A three-dimensional sand ripple terrain analysis method

Abstract

The present invention provides a three-dimensional sand ripple terrain analysis method, comprising the following steps: using a terrain scanning device to obtain terrain point cloud coordinates; obtaining three-dimensional terrain point cloud coordinates in a newly created coordinate system through preprocessing; extracting sand ripple signal sequences and performing interpolation processing; then performing wavelet transform to obtain a global wavelet spectrum diagram to confirm the wavelength value of sand dunes; obtaining an optimized sand ripple signal sequence through filter processing; analyzing the optimized sand ripple signal sequence using a zero-crossing analysis method, and counting the sand ripple length and sand ripple height. The three-dimensional sand ripple terrain analysis method described in the present invention solves the shortcomings of the zero-crossing method in sand ripple statistics when sand dunes exist, and effectively overcomes the influence of overall undulating sand dunes on the zero-crossing method.

Description

Technical Field

[0001] The invention belongs to the field of marine engineering, and in particular relates to a three-dimensional sand ripple terrain analysis method. Background Art

[0002] In a complex ocean dynamic environment, the morphology of sand ripples produced by the combined action of water flow and waves is quite different from that of sand ripples under laboratory conditions. The sand ripple topography under natural conditions mainly exists in the form of a composite superposition of sand ripples and sand dunes. The geometric size of sand ripples is of great significance to the development of near-bottom hydrodynamics and bed surface. Traditional methods for statistical sand ripples mainly use extreme value calibration method and zero-crossing method. The zero-crossing method is suitable for when the sand ripple signal is distributed on both sides of the horizontal zero line, while the presence of sand dunes seriously affects the statistical effect of the zero-crossing method. At the same time, the accuracy of the statistical results of the geometric size of sand ripples is not high due to the inclusion of sand dune information. The extreme value calibration method can overcome the requirements of the zero-crossing method for the overall signal level, but the noise points and local extreme values ​​have a great influence on the sand ripple statistics, and it also cannot overcome the interference of sand dune signals. If the extreme values ​​are manually calibrated, they have the disadvantages of low efficiency and large human errors. The above methods cannot well remove the influence of sand dunes, and the statistical efficiency and accuracy are low. Summary of the invention

[0003] In view of this, the present invention aims to propose a three-dimensional sand ripple terrain analysis method, which can decompose the dune and noise information in the sand ripples and improve the application efficiency of the zero-crossing method.

[0004] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0005] A three-dimensional sand ripple terrain analysis method comprises the following steps:

[0006] S1. Obtain terrain point cloud coordinates (x', y', η') using terrain scanning equipment;

[0007] S2. Preprocessing the terrain point cloud coordinates using a terrain point cloud coordinate transformation method to obtain three-dimensional terrain point cloud coordinates (x, y, η);

[0008] S3, extract a sand ripple signal sequence with a fixed width Δy along the X-axis direction of the three-dimensional terrain point cloud coordinates (x i ,η i ), and perform equally spaced piecewise linear interpolation processing;

[0009] S4, performing wavelet transform on the interpolated sand ripple signal sequence to obtain a global wavelet spectrum, and confirming the wavelength value of the sand dune by traversing the peak points of the wavelet spectrum;

[0010] S5, inputting the dune wavelength value into the filter, removing the dune signal from the interpolated sand ripple signal sequence by a low-pass filtering method, and further removing the sand ripple noise points in the interpolated sand ripple signal sequence by a high-pass filtering method to obtain an optimized sand ripple signal sequence;

[0011] S6. The optimized sand ripple signal sequence is analyzed using the zero-crossing analysis method, and the sand ripple length and sand ripple height are counted.

[0012] Furthermore, the terrain point cloud coordinate transformation method in step S2 includes the following steps:

[0013] All point cloud coordinates are converted into the new coordinate system through translation and rotation transformations;

[0014] Adjust the average value of point cloud coordinate elevation η′ m is the total number of terrain point clouds, and the average value of the point cloud coordinate elevation η′ is adjusted The horizontal plane is the zero elevation plane η = 0, and the corresponding coordinate transformation is

[0015] All point cloud coordinates are rotated clockwise around the η axis from the positive to the negative perspective by α, where α is the angle between the original coordinate x axis and the direction of the sand pattern. The three-dimensional point cloud (x, y, η) is obtained by the coordinate transformation method. The coordinate transformation method is:

[0016]

[0017] The equally spaced piecewise linear interpolation method in step S3 includes:

[0018] For the N nodes a=x0 in the extracted sand pattern signal sequence <x1…<x N-1 = b, interpolation is performed according to the set equally spaced δx values ​​and the interpolation function f(x);

[0019] The interpolation function, f(x) satisfies the condition f(x j )=η(x j ),j=0,1,…,N-1 and in each small interval [x j ,x j+1 ],j=0,1,…,N-1;

[0020] f(x) is a polynomial, that is, Substitute η(x j ), solve the interpolation function f(x), and finally obtain the signal interpolation result η(x0+K*δx) under equal spacing, K=0, 1, 2…K-1; where K represents an integer coefficient.

