A multi-feature water depth model construction and topographic evolution identification method

Abstract

The application discloses a kind of multi-feature water depth model construction and topographic evolution identification method.The method obtains the water depth measurement data of multiple time periods in study area, is corrected after systematic error, is removed after abnormal point and is uniformly processed after coordinate, forms multiple period sounding point set;Based on each period sounding point set, construct the multi-feature water depth model including depth, depth uncertainty, effective point number, the deepest and shallowest depth and other characteristics, realize the joint update of multiple characteristics by sounding point uncertainty propagation and measurement standard constraint, complete multi-period multi-feature water depth model construction;Further, different period water depth model is analyzed by the same difference, and topographic evolution is determined in combination with significance level and multi-feature constraint, and topographic evolution analysis result is generated.The application improves the quantification and reliability of seafloor topographic evolution analysis, and has important significance in high dynamic seafloor topographic area change monitoring and marine engineering application.

Description

Technical Field

[0001] This invention belongs to the field of marine surveying and seabed topography analysis technology, and specifically relates to a method for constructing a multi-feature water depth model and identifying topographic evolution. This method is particularly suitable for analyzing water depth changes and determining erosion and deposition in sea areas with significant measurement uncertainties and complex topographic evolution characteristics, and can effectively improve the quantification, reliability, and engineering applicability of seabed topographic evolution identification. Background Technology

[0002] Accurate acquisition and monitoring of seabed topography changes are fundamental tasks in marine surveying, marine engineering, and seabed scientific research, playing a crucial role in areas such as waterway maintenance, port dredging, marine engineering construction, and research on seabed environmental evolution. Particularly in areas with highly dynamic seabed topographic changes significantly influenced by tides, waves, and human activities, such as areas with developed sand waves and ridges, seabed morphology can change dramatically within short timescales, placing higher demands on the accuracy of depth measurements and the identification of changes.

[0003] Currently, multi-time-period seafloor topographic change analysis is typically based on regular grid depth models or direct differential comparison of bathymetric points to determine the evolution of seafloor topography, such as erosion and siltation. However, most of these methods only focus on the depth values ​​themselves and fail to systematically consider the uncertainties that are prevalent in the measurement process. This can easily lead to misjudging measurement noise or systematic errors as actual topographic changes, affecting the reliability of the identification results.

[0004] In addition, existing methods often use a single feature to describe the seabed topography during the construction of water depth models, lacking comprehensive utilization of auxiliary information such as the number of effective sounding points and extreme depths. This makes it difficult to control the consistency and reliability of change judgment results, especially in highly dynamic seabed topography areas or under multi-source and multi-phase measurement conditions, where the stability of the judgment results is poor.

[0005] In existing research, scholars have proposed multi-period depth analysis methods with joint uncertainties to address the difficulty of cross-sectional comparison of depth data from multiple time periods. These methods use single-beam bathymetry data as the research object, constructing time-series depth profiles through uncertainty propagation and filtering updates to analyze and evaluate the evolution of local topographic features. However, these methods are primarily geared towards single-beam bathymetry profile data, limiting their analysis to one-dimensional or quasi-one-dimensional topographic profiles, making them unsuitable for modeling and analyzing spatial variations in depth data with area coverage. Furthermore, these methods focus on single depth and its uncertainty characteristics, failing to comprehensively incorporate multi-dimensional features such as the number of effective points and extreme depths at the regular grid level, thus limiting their ability to constrain the spatial consistency and reliability of results for complex seafloor topographic changes.

[0006] Existing methods for analyzing water depth variations still have the following main problems:

[0007] 1. Insufficient handling of uncertainty: Existing methods usually only consider the measurement uncertainty of water depth data in a single time period, and do not use uncertainty as a key identification criterion, which makes the identification results susceptible to noise interference.

[0008] 2. Lack of multi-feature information fusion: Only water depth values ​​are used for change analysis, without comprehensively considering auxiliary information such as the number of effective sounding points and extreme depths, and there is a lack of control over the consistency and reliability of the results.

[0009] 3. Poor spatial consistency: Existing methods have difficulty maintaining consistency in identification results across spatial scales, especially in areas with highly dynamic seabed topography.

[0010] 4. Weak quantitative analysis capabilities: It lacks accurate quantitative and visual analysis of changing areas, making it difficult to meet the needs of engineering applications.

[0011] To address the aforementioned issues, there is an urgent need to propose a method that can introduce uncertainty constraints in the process of multi-time period water depth modeling and change analysis, and comprehensively utilize various water depth characteristic information, so as to improve the quantification, reliability and engineering applicability of seabed topography evolution identification. Summary of the Invention

[0012] This invention proposes a method for constructing a multi-feature water depth model and identifying terrain evolution, aiming to overcome the problem that the determination of water depth changes in multiple time periods in the prior art is easily affected by measurement noise, uncertainty differences and limitations of single features, resulting in unstable results.

