Stability analysis method and device for full-node environment noise collection and storage medium

Abstract

The invention discloses a stability analysis method and device for all-node environment noise collection and a storage medium, and relates to the technical field of signal processing, and the key points of the technical scheme are that the method comprises the steps: obtaining all-node environment noise, and extracting a to-be-detected sequence from the all-node environment noise; fourier transform and phase replacement are carried out on the to-be-detected sequence, and a plurality of replacement sequences are obtained; analyzing and processing the to-be-detected sequence and the plurality of alternative sequences to respectively obtain a two-dimensional scalar for representing the time-frequency characteristics of the to-be-detected sequence and a two-dimensional scalar set for representing the time-frequency characteristics of the plurality of alternative sequences; and comparing the two-dimensional scalar quantity representing the time-frequency characteristic of the to-be-detected sequence with the two-dimensional scalar quantity set representing the time-frequency characteristic of the alternative sequence, and judging the stability of the to-be-detected sequence. By means of the stability analysis method, stability testing can be conducted on the signals, and compared with other methods, the stability testing result of the method is more visual and effective, and the application range is wider.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method, device and storage medium for analyzing the stability of environmental noise collected by all nodes. Background Art

[0002] This section is intended to provide a background or context to the embodiments recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] Stationarity is a very important property in the field of time series analysis. If a time series is stationary, that is, the statistics of the time series have time-invariant characteristics, this will bring many conveniences to subsequent processing. However, not all time series are stationary. For non-stationary time series, their characteristics change over time. If the method of processing stationary time series is used to analyze the characteristics of non-stationary time series, it is likely to get wrong conclusions. Therefore, when analyzing and processing time series, it is often necessary to first judge the stationarity of the series, and on this basis, choose the appropriate method.

[0004] At present, most studies believe that seismic exploration random noise is a stationary random process. Previous researchers have not conducted scientific and systematic research on the stationarity of seismic exploration random noise. Most of them are based on intuitive observations and to simplify the problem, assuming that random noise has stationarity. On this basis, a series of noise reduction algorithms are proposed. In practical applications, the above methods have achieved certain results, but for seismic records with drastic changes in random noise properties, the denoising results are not satisfactory. This also indirectly shows that seismic exploration random noise is not a strict stationary random process, and previous researchers have limited understanding of it. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes a stationarity analysis method, device and storage medium for full-node acquisition of environmental noise, which can accurately evaluate the stationarity of the signal. The result of the stationarity judgment of the environmental noise is intuitive and effective, which is conducive to the next step of utilizing the characteristics of the environmental noise to obtain high-quality seismic data.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention includes four aspects.

[0007] In a first aspect, a method for analyzing the stationarity of environmental noise collected by all nodes is provided, comprising the following steps:

[0008] Obtain the environmental noise collected by all nodes and extract the sequence to be tested from it;

[0009] Performing Fourier transformation and phase replacement on the sequence to be tested to obtain a plurality of replacement sequences;

[0010] Analyzing and processing the sequence to be tested and the plurality of alternative sequences, respectively obtaining a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences;

[0011] The two-dimensional scalar representing the time-frequency characteristics of the sequence to be tested is compared with the set of two-dimensional scalars representing the time-frequency characteristics of the alternative sequence to determine the stationarity of the sequence to be tested.

[0012] In some embodiments, the Fourier transform and phase replacement are performed on the sequence to be tested to obtain a plurality of replacement sequences, including:

[0013] Perform Fourier transform on the sequence to be tested to obtain the expression of the amplitude spectrum and phase spectrum of the sequence to be tested;

[0014] Using the phase spectrum of the substitute sequence to be tested that obeys the uniform distribution within, a substitute sequence is obtained;

[0015] Repeat the above steps to generate several alternative sequences of the sequence to be tested.

[0016] In some embodiments, the analyzing and processing the sequence to be tested and the plurality of alternative sequences to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences respectively include:

[0017] Analyzing and processing the sequence to be tested to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested;

[0018] The plurality of substitute sequences are analyzed and processed in sequence in the same manner as the sequence to be tested, so as to obtain a two-dimensional scalar set for representing the time-frequency characteristics of the plurality of substitute sequences.

