Sediment particle size secondary distribution extraction method, device, equipment and storage medium

Abstract

The present invention relates to a method, apparatus, device, and storage medium for extracting sediment particle size secondary distribution. The method comprises: establishing a sediment particle size distribution model that integrates two probability density functions based on frequency data of the sediment particle size distribution; obtaining sediment particle size distribution model parameters based on the sediment particle size distribution model that integrates the two probability density functions; and determining particle size secondary distribution statistical parameters based on the sediment particle size distribution model parameters. The present invention solves the existing problem of extracting detrital sediment particle size secondary distribution, improving the accuracy and effectiveness of particle size secondary distribution extraction.

Description

Technical Field

[0001] The present invention relates to the technical fields of sedimentology, geology, hydraulics and geography for studying sediment particle size distribution, and in particular to a method, device, equipment and storage medium for extracting sediment particle size secondary distribution. Background Art

[0002] Detrital sediment particles are driven by complex sedimentary dynamics and controlled by sedimentary hydrodynamic conditions, sedimentary processes, and sedimentary environments. They have a particle size distribution composed of volume or weight percentages of particles of different sizes. They are not only powerful evidence for studying modern sedimentary environments, but also the most commonly used data for inferring paleoclimate and paleoenvironment.

[0003] In recent years, non-traditional methods based on mathematical algorithms such as fractals, clustering, and end-members have been developed to analyze the morphological characteristics of particle-size distribution frequency curves in different sedimentary environments. The particle-size distribution of clastic sediments is the final product of multiple sedimentary processes under specific sedimentary environments and hydrodynamic conditions. In other words, the particle-size distribution is the superposition of multiple secondary particle-size distributions from different sedimentary processes, and generally exhibits a bimodal characteristic on the particle-size distribution frequency curve.

[0004] In order to mathematically extract these secondary particle size distributions related to sedimentary hydrodynamic conditions, sedimentary processes, and sedimentary environments, the prior art typically uses a number of probability statistical distribution models, such as lognormal, loghyperbolic, logpartial Laplace, gamma, Weibull, and partial normal probability density functions, to decompose the particle size frequency curve. However, using only a single probability density function makes it difficult to accurately extract the secondary particle size distributions from different sedimentary processes. Therefore, a new method that integrates two probability density functions is urgently needed to extract the sediment particle size frequency curve and solve the problem of inaccurate extraction of secondary particle size distributions in the prior art. Summary of the Invention

[0005] In view of this, it is necessary to provide a sediment particle size secondary distribution extraction method, device, equipment and storage medium to solve the problem of inaccurate particle size secondary distribution extraction in the prior art.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a method for extracting sediment particle size secondary distribution, comprising:

[0007] According to the frequency data of sediment particle size distribution, a sediment particle size distribution model integrating two probability density functions is established.

[0008] According to the sediment particle size distribution model that integrates two probability density functions, the sediment particle size distribution model parameters are obtained;

[0009] The statistical parameters of the secondary distribution of particle size are determined based on the parameters of the sediment particle size distribution model.

[0010] Optionally, the two probability density functions include a skewed normal distribution probability density function and a Weibull distribution probability density function;

[0011] The probability density function of the skew normal distribution is: Among them, f SN represents the probability density function of skew normal distribution, x represents the particle size, μ1 represents the first position parameter, σ1≥0 represents the first scale parameter, λ1 represents the first shape parameter, and t is a parameter introduced for the convenience of calculation and has no actual physical meaning;

[0012] The probability density function of the Weibull distribution is: Among them, f w represents the probability density function of Weibull distribution, x represents the particle size, σ2≥0 represents the second scale parameter, and λ2 represents the second shape parameter.

[0013] Optionally, the sediment particle size distribution model that integrates the skew normal distribution probability density function and the Weibull distribution probability density function is: f = c1f SN +c2f w , where f represents the sediment particle size distribution model, c1 represents the first percentage of the particle size sub-distribution that obeys the skew normal distribution, c2 represents the second percentage of the particle size sub-distribution that obeys the Weibull distribution, and c1+c2=1, c1≥0, c2≥0.