[0013] To achieve the above objectives, the present invention proposes the following technical solution:

[0014] A method for constructing a multi-feature water depth model and identifying terrain evolution includes the following steps: Step 1: Data acquisition and preprocessing. Acquire raw water depth measurement data from multiple time periods within the study area. Perform systematic error elimination, abnormal noise point removal, and unified projection coordinate processing on the raw water depth measurement data to obtain a set of depth measurement points from multiple time periods. Step 2: Construction of Multi-Feature Depth Model and Uncertainty Constraint Fusion. For any set of sounding points in any time period, a multi-feature depth model in the form of a regular grid is initialized according to a certain spatial resolution. The multi-feature depth model includes features such as depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth. Based on the spatial relationship between the sounding points and the grid nodes of the multi-feature depth model, the uncertainty of the sounding points is propagated, and uncertainty constraints of the measurement standard are introduced. The Kalman filter method is used to update each feature of the multi-feature depth model, completing the construction of the multi-feature depth model for a single time period. The multi-time period sounding point sets are traversed, and corresponding multi-time period multi-feature depth models are constructed respectively. Step 3: Topographic evolution identification and result generation. Extract multi-feature water depth models from any two time periods, perform differential calculations on grid cells at the same spatial location to obtain depth change, depth change uncertainty and standardized statistics, and determine significance based on the set significance level. At the same time, introduce multiple features such as minimum effective point number and deepest and shallowest depth values ​​as auxiliary constraints to control consistency and reliability. Finally, generate a distribution map of topographic evolution identification results and an area statistics table.

[0015] The aforementioned multi-time period sounding point set In the formula, for Time period depth sounding point set, , , , , , , They are respectively Time Periods Individual sounding points, total number of sounding points, plane coordinates of sounding points, depth, horizontal uncertainty, and depth uncertainty. , , For natural numbers, It is a negative number.

[0016] The uncertainty constraint of the measurement standard is the maximum permissible depth uncertainty constraint based on the IHO S-44 international hydrographic standard, and the calculation formula is as follows: (1); In the formula, for Time Periods Depth of each sounding point The maximum permissible depth uncertainty calculated under the selected IHO S-44 international hydrographic standard measurement class conditions. , For natural numbers, It is a negative number; , These are the coefficients for the uncertainty that does not change with depth and the uncertainty that changes with depth for the selected measurement level, respectively. Their values ​​are obtained by referring to IHO S-44, the international hydrographic standard.

[0017] The multi-feature water depth model includes at least five types of features: depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth. Deep feature matrix , Deep uncertainty characteristic matrix , Valid point feature matrix , Deepest feature matrix , Shallowest depth feature matrix , In the formula, , , , , They are respectively The values ​​of depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth in the i-th row and j-th column of the corresponding feature matrix in the multi-feature depth model for a given time period are given. M and N are the number of nodes in the row and column directions of the corresponding feature matrix of the multi-feature depth model, respectively. i, j, M, and N are non-negative integers. It is a natural number.

[0018] The propagation of uncertainty at the sounding point includes: (2); (3); In the formula, for Time Periods One depth sounding point ( plane coordinates of ) , ) and the matrix node in the i-th row and j-th column of the multi-feature water depth model ( , The planar distance; For depth sounding points propagation to nodes ( , The depth uncertainty of ) , They are respectively The depth and level uncertainties are given by r, where r is the spatial resolution of the multi-feature depth model; i and j are non-negative integers, r > 0. , For natural numbers, <0.

[0019] The Kalman filtering method is used to update the features of the multi-feature depth model as follows: (4); (5); In the formula, The Kalman gain coefficient is... for The node value of the i-th row and j-th column of the depth uncertainty feature matrix in the time-period multi-feature water depth model; , , , To integrate depth sounding points Before and after the update The values ​​of the depth and the i-th row and j-th column of the depth uncertainty feature matrix in the time-period multi-feature water depth model; , , They are respectively The values ​​of the i-th row and j-th column of the feature matrix for the number of effective points, the deepest depth, and the shallowest depth in the time-period multi-feature water depth model; for The depth; i and j are non-negative integers. , For natural numbers, <0.

[0020] The depth change Uncertainty of depth change and standardized statistics The following formula is used for calculation: (6); In the formula, , , , They are respectively and The values ​​of the nodes in the i-th row and j-th column of the depth and depth uncertainty feature matrix in the time-period multi-feature water depth model. It approximately follows a normal distribution, where i and j are non-negative integers. <0, It is a natural number, and .

[0021] The significance determination includes: , which are grid nodes with no significant changes; when This significantly erodes the grid nodes; when , which are significantly silted-up grid nodes; in, significance level The corresponding two-sided test critical value; The number of valid points and the extreme value consistency constraints include: (7); In the formula, To be the minimum number of valid points, , They are respectively The value of the node in the i-th row and j-th column of the effective point number feature matrix in the time-period multi-feature water depth model; , , , They are respectively The values ​​of the nodes in the i-th row and j-th column of the feature matrix of the shallowest and deepest depths in the time-period multi-feature water depth model.

