Reservoir underwater topography rapid modeling method based on single-beam sonar sounding technology

Abstract

The invention discloses a reservoir underwater terrain rapid modeling method based on a single-beam sonar sounding technology. The method comprises the following steps: S10, drawing a reservoir range; s20, abnormal points in the measurement points are eliminated through a DBSCAN clustering algorithm; s30, determining the size of an initial grid; s40, arranging grids, calculating elevation values of the grids, and performing interpolation calculation on the grids without the elevation values; s50, setting a grid size change amplitude # imgabs0 # and a minimum grid size # imgabs1 #, and repeating iterative interpolation until the grid size is not greater than the minimum grid; and S60, modeling the digital elevation model of the underwater topography of the reservoir. The reservoir underwater terrain rapid modeling method based on the single-beam sonar sounding technology is simple in data processing step, high in automation degree, high in calculation efficiency and precision and high in universality.

Description

Technical Field

[0001] The present invention belongs to the technical field of reservoir underwater terrain modeling, and particularly relates to a rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology. Background Technique

[0002] The underwater terrain information of a reservoir is very important for reservoir management and is important supporting data for research in aspects such as reservoir basin governance and development, flood control and drought resistance defense, and water resource development and utilization. At present, underwater terrain measurement techniques mainly include single-beam sonar sounding technology and multi-beam sonar sounding technology, supplemented by artificial water depth measurement technology near the water line. The multi-beam sonar sounding technology has high measurement accuracy and high efficiency, but is expensive. Therefore, in actual work, underwater terrain measurement mainly relies on single-beam sonar sounding technology.

[0003] The single-beam sonar sounding technology conducts measurements according to cross-sections. The spacing between measurement points in the same cross-section is relatively close, while the spacing between cross-sections is relatively far. The overall data distribution is discrete, and the number of measurement point data is limited; the amount of measured data is small and the distribution is discrete, which will affect the accuracy of underwater terrain simulation and further affect the refined numerical simulation of the water environment based on the underwater terrain.

[0004] Therefore, for single-beam sonar sounding data, optimizing interpolation calculations for discrete measurement point data and quickly constructing a refined underwater terrain is a popular research direction, which can provide a scientific basis for reservoir basin governance and development, flood control and drought resistance defense, water resource development and utilization, etc.

[0005] In addition, the optimization methods for interpolating discrete measurement point data of underwater terrain mainly focus on underwater terrain modeling of rivers and river-type reservoirs. For example, the boundary lines on both banks of the river and the thalweg line are extracted, the intermediate line between the boundary lines on both banks of the river and the thalweg line is encrypted, and it intersects with the measured cross-section line of the river, and elevation points of the river are generated by directional interpolation; logical cross-sections are added between fixed cross-sections using the spline interpolation method, longitudinal lines are added along the river direction to form a grid, and the elevation of grid points in the fixed cross-section is interpolated using the Kriging interpolation algorithm; by incorporating the characteristic terrain boundary into the river grid meshing process, elevation interpolation of the interpolated cross-section nodes is performed based on the distance weighting method; by interpolating and supplementing cross-sections in the order from upstream to downstream and from left bank to right bank, and based on the river bottom elevation of the cross nodes between adjacent measured cross-sections, the node coordinates and the river bottom elevation are encoded and matched to achieve refined interpolation calculation of the river terrain. These methods have complex data processing steps, low automation, low calculation efficiency and accuracy, and are applicable to long-strip rivers and river-type reservoirs, but not applicable to planar reservoirs.

[0006] Therefore, based on the above existing problems, this application further studies the rapid reservoir underwater terrain modeling method. Summary of the Invention

[0007] In view of the deficiencies in the above-mentioned prior art, the present invention provides a rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology. This method has simple data processing steps, high automation, high calculation efficiency and accuracy, and strong versatility.

[0008] The present invention is solved by the following technical solutions.

[0009] A rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology, comprising the following steps: S10: Draw the reservoir range; S20: Eliminate abnormal points in the measurement points through the DBSCAN clustering algorithm; S30: Determine the initial grid size; S40: Layout the grid, calculate the grid elevation value, and perform interpolation calculation on the grid without elevation value; S50: Set the variation range of the grid size , the minimum grid size is , repeat iterative interpolation until the grid size is not greater than the minimum grid; S60: Model the digital elevation model of the reservoir underwater terrain.

