly data, sediment layer thickness data, and multibeam bathymetry data of multiple seamount samples with known seamount properties within the target sea basin; S2. Project all the data from step S1 onto a unified coordinate system to ensure that the latitude and longitude are consistent, and then process the data to obtain the topographic feature data and gravity and magnetic anomaly feature data of the seamount. S3. Construct a seamount property determination model, the specific implementation of which is as follows: S31 performs correlation analysis on the seamount topographic feature data and gravity and magnetic anomaly feature data to obtain a correlation coefficient matrix and extract strongly correlated feature pairs. S32 performs linear fitting based on the strongly correlated feature pairs, and constructs a judgment index to distinguish between mountains with and without mountain roots and seamounts by using the fitting slope of the feature pairs. S4. Input the topographic feature data and gravity and magnetic anomaly data of the seamount to be identified into the seamount nature determination model, calculate the weighted distance between the feature index of the seamount to be identified and the feature index of different types, determine the seamount type according to the minimum distance principle, and obtain the nature of the seamount to be identified.

[0008] Further, in step S2, the data processing includes: performing anomaly field correction on the spatial gravity anomaly data to obtain Bouguer gravity anomaly; eliminating the oblique magnetization effect of the magnetic anomaly data through polarization processing to obtain polarization magnetic anomaly; statistically analyzing multibeam bathymetry data to obtain the bottom and top depths of the seamount, and then calculating seamount topographic feature data including seamount height, bottom area, volume, slope, and length; The gravity and magnetic anomaly data include spatial gravity anomaly data, Bouguer gravity anomaly data, magnetic anomaly data, and polarized magnetic anomaly data.

[0009] Furthermore, in step S31, the extraction of the strongly correlated features is specifically implemented as follows: The correlation between the seamount topographic feature data and the gravity and magnetic anomaly data is calculated using the Pearson correlation coefficient method. If |r| ≥ r0, they are determined to be a strongly correlated feature pair, where r is the correlation coefficient and r0 is a preset correlation threshold.

[0010] Further, in step S32, using the strongly correlated feature pairs as variables, the relationship between the feature parameters is obtained through linear fitting. Using the fitting slopes of the feature pairs corresponding to samples with and without mountain roots, determination indices for mountain-rooted and mountain-sea-mountain features are constructed respectively. Among these, the determination index M for mountain-rooted and mountain-sea-mountain features is... root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r}; No mountain root or sea mountain determination index M without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w}

[0011] Furthermore, the determination criteria include at least the ratio of bottom area to height, the ratio of maximum slope to length, the ratio of bottom area to volume, the ratio of Bouguer gravity anomaly to magnetic anomaly, and the ratio of bottom area to spatial gravity anomaly.

[0012] Furthermore, step S4 is implemented as follows: Gravity anomaly data, magnetic anomaly data, sediment layer thickness data, and multibeam bathymetry data of the seamount area to be identified are obtained. After processing in steps S1 and S2, the topographic feature data and gravity and magnetic anomaly feature data of the seamount to be identified are obtained. The topographic feature data and gravity / magnetic anomaly feature data of the seamount to be identified are input into the seamount property determination model to calculate the determination index combination M corresponding to the seamount to be identified. unkown-root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r}、M unkown-without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w}, respectively corresponding to mountains with and without mountain roots; Calculate the weighted Euclidean distance between the combination of seamount identification indicators to be identified and the combination of seamount indicators with and without seamount indicators; , in, Indicates the seamount to be identified and the first The comprehensive distance between the combined indicators for determining seamount-like structures These respectively represent mountains with and without mountain roots; Indicates the seamount number to be identified Normalized index value; Indicates the first The first seamount-like structure Item normalization judgment index value; Indicates the first The weight coefficients corresponding to each indicator satisfy the following conditions: ; Determining seamount type based on the principle of minimum distance: If D root <D without-root The seamount to be identified was determined to be a seamount with a root. If D root >D without-root If so, the seamount to be identified is determined to be a seamount without a root. Among them, D root The distance between the seamount to be identified and the criteria for identifying seamounts with roots, The distance between the criteria for identifying seamounts to be identified and seamounts without roots.

[0013] Furthermore, the specific process for obtaining the Bouguer gravity anomaly through the anomaly field correction is as follows: The sedimentary layer is divided into multiple layers of equal thickness from top to bottom. The density of the sedimentary layer with depth is calculated using a sedimentary compaction model, as shown in the following formula: , in, Indicates the density of the sedimentary layer. For fluid density, For solid density, Porosity d Here, z is the depth attenuation parameter, representing the depth of the deposition layer; The gravity anomaly generated by the sedimentary layer can be calculated based on the sedimentary layer density, using the following formula: , Where G is the gravitational constant, This indicates a gravity anomaly generated by the sedimentary layer. This represents the difference between the density of the sedimentary layer and the density of the bedrock, where R is the Earth's radius and h is the thickness of the sedimentary layer. The gravitational effect produced by the water layer is calculated using the approximate formula for an infinitely large horizontal thin layer, as follows: , in, This indicates the gravitational effect produced by the water layer. Let the density of seawater be taken as... , Indicates seawater depth; After successively removing the effects of water layer gravity and sediment layer gravity from the spatial gravity anomaly data of the ocean basin, the corrected Bouguer gravity anomaly formula within the ocean basin is obtained as follows: , in, The longitude and latitude of the sea basin are The Bouguer gravity anomaly correction value at the location, This is due to a space gravity anomaly. This indicates the gravitational effect caused by the depth of the seabed. This indicates the gravitational effect caused by the sedimentary layer.