[0022] The spatial resolution ranges from 0.5m to 5m, preferably from 1m to 2m.

[0023] The method is applied to the fields of waterway maintenance, port dredging, offshore engineering, and seabed environment research.

[0024] The beneficial effects of this invention are:

[0025] 1. Improve the reliability of identification: By introducing uncertainty constraints and multi-feature consistency control, the influence of measurement noise and systematic errors on the identification results is effectively eliminated, which significantly improves the reliability of terrain evolution identification.

[0026] 2. Enhanced result stability: The construction of multi-feature water depth models and the constraint of multi-feature consistency make the identification results more stable in time and space, especially in areas with highly dynamic seabed topography.

[0027] 3. Enhanced quantitative analysis capabilities: By generating distribution maps and statistical reports of terrain evolution identification results, accurate quantitative and visual analysis of changing areas is achieved, meeting the needs of engineering applications.

[0028] 4. Expanding the scope of application: This method is applicable to various types of water depth measurement data and marine environments, and has strong versatility and engineering applicability.

[0029] 5. Optimize resource utilization: By reasonably setting parameters such as spatial resolution and minimum effective number of points, the computational efficiency and identification accuracy are balanced, thus optimizing resource utilization.

[0030] This invention can play an important role in fields such as waterway maintenance, port dredging, offshore engineering, and seabed environmental research. Attached Figure Description

[0031] Figure 1 This is a flowchart of the present invention, which shows the overall process of constructing a multi-feature water depth model and a terrain evolution identification method based on uncertainty constraints.

[0032] Figure 2 This is the depth measurement point set containing 29,235,951 depth measurement points, input in time period 1 of this embodiment of the invention. Schematic diagram.

[0033] Figure 3 This is under the condition of a spatial resolution of 1 m in the embodiment of the present invention ( The study area was regularly discretized according to the maximum rectangular spatial distribution range of the time period 1 sounding point set, and a multi-feature water depth model containing 5 feature matrices was constructed, namely the depth feature matrix. Deep uncertainty characteristic matrix Valid point feature matrix The deepest feature matrix Shallowest depth feature matrix ,in , Schematic diagram.

[0034] Figure 4 This is an example of the spatial relationship and uncertainty propagation between sounding points and grid nodes of a multi-feature water depth model in an embodiment of the present invention.

[0035] Figure 5 This is a schematic diagram of a multi-feature water depth model obtained in two time periods in an embodiment of the present invention, wherein... Figure 5 In the model, (a) and (b) are the deep feature matrices contained in the two periods. Figure 5 In the model, (c) and (d) are the five deep uncertainty characteristic matrices contained in the two periods. Figure 5 In the model, (e) and (f) are the feature matrices of the effective points contained in the two periods. Figure 5 In this context, (g) and (h) are the deepest feature matrices contained in the two periods of the model. Figure 5 In the model, (i) and (j) are the shallowest feature matrices contained in the two periods.

[0036] Figure 6 This is a schematic diagram illustrating the depth change, uncertainty of depth change, and standardized statistics calculated by the sub-multi-feature depth model for two time periods in this embodiment of the invention. Figure 6 In the example, (a) represents the depth change calculated by the sub-multi-feature depth model for two time periods in this embodiment of the invention. Figure 6 (b) in the figure represents the uncertainty of the calculated depth change. Figure 6 (c) in the equation is the standardized statistic obtained from the calculation.

[0037] Figure 7 These are the distribution map and area statistics table of terrain evolution identification results obtained in the embodiments of the present invention, wherein... Figure 7 (a) in the figure is a distribution map of the terrain evolution identification results obtained in the embodiments of the present invention. Figure 7 (b) in the middle is Figure 7(a) shows the area statistics of various regions in the evolution identification results (the key parameters selected are spatial resolution r=1 m, IHO S-44 standard level is the highest level of measurement, significance level α=0.05, and minimum number of effective points n_min=5). Figure 7 In the figure, (c) represents the water depth change obtained by the traditional single-depth difference method. Figure 7 (d) in the middle is Figure 7 (c) in the figure represents the area statistics of various regions in the water depth change.

[0038] Figure 8 This demonstrates the spatial resolution r, significance level α, and minimum number of significant points. n min The impact on the results of terrain change identification, among which Figure 8 In the example (a), the effect of spatial resolution r on the terrain change identification result is shown. Figure 8 (b) shows the impact of the significance level α on the topographic change identification results. Figure 8 (c) in the equation represents the minimum number of valid points. n min Impact on topographic change identification results. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0040] Example 1: Identification of Topographic Evolution in Submarine Sand Wave Zones

[0041] The specific implementation process of this invention is as follows: Figure 1 As shown, the main steps include data acquisition and preprocessing, construction of multi-feature water depth model and fusion of uncertainty constraints, and identification of terrain evolution and generation of results.