[0010] Preferably, in step S10, it includes the following steps: S11: Determine the reservoir range: Draw the reservoir range according to the remote sensing image data.

[0011] Preferably, in step S20, it includes the following steps: S21: Calculate the DBSCAN clustering algorithm parameters: Calculate the distance from measurement point to measurement point , that is:

[0012]

[0013] and

[0014] where the distance is calculated using the Euclidean distance formula, is all measurement points except .

[0015] Sort the distances in ascending order, and the sorted distance set is:

[0016] ;

[0017] Among them, is called -distance, which is the -th nearest distance from point to all points (except point); Calculate -distance for all measurement points to obtain the -distance set of all measurement points, as follows:

[0018]

[0019] Sort the th nearest neighbor distances of all measurement points in ascending order to obtain an ordered distance sequence; use the sorted distance sequence as the vertical axis and the corresponding number of each distance sequence as the horizontal axis to plot -distance graph; calculate the second derivative of the

[0020]

[0021] distance graph, that is: Capture the mutation points of the distance change through the second derivative of the -distance graph to determine the radius

[0022] value. For each measurement point, judge the neighboring points within the range with it as the center and radius That is, for the measurement point its neighboring points

[0023] satisfy the following formula:

[0024] Obtain the set of neighboring points of the measurement point and calculate its quantity as:

[0025]

[0026]

[0027] Statistical analysis to obtain the value with the highest frequency in the set which is the value.

[0028] Preferably, in step S20, it includes the following steps: S22: Abnormal rejection of underwater terrain data: Use the and MinPts values calculated in step S21 to judge each measurement point, mark the points that are neither core points nor boundary points as abnormal points, and reject the abnormal points.

[0029] Preferably, in step S30, it includes the following steps: S31: Determine the initial grid size: According to the average spacing between adjacent survey lines and the average value of the minimum spacing between measurement points, determine the initial grid size , is and an integer between.

[0030] Preferably, in step S40, the following steps are included: S41: Grid layout: Layout a grid of corresponding size, and the layout range covers the reservoir area.

[0031] Preferably, in step S40, the following steps are included: S42: Calculation of grid elevation values in the reservoir area: Overlay and analyze the elevation point data with the grid. If there is no elevation point in the grid, the grid is a grid to be interpolated; if there is 1 elevation point in the grid, the elevation value of the elevation point is the elevation value of the grid ; if there are multiple elevation points in the grid, the elevation values of the elevation points are , then the elevation value of this grid is the average of the elevation values of multiple elevation points in the grid, that is:

[0032]

[0033] Preferably, in step S40, the following steps are included: S43: Grid interpolation calculation: For the grids to be interpolated obtained in step S41, weighted interpolation is performed according to the elevation values of 24 adjacent grids around each grid to be interpolated, that is:

[0034]

[0035] Among them, if the adjacent grids around have elevation values, then is 1; if the adjacent grids around have no elevation values, then is 0, that is:

[0036]

[0037] is the weight of the adjacent grids around, and is the reciprocal of the distance from the adjacent grids around to the central grid.

[0038] Preferably, in step S50, the following steps are included: S51: Repeated iterative interpolation: Set the variation range of the grid size, and the minimum grid size is , then: ;

[0039] When , repeat step S40 according to the new grid size until , where the elevation point data is changed to the set of measurement point data and interpolated point data.

[0040] Preferably, in step S60, the following steps are included: S61: Digital elevation model modeling: Generate a digital elevation model of the reservoir using the set data of measurement point data and interpolation data.

[0041] Compared with the prior art, the present invention has the following beneficial effects: It provides a rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology, which uses the DBSCAN clustering algorithm to eliminate abnormal data of measurement points, improving the accuracy of underwater terrain modeling; it uses a 24-neighborhood to interpolate discrete measurement point data, which not only ensures the interpolation accuracy but also reduces the computational amount and improves the computational efficiency; it sets the grid width step size, iterates the 24-neighborhood grid size, and further encrypts the interpolation points to improve the overall underwater terrain modeling quality; the grid width step size is controllable and can be set according to actual needs to control the underwater terrain modeling accuracy and the total calculation time. This method has simple data processing steps, high automation, high computational efficiency and accuracy, and strong versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic flow chart of the rapid reservoir underwater terrain modeling method in the present invention.

[0043] Figure 2 It is a graphical schematic diagram of abnormal elimination of underwater terrain data in step S22.