[0014] Furthermore, the polarization treatment is specifically as follows: The magnetic tilt and declination data of the ocean basin were obtained, and deconvolution filtering was used to balance signal fidelity and noise suppression. A damping term was introduced into the denominator of the polarization factor, and the calculation formula is as follows: ; in, This indicates the abnormal magnetic force value after pole shifting. Let u and v represent the space wavenumbers in the x and y directions, respectively, D be the magnetic declination, I be the magnetic inclination, i be the imaginary unit, k be the total space wavenumber, and β be the damping term. The Fourier transform result represents the magnetic anomaly data.

[0015] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: (1) This invention breaks through the limitations of traditional empirical judgment methods. Starting from physical laws, it uses the correlation between the shallow geometric features of seamounts and gravity and magnetic anomalies as the basis for judgment, and realizes the standardized quantitative classification of seamounts with and without roots. By analyzing the correlation between topographic parameters such as volume, slope, length and bottom area and spatial gravity anomalies, Bouguer gravity anomalies, magnetic anomalies and polarization magnetic anomalies, this method can reveal the inherent coupling relationship between shallow morphology and deep compensation structure, and can still achieve reliable judgment in areas where high-resolution measured data is lacking.

[0016] (2) This invention constructs key ratio indicators and a comprehensive judgment mechanism, which improves the accuracy and repeatability of judgment. By calculating seamount characteristic indicators such as volume-slope ratio (V / θmax), length-volume ratio (L / V), bottom area-height ratio (A / H), and slope-length ratio (θmax / L), and combining them with correlation coefficients to form a comprehensive judgment index, this method can quantitatively describe the relationship between shallow morphology and deep anomaly characteristics of seamounts, thereby realizing automated and standardized classification of seamount types and significantly reducing the uncertainty of subjective judgment.

[0017] (3) This invention establishes a standardized analysis process and a quantitative evaluation system, realizing a complete closed loop of data processing, feature extraction, correlation analysis, judgment model construction, and classification evaluation. By generating the deep compensation intensity index, morphological consistency index, and anomaly response index, and outputting a two-dimensional distribution map, this method can not only be used for seamount nature determination, but also provides reliable and repeatable quantitative support for large-scale ocean basin tectonic analysis, geodynamics research, and marine resource assessment. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to the present invention; Figure 2 This is a flowchart of the Bouguer gravity anomaly correction process of the present invention; Figure 3 This is a Pearson correlation matrix diagram of the topographic features of mountain and sea mountain and gravity and magnetic anomaly features in an embodiment of the present invention; Figure 4 This is a Pearson correlation matrix diagram of topographic features and gravity and magnetic anomaly features of mountains and seamounts without mountain roots in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention aims to overcome the limitations of existing methods for determining seamount properties in large-scale ocean basins or topographically complex regions, including inaccurate deep structure identification and high dependence on high-resolution field observations. It comprehensively utilizes the correlation and non-correlation characteristics between seamount topographic features and spatial gravity anomalies, Bouguer gravity anomalies, magnetic anomalies, and polarization magnetic anomalies. Topographic features such as seamount height, slope, and volume exhibit different correlation characteristics with anomaly features in different types of seamounts, forming a physical basis for determining seamounts with and without roots. Based on this physical law, such as... Figure 1 As shown, the present invention provides a method for determining the nature of seamounts by utilizing the correlation between seabed topography and gravity / magnetic anomalies, as detailed below: Step S1: Acquire gravity anomaly data, magnetic anomaly data, sediment layer thickness data, and multibeam bathymetry data from multiple known seamount sample areas; among which, seamount samples include seamount samples with roots and seamount samples without roots; Among them, seamount gravity anomaly data, magnetic anomaly data and sediment thickness data are usually stored in .dat, .grd or ASCII table format, containing latitude and longitude and gravity and magnetic anomaly values; multibeam bathymetry data adopts water depth products that are publicly obtained or measured in the field, such as data provided by GEBCO global ocean topography data, and is uniformly projected to the CGCS2000 coordinate system.