[0019] In some embodiments, the analyzing and processing the sequence to be tested to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested includes:

[0020] Determining the time-frequency spectrum of the sequence to be tested;

[0021] Normalizing the time-frequency spectrum of the sequence to be tested;

[0022] According to the normalized time-frequency spectrum, determining the characteristic quantity of the signal in the sequence to be tested that changes with time and the local frequency characteristic quantity of the signal;

[0023] The characteristic quantity of the signal in the sequence to be tested that varies with time and the local frequency characteristic quantity of the signal are defined as time-frequency characteristics;

[0024] The time-frequency characteristics of the sequence to be measured are represented by a two-dimensional scalar, and the two-dimensional scalar of the time-frequency characteristics of the sequence to be measured is obtained.

[0025] In some embodiments, determining the time-frequency spectrum of the sequence to be tested includes:

[0026] The k-order Hermite function is obtained through the Hermite polynomial;

[0027] Calculate the time-frequency spectrum according to the k-order Hermite function;

[0028] Determine the time-frequency spectrum of the sequence to be tested according to the time-frequency spectrum calculated by the k-order Hermite function;

[0029] The preset length of the k-order Hermite function is used to determine the frequency spectrum of the sequence to be tested at a specific time point in discretized time.

[0030] In some embodiments, the formula for obtaining the k-order Hermite function through the Hermite polynomial is:

[0031]

[0032] In the formula, k represents the order; k! represents the factorial of k; H k (t) = 2tH k-1 (t)-2(k-2)H k-2 (t), k≥2, H0(t)=1, H1(t=2t.; t represents the time;

[0033] The calculation formula for calculating the time-frequency spectrum of the sequence to be tested according to the k-order Hermite function is:

[0034]

[0035] In the formula, Represents the time-frequency spectrum calculated by the k-order Hermite function:

[0036] In some embodiments, the k-order Hermite window function h is used to determine the frequency spectrum of the sequence to be tested at a specific time point by using a preset length of the k-order Hermite function to discretize the time. k The length of (t) is T h , the discretized time is t m , m=1…,M.; the frequency spectrum of the sequence to be tested at a specific time point is expressed as:

[0037]

[0038] Where, t mis the discretized time; x(n) is the sequence to be tested; k=1,...,N, N represents the number of sample points contained in the sequence to be tested; n is the serial number of the sample point of the sequence to be tested; j is the imaginary unit; f is the frequency; π is the circumference of a circle.

[0039] In some embodiments, the formula for performing Fourier transform on the sequence to be tested is:

[0040]

[0041] The formula for converting it into the expression of amplitude spectrum and phase spectrum is:

[0042] X(k)=|F(k)|e jφ(k) ,k=1,…,N.

[0043] The processing formula for replacing the phase spectrum of the sequence to be measured by using ψ(k) that obeys a uniform distribution within [-π, π] to obtain the replacement sequence is:

[0044]

[0045] Wherein, N represents the number of samples contained in the sequence to be tested; k represents the sample point number; F(k) represents the amplitude spectrum; ψ(k) represents the phase spectrum; n is the sample point number of the sequence to be tested; j is the imaginary unit; f is the frequency; and π is the circumference of a circle.

[0046] In some embodiments, comparing the two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the replacement sequence to determine the stationarity of the sequence to be tested includes:

[0047] The SVDD method is used to compare the differences between the two-dimensional scalar set of the time-frequency characteristics of the test sequence and the two-dimensional scalar set of the time-frequency characteristics of the replacement sequence;

[0048] When the comparison result is less than the judgment threshold, the sequence signal to be tested is determined to be a stable signal;

[0049] When the comparison result is greater than the judgment threshold, the sequence signal to be tested is determined to be a non-stationary signal.