[0044] Figure 3 It is a grid weight schematic diagram of grid interpolation calculation in step S43. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0046] In the following embodiments, the same or similar reference numerals represent the same or similar components or components with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention.

[0047] The following is a specific embodiment in the present application.

[0048] See Figure 1 , a rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology in the present application, includes the following steps.

[0049] S10: Draw the reservoir range: Determine and draw the reservoir range according to remote sensing image data.

[0050] S20: Eliminate abnormal points in the measurement points through the DBSCAN clustering algorithm. The specific method is as follows:

[0051] S21: Calculate the DBSCAN clustering algorithm parameters: Calculate the distance from measurement point to measurement point , that is

[0052]

[0053] And

[0054] where the distance is calculated using the Euclidean distance formula, is all the measurement points except .

[0055] Sort the distances in ascending order, and the sorted distance set is

[0056]

[0057] is called -distance, which is the distance from point to all points (except point) that is the th closest distance. Calculate the -distance for all measurement points to obtain the -distance set of all measurement points, as follows:

[0058]

[0059] Sort the th closest neighbor distances of all measurement points in ascending order to obtain an ordered distance sequence. Use the sorted distance sequence as the vertical axis and the corresponding number of each distance sequence as the horizontal axis to plot the -distance graph. Calculate the second derivative of the -distance graph, that is

[0060]

[0061] Capture the mutation points of the distance change through the second derivative of the -distance graph to determine the radius value.

[0062] For each measurement point, judge the neighboring points within the range of the radius centered on it, that is, for the measurement point , its neighboring points satisfy

[0063]

[0064]

[0065] Obtain the neighboring point set of the measurement point , and calculate its quantity as

[0066]

[0067]

[0068] The value with the highest frequency obtained from statistical analysis in the set is the value.

[0069] S22: Abnormal elimination of underwater terrain data: Use the and MinPts values calculated in step S21 to judge each measurement point, mark the points that are neither core points nor boundary points as abnormal points, and eliminate the abnormal points. As Figure 2 shown, the marked abnormal points can be deleted.

[0070] S30: Determine the initial grid size: According to the average spacing between adjacent survey lines , the average value of the minimum spacing between measurement points , determine the initial grid size , is and is an integer between.

[0071] S40: Layout the grid, calculate the elevation value of the grid, and perform interpolation calculation on the grid without elevation value; the specific steps are as follows:

[0072] S41: Layout the grid: Layout the grid of the corresponding size, and the layout range covers the reservoir area.

[0073] S42: Calculation of the elevation value of the grid in the reservoir area: Perform overlay analysis on the elevation point data and the grid. If there is no elevation point in the grid, the grid is the grid to be interpolated; if there is 1 elevation point in the grid, the elevation value of the elevation point is the elevation value of the grid ; if there are multiple elevation points in the grid, the elevation value of the elevation point is , then the elevation value of the grid is the average value of the elevation values of multiple elevation points in the grid, that is:

[0074]

[0075] S43: Grid interpolation calculation: For the grid to be interpolated obtained in step S41, perform weighted interpolation according to the elevation values of 24 adjacent grids around each grid to be interpolated, that is

[0076]

[0077] Among them, if the adjacent surrounding grid has an elevation value, then is 1; if the adjacent surrounding grid has no elevation value, then is 0, that is

[0078]

[0079] is the weight of the surrounding adjacent grids, and is the reciprocal of the distance from the surrounding adjacent grids to the central grid, as shown in the grids in Figure 3 .

[0080] S50: Set the variation range of the grid size , and the minimum grid size is . Repeat the iterative interpolation until the grid size is not greater than the minimum grid; specifically, set the variation range of the grid size , and the minimum grid size is , then ; when , repeat step S40 according to the new grid size until , where the elevation point data is changed to the set of the measurement point data and the interpolated point data.

[0081] S60: Model the digital elevation model of the underwater terrain of the reservoir: Generate the digital elevation model of the reservoir with the set data of the measurement point data and the interpolated data.

[0082] As can be seen from the above description, the present invention provides a method for quickly modeling the underwater terrain of a reservoir based on the single-beam sonar sounding technology. The DBSCAN clustering algorithm is used to eliminate the abnormal measurement point data, improving the accuracy of the underwater terrain modeling; the 24-neighborhood is used to interpolate the discrete measurement point data, which not only ensures the interpolation accuracy but also reduces the calculation amount and improves the calculation efficiency; the grid width step is set, and the grid size of the 24-neighborhood is iterated to further encrypt the interpolated points, improving the overall quality of the underwater terrain modeling; the grid width step is controllable and can be set according to actual needs to control the accuracy of the underwater terrain modeling and the total calculation time. This method has simple data processing steps, high automation degree, high calculation efficiency and accuracy, and strong versatility.

