CN-122020072-A - Multi-feature water depth model construction and topography evolution identification method

CN122020072ACN 122020072 ACN122020072 ACN 122020072ACN-122020072-A

Abstract

The invention discloses a multi-feature water depth model construction and terrain evolution judgment method. The method comprises the steps of obtaining multi-time-period secondary water depth measurement data of a research area, forming a multi-period secondary depth measurement point set through systematic error correction, abnormal point elimination and unified coordinate processing, constructing a multi-feature water depth model comprising depth, depth uncertainty, effective point number, deepest depth, shallowest depth and other features based on the multi-period secondary depth measurement point set, realizing multi-feature joint updating through depth measurement point uncertainty propagation and measurement standard constraint, completing multi-period secondary multi-feature water depth model construction, further carrying out parity differential analysis on different-period secondary water depth models, and carrying out terrain evolution judgment by combining significance level and multi-feature constraint to generate a terrain evolution analysis result. The invention improves the quantification and reliability of the submarine topography evolution analysis, and has important significance in the high-dynamic submarine topography area change monitoring and ocean engineering application.

Inventors

ZHAO DINENG
Lv Yinghao
He Kuangsheng
WU ZIYIN
ZHOU JIEQIONG
SHANG JIHONG
LUO XIAOWEN
WANG MINGWEI
QIN XIAOMING
CHEN JIANBING
ZENG YULIN

Assignees

自然资源部第二海洋研究所

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (10)

1. A multi-feature water depth model construction and terrain evolution identification method is characterized by comprising the following steps: step 1, data acquisition and preprocessing, namely acquiring multi-time-period secondary original water depth measurement data in a research area, and performing systematic error elimination, abnormal noise point removal and projection coordinate unified processing on the original water depth measurement data to obtain a multi-time-period secondary sounding point set; Step 2, constructing a multi-feature water depth model and carrying out uncertainty constraint fusion, and initializing the multi-feature water depth model in a regular grid form according to spatial resolution aiming at a sounding point set of any time period, wherein the multi-feature water depth model comprises the characteristics of depth, depth uncertainty, effective point number, deepest depth, shallowest depth and the like; based on the spatial relationship between the sounding points and the grid nodes of the multi-feature water depth model, the uncertainty of the sounding points is transmitted, uncertainty constraint conditions of measurement standards are introduced, and each feature of the multi-feature water depth model is updated by adopting a Kalman filtering method to complete the construction of the multi-feature water depth model for single time period; And 3, terrain evolution judgment and result generation, namely extracting multi-feature water depth models of any two time periods, carrying out differential calculation on grid cells of the same space position to obtain depth variation, uncertainty of the depth variation and standardized statistics, carrying out significance judgment by combining the set significance level, introducing the minimum effective point number and the multi-feature of the deepest and shallowest depth values as auxiliary constraints to carry out consistency and reliability control, and finally generating a terrain evolution judgment result distribution diagram and an area statistics table.
2. The method of claim 1, wherein the multiple time period subscales are set of depth points In which, in the process, Is that A time period secondary sounding point set, 、、、、、、 Respectively is Time period of next time A plurality of sounding points, a total sounding point number, sounding point plane coordinates, depth, horizontal uncertainty and depth uncertainty, 、、 Is a natural number of the Chinese characters, And is negative.
3. The method of claim 1, wherein the uncertainty constraint of the measurement standard is a maximum allowable depth uncertainty constraint based on IHO S-44 international sea-course measurement standards, and the calculation formula is: (1) ; In the formula, Is that Time period of next time Depth of each sounding site The maximum allowable depth uncertainty calculated under the measurement grade conditions in the selected IHO S-44 international sea-course measurement standard, 、 Is a natural number of the Chinese characters, Is a negative number; 、 the uncertainty corresponding to the selected measurement level is not changed with the depth and the coefficient of the part changed with the depth are respectively obtained by consulting IHO S-44 international sea-course measurement standards.
4. The method of claim 1, wherein the multi-feature water depth model comprises at least five types of features of depth, depth uncertainty, number of valid points, deepest, and shallowest depth values, wherein: depth feature matrix , Depth uncertainty feature matrix , Effective point number characteristic matrix , Feature matrix with deepest depth , Shallowest depth feature matrix , In the formula, 、、、、 Respectively is The i-th row and j-th column depth, the depth uncertainty, the effective point number, the deepest depth and the shallowest depth of the corresponding feature matrix in the time period multi-feature water depth model are M, N, the node numbers in the row and column directions of the corresponding feature matrix of the multi-feature water depth model are respectively i, j, M, N and are non-negative integers, Is a natural number.
5. The method of claim 1, wherein said propagating uncertainty of the depth point comprises: (2) ; (3) ; In the formula, Is that Time period of next time Each sounding site Plane coordinates of [ ((II)) 、 ) Matrix nodes of ith row and j columns of multi-feature water depth model 、 ) Is a plane distance of (2); Is the sounding point Propagated to node 、 ) Is used for the depth uncertainty of (a), 、 Respectively is And the depth and horizontal uncertainty of (2), r is the spatial resolution of the multi-feature water depth model, i and j are non-negative integers, r > 0, 、 Is a natural number of the Chinese characters, <0。
6. The method of claim 1, wherein the updating each feature of the multi-feature water depth model by using a kalman filter method is as follows: (4); (5); In the formula, In order to be a kalman gain factor, Is that The node values of the ith row and the jth column of the depth uncertainty feature matrix in the time period multi-feature water depth model; 、、、 To fuse depth-finding points Before and after updating Values of the ith row and the jth column of the depth uncertainty feature matrix in the time period multi-feature water depth model; 、、 Respectively is The values of the ith row and the jth column of the feature matrix of the effective point number, the deepest depth and the shallowest depth in the time period multi-feature water depth model; Is that I, j are non-negative integers, 、 Is a natural number of the Chinese characters, <0。
7. The method of claim 6, wherein the depth variation Uncertainty of depth variation And normalized statistics The following formula is adopted for calculation: (6); In the formula, 、、、 Respectively is And Values of the ith row and jth column nodes of the depth uncertainty feature matrix in the time period secondary multi-feature water depth model, Approximately obeys normal distribution, i and j are nonnegative integers, <0, Is a natural number, and 。
8. The method of claim 7, wherein the significance determination comprises: The grid nodes are not significantly changed; When (when) To significantly flush the mesh nodes; When (when) Mesh nodes are significantly silted; Wherein, the At a level of significance Corresponding double-sided inspection threshold; the valid point and extremum consistency constraints include: (7); In the formula, In order to minimize the number of significant points, 、 Respectively is Values of the ith row and jth column nodes of the feature matrix of the effective point number in the time period multi-feature water depth model; 、、、 Respectively is The values of the ith row and jth column nodes of the characteristic matrix of the shallowest depth and the deepest depth in the time period multi-characteristic water depth model.
9. The method according to claim 1, wherein the spatial resolution has a value in the range of 0.5m-5m.
10. The method according to claim 1, wherein the method is applied in the areas of channel maintenance, port dredging, offshore engineering and subsea environment research.