[0014] Optionally, sediment particle size distribution model parameters include:

[0015] a first location parameter, a first scale parameter, a first shape parameter of the probability density function of the skew normal distribution, and a first percentage of the particle size sub-distribution that obeys the skew normal distribution;

[0016] The second scale parameter, the second shape parameter of the Weibull distribution probability density function and the second percentage of the particle size secondary distribution obeying the Weibull distribution.

[0017] Optionally, according to the sediment particle size distribution model that integrates the two probability density functions, sediment particle size distribution model parameters are obtained, including:

[0018] The least squares method is used to solve the sediment particle size distribution model parameters. The mathematical form of the least squares method is: Among them, F(x i ) represents the theoretical value of the i-th particle size of the objective function, y(x i ) represents the actual measured value of the i-th particle size of the objective function, n represents the number of particle size intervals, i = 1, 2…n.

[0019] Optionally, the particle size secondary distribution statistical parameters include a first mean, a first variance, a first skewness, and a first kurtosis of the particle size secondary distribution that obeys a skewed normal distribution. The particle size secondary distribution statistical parameters are determined according to the sediment particle size distribution model parameters, including:

[0020] Determine the first mean, first variance, first skewness, and first kurtosis of the particle size secondary distribution that obeys the skewed normal distribution. The calculation method is: Among them, M1, V1, S1, and K1 represent the first mean, first variance, first skewness, and first kurtosis of the particle size secondary distribution that obeys the skewed normal distribution, respectively. γ is a parameter introduced for the convenience of calculation and has no actual physical meaning.

[0021] Optionally, the particle size secondary distribution statistical parameters further include a second mean, a second variance, a second skewness, and a second kurtosis of the particle size secondary distribution that obeys the Weibull distribution. Determining the particle size secondary distribution statistical parameters based on the sediment particle size distribution model parameters further includes:

[0022] Determine the second mean, second variance, second skewness, and second kurtosis of the particle size secondary distribution that obeys the Weibull distribution. The calculation method is:

[0023] Wherein, M2, V2, S2, and K2 represent the second mean, second variance, second skewness, and second kurtosis of the particle size secondary distribution obeying the Weibull distribution, respectively, and Γ represents the gamma function.

[0024] In a second aspect, the present invention further provides a device for extracting sediment particle size secondary distribution, comprising:

[0025] Establishing a module for establishing a sediment particle size distribution model that integrates two probability density functions based on the frequency data of the sediment particle size distribution;

[0026] An acquisition module is used to obtain sediment particle size distribution model parameters according to a sediment particle size distribution model that integrates two probability density functions;

[0027] The determination module is used to determine the statistical parameters of the secondary distribution of particle size according to the sediment particle size distribution model parameters.

[0028] In a third aspect, the present invention also provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned sediment particle size secondary distribution extraction method are implemented.

[0029] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, which implements the steps of the above-mentioned sediment particle size secondary distribution extraction method when executed by a processor.

[0030] Compared with the prior art, the present invention has the following beneficial effects: the present invention establishes a sediment particle size distribution model that integrates two probability density functions based on the frequency data of the sediment particle size distribution; obtains sediment particle size distribution model parameters based on the sediment particle size distribution model; and finally, determines the statistical parameters of the sediment particle size secondary distribution based on the sediment particle size distribution model parameters, thereby improving the accuracy and effectiveness of the sediment particle size secondary distribution extraction and further exploring the sedimentological information hidden in the particle size distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flow chart of a method for extracting sediment particle size secondary distribution provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of a particle size secondary distribution extraction result provided by an embodiment of the present invention;

[0033] Figure 3 A schematic structural diagram of a sediment particle size secondary distribution extraction device provided by an embodiment of the present invention;

[0034] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0036] A specific embodiment of the present invention, as Figure 1 As shown, a method for extracting sediment particle size secondary distribution is disclosed, comprising:

[0037] Step S101: Based on the frequency data of sediment particle size distribution, a sediment particle size distribution model is established by integrating two probability density functions.