[0021] Step S2: Process the data from step S1 to obtain seamount gravity and magnetic anomaly characteristics and topographic feature data; In the data processing stage, all data in step A are projected to the CGCS2000 coordinate system to ensure that the latitude and longitude are consistent. Then, Bouguer gravity anomaly correction, magnetic anomaly data polarization, and seamount topographic feature data calculation are performed on the spatial gravity anomaly. Step S21: Perform anomaly field correction on the spatial gravity anomaly to obtain the Bouguer gravity anomaly; Calculate the gravity effect of the sedimentary layer: Divide the sedimentary layer into 100 layers of equal thickness from top to bottom. The density of each layer varies with depth. Use a sedimentary compaction model to calculate the change of sedimentary layer density with depth, as shown in the following formula: , in, Indicates the density of the sedimentary layer. For fluid density, For solid density, Porosity d Here, z is the depth attenuation parameter, representing the depth of the deposition layer; Using Parker's forward modeling formula for gravity anomalies, the gravity anomalies generated by the sedimentary layer are calculated based on the sedimentary layer density, as shown in the following formula: , Where G is the gravitational constant, taken as... , This represents the gravity anomaly generated by the sedimentary layer, where R is the Earth's radius (taken as R = 6371 km) and h is the thickness of the sedimentary layer. This represents the difference between the density of the sedimentary layer and the density of the bedrock, i.e. ,in, The density of the sedimentary layer, Indicates the density of bedrock; The gravitational effect produced by the water layer is calculated using the approximate formula for an infinitely large horizontal thin layer, as follows: , in, This indicates the gravitational effect produced by the water layer. Let the density of seawater be taken as... , Indicates seawater depth; By sequentially removing the gravity effects of the water layer and sedimentary layer using spatial gravity anomaly data of the ocean basin, the corrected Bouguer gravity anomaly within the basin is obtained, such as... Figure 2 As shown, the formula is as follows: , in, The longitude and latitude of the sea basin are The Bouguer gravity anomaly correction value at the location, This is due to a space gravity anomaly. This indicates the gravitational effect caused by the depth of the seabed. This indicates the gravitational effect caused by the sedimentary layer.

[0022] Step S22: Perform polarization processing on the magnetic anomaly data; Since magnetic anomaly data can be affected by oblique magnetization, leading to deviations in analysis results, polarization processing is used to eliminate the oblique magnetization effect in low-latitude regions, so that the magnetic anomaly morphology can more clearly reflect the true spatial distribution of underground magnetic bodies.

[0023] Specifically, magnetic tilt and magnetic declination data of the ocean basin are obtained, and deconvolution filtering technology is used to balance signal fidelity and noise suppression. At the same time, a damping term is introduced into the denominator of the polarization factor. The calculation formula is as follows: , in, This indicates the abnormal magnetic force value after pole shifting. The space wavenumbers are represented by u and v, respectively, D is the magnetic declination, I is the magnetic inclination, i is the imaginary unit, and k is the total space wavenumber. β represents the damping term, and in this embodiment of the invention, β = 0.002. The Fourier transform result represents the magnetic anomaly data.

[0024] This invention introduces a damping term to avoid the problem of abnormal spectrum amplification caused by the denominator of the polarization factor approaching zero when the magnetic tilt angle and magnetic declination angle are small or the spatial frequency is close to the singular region. This improves the stability and noise resistance of the polarization process, reduces the impact of high-frequency noise on the magnetic anomaly conversion results, and improves the reliability of polarization magnetic anomaly data.

[0025] Step S23: Obtain seamount topographic feature data; The maximum and minimum water depths of the seamount are statistically determined using multibeam echo sounding data at its location, and recorded as the bottom and top depths, respectively. The height of the seamount is calculated from the top depth and the ground depth, and the bottom area is also statistically determined. The volume of the seamount is calculated by integrating its horizontal cross-sectional area vertically, using the following formula: , Where V is the volume of the seamount. The depth of the seamount summit. Let A(z) be the depth of the seamount's base and A(z) be the area of ​​the seamount's base. The slope of a seamount is calculated from the vertical elevation difference and the horizontal distance, using the following formula: , in, The slope of the sea mountain. This represents the change in seabed elevation between adjacent measuring points. Indicates the horizontal distance between adjacent measuring points; The multibeam bathymetry data along the coastal mountain profile is segmented and calculated to obtain the slope value of each segment. The maximum slope value is then used as the characteristic parameter of the seamount slope. The length of a seamount is calculated based on its height and maximum slope: , Where L is the length of the seamount and H is the height of the seamount. This represents the maximum slope of the seamount.

[0026] Using the methods described above, key topographic feature data such as elevation difference, volume, slope and length of seamounts are obtained, providing accurate input data for quantifying the shallow morphological features of seamounts, determining the deep root structure, and establishing a standardized judgment model.

[0027] Step S3: Construct a seamount property determination model; Specifically, the correlation analysis is performed on the seamount topographic feature data and gravity and magnetic anomaly data obtained in step S2 for seamounts with and without roots, respectively, to quantify the physical coupling relationship between topographic features and anomaly features. By utilizing the different correlation relationships between topographic features and gravity and magnetic anomaly features of the two types of seamounts, and through strong correlation feature pairs, the different manifestations of the two types of seamounts in topography and deep structure are characterized, and the different developmental patterns of the two types of seamounts are obtained to distinguish between seamounts with and without roots. Step S31: Perform correlation analysis on the seamount topographic feature data and gravity and magnetic anomaly data; The Pearson correlation coefficient was used to calculate the correlation between seven sets of seamount topographic features, including top surface depth, bottom surface depth, height, bottom area, maximum slope, length, and volume, and four sets of seamount gravity and magnetic anomaly features, including spatial gravity anomaly, Bouguer gravity anomaly, magnetic anomaly, and polarization magnetic anomaly. A correlation matrix heatmap was created to analyze the correlation relationships between these features, yielding correlation analysis indicators for various seamount data. The formula for calculating the Pearson correlation coefficient between the various seamount features is as follows: , Among them, X i and Y i These are the feature parameter values ​​of the i-th seamount. and These are the average values ​​of the corresponding feature parameters. n Given the number of seamount samples, a heatmap of the correlation matrix between seamount topography and gravity / magnetic anomalies was obtained through Pearson correlation coefficient analysis, thereby revealing the differences in topography and deep structure among different types of seamounts.

