Passive source surface wave data screening and imaging method based on noise wavefield feature analysis

Abstract

The present invention provides a method for screening and imaging passive source surface wave data based on noise wave field feature analysis, including: preprocessing the original passive source noise record and segmenting the processed original data; statistically calculating the RMS of the segmented original record row by row to calculate the coefficient of variation of each data segment with respect to the RMS curve; setting a threshold according to the coefficient of variation of the RMS curve to screen data segments; using the difference in the coefficient of variation to set a threshold to distinguish data segments that are beneficial for imaging from data segments that interfere with imaging; performing interference superposition processing on the screened data segments to obtain high-quality surface wave signals. The present invention can extract effective surface wave signals from short noise records through wave field feature analysis and statistics, reduce the requirement for the duration of noise data, lower the acquisition cost, and improve the processing efficiency of noise data. The present invention can effectively screen noise data types, extract higher-quality surface wave signals, and improve the signal-to-noise ratio of imaging results.

Description

Technical Field

[0001] The invention provides a passive surface wave data screening and imaging method based on noise wave field characteristic analysis, belonging to the technical field of geological exploration. Background Art

[0002] Surface wave exploration is a shallow surface exploration method that has developed rapidly in recent years. It can be divided into active and passive surface wave exploration. Passive surface wave exploration can extract reliable surface wave signals from long-term background noise recordings to invert the shallow shear wave velocity structure, thereby enabling accurate analysis and evaluation of shallow geological structures and physical properties. Due to its advantages such as ease of operation, susceptibility to interference, high exploration accuracy, and low economic cost, passive surface wave exploration has become increasingly popular and has become an indispensable mainstream method in shallow geological surveys in urban environments. In urban environments, traffic noise, as an important type of passive surface wave data, generally contains strong surface wave information and is easy to obtain, and has attracted widespread attention and research.

[0003] The key to passive surface-wave imaging lies in data processing. Long-duration data is first segmented and interferometrically processed, and then the interferometric results are superimposed to obtain valid surface-wave information. Traditional methods simply segment the data and perform interferometric superposition. This process requires a long period of noise recording (hours or even days) to obtain valid surface-wave information, and the extracted surface-wave signal has a relatively low signal-to-noise ratio. However, since the signal-to-noise ratios of different data segments often vary significantly, the imaging results can also vary significantly. If only data segments with good signal-to-noise ratios are interferometrically superimposed, not only will the imaging quality be greatly improved, but data acquisition time and costs can also be reduced. Including data segments with poor signal-to-noise ratios in the superposition calculation not only reduces the imaging effect, but the noise contained in them may also drown out the weak valid signal. Therefore, in the process of passive surface-wave imaging, effectively screening the segmented data segments is both a key and a challenge.

[0004] In recent years, researchers and experts have conducted extensive research on the problem of passive surface wave data screening. Most approaches involve converting noise records from the time-space domain to other domains. For example, Fourier transforms are used to convert them to the frequency-wavenumber domain (fk), or Tau-p transforms are used to convert them to the Tau-p domain. These approaches then utilize the differences in the performance of effective signals and noise in other domains to distinguish and separate them. This approach can, to a certain extent, distinguish noise from effective signals, retaining noisy data segments with a good signal-to-noise ratio and eliminating data with a poor signal-to-noise ratio.

[0005] Using methods such as f-k transformation or Tau-p transformation to convert the original data to other domains for signal recognition can screen data segments to a certain extent, but it ignores some characteristics of the noise data itself. Especially for traffic noise, since the acquisition array is usually linearly arranged along the road. There is a certain positional relationship between the geophones and the passive sources (vehicles), so the wave field characteristic transformation is relatively obvious and there are certain rules to follow. However, the above methods do not fully identify and analyze this regular characteristic, and the intermediate calculation is relatively cumbersome when screening and identifying after data conversion, and the computational complexity of this type of method is relatively large.

[0006] In addition, for the processing of passive source surface wave data, the important steps are as follows: segment the noise data, then perform interference calculation on it, stack the interference results, and finally obtain surface wave information. In order to improve the signal-to-noise ratio of the final stacked result, many scholars have proposed to screen and stack the interference results. Different from the previous methods, this type of method does not screen the original noise data segments, but only screens the interference results of each data segment, and then retains the results with better signal-to-noise ratio. This method can improve the effect of passive source surface wave imaging to a certain extent.