[0042] Step 1: Data Acquisition and Preprocessing

[0043] This example uses the RESON Seabat 7125 shallow-water multibeam echo sounder system to conduct full-coverage seabed topographic surveys of a shallow shoal area with well-developed sand waves. Both depth measurements were performed according to the IHO S-44 Special Grade Measurement Standard, in June 2018 (Time Period 1) and June 2019 (Time Period 2). The resulting unprocessed raw echo sounders all contain the geographic coordinates, depth values, and horizontal and depth uncertainties for each point. First, systematic errors were eliminated from the raw depth measurement data by calculating the installation angle deviation using the calibration survey lines. Then, outlier noise points were removed using statistical methods (3σ criterion) and interactive editing. Finally, the Global Mapper software was used to project the echo sounder set onto the WGS_1984_UTM_Zone_50N projection coordinate system, obtaining the echo sounder sets for the seabed sand wave area in both time periods. Each sounding point in the two periods contains its plane coordinates (x, y), depth (z), horizontal uncertainty (u_h), and depth uncertainty (u_z). The data volume is 29,235,951 sounding points (period 1) and 18,152,634 sounding points (period 2).

[0044] Step 2: Construction of multi-feature water depth model and fusion of uncertainty constraints

[0045] like Figure 2 As shown, the input time period 1 contains a set of 29,235,951 sounding points. The white box represents the maximum rectangular spatial distribution of this point set, and each sounding point has its own planar coordinates. ),depth( ), composition of horizontal and depth uncertainties ( The set spatial resolution is... 1 m, the study area is regularly discretized according to the maximum rectangular spatial distribution range and spatial resolution of the sounding point set, resulting in the following: Figure 3 The example shown is an initialized multi-feature water depth model containing 1270 rows (M=1270) and 1940 columns (N=1940). , , , in, For a multi-feature water depth model of time period 1, for Spatial grid, This is a multi-feature attribute matrix for time period 1. The total feature dimension, The first feature of the multi-feature water depth model represents the time period 1. The matrix representation of each feature on the grid.

[0046] Figure 3 The feature matrices of the multi-feature depth model are shown. It can be seen that this initialized multi-feature depth model... Features include depth, depth uncertainty, number of valid points, deepest and shallowest depths, i.e., a depth matrix. Depth uncertainty matrix Valid point matrix The deepest depth matrix Shallowest depth matrix The depth matrix records the depth value of each grid node, the depth uncertainty matrix records the depth uncertainty of each grid node, the effective point number matrix records the number of effective sounding points participating in the node update, the deepest depth matrix records the deepest depth value among the effective sounding points absorbed by the node, and the shallowest depth matrix records the shallowest depth value among the effective sounding points absorbed by the node.

[0047] Based on the spatial relationship between sounding points and the grid nodes of the multi-feature depth model, all sounding points are traversed. The uncertainty is propagated to every grid node, and the uncertainty constraint of the IHO S-44 Special Measurement Standard is introduced (the value of which is obtained by referring to the IHO S-44 International Hydrographic Standard). =0.25, =0.0075).

[0048] To facilitate understanding of the underlying principles, according to Figure 4 The top and side views illustrate the uncertainty propagation and constraint implementation process. First, the sounding point is calculated. The distance to the node in the second row and first column of the multi-feature depth model matrix (solid green line) is calculated. Next, the depth uncertainty propagated from this sounding point to this node is calculated (dashed green line). Then, the maximum permissible depth uncertainty of this sounding point under the IHO S-44 special standard is calculated (denoted as ). Its value is Figure 4 (The side view is represented by a red dashed line). Comparison shows that the propagation depth uncertainty of this sounding point (green dashed line) is greater than the maximum permissible depth uncertainty. Therefore, this sounding point does not participate in the multi-feature update of node (2,1). Similarly, it can be concluded that this sounding point can participate in the multi-feature update of node (2,2). The specific calculation process is as follows:

[0049] like Figure 4 As shown in the top view, the currently traversed depth sounding point is the black depth sounding point. First, use the following formula to calculate the distance between the sounding point and node (2,1) (the distance is represented by the solid green line): ; In the formula, For the 102nd sounding point in time period 1 plane coordinates ( , ) and the matrix nodes in the second row and first column of the multi-feature depth model ( , The planar distance between ).

[0050] Next, use the following formula to calculate the depth uncertainty propagating from the sounding point to the grid node (its value is as follows). Figure 4 (As shown by the green dashed line in the side view) ; In the formula, For depth sounding points propagation to nodes ( , The depth uncertainty of ) , They are respectively Depth and level uncertainty, This represents the spatial resolution of the multi-feature water depth model.