[0083] The protection scope of the present invention includes but is not limited to the above embodiments. The protection scope of the present invention is subject to the claims, and any replacement, deformation, and improvement that are easily conceivable by those skilled in the art to this technology fall within the protection scope of the present invention.

Claims

1. A rapid underwater terrain modeling method for reservoirs based on single-beam sonar sounding technology, characterized in that It includes the following steps: S10: Draw the reservoir scope; S20: Remove the abnormal points in the measurement points through the DBSCAN clustering algorithm; S30: Determine the initial grid size; S40: Layout the grid, calculate the grid elevation value, and perform interpolation calculation on the grids without elevation values; S50: Set the variation range of the grid size , the minimum grid size is , repeat the iterative interpolation until the grid size is not greater than the minimum grid; S60: Model the digital elevation model of the underwater topography of the reservoir.

2. A rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology according to claim 1, characterized in that In step S10, it includes the following steps: S11: Determine the reservoir scope: Draw the reservoir scope according to the remote sensing image data.

3. A rapid underwater terrain modeling method for reservoirs based on single-beam sonar sounding technology according to claim 2, characterized in that, In step S20, it includes the following steps: S21: Calculate the parameters of the DBSCAN clustering algorithm: Calculate the distance from the measurement point to the measurement point where the distance is calculated using the Euclidean distance formula, is all measurement points except ; Sort the distances, and after sorting, obtain a distance set; Obtain from this set of distances - distances, and calculate for all measurement points - distances, obtaining the - distance set Sort the nearest neighbor distances of all measurement points to obtain a distance sequence; Plot the distance sequence as the vertical axis and the corresponding number of each distance sequence as the horizontal axis to draw -distance graph; Calculation - Second derivative of the distance map, capturing the mutation points of distance changes through this second derivative to determine the radius value; For each measurement point, determine the neighboring points within a radius to obtain the set of neighboring points of the measurement point , calculate its quantity, and form a set ; The set obtained by statistical analysis The value with the highest frequency in it is the value.

4. A rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology according to claim 3, characterized in that, In step S20, it includes the following steps: S22: Abnormal rejection of underwater terrain data: Using the and MinPts values calculated in step S21, judge each measurement point, mark the points that are neither core points nor boundary points as abnormal points, and remove the abnormal points.

5. A rapid reservoir underwater terrain modeling method based on single-beam sonar sounding technology according to claim 4, characterized in that In step S30, it includes the following steps: S31: Determine the initial grid size: Determine the initial grid size according to the average spacing of adjacent survey lines and the average value of the minimum spacing between measurement points.

6. A rapid underwater terrain modeling method for a reservoir based on single-beam sonar sounding technology according to claim 5, characterized in that In step S40, it includes the following steps: S41: Layout the grid: Layout grids of corresponding size, and the layout range covers the reservoir area; S42: Calculate the grid elevation value of the reservoir area: Overlay and analyze the elevation point data with the grid. If there is no elevation point in the grid, the grid is the grid to be interpolated; if there is one elevation point in the grid, the elevation value of the elevation point is the elevation value of the grid; if there are multiple elevation points in the grid, the elevation value of the grid is the average value of the elevation values of multiple elevation points in the grid; S43: Grid interpolation calculation: For the grids to be interpolated obtained in step S41, perform weighted interpolation according to the elevation values of several adjacent grids around each grid to be interpolated.

7. A rapid underwater terrain modeling method for reservoirs based on single-beam sonar sounding technology according to claim 6, characterized in that, In step S50, it includes the following steps: S51: Repeated iterative interpolation: Set the variation range of the grid size , the minimum grid size is , then: When occurs, repeat step S40 according to the new grid size until , where the elevation point data is changed to the set of measurement point data and interpolated point data.

8. A rapid underwater terrain modeling method for a reservoir based on single-beam sonar sounding technology according to claim 7, characterized in that, In step S60, it includes the following steps: S61: Digital elevation model modeling: Generate the digital elevation model of the reservoir with the set data of the measurement point data and the interpolation data.

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