[0050] In a second aspect, a device for analyzing the stability of environmental noise collected by all nodes is provided, comprising:

[0051] The data extraction module obtains the environmental noise collected by all nodes and extracts the sequence to be tested from it;

[0052] A data processing module performs Fourier transform and phase replacement on the sequence to be tested to obtain a plurality of replacement sequences;

[0053] A data analysis module, used to analyze and process the sequence to be tested and the plurality of alternative sequences, and obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences;

[0054] The comparison evaluation module is used to compare the two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the alternative sequence to determine the stationarity of the sequence to be tested.

[0055] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the aforementioned stationarity analysis method when executing the computer program.

[0056] In a fourth aspect, a computer-readable storage medium is provided, and when the computer program is executed by a processor, the steps of the aforementioned stationarity analysis method are implemented.

[0057] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0058] The present application provides a method, device and storage medium for analyzing the stability of environmental noise collected by all nodes. The analysis method includes: obtaining the environmental noise collected by all nodes and extracting a sequence to be tested from it; performing Fourier transform and phase replacement on the sequence to be tested to obtain several alternative sequences; analyzing and processing the sequence to be tested and several alternative sequences to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a two-dimensional scalar set for representing the time-frequency characteristics of several alternative sequences; comparing the two-dimensional scalar representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the alternative sequence to determine the stability of the sequence to be tested. Through this stability analysis method, it is possible to compare the difference in time-frequency characteristics between the sequence to be tested and the Fourier transform alternative sequence based on the method combining Fourier transform as an alternative sequence with time-frequency analysis, that is, to compare whether the energy of the two is significantly different in the time-frequency domain, and then perform a stability test. Compared with other methods, the stability test results of this method are more intuitive and effective, and have a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings;

[0060] Figure 1 An exemplary flow chart of a method for analyzing the stationarity of environmental noise collected by all nodes provided in the first embodiment of the present invention;

[0061] Figure 2 In the embodiment of the present invention, Figure 1An exemplary flow chart of step S2 shown in;

[0062] Figure 3 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S3 shown in FIG.

[0063] Figure 4 In the embodiment of the present invention, Figure 3 An exemplary flow chart of step S31 shown in FIG.

[0064] Figure 5 In the embodiment of the present invention, Figure 4 An exemplary flow chart of step S311 shown in FIG.

[0065] Figure 6 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S4 shown in;

[0066] Figure 7 In the embodiment of the present invention, the analysis method is used to analyze the Gaussian signal at T h =Stability test results when 0.2T;

[0067] Figure 8 The test results of Gaussian signals under different Hermite window function lengths using the analysis method in the embodiment of the present invention are shown in FIG.

[0068] Fig. 9 In the embodiment of the present invention, the chirp signal is analyzed by the analysis method in T h =Stability test results when 0.3T;

[0069] Fig.10 The test results of chirp signals under different Hermite window function lengths using the analysis method in the embodiment of the present invention are as follows;

[0070] Fig.11 This is a schematic block diagram of a device for analyzing the stability of environmental noise collected by all nodes provided in the second embodiment of the present invention;

[0071] Fig.12 A schematic block diagram of an electronic device provided in Embodiment 3 of the present invention;

[0072] Fig.13 This is a schematic diagram of a computer-readable storage medium provided in Embodiment 4 of the present invention.

[0073] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0074] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0075] Embodiment 1:

[0076] Most studies believe that random noise in seismic exploration is a stationary random process. Previous researchers have not conducted scientific and systematic research on the stationarity of random noise in seismic exploration. Most of them are based on intuitive observations and to simplify the problem, assuming that random noise is stationary. On this basis, a series of noise reduction algorithms have been proposed. In practical applications, the above methods have achieved certain results, but for seismic records with drastic changes in random noise properties, the denoising results are not satisfactory. This also indirectly shows that random noise in seismic exploration is not a strictly stationary random process, and previous researchers have limited understanding of it.