[0007] The above method screens and processes the interference results, retains the results with higher signal-to-noise ratio for stacking, and can obtain effective surface wave information. However, this method requires interference processing of all original data segments, which is relatively time-consuming, and this method requires noise records with long-term recording to extract high-quality surface wave signals. It is not only time-consuming in processing, but also increases the acquisition cost. Summary of the Invention

[0008] The passive source surface wave imaging technology not only has high imaging accuracy but also can achieve non-destructive exploration, which is undoubtedly the preferred choice for exploring urban underground structures. Many scholars have conducted corresponding research on how to improve the passive source surface wave imaging accuracy, and one of the more important aspects is how to extract higher-quality surface wave signals from noise records. Taking actual traffic noise as an example, the present invention establishes a set of passive source surface wave data screening and imaging methods based on noise wave field characteristic analysis. By analyzing the wave field characteristics of noise records and using statistical methods to analyze the wave field changes of noise data segments, data segments that contribute well to surface wave imaging are selected preferentially, and then interference calculation is performed on them, and finally higher-quality surface wave signals are obtained by stacking. This method can not only extract effective surface wave signals from shorter noise records, thereby shortening the acquisition cost, but also effectively improve the surface wave imaging accuracy and extract higher-quality surface wave signals.

[0009] The complete technical solution provided by the present invention:

[0010] A passive source surface wave data screening and imaging method based on noise wave field characteristic analysis, comprising the following steps:

[0011] Step 1: Preprocess the original passive source noise record, and then cut and segment the processed original data.

[0012] Step 2: Statistically calculate the root mean square value (RMS) of the segmented original record row by row, and calculate the coefficient of variation of each data segment;

[0013] Statistically analyze the RMS values of the original data using sampling statistics to obtain the RMS value distribution in the time direction. After smoothing the discrete points, an RMS curve is obtained, and then the coefficient of variation of the curve is calculated. The coefficient of variation obtained by statistics is used to reflect the discreteness of the RMS curve, thereby judging the change in the wave field characteristics of the corresponding original data.

[0014] The implementation process is as follows:

[0015] S2.1. Let each segment of the original data be data m (i, j), where m represents dividing the original data into m segments, and i and j represent that each segment of data is a two-dimensional array of i rows * j columns. Horizontally, that is, row by row, calculate the root mean square value (RMS) of each row of the original record. The calculation formula is as follows:

[0016]

[0017] Where: N is the number of data points in each row, x i,j is the data at the i-th row and j-th column, and RMS i represents the root mean square value at the i-th moment.

[0018] S2.2. Obtain the root mean square value of each row. Each segment of data obtains i root mean square values. Then, obtain the change trend of the statistically calculated RMS values. Remove some noise or short-term fluctuations through smoothing to obtain the change of the RMS values. The specific calculation formula is as follows:

[0019]

[0020] Where: RMS smoothed,k represents the smoothed RMS value, k represents the current sliding window position, w represents the smoothing window size, represents the left and right half widths of the window, rounded down;

[0021] S2.3. Calculate and smooth the RMS of the original noise data segment to obtain the corresponding curve. The characteristics of the smoothed RMS curve correspond to the wave field characteristics of the original data segment. Use the coefficient of variation of the RMS curve to describe the change in the wave field characteristics of the original data.

[0022] Calculate the standard deviation of the curve, which reflects the overall fluctuation of the curve relative to the mean. The specific calculation formula is as follows:

[0023]

[0024] Where: μ is the mean of the curve, and n is the number of samples.

[0025] S2.4. The standard deviation reflects the fluctuation of the curve. Larger fluctuations indicate more significant changes in the original data's wavefield, while smaller fluctuations indicate less significant changes in the original data's wavefield and more stable surface wave propagation. Based on the standard deviation, the coefficient of variation is calculated, which is the ratio of the standard deviation to the mean. The coefficient of variation directly reflects the fluctuation of the curve. The specific calculation formula is as follows:

[0026]

[0027] Where CV is the coefficient of variation of each data segment with respect to the RMS curve, std is the standard deviation of the curve, and μ is the mean of the curve. A larger CV value indicates greater curve fluctuations, meaning the raw data wavefield is experiencing dramatic changes. A smaller CV value indicates less curve fluctuations, meaning the corresponding raw data wavefield is experiencing less change and the signal propagation is relatively stable.

[0028] Step 3: Set a threshold based on the coefficient of variation of the RMS curve to filter the data segments. By using the difference in coefficient of variation, setting a reasonable threshold will distinguish the data segments that are beneficial to imaging from the interference data segments, thereby improving the imaging quality. The specific expression is as follows:

[0029]

[0030] Data i is the data segment retained after screening, CV is the coefficient of variation of each data segment with respect to the RMS curve, and T is the threshold.