[0021] Furthermore, in step S4, the method of obtaining the global wavelet spectrum by wavelet transform includes: performing wavelet transform using the morlet wavelet basis function ψ0(t). According to the convolution theorem, the wavelet transform is the inverse Fourier transform of the product:

[0022]

[0023] Where * represents complex conjugate, s is the wavelet scale to be changed, N is the number of sequence signal points, k = 0, 1, 2, ..., N-1 is the frequency index, about x n The discrete Fourier transform is:

[0024]

[0025] Angular frequency ω k Defined as:

[0026]

[0027] Define real number |W n (s)| 2 is the wavelet power density function, and the power spectrum density diagram is plotted according to the transformation frequency.

[0028] Furthermore, the morlet wavelet basis function is as follows:

[0029]

[0030] The longitudinal x-axis spatial sequence in the sand ripple signal is equivalent to the time series t, ω0 is the dimensionless frequency,

[0031] Furthermore, the specific process of obtaining the optimized sand ripple signal sequence is as follows:

[0032] S51, select a mature Chebyshev filter, use the dune wavelength value as a low-pass filter parameter, input the sand ripple signal through the filter to obtain the sand dune signal;

[0033] S52, for the noise points existing in the sand ripple signal in the sand ripple signal sequence, set a fixed noise frequency parameter of 1 / 10, and then obtain a high-frequency noise point signal through high-pass filtering;

[0034] S53, using the original sand ripple signal to subtract the sand dune signal and the high-frequency noise signal to finally obtain an optimized sand ripple signal.

[0035] Furthermore, the zero-crossing analysis method includes the following contents: a zero line is added to the optimized sand ripple signal, and when the sand ripple signal crosses the zero line from left to right upward, it is recorded as a zero-crossing point, and the height difference between the maximum elevation and the minimum elevation between adjacent zero-crossing points is the wave height of the sand ripple, and the distance between adjacent zero-crossing points is the wavelength.

[0036] Compared with the prior art, the three-dimensional sand ripple terrain analysis method described in the present invention has the following advantages:

[0037] (1) A three-dimensional sand ripple terrain analysis method described in the present invention uses a laser scanner to scan the terrain after wave and water flow to form point cloud data, and the coordinate transformation of the point cloud coordinates facilitates the processing of sand ripple sequence numbers; since the development of sand ripples is accompanied by the development of sand dunes, the sand ripples appear in an undulating shape as a whole; the zero-crossing method is a common method for sand ripple statistics. When facing the overall undulating sand ripples, many sand ripples that do not cross zero cannot be counted; the combination of wavelet transform and filtering can effectively eliminate the influence of sand dunes. The wavelet transform performs multi-scale refinement analysis on functions or signals through calculation functions such as scaling and translation, and can effectively analyze the main frequency signals contained in the sand ripple signal, and accurately determine the wavelength value of the sand dune according to the length threshold of 0.6m between the sand ripple and the sand dune wavelength; the sand dune wavelength value is used as a low-pass filter parameter in the filter to obtain the sand ripple signal after the sand dunes are eliminated, and the high-pass filter removes the influence of noise to obtain the optimized sand ripple signal; the shortcomings of the zero-crossing method in sand ripple statistics when sand dunes exist are solved, and the influence of the overall undulating sand dunes on the zero-crossing method is effectively overcome.

[0038] (2) The three-dimensional sand ripple terrain analysis method described in the present invention will effectively improve the efficiency and accuracy of sand ripple statistics compared to traditional sand ripple statistics methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0040] Figure 1 A flow chart of a three-dimensional sand ripple terrain analysis method according to an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of terrain point cloud coordinate scanning and preprocessing according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a wavelet spectrum according to an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of filtering processing according to an embodiment of the present invention;

[0044] Figure 5 Schematic diagram of the zero-crossing method for sand ripple signals according to an embodiment of the present invention. Description of the drawings:

[0046] 1. Terrain scanning equipment; 2. Scanning terrain; 3. Sand ripple signal array; 4. Terrain point cloud coordinates. DETAILED DESCRIPTION

[0047] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0048] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0049] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0050] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0051] like Figure 1 and Figure 2 As shown, a three-dimensional sand ripple terrain analysis method includes the following steps:

[0052] S1: Obtain terrain point cloud coordinates (x', y', η') using terrain scanning equipment 1;

[0053] S2: Preprocessing the terrain point cloud coordinates 4 using the terrain point cloud coordinates 4 transformation method to obtain the three-dimensional point cloud coordinates (x, y, η), wherein the specific steps include:

[0054] Terrain point cloud coordinates 4 transformation, the specific steps are to transform the point cloud coordinates into the new coordinate system through translation and rotation transformation;

[0055] The conversion process specifically includes: adjusting the average value of the point cloud coordinate elevation η′ m is the total number of terrain point clouds, and the average value of the point cloud coordinate elevation η′ is adjusted The horizontal plane is the zero elevation plane η = 0, and the corresponding coordinate transformation is

[0056] All point cloud coordinates are rotated clockwise around the η axis from the positive to the negative perspective by α, where α is the angle between the original coordinate x axis and the direction of the sand pattern. The three-dimensional point cloud (x, y, η) is obtained by the coordinate transformation method. The coordinate transformation method is:

[0057] S3: Extract a fixed width Δy sand ripple signal sequence along the x-axis from the 3D terrain point cloud (x i ,η i ), i = 0, 1, ..., n, and perform equally spaced piecewise linear interpolation processing;

[0058] In S3, the determination of the fixed width Δy value satisfies the requirement that the sand ripple signal selected within the single width of the sand ripple reaches the minimum density requirement for forming a waveform signal. In order to increase the density, Δy can be appropriately increased, but the noise point will increase accordingly.

[0059] The equally spaced piecewise linear interpolation method includes: setting an equally spaced piecewise linear interpolation of δx, for the N nodes a=x0 on the extracted signal <x1…<x N-1 = b, there exists an interpolation function f(x) that satisfies the condition f(x j )=η(x j ),j=0,1,…,N-1 and in each small interval [x j ,x j+1 ], j = 0, 1, ..., N-1, f(x) is a polynomial, that is, Substitute η(x j ), solve the interpolation function f(x), and finally obtain the signal interpolation result η(x0+K*δx) under equal spacing, K=0, 1, 2…K-1; K represents an integer coefficient.

[0060] S4: Perform wavelet transform on the interpolated sand ripple signal sequence to obtain a global wavelet spectrum, and confirm the wavelength value of the sand dune by traversing the peak points of the wavelet spectrum;

[0061] The method of obtaining a global wavelet spectrum diagram by wavelet transformation includes: performing wavelet transformation using a file for extracting point cloud coordinates, constructing an energy spectrum diagram of wavelet transformation; selecting a morlet wavelet basis function:

[0062]

[0063] The longitudinal x-axis spatial sequence in the sand ripple signal is equivalent to the time series t, ω0 is the dimensionless frequency, which is 6 here to meet the conditions. According to the convolution theorem, the wavelet transform is the inverse Fourier transform of the product:

[0064]

[0065] Where * represents complex conjugate, s is the wavelet scale to be changed, N is the number of sequence signal points, k = 0, 1, 2, ..., N-1 is the frequency index, about x n The discrete Fourier transform is:

[0066]

[0067] Angular frequency ω k Defined as:

[0068]

[0069] Since the wavelet function ψ0(t) is generally complex, the wavelet transform W n (s) is also very complex, so we can define the real number |W n (s)| 2 is the wavelet power density function, and the power spectrum density diagram is plotted according to the transformation frequency;

[0070] The wavelet spectrum is used to determine the wavelength of sand dunes. The global wavelet energy spectrum is generated by the energy spectrum. The specific method is to superimpose the energy of the wavelet power spectrum at the same frequency to finally form a global wavelet energy frequency diagram. Then, the maximum peak point of the corresponding frequency below 600 mm is found in the global wavelet energy frequency diagram. The corresponding distance length is the wavelength of the sand ripple, such as Figure 3 As shown, the wavelength value of this sand dune is 2150mm.

[0071] S5: inputting the dune wavelength value into the filter, removing the dune signal from the interpolated sand ripple signal sequence by a low-pass filtering method, and further removing the sand ripple noise points in the interpolated sand ripple signal sequence by a high-pass filtering method to obtain an optimized sand ripple signal sequence;

[0072] like Figure 4 As shown, the specific process of obtaining the optimized sand ripple signal sequence is as follows:

[0073] S51: A mature Chebyshev filter is selected, and the dune wavelength value is used as a low-pass filter parameter. The input sand ripple signal is filtered through the filter to obtain the sand dune signal.

[0074] S52: For the noise points existing in the sand pattern, the noise frequency parameter is fixed to 1 / 10, and then the high-pass filter is used to obtain the high-frequency noise signal.

[0075] S53: Subtract the sand dune signal and the noise signal from the original sand ripple signal to finally obtain an optimized sand ripple signal.

[0076] S6. The optimized sand ripple signal sequence is analyzed using the zero-crossing analysis method, and the sand ripple length and sand ripple height are counted.