[0077] The present application embodiment discloses a method for analyzing the stability of environmental noise collected by all nodes, such as Figure 1 As shown, the analysis method includes: obtaining the environmental noise collected by all nodes and extracting the sequence to be tested from it; performing Fourier transform and phase replacement on the sequence to be tested to obtain several alternative sequences; analyzing and processing the sequence to be tested and several alternative sequences, respectively obtaining a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a two-dimensional scalar set for representing the time-frequency characteristics of several alternative sequences; comparing the two-dimensional scalar representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the alternative sequence to determine the stationarity of the sequence to be tested. Through this stationarity analysis method, it is possible to compare the difference in time-frequency characteristics between the sequence to be tested and the Fourier transform alternative sequence based on the method combining Fourier transform as an alternative sequence with time-frequency analysis, that is, to compare whether the energy of the two is significantly different in the time-frequency domain, and then perform a stationarity test. Compared with other methods, the stationarity test results of this method are more intuitive and effective, and have a wider range of applications.

[0078] A method for analyzing the stationarity of environmental noise collected by all nodes provided by at least one embodiment of the present disclosure can be implemented in the form of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, tablet computer, laptop computer, desktop computer, network server, etc., so as to achieve accurate stationarity evaluation. The result of the stationarity judgment of the environmental noise is intuitive and effective, which is conducive to the next step of utilizing the characteristics of the environmental noise to obtain high-quality seismic data.

[0079] Reference below Figure 1 As shown, a stationarity analysis method for collecting environmental noise at all nodes provided by at least one embodiment of the present disclosure is described, and the stationarity analysis method includes steps S1 to S4.

[0080] S1. Obtain the environmental noise collected by all nodes and extract the sequence to be tested from it.

[0081] In some embodiments, the extracted sequence to be tested is recorded as x(n), n=1, ..., N, where N represents the number of samples included in the sequence to be tested; and n is the serial number representing the sample of the sequence to be tested.

[0082] S2. Perform Fourier transform and phase replacement on the sequence to be tested to obtain several replacement sequences.

[0083] In some embodiments, step S2, such as Figure 2 As shown, including S21-S23, specifically:

[0084] S21. Perform Fourier transform on the sequence to be tested to obtain the amplitude spectrum and phase spectrum of the sequence to be tested.

[0085] In some embodiments, the formula for Fourier transforming the sequence to be tested is:

[0086] Wherein, N represents the number of samples contained in the sequence to be tested; k represents the sample point sequence number; n is the sequence number of the sample point in the sequence to be tested; j is the imaginary unit; f is the frequency; and π is the circumference of a circle.

[0087] Furthermore, X(k) is expressed as an amplitude spectrum |F(k)| and a phase spectrum φ(k):

[0088] X(k)=|F(k)|e jφ(k) ,k=1,…,N.

[0089] Wherein, N represents the number of samples contained in the sequence to be tested; k represents the sample point number; F(k) represents the amplitude spectrum; φ(k) is the phase spectrum; n is the sample point number of the sequence to be tested; j is the imaginary unit; f is the frequency; and π is the circumference of a circle.

[0090] S22, using ψ(k) that obeys a uniform distribution within [-π, π] to replace the phase spectrum of the sequence to be measured, to obtain a replacement sequence;

[0091] Specifically, the obtained replacement sequence is:

[0092]

[0093] Wherein, N represents the number of samples contained in the sequence to be tested; k represents the sample point number; F(k) represents the amplitude spectrum; ψ(k) represents the phase spectrum; n is the sample point number of the sequence to be tested; j is the imaginary unit; f is the frequency; and π is the circumference of a circle.

[0094] S23. Repeat the above steps (S21-S22) to generate several replacement sequences of the sequence to be tested.

[0095] In some embodiments, a total of J alternative sequences are generated for the sequence to be tested, sj ,j=1,…,J.; so as to provide multiple sets of data for comparison with the sequence to be tested and improve the accuracy of stationarity evaluation.

[0096] S3. Analyze and process the sequence to be tested and several alternative sequences to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of several alternative sequences.

[0097] In some embodiments, step S3, such as Figure 3 As shown, including S31-S32, specifically:

[0098] S31, analyzing and processing the sequence to be tested to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested;

[0099] S32. Analyze and process several alternative sequences in sequence in the same manner as that of analyzing and processing the sequence to be tested, and obtain a two-dimensional scalar set for representing time-frequency features of the several alternative sequences.