[0051] Then, use the following formula to calculate the maximum permissible depth uncertainty of the sounding point under the IHO S-44 Special Measurement Standard (its value is as follows). Figure 4 (As shown by the red dashed line in the side view): ; In the formula, The depth of the 102nd sounding point in time period 1 ( The maximum permissible depth uncertainty calculated under the selected IHOS-44 international hydrographic standard measurement class conditions. , These are the coefficients for the uncertainty that does not change with depth and the uncertainty that changes with depth, respectively, corresponding to the selected measurement level. Their values ​​were determined by referring to IHO S-44, the International Hydrographic Standard. =0.25, =0.0075.

[0052] from Figure 4 The side view clearly shows that the depth uncertainty propagating from the sounding point to node (2,1) is greater than the maximum permissible depth uncertainty. This indicates that the sounding point (k=102) does not participate in the multi-feature update of node (2,1).

[0053] Repeating the same steps above, it was found that the depth uncertainty propagated from the sounding point (k=102) to node (2,2) is less than the maximum permissible depth uncertainty. ), can participate in the multi-feature update of node (2,2).

[0054] For sounding points that can participate in the multi-feature update of the multi-feature depth model, the Kalman filter method will be used to update each feature of the time period 1 multi-feature depth model. Here, the Kalman gain is first calculated using the formula ( ): ; Then, use the following set of formulas to update the depth of the mesh nodes ( ), depth uncertainty ( ), number of valid points ( ), deepest depth ( ) and shallowest depth ( ); .

[0055] In the two formulas above, The Kalman gain coefficient is... For the node value of the i-th row and j-th column of the depth uncertainty feature matrix in the time period 1 multi-feature water depth model; , , , To integrate sounding points ( Before and after the update The values ​​of the depth and the i-th row and j-th column of the depth uncertainty feature matrix in the multi-feature water depth model of time period 1; , , These represent the values ​​of the effective number of points, the deepest depth, and the shallowest depth feature matrix in the multi-feature water depth model for time period 1, respectively; for The depth; i and j are non-negative integers. For natural numbers, <0.

[0056] Following the steps described above, the sounding points are traversed sequentially, and the various features of the multi-feature depth model (depth, depth uncertainty, number of valid points, deepest and shallowest depths) are updated synchronously accordingly, ultimately yielding the multi-feature depth model for time period 1. The input is a sounding point set for time period 2 containing 18,152,634 sounding points. Repeat step 2 to obtain a multi-feature water depth model for time period 2.

[0057] like Figure 5 As shown, this is the final multi-feature water depth model for two periods, where each period model contains a depth feature matrix ( Figure 5(a) and (b) in the figure, and the characteristic matrix of depth uncertainty ( Figure 5 (c), (d) and the effective point feature matrix ( Figure 5 (e), (f)), and the deepest feature matrix ( Figure 5 (g), (h)), and the shallowest depth feature matrix ( Figure 5 There are 10 feature matrices in total, consisting of 5 types (i) and (j). Figure 5 The results of constructing a multi-period, multi-feature water depth model are presented, which intuitively reflects the spatial distribution characteristics of different feature matrices.

[0058] Step 3: Topographic Evolution Identification and Result Generation

[0059] Extract the depth matrix from the multi-feature depth model for these two time periods, and use the following set of formulas to calculate the depth variation for grid cells at the same spatial location. Uncertainty of differential calculation and its depth variation ) calculate, and further calculate the standardized statistic that approximately follows a normal distribution ( ). ), to obtain the depth change matrix ( Figure 6 (a) in the figure, and the uncertainty matrix of the depth change ( Figure 6 (b) and the standardized statistics matrix ( Figure 6 (c) in the middle); Figure 6 The key intermediate results in the process of terrain evolution identification are presented, providing data support for subsequent significance determination.

[0060] ; In the formula, , , , These represent the values ​​of the nodes in the i-th row and j-th column of the depth and depth uncertainty feature matrix in the multi-feature water depth models for time periods 1 and 2, respectively. It approximately follows a normal distribution, where i and j are non-negative integers. <0, It is a natural number.

[0061] Set the significance level as The corresponding two-tailed test critical value is For each node The following rules shall be followed for determination: When , represents nodes with no significant change; when , is a significant scour node; when , which are significant siltation nodes.

[0062] Set minimum number of valid points Using the number of valid points and the deepest and shallowest depth values ​​as auxiliary constraints for consistency and reliability control, the study area was identified as erosion areas, sedimentation areas, and areas with no significant changes. Finally, a distribution map of the topographic evolution identification results and an area statistics table were generated. Figure 7 ). Figure 7 The invention demonstrates the final identification results, including the distribution of scour areas, siltation areas, and areas with no significant changes, as well as area statistics for each type of area, enabling an intuitive and quantitative analysis of seabed topographic evolution. Traditional methods, however, rely solely on changes in water depth for seabed topographic scour and siltation evolution analysis, failing to systematically consider the inherent uncertainties in the measurement process. This can easily lead to misinterpreting measurement noise or systematic errors as topographic changes, thus affecting the reliability of the identification results.