[0028] Step S32: Based on the correlation matrix, summarize the different characteristic judgment index combinations for mountains with and without mountain roots and seamounts; Based on the Pearson correlation matrix calculation results, feature combinations with strong correlations between seamount topographic features and gravity / magnetic anomaly features are extracted. A preset correlation threshold r0 is set according to the absolute value of the correlation coefficient. This preset correlation threshold can be adjusted according to the data quality, sample size, and practical application needs of the study area. The correlation between different feature parameters is evaluated, and feature pairs with high correlation (|r|≥r0) and clear geological significance are selected, while redundant feature combinations with weak correlation and unclear coupling relationships are removed.

[0029] Using the strongly correlated feature parameter pairs obtained from the above screening as variables, a scatter plot relationship model is constructed. The changing relationships between feature parameters are obtained through linear fitting. Combining this with known combinations of strongly correlated features from samples of seamounts with and without mountain roots, feature relationships that characterize different types of seamount morphology or deep compensation structures are calculated. Specifically, the fitting slope of the feature relationship pair is calculated to characterize the degree to which a change in one feature parameter causes a change in another feature parameter. These include, but are not limited to, the ratio of base area to height (I1=A / H) representing planar expansion capability, and the ratio of maximum slope to length (I2=) representing seamount steepness. / L, the ratio of base area to volume representing mass distribution, I3=A / V; the ratio of Bouguer gravity anomaly to magnetic anomaly representing the coupling characteristics of deep density anomalies and magnetic anomalies, I4= / And the ratio of the base area representing the density response to the spatial gravity anomaly, I5 = A / The above feature combinations are only representative; in practice, feature combinations should be selected based on the results of correlation analysis.

[0030] By statistically analyzing the strongly correlated characteristic indicators corresponding to different types of known seamount samples, characteristic data combinations of indicators were established for seamounts with and without roots. Since seamounts with and without roots have different seamount properties, different characteristic combinations are corresponding to the two types of seamounts, and the characteristic indicators corresponding to the two types of seamounts may not be the same. The specific characteristic indicators should be selected based on the correlation analysis results. The seamount property determination model is used to describe the coupling relationship between the shallow geometric morphology characteristics of seamounts and the anomaly response of deep physical properties, and provides an evaluation basis for the subsequent identification of unknown seamount properties.

[0031] Seamounts with and without roots exhibit different patterns, showing differences in several indicators. Specifically, seamounts with roots show a higher correlation in magnetic polarization anomalies, magnetic ΔT anomalies, and spatial gravity anomalies; magnetic material is concentrated near the root; the ratio of base area to slope is significantly lower than that of seamounts without roots; their planar expansion is limited and their slopes are steep; the seamount mass is concentrated near the root; and their three-dimensional morphology is sharply pointed. These characteristics can describe not only the shallow structure of the seamount but also determine the presence of deep roots. Seamounts without roots, on the other hand, typically have shallower bases and more extensive planar expansion; they extend horizontally and have a more uniform rock mass distribution; they are larger in size and have a more stable structure; and their average slope-to-length ratio is higher, indicating a gentler overall profile.

[0032] Based on known samples of mountains with and without mountain roots, the slope parameters of each feature pair are obtained through linear fitting. These slope parameters are then used as a combination of feature indicators for mountains with and without mountain roots to establish a determination index for mountains with and without mountain roots.

[0033] For samples with mountain roots and seamounts, calculate: Mroot ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r}, where M root I represents the set of characteristic indicators of known mountain roots and seamounts. 1r I 2r I 3r I 4r I 5r These represent five characteristic indicators of mountains and seas.

[0034] For samples without mountain roots and seamounts, calculate: M without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w}, where M without-root I represents the set of characteristic indicators of a known mountain without a root or seamount. 1w I 2w I 3w I 4w I 5w These represent five characteristic indicators of mountains without roots or seas.

[0035] The above-mentioned seamount nature determination model uses a combination of seamount characteristic indicators as input parameters. Subsequently, the model will perform matching analysis between the seamount indicator combination to be identified and the established determination indicators to achieve the classification of seamount types.

[0036] Step S4: After processing the data in steps S1 and S2, the seamount region to be identified is input into the seamount nature determination model to classify seamounts with and without mountain roots. This invention collects gravity anomaly data, magnetic anomaly data, sediment layer thickness data, and multibeam bathymetry data of the seamount area to be identified. After data processing, it obtains seamount gravity and magnetic anomaly characteristics and topographic characteristics data. The characteristic data is then input into the seamount property determination model constructed in step S3. The characteristic parameters of the seamount to be identified are calculated, and the seamounts are finally classified into those with roots and those without roots based on the combination of characteristic indicators.