[0038] In one embodiment of the present invention, the two probability density functions include a skew normal distribution probability density function and a Weibull distribution probability density function;

[0039] Specifically, the probability density function of the skew normal distribution is: Among them, f SN represents the probability density function of skew normal distribution, x represents the particle size, μ1 represents the first position parameter, σ1≥0 represents the first scale parameter, λ1 represents the first shape parameter, and t is a parameter introduced for the convenience of calculation and has no actual physical meaning.

[0040] The probability density function of the Weibull distribution is: Among them, f w represents the probability density function of Weibull distribution, x represents the particle size, σ2≥0 represents the second scale parameter, and λ2 represents the second shape parameter.

[0041] Furthermore, the sediment particle size distribution model that integrates the skew normal distribution probability density function and the Weibull distribution probability density function is: f = c1f SN +c2f w , where f represents the sediment particle size distribution model, c1 represents the first percentage of the particle size sub-distribution that obeys the skew normal distribution, c2 represents the second percentage of the particle size sub-distribution that obeys the Weibull distribution, and c1+c2=1, c1≥0, c2≥0.

[0042] Step S102: obtaining sediment particle size distribution model parameters according to the sediment particle size distribution model that integrates the two probability density functions.

[0043] Specifically, the sediment particle size distribution model parameters include: the first position parameter, the first scale parameter, the first shape parameter and the first percentage of the particle size sub-distribution that obeys the skew normal distribution probability density function; and the second scale parameter, the second shape parameter and the second percentage of the particle size sub-distribution that obeys the Weibull distribution probability density function, a total of 7 parameters.

[0044] Furthermore, according to the sediment particle size distribution model that integrates the two probability density functions, the sediment particle size distribution model parameters are obtained, including:

[0045] The least squares method is used to solve the sediment particle size distribution model parameters. The mathematical form of the least squares method is: Among them, F(x i ) represents the theoretical value of the i-th particle size of the objective function, y(x i ) represents the actual measured value of the i-th particle size of the objective function, n represents the number of particle size intervals, i = 1, 2…n.

[0046] In a specific embodiment of the present invention, exemplarily, a sediment particle size distribution model is established based on the frequency data of the sediment particle size distribution, and the least squares method is used to calculate the sediment particle size distribution model parameters: the first percentage c1=68.708% of the particle size secondary distribution that obeys the skew normal distribution, the first position parameter μ1=0.500, the first scale parameter σ1=0.766, and the first shape parameter λ1=2.439; the second percentage c2=31.292% of the particle size secondary distribution that obeys the Weibull distribution, the second scale parameter σ2=5.349, and the second shape parameter λ2=8.944.

[0047] According to the above sediment particle size distribution model parameters, the particle size secondary distribution of skew normal distribution and the particle size secondary distribution of Weibull distribution are further obtained, as shown in Figure 2 As shown in Table 1, the parameter calculation of the sediment particle size distribution model is now complete.

[0048] Step S103: Determine the statistical parameters of the secondary distribution of particle size according to the sediment particle size distribution model parameters.

[0049] Specifically, the statistical parameters of the particle size sub-distribution include the first mean, first variance, first skewness, and first kurtosis of the particle size sub-distribution that obeys the skewed normal distribution. The statistical parameters of the particle size sub-distribution are determined based on the sediment particle size distribution model parameters, including:

[0050] Determine the first mean, first variance, first skewness, and first kurtosis of the particle size secondary distribution that obeys the skewed normal distribution. The calculation method is: Among them, M1, V1, S1, and K1 represent the first mean, first variance, first skewness, and first kurtosis of the particle size secondary distribution that obeys the skewed normal distribution, respectively. γ is a parameter introduced for the convenience of calculation and has no actual physical meaning.

[0051] In addition, the particle size secondary distribution statistical parameters also include the second mean, second variance, second skewness and second kurtosis of the particle size secondary distribution that obeys the Weibull distribution. According to the sediment particle size distribution model parameters, the particle size secondary distribution statistical parameters are determined, which also include:

[0052] Determine the second mean, second variance, second skewness, and second kurtosis of the particle size secondary distribution that obeys the Weibull distribution. The calculation method is:

[0053] Wherein, M2, V2, S2, and K2 represent the second mean, second variance, second skewness, and second kurtosis of the particle size secondary distribution obeying the Weibull distribution, respectively, and Γ represents the gamma function.