[0063] Figure 7 Figures (a) and (b) show the distribution map of the seafloor topographic evolution identification results and the area statistics table of various regions in the seafloor sand wave area over one year, respectively. The key parameters selected are spatial resolution r=1 m, IHO S-44 standard level of the highest level of measurement, significance level α=0.05, and minimum number of effective points n_min=5. It is found that the area of ​​significant erosion in the study area over one year is 0.14 square kilometers, the area of ​​significant siltation is 0.95 square kilometers, and the area with no significant change is 1.38 square kilometers (α=0.05, 95% confidence level).

[0064] from Figure 7 As shown in (a), the significant scouring area is mainly located at the crest of the sand waves, while the significant sedimentation area is mainly located south of the crest. The flat topography area between the two sand waves did not change significantly from year to year. The average water depth variation range of the significant scouring area is 0.38~3.99m, with a mean of 0.83m; while the average water depth variation range of the significant sedimentation area is -0.38~-4.39m, with a mean of -0.69m. This provides a direct and quantitative analysis of the seafloor topographic evolution of the entire study area.

[0065] Compared with the traditional single-depth difference method (see...) Figure 7 Compared to (d), traditional methods rely solely on changes in water depth for seafloor topographic erosion and deposition analysis, failing to systematically consider the inherent uncertainties in the measurement process. This can easily lead to misinterpretations of measurement noise or systematic errors as topographic changes, thus affecting the reliability of the identification results. It can be seen that traditional methods cannot effectively identify areas without significant changes, nor can they provide corresponding confidence levels when determining areas of change, resulting in a lack of sufficient mathematical information to support the conclusions regarding topographic evolution.

[0066] Example 2: Experiment on the optimization of key parameters for the construction of multi-feature water depth models and the identification of terrain evolution.

[0067] Using the same two-phase sounding point set for the seabed sand wave area as in Example 1, and since the water depth measurement standard is performed according to IHO S-44 Special Grade Measurement Standard, this example remains unchanged. We selected key parameter 1, spatial resolution r = 0.1, 0.5, 1, and 5 m, and key parameter 2, significance level. =0.001, 0.01, 0.05 and 0.1, key parameter 3: minimum number of valid points =5, 10, 20, 50, keep one of the key parameters unchanged (set to the same value as used in Example 1), compare the impact of different parameter selections on the seabed topography evolution identification results, and thus optimize the key parameters for multi-feature water depth model construction and topography evolution identification.

[0068] Figure 8 Figure (a) illustrates the impact of spatial resolution *r* on the topographic change identification results. When *r* = 0.1 m, the area statistics of each changed region are at their minimum. Analysis shows that excessively low spatial resolution prevents grid nodes from absorbing a sufficient number of depth points, resulting in a large number of null values ​​in the multi-feature depth model, thus hindering effective identification of topographic changes. As the value of *r* increases, the areas of significant erosion, significant siltation, areas with no significant changes, and the total area all show a significant increasing trend; however, when *r* increases from 1 m to 5 m, the area statistics of the above four types remain basically stable. Considering both the high-resolution requirement and the stability of the results for topographic evolution identification, this example selects *r* = 1 m as the optimal spatial resolution parameter for this sounding point set.

[0069] Figure 8 (b) illustrates the impact of the significance level α on the topographic change identification results. When α = 0.001 (the threshold often described as "extremely significant," allowing a maximum false rejection risk of 0.1%), the areas of significant erosion and significant sedimentation are the smallest, while the area of ​​areas with no significant change is the largest. As the α value increases, the areas of significant erosion and significant sedimentation gradually expand, while the area of ​​areas with no significant change decreases accordingly. In this example, α = 0.05 is selected as the significance level, allowing a maximum false rejection risk of 5%. This level is currently the most commonly used and widely accepted statistical standard, and it is also the threshold adopted by the vast majority of confirmatory studies. If this method is applied to topographic evolution identification with lower accuracy requirements, it is recommended to use α = 0.1; if applied to situations requiring high accuracy, it is recommended to use α = 0.01 or even 0.001.

[0070] Figure 8 (c) in the diagram shows the minimum number of valid points. The impact on the results of terrain change identification. When As the value increases from 1 to 10, the areas of significant scour, significant siltation, no significant change, and total area all show a gradual decreasing trend; while when... When the area is increased from 10 to 50, the areas of all four types decrease rapidly. This is because... The larger the value, the stricter the constraint on the number of effective sounding points, leading to a sharp decrease in the number of grid nodes that can meet the constraints. According to relevant literature, when constructing a depth model using multibeam bathymetry data, it is generally required that each grid node absorb at least 10 depth points. To ensure the breadth and reliability of the terrain change identification area, this example selects... As a constraint for the minimum number of valid points.