[0037] Specifically, the following steps are taken: Based on the gravity and magnetic anomaly characteristics and topographic features of the seamount to be identified, a feature index system is established according to the seamount nature determination model. The five-dimensional comprehensive determination index combination corresponding to the seamount to be identified is calculated: M corresponds to seamounts with mountain roots. unkown-root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r}, where M unkown-rootThis indicates that an unknown seamount corresponds to a combination of characteristic indicators of a seamount with a mountain root, which is calculated and compared with M. root The same indicators will be used for subsequent judgments; and the corresponding M for mountains without roots and seas. unkown-withot-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w}, where M unkown-withot-root This represents the combination of characteristic indicators corresponding to an unknown seamount with no root, calculated and compared with M. without-root The same indicators will be used for subsequent judgments. The characteristic parameters in the combination of characteristic indicators for the two types of seamounts may differ; the specific selection should be based on the correlation matrix analysis results in step C.

[0038] The feature index combination corresponding to the seamount to be identified is combined with the seamount index combination M established in step S3. root Combined with the data of the mountain root and sea mountain indicators M without-root Weighted distance analysis was performed to evaluate the similarity between the combination of feature indicators of the seamount to be identified and different types of seamounts.

[0039] Further calculation of the weighted Euclidean distance between the combination of seamount feature indicators to be identified and the combination of seamount indicators with and without roots: , in, Indicates the seamount to be identified and the first The comprehensive distance between the combined indicators for determining seamount-like structures Let k represent mountains with and without roots, and k take the values ​​{root, without-root}. Indicates the seamount number to be identified Normalized index value; Indicates the first The first seamount-like structure The normalized judgment index value; i represents the i-th item in the feature combination, i takes the values ​​1, 2, 3, 4, 5; Indicates the first The weight coefficients corresponding to each indicator satisfy the following conditions: Since the five comprehensive judgment indicators in this invention are used to characterize the seamount's planar expansion characteristics, overall morphological characteristics, volume distribution characteristics, magnetic anomaly response characteristics, and density anomaly response characteristics, and each indicator has independent geological significance, different weights are set sequentially according to the range and geological significance of each characteristic indicator in practice; Calculate the distance between the seamount to be identified and the combined criteria for identifying seamounts with roots, respectively: , in, M represents unkown-root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r The normalized index value of the i-th term in} M represents root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r The normalized index value of the i-th term in}; And the distance between the combined criteria for identifying seamounts and seamounts without roots: , in, M represents unkown-withot-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w The normalized index value of the i-th term in} M represents without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w The normalized index value of the i-th term in}; Finally, the seamount type is determined based on the principle of minimum distance: If D root <D without-root They believed that the five-dimensional comprehensive judgment index combination of the seamount to be identified was more similar to the comprehensive judgment index combination of seamounts with roots, and therefore determined that the seamount to be identified was a seamount with roots. If D root >D without-root If the five-dimensional comprehensive judgment index combination of the seamount to be identified is more similar to the comprehensive judgment index combination of seamounts without roots, then the seamount to be identified is determined to be a seamount without roots.

[0040] Through the above steps, the classification of seamount properties is finally achieved.

[0041] In summary, this invention, through systematic processing of multibeam bathymetry and gravity / magnetic anomaly data of seamounts, combined with topographic feature extraction, correlation analysis, and a quantitative determination model, successfully achieves high-precision determination of seamount properties in topographically complex sea basins. This invention can distinguish between seamounts with and without roots, and generates quantitative indicators such as depth compensation and topography, thus enabling seamount property analysis.

[0042] To further verify the effectiveness of the method for determining seamount properties using the correlation between seafloor topography and gravity / magnetic anomalies described in this invention, a specific implementation example is presented below, using a widely distributed seamount in the northern part of a central sub-basin of a certain sea area as the research object. This region exhibits a disordered distribution of seamounts, uneven basin topography, complex deep-sea tectonic background, and dramatic changes in gravity / magnetic anomalies, making it a typical representative of areas with highly variable seamount properties. The specific steps are as follows: Step A: Obtain water depth and topographic data (in .grd and .dat formats) of the central basin of a certain sea area. The data comes from the marine geological map of this sea area. The spatial gravity anomaly data and magnetic anomaly data of the basin come from the geological and geophysical maps of the sea area and its adjacent areas. The sedimentary layer thickness data comes from the geological survey report of the marine area by the China National Marine Geological Survey. Project the above water depth and topographic data and spatial gravity anomaly data onto a unified latitude and longitude benchmark under the CGCS2000 coordinate system, perform spatial matching and coordinate transformation, and then perform gridding to establish a unified water depth and spatial gravity anomaly dataset. Identify the seamount areas to be identified in the basin based on the topographic relief and mark the locations of the seamounts. There are 15 seamounts in the basin. Obtain the seamounts with known seamount properties based on the literature. Among them, seamounts F, G, and I are known to be seamounts without roots; seamounts E, A, and B are seamounts with roots; the rest are seamounts to be identified.

[0043] Step B: Perform Bouguer gravity anomaly correction on the acquired spatial gravity anomaly data, calculate the gravity effects generated by the water layer and sediment layer, and use the forward modeling formula of gravity anomaly to obtain the Bouguer gravity anomaly in the ocean basin.

[0044] Step B1: First, calculate the gravity effect generated by the sedimentary layer. The physical parameters are based on the fitting results of drilling data from the ODP ocean drilling program. =1.03×103 kg / m3, =2.8×103 kg / m 3 , =0.8, d=1.5 km, and the gravity effect generated by the sedimentary layer is calculated based on the above parameters. Then, based on the obtained water depth data, take... =1.03×103 kg / m3, using the approximate formula for an infinitely large horizontal thin layer to calculate the gravity effect generated by the water layer.