[0100] The time-frequency characteristics of the test sequence and the replacement sequence are calculated through the same processing process to obtain a two-dimensional scalar in order to improve the accuracy of stationarity analysis.

[0101] In some embodiments, in step S31, the sequence to be tested is analyzed and processed to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested, such as Figure 4 As shown, including S311-S315, specifically:

[0102] S311: Determine the time-frequency spectrum of the sequence to be tested.

[0103] In some embodiments, Figure 5 As shown, step S311 includes S3101-S3104, namely:

[0104] S3101, obtaining a k-order Hermite function through a Hermite polynomial;

[0105] S3102, calculating a time-frequency spectrum according to the k-order Hermite function;

[0106] S3103, determining the time-frequency spectrum of the sequence to be tested according to the time-frequency spectrum calculated by the k-order Hermite function;

[0107] S3104 , using a preset length of the k-order Hermite function to determine the frequency spectrum of a specific time point of the sequence to be tested in a discretized time.

[0108] Among them, the formula for obtaining the k-order Hermite function through the Hermite polynomial is:

[0109]

[0110] In the formula, k represents the order; k! represents the factorial of k; H k (t) = 2tH k-1 (t)-2(k-2)H k-2 (t), k≥2, H0(t)=1, H1(t=2t.; t represents the time;

[0111] The calculation formula for calculating the time-frequency spectrum of the sequence to be tested according to the k-order Hermite function is:

[0112]

[0113] In the formula, Represents the time-frequency spectrum calculated by the k-order Hermite function: Wherein, x(n) is the sequence to be tested; k=1,...,N, N represents the number of sample points contained in the sequence to be tested; n is the serial number of the sample point of the sequence to be tested; j is the imaginary unit; f is the frequency; and π is the circumference of a circle.

[0114] In some embodiments, the k-order Hermite window function h is used to determine the frequency spectrum of a specific time point of the sequence to be tested by using a preset length of the k-order Hermite window function h. k The length of (t) is T h , the discretized time is t m , m=1…,M.; the frequency spectrum of the sequence to be tested at a specific time point is expressed as:

[0115]

[0116] Where, t m is the discretized time; x(n) is the sequence to be tested; k=1,...,N, N represents the number of sample points contained in the sequence to be tested; n is the serial number of the sample point of the sequence to be tested; j is the imaginary unit; f is the frequency; π is the circumference of a circle.

[0117] S312: normalize the time-frequency spectrum of the sequence to be tested.

[0118] In some embodiments, the time-frequency spectrum of the sequence to be tested is further normalized, and the obtained time-frequency spectrum is:

[0119]

[0120] S313: Determine, according to the normalized time-frequency spectrum, the characteristic quantity of the signal in the sequence to be tested that varies with time and the local frequency characteristic quantity of the signal.

[0121] In some embodiments, the expression of the characteristic quantity of the signal changing with time is:

[0122]

[0123] Where D m (f) is the normalized time-frequency spectrum.

[0124] The expression of the local frequency characteristic of the signal is:

[0125]

[0126] Where D m (f) is the normalized time-frequency spectrum.

[0127] S314: define the characteristic quantity of the signal in the sequence to be tested that varies with time and the local frequency characteristic quantity of the signal as time-frequency characteristics.

[0128] P x =std{P x,m} m＝1,…,M

[0129] F x =std{F x,m} m＝1,…,M

[0130] In some embodiments, the positioning formula defined as the time-frequency feature is:

[0131] Where P x,m is the characteristic quantity of the signal changing with time; F x,m is the local frequency characteristic of the signal.

[0132] S315. Using a two-dimensional scalar to represent the time-frequency characteristics of the sequence to be measured, to obtain a two-dimensional scalar of the time-frequency characteristics of the sequence to be measured.

[0133] In some embodiments, the two-dimensional scalar is denoted by U x , then the two-dimensional scalar expression of the time-frequency characteristics of the sequence to be tested is:

[0134] U x =(P x ,F x ).