[0071] Example 3: Identification of Topographic Evolution in Port and Waterway Areas

[0072] according to Figure 1 The method for identifying terrain evolution shown in the figure mainly includes three steps: data acquisition and preprocessing, construction of a multi-feature water depth model and fusion of uncertainty constraints, and identification of terrain evolution and generation of results.

[0073] Step 1: Data Acquisition and Preprocessing

[0074] Our team used the R2SONIC 2026 multibeam echo sounder to conduct full-coverage seabed topographic surveys of a large port channel area. Two depth measurements were performed according to IHO S-44 1a and other measurement standards, in May 2022 (period 1) and June 2023 (period 2), respectively. The resulting unprocessed raw echo sounders all included the geographic coordinates, depth values, and horizontal and depth uncertainties for each point. First, we eliminated systematic errors in the raw depth measurement data by using installation angle deviations and sound velocity profile corrections calculated from the calibration survey lines. Then, we used an interactive editing method to remove abnormal noise points. Finally, we used Global Mapper software to project the echo sounder set onto the WGS_1984_UTM_Zone_49N projection coordinate system, obtaining the echo sounder set for the port channel area in both time periods. Each sounding point in the two time periods contains its plane coordinates (x, y), depth (z), and horizontal and depth uncertainties. The data volume is 45,327,896 sounding points (time period 1) and 36,124,875 sounding points (time period 2).

[0075] Step 2: Construction of multi-feature water depth model and fusion of uncertainty constraints

[0076] Similar to the corresponding steps in Example 1, the spatial resolution is set to... The study area is discretized regularly according to the maximum rectangular spatial distribution range and spatial resolution of the sounding point set, resulting in an initial multi-feature water depth model containing 1000 rows (M=1000) and 2000 columns (N=2000). Uncertainty constraints of measurement standards such as IHO S-44 1a are introduced (defined). =0.5, =0.013). The resulting multi-feature water depth models for two time periods are as follows: each time period model contains five feature matrices: depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth.

[0077] Step 3: Topographic Evolution Identification and Result Generation

[0078] Similar to the corresponding steps in Example 1, a significance level is set. Set the minimum number of valid points for consistency constraints. =10. Using the number of valid points and the deepest and shallowest depth values ​​as auxiliary constraints for consistency and reliability control, the scour area, siltation area and area without significant change in the port channel area are determined, and finally, a distribution map of the terrain evolution identification results and an area statistics table are generated.

[0079] The results showed that the area of ​​significant scour was 1.5 square kilometers, the area of ​​significant siltation was 2.6 square kilometers, the area of ​​no significant change was 3.9 square kilometers, the maximum scour volume was 0.85 meters, and the maximum siltation volume was 1.15 meters.

[0080] The innovative aspects of this invention:

[0081] 1. Uncertainty Constraint Fusion: This invention innovatively introduces the uncertainty constraint conditions in the IHO S-44 international hydrographic standard into the water depth model construction process, ensuring that the propagation uncertainty of the sounding point at the grid node does not exceed the allowable threshold, effectively eliminating the influence of measurement noise on the identification results.

[0082] 2. Construction of a multi-feature water depth model: This invention constructs a multi-feature water depth model that includes five types of features: water depth value, depth uncertainty, number of effective points, deepest and shallowest depth values, which comprehensively describes the spatiotemporal variation characteristics of seabed topography.

[0083] 3. Kalman Filter Update: This invention uses the Kalman filter method to synchronously update each feature of the multi-feature water depth model, which not only ensures the accuracy of the water depth values, but also reasonably transmits uncertainty information, thereby improving the reliability and stability of the model.

[0084] 4. Multi-feature consistency constraint: This invention introduces multiple features such as the number of effective points and the deepest and shallowest depth values ​​as auxiliary constraints to control the consistency and reliability of the change judgment results, which significantly improves the credibility of the judgment results.

[0085] 5. Spatial Consistency Identification: This invention achieves consistent topographic evolution identification at a spatial scale by combining co-location difference analysis with multi-feature constraints, and is particularly suitable for highly dynamic seabed topographic areas.

[0086] The embodiments described above can be further combined or replaced, and these embodiments are merely descriptions of preferred embodiments of the present invention, not limitations on the concept and scope of the present invention. Various changes and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept are all within the protection scope of the present invention. The protection scope of the present invention is given by the appended claims and any equivalents.