[0054] In a specific embodiment of the present invention, for example, according to the two calculation methods of the above-mentioned particle size secondary distribution, the first mean, first variance, first skewness and first kurtosis of the skewed normal distribution are calculated to be M1=2.712φ, V1=0.512, S1=0.024, K1=0.006, respectively, and the first mean, first variance, first skewness and first kurtosis of the Weibull distribution are calculated to be M2=5.064φ, V2=0.458, S2=-0.588, K2=0.448, respectively, where φ represents particle size. At this point, the statistical parameters of the sediment particle size secondary distribution are calculated. Specific statistical parameters are as follows:

[0055] As shown in Table 1.

[0056]

[0057] Table 1

[0058] Note: c is the percentage of the secondary particle size distribution, σ is the scale parameter of the sediment particle size distribution model, λ is the shape parameter of the sediment particle size distribution model, μ is the position parameter of the sediment particle size distribution model, M is the mean of the secondary particle size distribution, V is the variance of the secondary particle size distribution, S is the skewness of the secondary particle size distribution, and K is the kurtosis of the secondary particle size distribution.

[0059] The present invention establishes a sediment particle size distribution model that integrates two probability density functions based on the frequency data of sediment particle size distribution. Sediment particle size distribution model parameters are obtained based on the sediment particle size distribution model. Finally, based on the sediment particle size distribution model parameters, particle size secondary distribution statistical parameters such as mean, variance, skewness and kurtosis of the sediment particle size secondary distribution are determined. This improves the accuracy and effectiveness of sediment particle size secondary distribution extraction and further explores the sedimentological information hidden in the particle size distribution.

[0060] Based on the above sediment particle size secondary distribution extraction method, an embodiment of the present invention further provides a sediment particle size secondary distribution extraction device, which corresponds one-to-one to the sediment particle size secondary distribution extraction method in the above embodiment.

[0061] A specific embodiment of the present invention, as Figure 3 As shown, a device for extracting sediment particle size secondary distribution is disclosed, including: an establishment module 301, an acquisition module 302 and a determination module 303.

[0062] Establishing module 301, establishing a sediment particle size distribution model that integrates two probability density functions based on the frequency data of the sediment particle size distribution;

[0063] An acquisition module 302 is used to acquire sediment particle size distribution model parameters according to a sediment particle size distribution model that integrates two probability density functions;

[0064] The determination module 303 is used to determine the statistical parameters of the secondary particle size distribution according to the sediment particle size distribution model parameters.

[0065] The specific limitations of the sediment particle size secondary distribution extraction device can be found in the limitations of the sediment particle size secondary distribution extraction method described above and will not be further elaborated here. Each module in the aforementioned sediment particle size secondary distribution extraction device can be implemented in whole or in part via software, hardware, or a combination thereof. Each of the aforementioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each of the aforementioned modules.

[0066] Based on the above-mentioned sediment particle size secondary distribution method, an embodiment of the present invention also provides an electronic device, including: a processor and a memory, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps in the sediment particle size secondary distribution method as described in the above-mentioned embodiments are implemented.

[0067] Figure 4 4 shows a schematic diagram of the structure of an electronic device 400 suitable for implementing an embodiment of the present invention. The electronic devices in the embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0068] The electronic device includes: a memory and a processor, wherein the processor here may be referred to as the processing device 401 below, and the memory may include at least one of the read-only memory (ROM) 402, the random access memory (RAM) 403, and the storage device 408 below, as shown below:

[0069] like Figure 4 As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. Various programs and data required for the operation of the electronic device 400 are also stored in the RAM 403. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0070] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0071] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present invention are performed.

[0072] Based on the above-mentioned sediment particle size secondary distribution method, an embodiment of the present invention also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the sediment particle size secondary distribution method as described in the above-mentioned embodiments.