[0135] In some embodiments, the set of two-dimensional scalars composed of video features of several alternative sequences is recorded as Thus, U x and U s By comparing, we can determine the stationarity of the sequence to be tested.

[0136] S4. Compare the two-dimensional scalar representing the time-frequency characteristics of the sequence to be tested with the set of two-dimensional scalars representing the time-frequency characteristics of the replacement sequence to determine the stationarity of the sequence to be tested.

[0137] In some implementations, a two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested is compared with a two-dimensional scalar set representing the time-frequency characteristics of the alternative sequence to determine the stationarity of the sequence to be tested, such as Figure 6 As shown, including:

[0138] S41, using the SVDD method to compare the difference between the two-dimensional scalar set of the time-frequency characteristics of the sequence to be tested and the two-dimensional scalar set of the time-frequency characteristics of the replacement sequence;

[0139] S42, when the comparison result is less than the judgment threshold, the sequence signal to be tested is determined to be a stable signal;

[0140] S43: When the comparison result is greater than the judgment threshold, it is determined that the sequence signal to be tested is a non-stationary signal.

[0141] In some embodiments, the difference between the two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested and the two-dimensional scalar set representing the time-frequency characteristics of the replacement sequence is set as a stationary test value, so as to compare the stationary test value with the judgment threshold. When the stationary test value is always less than the judgment threshold, the Gaussian signal is determined to be a stationary signal; when the stationary test value is not always less than the judgment threshold, the signal is determined to be a non-stationary signal.

[0142] In some specific embodiments, MATLAB is used to generate a random Gaussian signal x1 and a chirp signal x2, and the signal stationarity analysis method provided in the above embodiment is used to analyze the Gaussian signal x1 and the chirp signal x2 respectively. The analysis results are as follows: Figure 7 , Figure 8 , Fig. 9 and Fig.10 As shown in the figure, the lengths of Gaussian signal x1 and chirp signal x2 are both 3000, and the lengths of Hermite window function are 0.03T, 0.05T, 0.1T, 0.15T, 0.2T, 0.25T, 0.3T, and 0.5T respectively. The number of substitution sequences and the penalty parameters in the support vector machine are C=0.03, 0.1, 0.15, and 0.2 respectively.

[0143] By the attached Figure 7 It can be seen that the statistics of the Gaussian signal are in the circle where the alternative sequence is located, so at T h =0.2T, the signal is stable; Figure 8 It can be seen that the stationarity test value is always smaller than the judgment threshold, so the Gaussian signal is judged to be a stationary signal.

[0144] By the attached Fig. 9It can be seen that the statistics of the chirp signal are not in the circle where the alternative sequence is located, so in T h =0.3T, the signal is non-stationary; Fig.10 It can be seen that not all stationary tests are less than the judgment threshold, so the signal is determined to be a non-stationary signal.

[0145] The stationarity analysis method for full-node acquisition environmental noise provided by the embodiments of the present disclosure determines the participation of the test sequence and the alternative sequence by adopting the time-frequency analysis method and the Fourier transform alternative sequence method, so as to facilitate the rapid judgment of stationarity. The result of the stationarity judgment of the environmental noise is intuitive and effective, which is conducive to the next step of obtaining high-quality seismic data by utilizing the characteristics of the environmental noise.

[0146] Embodiment 2:

[0147] At least one embodiment of the present disclosure further provides a device for analyzing the stability of environmental noise of a full node. Each module in the device corresponds to the steps in the above method. The effects achieved and the problems solved have been described and will not be repeated here. Fig.11 As shown, including:

[0148] The data extraction module 11 obtains the environmental noise collected by all nodes and extracts the sequence to be tested from it;

[0149] The data processing module 12 performs Fourier transform and phase replacement on the sequence to be tested to obtain a plurality of replacement sequences;

[0150] A data analysis module 13 is used to analyze and process the sequence to be tested and the plurality of alternative sequences, and obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences;

[0151] The comparison and evaluation module 14 is used to compare the two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the replacement sequence to determine the stationarity of the sequence to be tested.