[0045] Finally, the spatial gravity anomaly of the ocean basin was analyzed by removing the gravity effects of the water layer and sediment layer, and the Bouguer gravity anomaly within the ocean basin was obtained according to the processing procedure. The spatial gravity anomaly values ​​and Bouguer gravity anomaly values ​​at the location of the seamount were then statistically analyzed.

[0046] Step B2: To avoid distortion, the magnetic anomaly data was processed using a zone-based variable tilt polarization method, dividing the data into three latitudinal zones: north, central, and south. The magnetic tilt and declination data were obtained from a website of a marine and atmospheric administration. In the southern low-latitude zone, deconvolution filtering was used to balance signal fidelity and noise suppression. A damping term β=0.002 was introduced into the denominator of the polarization factor. In the north and central zones, conventional polarization methods were directly applied. The processed results were linearly stitched together, and the magnetic anomaly (ΔT) value and polarization magnetic anomaly value at the seamount were statistically analyzed.

[0047] Step B3: Using multibeam echo sounding data from GEBCO_2023 at the location of the seamount, a three-dimensional water depth topographic map of the seamount is drawn, the bottom and top depths of the seamount are statistically analyzed, and the height difference of the seamount is obtained. The maximum horizontal cross-sectional area, slope and volume of the seamount are calculated by formula, and the topographic features of the seamount are statistically analyzed as shown in Table 1.

[0048] Table 1. Topographic features of seamounts in the central basin of a certain sea area. Step C: Construct a seamount determination model Step C1: Through the above data processing, seven sets of seamount topographic feature data and four sets of seamount gravity and magnetic anomaly feature data were obtained for seamounts with known seamount properties and seamounts to be identified. Pearson correlation coefficient was used for correlation analysis to obtain a heatmap of the correlation matrix of the two types of seamounts. Figure 3 , Figure 4 ).

[0049] Step C2: Select five pairs of features with strong correlation as variables for studying the topography and internal structure of seamounts, and conduct in-depth analysis. Through matrix analysis of the correlation between seamount topographic features and gravity / magnetic anomaly features, strongly correlated feature pairs are selected.

[0050] Based on the correlation analysis results of the known characteristics of two types of seamounts, we selected feature pairs with strong correlation between the two types of seamounts and constructed a combination of feature indicators that can reflect the different characteristics of the two types of seamounts in terms of topography and deep structure. A correlation threshold r0=0.95 was preset based on the sample quality and quantity in the study area, and feature pairs with high correlation (|r|≥r0) and clear geological significance were selected, while redundant feature combinations with weak correlation and unclear coupling were removed.

[0051] The selection process yielded feature combinations that reflected the different patterns of the two types of seamounts. For seamounts with roots: M root ={I 1r ,I 2r ,I 3r ,I 4r ,I5r}; where I 1r =A / H is the ratio of base area to height, I 2r = / L is the ratio of maximum slope to length, I 3r =A / V is the ratio of base area to volume, I 4r = / I represents the ratio of Bouguer gravity anomaly to polarization magnetic anomaly. 5r =A / This is the ratio of the base area to the spatial gravity anomaly.

[0052] For mountains without roots and seas: M without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w}. Among them, M root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r}; where I 1w =A / L is the ratio of base area to length, I 2w = / H is the ratio of maximum slope to height, I 3w =A / V is the ratio of base area to volume, I 4w = / I represents the ratio of Bouguer gravity anomaly to magnetic anomaly. 5w =A / This is the ratio of the base area to the spatial gravity anomaly.

[0053] Based on the above comprehensive indicators, using known seamount data, the slope of each feature pair is calculated through linear fitting, and the slope parameter is used as a combination of feature indicators for seamounts with mountain roots.

[0054] Finally, the slope parameters of each feature pair in the two types of seamount feature index combinations were calculated as follows: M root ={0.52, 2.8 × 10 -4 ,0.74,-0.18,28.45}; M without-root ={2.19, -2.9×10 -4 ,0.64,-4.17,57.36}.

[0055] Step D: Match the combination of seamount indicators to be identified with the judgment indicators of the established seamount nature determination model to achieve the classification of seamount nature; For the seamount areas to be identified, the corresponding five-dimensional comprehensive judgment index combination is calculated, among which, For the nine seamounts to be identified (O, N, L, M, C, H, D, J, and K), calculate the number of seamounts with roots corresponding to each seamount. unkown-root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r}; and M corresponding to the mountain without roots and sea. unkown-withot-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w}