[0073] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0074] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for extracting sediment particle size secondary distribution, characterized in that: include: According to the frequency data of the sediment particle size distribution, a sediment particle size distribution model is established by integrating two probability density functions, wherein the two probability density functions include a skew normal distribution probability density function and a Weibull distribution probability density function. The sediment particle size distribution model integrating the skew normal distribution probability density function and the Weibull distribution probability density function is: ,in, represents the sediment particle size distribution model, is the probability density function of the skew normal distribution, is the probability density function of the Weibull distribution, Indicates the first percentage of the secondary distribution of particle size that obeys the skew normal distribution, represents the second percentile of the Weibull-distributed particle size sub-distribution, and ; According to the sediment particle size distribution model that integrates the two probability density functions, sediment particle size distribution model parameters are obtained, wherein the sediment particle size distribution model parameters include: a first position parameter, a first scale parameter, a first shape parameter, and a first percentage of a particle size sub-distribution that obeys the skew normal distribution probability density function; a second scale parameter, a second shape parameter, and a second percentage of a particle size sub-distribution that obeys the Weibull distribution probability density function; Determining particle size secondary distribution statistical parameters based on the sediment particle size distribution model parameters; Wherein, obtaining sediment particle size distribution model parameters according to the sediment particle size distribution model that integrates the two probability density functions includes: The sediment particle size distribution model parameters are solved using the least squares method, and the mathematical form of the least squares method is: ,in, Represents the objective function The theoretical value of particle size, Represents the objective function The actual measured value of the particle size, Indicates the number of granularity intervals, .

2. The method according to claim 1, characterized in that The skewed normal distribution probability density function is: ,in, represents the probability density function of the skew normal distribution, x Indicates particle size, μ 1 represents the first position parameter, σ 1≥0 represents the first scale parameter, λ 1 represents the first shape parameter, The parameters introduced for the convenience of calculation have no actual physical meaning; The Weibull distribution probability density function is: ,in, represents the probability density function of the Weibull distribution, x Indicates particle size, σ 2≥0 indicates the second scale parameter, λ 2 represents the second shape parameter.

3. The method according to claim 1, wherein the particle size sub-distribution statistical parameters include a first mean, a first variance, a first skewness, and a first kurtosis of a particle size sub-distribution that obeys a skewed normal distribution, and determining the particle size sub-distribution statistical parameters based on the sediment particle size distribution model parameters comprises: Determine the first mean, first variance, first skewness, and first kurtosis of the particle size secondary distribution that obeys the skewed normal distribution. The calculation method is: ,in, They represent the first mean, first variance, first skewness and first kurtosis of the particle size secondary distribution that obeys the skew normal distribution, The parameters introduced for the convenience of calculation have no actual physical meaning.

4. The method according to claim 1, wherein the particle size sub-distribution statistical parameters further include a second mean, a second variance, a second skewness, and a second kurtosis of the particle size sub-distribution obeying a Weibull distribution, and wherein determining the particle size sub-distribution statistical parameters based on the sediment particle size distribution model parameters further includes: Determine the second mean, second variance, second skewness, and second kurtosis of the particle size secondary distribution that obeys the Weibull distribution. The calculation method is: in, They represent the second mean, second variance, second skewness and second kurtosis of the particle size secondary distribution that obeys the Weibull distribution, represents the gamma function.

5. A device for extracting sediment particle size secondary distribution, used to perform the sediment particle size secondary distribution extraction method according to any one of claims 1 to 4, characterized in that: include: An establishment module is used to establish a sediment particle size distribution model that integrates two probability density functions based on the frequency data of the sediment particle size distribution; An acquisition module, configured to acquire sediment particle size distribution model parameters according to the sediment particle size distribution model that integrates the two probability density functions; The determination module is used to determine the statistical parameters of the secondary distribution of particle size according to the sediment particle size distribution model parameters.

6. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the sediment particle size secondary distribution extraction method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the sediment particle size secondary distribution extraction method according to any one of claims 1 to 4 are implemented.

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