Description

Multi-feature water depth model construction and topography evolution identification method Technical Field The invention belongs to the technical field of ocean mapping and submarine topography analysis, and particularly relates to a multi-feature water depth model construction and topography evolution judgment method. The method is particularly suitable for sea area water depth change analysis and dredging judgment with obvious measurement uncertainty and complex terrain evolution characteristics, and can effectively improve the quantification, reliability and engineering applicability of submarine terrain evolution judgment. Background The accurate acquisition and change monitoring of the submarine topography are basic works in marine mapping, marine engineering and submarine scientific research, and have important roles in the fields of airway maintenance, port dredging, marine engineering construction, submarine environment evolution research and the like. Particularly in high-dynamic submarine topography change areas which are obviously affected by tide, wave and human activities, such as submarine sand waves and sand ridge development sea areas, the submarine morphology can be obviously changed in a short time scale, and higher requirements are put on the water depth measurement precision and evolution judgment. At present, multi-time-period sub-sea floor topography change analysis is generally based on a regular grid water depth model or directly carries out differential comparison on depth measurement points so as to judge evolution conditions such as scouring, silting and the like of sea floor topography. However, most of the methods only pay attention to the water depth value, fail to systematically consider the uncertainty difference commonly existing in the measurement process, and easily misjudge measurement noise or systematic errors as real terrain changes, so that the reliability of the judgment result is affected. In addition, in the existing method, in the process of constructing a water depth model, single characteristics are mostly adopted to describe the submarine topography, the comprehensive utilization of auxiliary information such as the number of effective sounding points, extreme value depth and the like is lacked, consistency and reliability control on a change judgment result are difficult, and particularly, the stability of the judgment result is poor in a high-dynamic submarine topography area or under multi-source and multi-period measurement conditions. In the prior study, aiming at the problem that the secondary water depth data of multiple time periods are difficult to be compared in a coordinated manner, a multi-period water depth analysis method combining uncertainty is provided by the scholars. The method takes single beam sounding data as a research object, and builds a time sequence water depth section through uncertainty propagation and filtering updating means, so as to realize analysis and evaluation of local section topography evolution. However, the method is mainly oriented to single-beam sounding profile data, an analysis object of the method is limited to one-dimensional or quasi-one-dimensional topographic profile, the method is difficult to be suitable for modeling and space change analysis of water depth data covered in a planar mode, meanwhile, the method is focused on single water depth and uncertainty characteristics thereof, multi-dimensional characteristic information such as effective points, extreme value depths and the like cannot be comprehensively introduced in a regular grid layer, and the constraint capability of spatial consistency and result reliability of the complex submarine topographic change is limited. The existing water depth change analysis method also has the following main problems: 1. the uncertainty is insufficiently processed, namely, the existing method only considers the measurement uncertainty of the secondary water depth data in a single time period, and the uncertainty is not used as a key judgment basis, so that the judgment result is easy to be interfered by noise. 2. The multi-feature information fusion is lacking, namely, only the water depth value is used for carrying out change analysis, the auxiliary information such as the number of effective sounding points, the extreme value depth and the like is not comprehensively considered, and the consistency and the credibility of the result are not controlled. 3. The space consistency is poor, and the consistency of the identification result is difficult to maintain on the space scale by the existing method, especially in the high-dynamic submarine topography area. 4. The quantitative analysis capability is weak, the accurate quantization and visual analysis of the change area are lacking, and the engineering application requirements are difficult to meet. In view of the above problems, it is needed to provide a method capable of introducing uncertainty constraint in the mul