[0056] O Haishan: M unkown-root ={0.599,0.00013,0.848,1.441,18.373}, M unkown-withot-root ={0.0178,0.0042,0.848,-3.275,18.373}; N Haishan: M unkown-root ={1.021,0.00017,0.985,-2.881,58.913},M unkown-withot-root ={0.0479,0.0037,0.985, 10.468,58.913}; L Haishan: M unkown-root ={1.414,0.00019,1.803,10.994,260.865},M unkown-withot-root ={0.0377,0.0071,1.803,46.843,260.865}; M Haishan: M unkown-root ={0.477,0.00021,1.232,-1.310,43.434}, M unkown-withot-root ={0.0182,0.0056,1.232,90.937,43.434}; C Haishan: M unkown-root ={0.416,0.00041,0.767,-30.076,21.609}, M unkown-withot-root ={0.0664,0.0026,0.767,-7.264,21.609}; D Haishan: M unkown-root ={0.378,0.00039,1.075,-2.173,89.474}, M unkown-withot-root={0.0302,0.0049,1.075,-3.445,89.474}; H Haishan: M unkown-root ={0.381,0.00025,0.772,11.153,33.311},M unkown-withot-root ={0.0325,0.0029,0.772,-3.862,33.311}; J Haishan: M unkown-root ={0.713,0.00024,0.896,25.784,48.242}, M unkown-withot-root ={0.0459,0.0038,0.896,-3.622,48.242}; K Haishan: M unkown-root ={2.143,0.00022,0.917,2.088,102.441},M unkown-withot-root ={0.1128,0.0042,0.917,-6.627,102.441}.

[0057] After obtaining the M corresponding to each seamount to be identified, the mountain roots and seamounts are found. unkown-root And corresponding to M, which has no mountain root and sea mountain unkown-withot-root Further calculations were performed to determine the weighted Euclidean distance between the combination of seamount feature indicators to be identified and the combinations of seamount indicators with and without roots: Considering that different criteria reflect the geometric morphology, rock mass distribution, and deep physical property response of seamounts, respectively, to avoid excessive influence of a single criterion on the distance calculation results, weighting coefficients are set according to the geological significance of each criterion, its contribution to the deep compensation structure of the seamount, and the criterion range. This embodiment uses a weighted combination: .

[0058] Based on this, the distance between the seamount to be identified and the comprehensive judgment index combination of seamounts with roots, and the distance between the seamount to be identified and the comprehensive judgment index combination of seamounts without roots were calculated respectively, and the final calculation results are as follows: O Haishan: D root =2.42, D without-root =8.81; D root <D without-root They believed that the five-dimensional comprehensive judgment index combination of O seamount was more similar to the comprehensive judgment index combination of seamount with mountain root, and judged O seamount as seamount with mountain root. N Haishan: D root =6.98, D without-root =8.11; D root <D without-root The N seamount is determined to be a seamount with a root. L Haishan: D root =52.33, D without-root =53.41; D root <D without-root The L-shaped seamount was determined to be a seamount with a mountain root. M Haishan: D root =3.42, D without-root =52.20; D root <D without-root The seamount M is determined to be a seamount with a root. C Haishan: D root =16.45, D without-root =8.25; D root >D without-root They believed that the five-dimensional comprehensive judgment index combination of seamount C was more similar to the comprehensive judgment index combination of seamount without roots, and judged the seamount to be identified as a seamount without roots. D Haishan: D root =13.69, D without-root =7.29; D root >D without-root The seamount D is determined to be a seamount without a root. H Haishan: D root =6.30, D without-root =5.51; D root >D without-root The H seamount is determined to be a seamount without a root. J Haishan: D root =14.89, D without-root =2.38; D root >D without-root The J-type seamount was determined to be a seamount without a root. K Haishan: D root =16.62, D without-root =10.23; D root >D without-root Therefore, K seamount is determined to be a seamount without a root.

[0059] Finally, using the comprehensive seamount identification index combination constructed above, the weighted distance between all seamounts to be identified and the identification index combinations of seamounts with and without roots was calculated. Based on the principle of minimum distance, the seamounts were classified into seamounts with and without roots. Seven seamounts with roots were identified: A, B, O, N, L, E, and M. Eight seamounts without roots were identified: C, D, F, G, H, I, J, and K. The seamount properties of 15 seamounts were obtained.

[0060] The embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for determining the nature of seamounts by utilizing the correlation between seabed topography and gravity / magnetic anomalies, characterized in that, Includes the following steps: S1. Acquire gravity anomaly data, magnetic anomaly data, sediment layer thickness data, and multibeam bathymetry data of multiple seamount samples with known seamount properties within the target sea basin; S2. Project all the data from step S1 onto a unified coordinate system to ensure that the latitude and longitude are consistent, and then process the data to obtain the topographic feature data and gravity and magnetic anomaly feature data of the seamount. S3. Construct a seamount property determination model, the specific implementation of which is as follows: S31 performs correlation analysis on the seamount topographic feature data and gravity and magnetic anomaly feature data to obtain a correlation coefficient matrix and extract strongly correlated feature pairs. S32 performs linear fitting based on the strongly correlated feature pairs, and constructs a judgment index to distinguish between mountains with and without mountain roots and seamounts by using the fitting slope of the feature pairs. S4. Input the topographic feature data and gravity and magnetic anomaly data of the seamount to be identified into the seamount nature determination model, calculate the weighted distance between the feature index of the seamount to be identified and the feature index of different types, determine the seamount type according to the minimum distance principle, and obtain the nature of the seamount to be identified.

2. The method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to claim 1, characterized in that, In step S2, the data processing includes: performing anomaly field correction on the spatial gravity anomaly data to obtain Bouguer gravity anomaly; eliminating the oblique magnetization effect of magnetic anomaly data through polarization processing to obtain polarization magnetic anomaly; statistically analyzing multibeam bathymetry data to obtain the bottom and top depths of the seamount, and then calculating seamount topographic feature data including seamount height, bottom area, volume, slope, and length. The gravity and magnetic anomaly data include spatial gravity anomaly data, Bouguer gravity anomaly data, magnetic anomaly data, and polarized magnetic anomaly data.