[0152] In some embodiments, the output end of the data extraction module 11 is connected to the input end of the data processing module 12, the output end of the data processing module 12 is connected to the input end of the data analysis module 13, and the output end of the data analysis module 13 is connected to the input of the comparison and evaluation module 14, thereby executing the steps of the stationarity analysis method of embodiment one.

[0153] In some embodiments, the device may be a part of the software, or may be implemented in combination with corresponding hardware, which will not be described in detail here.

[0154] Embodiment three:

[0155] At least some embodiments of the present disclosure also provide an electronic device, such as Fig.12 As shown, the electronic device includes a memory 21 and a processor 22, wherein the memory 21 stores a computer program, and when the computer program is executed by the processor, the steps of the stationarity analysis method provided in any embodiment of the present disclosure are performed.

[0156] In some embodiments, the processor 22 is used to execute all or part of the steps in the stability analysis method of any embodiment of the present disclosure. The memory 21 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0157] The processor 22 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the stability analysis method in the above-mentioned embodiment 1.

[0158] The memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0159] Embodiment 4:

[0160] At least some embodiments of the present disclosure also provide a computer-readable storage medium, such as Fig.13As shown, a computer program 31 is stored on the readable storage medium, and when the computer program 31 is executed by a processor, the steps of the stationarity analysis method provided by any embodiment of the present disclosure are implemented.

[0161] In some embodiments, the storage medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0162] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0163] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0164] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0165] Embodiment five:

[0166] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the stationarity analysis method provided in any embodiment of the present disclosure.

[0167] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0168] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A method for analyzing the stability of environmental noise collected by all nodes, characterized in that: The following steps are involved: Obtain the environmental noise collected by all nodes and extract the sequence to be tested from it; Performing Fourier transformation and phase replacement on the sequence to be tested to obtain a plurality of replacement sequences; Analyzing and processing the sequence to be tested and the plurality of alternative sequences, respectively obtaining a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences; The two-dimensional scalar representing the time-frequency characteristics of the sequence to be tested is compared with the set of two-dimensional scalars representing the time-frequency characteristics of the alternative sequence to determine the stationarity of the sequence to be tested.

2. According to claim 1, a method for analyzing the stability of full-node environmental noise collection is characterized in that: The method of performing Fourier transform and phase replacement on the sequence to be tested to obtain a plurality of replacement sequences includes: Perform Fourier transform on the sequence to be tested to obtain the expression of the amplitude spectrum and phase spectrum of the sequence to be tested; The phase spectrum of the sequence to be measured is replaced by ψ(k) which obeys uniform distribution in [-π, π] to obtain a replacement sequence; Repeat the above steps to generate several alternative sequences of the sequence to be tested.

3. According to the method for analyzing the stability of full-node environmental noise collection according to claim 1, it is characterized in that: The analyzing and processing the sequence to be tested and the plurality of alternative sequences to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences respectively include: Analyzing and processing the sequence to be tested to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested; The plurality of substitute sequences are analyzed and processed in sequence in the same manner as the sequence to be tested, so as to obtain a two-dimensional scalar set for representing the time-frequency characteristics of the plurality of substitute sequences.

4. According to claim 3, a method for analyzing the stability of full-node environmental noise collection is characterized in that: The analyzing and processing the sequence to be tested to obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested includes: Determining the time-frequency spectrum of the sequence to be tested; Normalizing the time-frequency spectrum of the sequence to be tested; According to the normalized time-frequency spectrum, determining the characteristic quantity of the signal in the sequence to be tested that changes with time and the local frequency characteristic quantity of the signal; The characteristic quantity of the signal in the sequence to be tested that varies with time and the local frequency characteristic quantity of the signal are defined as time-frequency characteristics; The time-frequency characteristics of the sequence to be measured are represented by a two-dimensional scalar, and the two-dimensional scalar of the time-frequency characteristics of the sequence to be measured is obtained.