Claims

1. A method for constructing a multi-feature water depth model and identifying terrain evolution, characterized in that, include: Step 1: Data acquisition and preprocessing. Acquire raw water depth measurement data from multiple time periods within the study area. Perform systematic error elimination, abnormal noise point removal, and unified projection coordinate processing on the raw water depth measurement data to obtain a set of depth measurement points from multiple time periods. Step 2: Construction of multi-feature water depth model and fusion of uncertainty constraints. For the set of sounding points in any time period, a multi-feature water depth model in the form of a regular grid is initialized according to the spatial resolution. The multi-feature water depth model includes features such as depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth. Based on the spatial relationship between sounding points and the grid nodes of the multi-feature depth model, the uncertainty of the sounding points is propagated, and the uncertainty constraint of the measurement standard is introduced. The Kalman filter method is used to update each feature of the multi-feature depth model to complete the construction of the single-time-period multi-feature depth model. The multi-time-period sounding point set is traversed to construct the corresponding multi-time-period multi-feature depth model. Step 3: Topographic evolution identification and result generation. Extract multi-feature water depth models from any two time periods, perform differential calculations on grid cells at the same spatial location to obtain depth change, depth change uncertainty and standardized statistics, and determine significance based on the set significance level. At the same time, introduce multiple features such as minimum effective point number and deepest and shallowest depth values ​​as auxiliary constraints to control consistency and reliability. Finally, generate a distribution map of topographic evolution identification results and an area statistics table.

2. The method according to claim 1, characterized in that, The aforementioned multi-time period sounding point set In the formula, for Time period depth sounding point set, , , , , , , They are respectively Time Periods Individual sounding points, total number of sounding points, plane coordinates of sounding points, depth, horizontal uncertainty, and depth uncertainty. , , For natural numbers, It is a negative number.

3. The method according to claim 1, characterized in that, The uncertainty constraint of the measurement standard is the maximum permissible depth uncertainty constraint based on the IHO S-44 international hydrographic standard, and the calculation formula is as follows: (1) ； In the formula, for Time Periods Depth of each sounding point The maximum permissible depth uncertainty calculated under the selected IHO S-44 international hydrographic standard measurement class conditions. , For natural numbers, It is a negative number; , These are the coefficients for the uncertainty that does not change with depth and the uncertainty that changes with depth for the selected measurement level, respectively. Their values ​​are obtained by referring to IHO S-44, the international hydrographic standard.

4. The method according to claim 1, characterized in that, The multi-feature water depth model includes at least five types of features: depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth. Deep feature matrix , Deep uncertainty characteristic matrix , Valid point feature matrix , Deepest feature matrix , Shallowest depth feature matrix , In the formula, , , , , They are respectively The values ​​of depth, depth uncertainty, number of effective points, deepest depth, and shallowest depth in the i-th row and j-th column of the corresponding feature matrix in the multi-feature depth model for a given time period are given. M and N are the number of nodes in the row and column directions of the corresponding feature matrix of the multi-feature depth model, respectively. i, j, M, and N are non-negative integers. It is a natural number.

5. The method according to claim 1, characterized in that, The propagation of uncertainty at the sounding point includes: (2) ； (3) ； In the formula, for Time Periods Each depth measurement point plane coordinates ( , ) and the matrix node in the i-th row and j-th column of the multi-feature water depth model ( , The planar distance; For depth sounding points propagation to nodes ( , The depth uncertainty of ) , They are respectively The depth and level uncertainties are given by r, where r is the spatial resolution of the multi-feature depth model; i and j are non-negative integers, r >

0. , For natural numbers, <0.

6. The method according to claim 1, characterized in that, The Kalman filtering method is used to update the features of the multi-feature depth model as follows: (4)； (5)； In the formula, The Kalman gain coefficient is... for The node value of the i-th row and j-th column of the depth uncertainty feature matrix in the time-period multi-feature water depth model; , , , To integrate depth sounding points Before and after the update The values ​​of the depth and the i-th row and j-th column of the depth uncertainty feature matrix in the time-period multi-feature water depth model; , , They are respectively The values ​​of the i-th row and j-th column of the feature matrix for the number of effective points, the deepest depth, and the shallowest depth in the time-period multi-feature water depth model; for The depth; i and j are non-negative integers. , For natural numbers, <0.

7. The method according to claim 6, characterized in that, The depth change Uncertainty of depth change and standardized statistics The following formula is used for calculation: (6)； In the formula, , , , They are respectively and The values ​​of the nodes in the i-th row and j-th column of the depth and depth uncertainty feature matrix in the time-period multi-feature water depth model. It approximately follows a normal distribution, where i and j are non-negative integers. <0, It is a natural number, and .

8. The method according to claim 7, characterized in that, The significance determination includes: , which are grid nodes with no significant changes; when This significantly erodes the grid nodes; when , which are significantly silted-up grid nodes; in, significance level The corresponding two-sided test critical value; The number of valid points and the extreme value consistency constraints include: (7)； In the formula, To be the minimum number of valid points, , They are respectively The value of the node in the i-th row and j-th column of the effective point number feature matrix in the time-period multi-feature water depth model; , , , They are respectively The values ​​of the nodes in the i-th row and j-th column of the feature matrix of the shallowest and deepest depths in the time-period multi-feature water depth model.

9. The method according to claim 1, characterized in that, The spatial resolution ranges from 0.5m to 5m.

10. The method according to claim 1, characterized in that, The method is applied to the fields of waterway maintenance, port dredging, offshore engineering, and seabed environment research.

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