3. The method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to claim 2, characterized in that, In step S31, the extraction of the strongly correlated features is specifically implemented as follows: The correlation between the seamount topographic feature data and the gravity and magnetic anomaly data is calculated using the Pearson correlation coefficient method. If |r| ≥ r0, they are determined to be a strongly correlated feature pair, where r is the correlation coefficient and r0 is a preset correlation threshold.

4. The method for determining the nature of seamounts by utilizing the correlation between seabed topography and gravity / magnetic anomalies according to claim 3, characterized in that, In step S32, using the strongly correlated feature pairs as variables, the relationship between the feature parameters is obtained through linear fitting. The fitting slopes of the feature pairs corresponding to samples with and without mountain roots are used to construct criteria for determining whether a mountain or sea feature exists. Specifically, the criteria for determining whether a mountain or sea feature exists is M. root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r }; No mountain root or sea mountain determination index M without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w } 5. The method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to claim 4, characterized in that, The determination criteria include at least the ratio of bottom area to height, the ratio of maximum slope to length, the ratio of bottom area to volume, the ratio of Bouguer gravity anomaly to magnetic anomaly, and the ratio of bottom area to spatial gravity anomaly.

6. The method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to claim 4, characterized in that, Step S4 is implemented as follows: Gravity anomaly data, magnetic anomaly data, sediment layer thickness data, and multibeam bathymetry data of the seamount area to be identified are obtained. After processing in steps S1 and S2, the topographic feature data and gravity and magnetic anomaly feature data of the seamount to be identified are obtained. The topographic feature data and gravity / magnetic anomaly feature data of the seamount to be identified are input into the seamount property determination model to calculate the determination index combination M corresponding to the seamount to be identified. unkown-root ={I 1r ,I 2r ,I 3r ,I 4r ,I 5r }、M unkown-without-root ={I 1w ,I 2w ,I 3w ,I 4w ,I 5w }, respectively corresponding to mountains with and without mountain roots; Calculate the weighted Euclidean distance between the combination of seamount identification indicators to be identified and the combination of seamount indicators with and without seamount indicators; ， in, Indicates the seamount to be identified and the first The comprehensive distance between the combined indicators for determining seamount-like structures These respectively represent mountains with and without mountain roots; Indicates the seamount number to be identified Normalized index value; Indicates the first The first seamount-like structure Item normalization judgment index value; Indicates the first The weight coefficients corresponding to each indicator satisfy the following conditions: ; Determining seamount type based on the principle of minimum distance: If D root <D without-root The seamount to be identified was determined to be a seamount with a root. If D root >D without-root If so, the seamount to be identified is determined to be a seamount without a root. Among them, D root The distance between the seamount to be identified and the criteria for identifying seamounts with roots, The distance between the criteria for identifying seamounts and seamounts without roots.

7. The method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to claim 2, characterized in that, The specific process for obtaining the Bouguer gravity anomaly through anomaly field correction is as follows: The sedimentary layer is divided into multiple layers of equal thickness from top to bottom. The density of the sedimentary layer with depth is calculated using a sedimentary compaction model, as shown in the following formula: ， in, Indicates the density of the sedimentary layer. For fluid density, For solid density, Porosity d Here, z is the depth attenuation parameter, representing the depth of the deposition layer; The gravity anomaly generated by the sedimentary layer can be calculated based on the sedimentary layer density, using the following formula: ， Where G is the gravitational constant, This indicates a gravity anomaly generated by the sedimentary layer. This represents the difference between the density of the sedimentary layer and the density of the bedrock, where R is the Earth's radius and h is the thickness of the sedimentary layer. The gravitational effect produced by the water layer is calculated using the approximate formula for an infinitely large horizontal thin layer, as follows: ， in, This indicates the gravitational effect produced by the water layer. Let the density of seawater be taken as... , Indicates seawater depth; After successively removing the effects of water layer gravity and sediment layer gravity from the spatial gravity anomaly data of the ocean basin, the corrected Bouguer gravity anomaly formula within the ocean basin is obtained as follows: ， in, The longitude and latitude of the sea basin are The Bouguer gravity anomaly correction value at the location, This is due to a space gravity anomaly. This indicates the gravitational effect caused by the depth of the seabed. This indicates the gravitational effect caused by the sedimentary layer.

8. The method for determining the nature of seamounts using the correlation between seabed topography and gravity / magnetic anomalies according to claim 2, characterized in that, The polarization treatment is as follows: The magnetic tilt and declination data of the ocean basin were obtained, and deconvolution filtering was used to balance signal fidelity and noise suppression. A damping term was introduced into the denominator of the polarization factor, and the calculation formula is as follows: ； in, This indicates the abnormal magnetic force value after pole shifting. Let u and v represent the space wavenumbers in the x and y directions, respectively, D be the magnetic declination, I be the magnetic inclination, i be the imaginary unit, k be the total space wavenumber, and β be the damping term. The Fourier transform result represents the magnetic anomaly data.

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