[0077] like Figure 5 As shown, a zero line 5 is added to the optimized sand ripple signal. When the sand ripple signal crosses the zero line from left to right upward, it is recorded as a zero crossing point 6. The height difference between the maximum elevation and the minimum elevation between adjacent zero crossing points is the wave height of the sand ripple, and the distance between adjacent zero crossing points is the wavelength.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A three-dimensional sand ripple terrain analysis method, characterized in that The following steps are involved: S1. Obtain terrain point cloud coordinates (x', y', η') using terrain scanning equipment; S2. Preprocessing the terrain point cloud coordinates using a terrain point cloud coordinate transformation method to obtain three-dimensional terrain point cloud coordinates (x, y, η); S3, extract a sand ripple signal sequence with a fixed width Δy along the X-axis direction of the three-dimensional terrain point cloud coordinates (x i ,η i ), and perform equally spaced piecewise linear interpolation processing; S4, performing wavelet transform on the interpolated sand ripple signal sequence to obtain a global wavelet spectrum, and confirming the wavelength value of the sand dune by traversing the peak points of the wavelet spectrum; S5, inputting the dune wavelength value into the filter, removing the dune signal from the interpolated sand ripple signal sequence by a low-pass filtering method, and further removing the sand ripple noise points in the interpolated sand ripple signal sequence by a high-pass filtering method to obtain an optimized sand ripple signal sequence; S6. The optimized sand ripple signal sequence is analyzed using the zero-crossing analysis method, and the sand ripple length and sand ripple height are counted; The equally spaced piecewise linear interpolation method in step S3 includes: For the N nodes a=x0 in the extracted sand pattern signal sequence <x1…<x N-1 = b, interpolation is performed according to the set equally spaced δx values ​​and the interpolation function f(x); The interpolation function, f(x) satisfies the condition f(x j )=η(x j ),j=0,1,…,N-1 and in each small interval [x j ,x j+1 ],j=0,1,…,N-1; f(x) is a polynomial, that is, Substitute η(x j ), solve the interpolation function f(x), and finally obtain the signal interpolation result η(x0+K*δx) under equal spacing, K=0, 1, 2…K-1; where K represents an integer coefficient; The specific process of obtaining the optimized sand ripple signal sequence is as follows: S51, select a mature Chebyshev filter, use the dune wavelength value as a low-pass filter parameter, input the sand ripple signal through the filter to obtain the sand dune signal; S52, for the noise points existing in the sand ripple signal in the sand ripple signal sequence, set a fixed noise frequency parameter of 1 / 10, and then obtain a high-frequency noise point signal through high-pass filtering; S53, using the original sand ripple signal to subtract the sand dune signal and the high-frequency noise signal to finally obtain an optimized sand ripple signal; The zero-crossing analysis method includes the following contents: a zero line is added to the optimized sand ripple signal. When the sand ripple signal crosses the zero line from left to right and upward, it is recorded as a zero-crossing point. The height difference between the maximum elevation and the minimum elevation between adjacent zero-crossing points is the wave height of the sand ripple, and the distance between adjacent zero-crossing points is the wavelength.

2. A three-dimensional sand ripple terrain analysis method according to claim 1, characterized in that: The terrain point cloud coordinate transformation method in step S2 includes the following steps: All point cloud coordinates are converted into the new coordinate system through translation and rotation transformations; Adjust the average value of point cloud coordinate elevation η′ m is the total number of terrain point clouds, and the average value of the point cloud coordinate elevation η′ is adjusted The horizontal plane is the zero elevation plane η = 0, and the corresponding coordinate transformation is All point cloud coordinates are rotated clockwise around the η axis from the positive to the negative perspective by α, where α is the angle between the original coordinate x axis and the direction of the sand pattern. The three-dimensional point cloud (x, y, η) is obtained by the coordinate transformation method. The coordinate transformation method is:

3. A three-dimensional sand ripple terrain analysis method according to claim 1, characterized in that: In step S4, the method of obtaining the global wavelet spectrum by wavelet transform includes: using the morlet wavelet basis function ψ0(t) to perform wavelet transform. According to the convolution theorem, the water wave transform is the inverse Fourier transform of the product: Where (*) represents complex conjugate, s is the wavelet scale to be changed, N is the number of sequence signal points, k = 0, 1, 2, ..., N-1 is the frequency index, about x n The discrete Fourier transform is: Angular frequency ω k Defined as: Define real number |W n (s)| 2 is the wavelet power density function, and the power spectrum density diagram is plotted according to the transformation frequency.

4. A three-dimensional sand ripple terrain analysis method according to claim 3, characterized in that: The morlet wavelet basis function is as follows: The longitudinal x-axis spatial sequence in the sand ripple signal is equivalent to the time series t, and ω0 is the dimensionless frequency.

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