5. According to claim 4, a method for analyzing the stability of full-node environmental noise collection is characterized in that: The determining of the time-frequency spectrum of the sequence to be tested comprises: The k-order Hermite function is obtained through the Hermite polynomial; Calculate the time-frequency spectrum according to the k-order Hermite function; Determine the time-frequency spectrum of the sequence to be tested according to the time-frequency spectrum calculated by the k-order Hermite function; The preset length of the k-order Hermite function is used to determine the frequency spectrum of the sequence to be tested at a specific time point in discretized time.

6. A method for analyzing the stability of full-node environmental noise collection according to claim 5, characterized in that: The formula for obtaining the k-order Hermite function through the Hermite polynomial is: In the formula, k represents the order; k! represents the factorial of k; H k (t)=2tH k-1 (t)-2(k-2)H k-2 (t), k≥2, H0(t)=1, H1(t=2t.; t represents the time; The calculation formula for calculating the time-frequency spectrum of the sequence to be tested according to the k-order Hermite function is: In the formula, Represents the time-frequency spectrum calculated by the k-order Hermite function:

7. A method for analyzing the stability of full-node environmental noise collection according to claim 6, characterized in that: In the method of using the preset length of the k-order Hermite function to determine the frequency spectrum of the specific time point of the sequence to be tested with discretized time, the k-order Hermite window function h k The length of (t) is T h , the discretized time is t m , m=1…,M.; the frequency spectrum of the sequence to be tested at a specific time point is expressed as: Where, t m is the discretized time; x(n) is the sequence to be tested; k=1,...,N, N represents the number of sample points contained in the sequence to be tested; n is the serial number of the sample point of the sequence to be tested; j is the imaginary unit; f is the frequency; π is the circumference of a circle.

8. The method for analyzing the stability of environmental noise collected by all nodes according to claim 1 is characterized in that: The formula for Fourier transforming the sequence to be tested is: The formula for converting it into the expression of amplitude spectrum and phase spectrum is: X(k)=|F(k)|e jφ (k),k=1,…,N. The processing formula for replacing the phase spectrum of the sequence to be measured by using ψ(k) that obeys a uniform distribution within [-π, π] to obtain the replacement sequence is: Wherein, N represents the number of samples contained in the sequence to be tested; k represents the sample point number; F(k) represents the amplitude spectrum; ψ(k) represents the phase spectrum; n is the sample point number of the sequence to be tested; j is the imaginary unit; f is the frequency; and π is the circumference of a circle.

9. The method for analyzing the stability of environmental noise collected by all nodes according to claim 1 is characterized in that: The step of comparing the two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the replacement sequence to determine the stationarity of the sequence to be tested includes: The SVDD method is used to compare the differences between the two-dimensional scalar set of the time-frequency characteristics of the test sequence and the two-dimensional scalar set of the time-frequency characteristics of the replacement sequence; When the comparison result is less than the judgment threshold, the sequence signal to be tested is determined to be a stable signal; When the comparison result is greater than the judgment threshold, the sequence signal to be tested is determined to be a non-stationary signal.

10. A device for analyzing the stability of environmental noise collected by all nodes, characterized in that: include: The data extraction module obtains the environmental noise collected by all nodes and extracts the sequence to be tested from it; A data processing module performs Fourier transformation and phase replacement on the sequence to be tested to obtain a plurality of replacement sequences; A data analysis module, used to analyze and process the sequence to be tested and the plurality of alternative sequences, and obtain a two-dimensional scalar for representing the time-frequency characteristics of the sequence to be tested and a set of two-dimensional scalars for representing the time-frequency characteristics of the plurality of alternative sequences; The comparison and evaluation module is used to compare the two-dimensional scalar set representing the time-frequency characteristics of the sequence to be tested with the two-dimensional scalar set representing the time-frequency characteristics of the replacement sequence to determine the stationarity of the sequence to be tested.

11. An electronic 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 the processor implements the steps of the stationarity analysis method according to any one of claims 1 to 9 when executing the computer program.

12. A computer-readable storage medium, characterized in that: When the computer program is executed by a processor, the steps of the stationarity analysis method according to any one of claims 1 to 9 are implemented.

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