[0031] Step 4: Interference superposition calculation.

[0032] By interfering the noise records of the two receivers, the empirical Green's function between the two points is reconstructed to obtain the effective signal.

[0033] This invention mainly uses the mutual interference method and normalizes the amplitude of each frequency to expand the effective frequency band. The specific expression formula is as follows:

[0034]

[0035] Where: u(A,s,ω) represents the frequency domain signal excited by the source at S and received by the receiver at A.

[0036] u(B,s,ω) represents the frequency domain signal excited by the source at S and received by the receiver at B.

[0037] Among them, H AB (ω) represents the surface wave signal propagated from point A to point B extracted through mutual interference.

[0038] The passive source surface wave data screening and imaging method based on noise wave field feature analysis proposed by the present invention can achieve the following benefits:

[0039] 1. By analyzing and statistically analyzing the wave field characteristics, effective surface wave signals can be extracted from short noise records, further reducing the requirement for the duration of noise data, thereby reducing the acquisition cost and improving the processing efficiency of noise data at the same time.

[0040] 2. Using the method proposed by the present invention can effectively screen the types of noise data, extract higher-quality surface wave signals, and improve the signal-to-noise ratio of the imaging results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a record diagram of the background noise generated by a high-speed train in the original one minute of the embodiment;

[0042] Figure 2 It is a schematic diagram of the segmentation of Section 1 of the embodiment;

[0043] Figure 3 It is a schematic diagram of the segmentation of Section 2 of the embodiment;

[0044] Figure 4 It is a schematic diagram of the segmentation of Section 3 of the embodiment;

[0045] Figure 5 It is a schematic diagram of the segmentation of Section 4 of the embodiment;

[0046] Figure 6 It is a schematic diagram of the segmentation of Section 5 of the embodiment;

[0047] Figure 7 It is a schematic diagram of the segmentation of Section 6 of the embodiment;

[0048] Figure 8 It is an RMS statistical chart of the original noise data in Section 1 of the embodiment;

[0049] Figure 9 It is an RMS statistical chart of the original noise data in Section 2 of the embodiment;

[0050] Figure 10 It is an RMS statistical chart of the original noise data in Section 3 of the embodiment;

[0051] Fig.11 It is an RMS statistical chart of the original noise data in Section 4 of the embodiment;

[0052] Figure 12 It is an RMS statistical chart of the original noise data in Section 5 of the embodiment;

[0053] Fig.13 6-segment RMS statistical graph of original noise data in the embodiment;

[0054] Fig.14 This is the interference result diagram of the noise data of the embodiment 1;

[0055] Figure 15 This is the result diagram of the 2-segment interference of noise data in the embodiment;

[0056] Figure 16 This is a graph showing the interference results of three sections of noise data in the embodiment;

[0057] Fig.17 This is a graph showing the interference results of four sections of noise data in the embodiment;

[0058] Figure 18 This is a graph showing the interference results of 5 segments of noise data in the embodiment;

[0059] Fig.19 This is a graph showing the interference results of 6 segments of noise data in the embodiment;

[0060] Figure 20 This is the result of traditional method processing noise examples;

[0061] Figure 21 This is a diagram showing the processing effect of an actual example of the method of the present invention;

[0062] Figure 22 This is a process flow chart of the present invention. DETAILED DESCRIPTION

[0063] The specific technical solutions of the present invention are described with reference to the embodiments.

[0064] like Figure 22 As shown in Figure 2, the passive surface wave data screening and imaging method based on noise wave field characteristic analysis mainly includes the following steps:

[0065] Step 1: Preprocess the raw passive noise recordings (conventional processing: filtering, removing instrument response, removing bad tracks, etc.), then segment the processed raw data. Generally, split the raw noise recordings of tens of minutes or hours into data segments of a few seconds.

[0066] like Figures 1 to 7 As shown, Figure 1 This is the original one-minute recording of the background noise generated by the high-speed rail. Figure 1 Two strong energy axes (white dashed lines) can be seen in the figure, which means that there are two high-speed rail passing events in the record within this minute. The original high-speed rail record is split into multiple data segments of 4 seconds each (a total of 15 segments), of which the first 6 segments are shown in the figure. Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 , Figure 6 and Figure 7 as shown. From the first three data segments ( Figure 2 , Figure 3 and Figure 4 ), it can be seen that the original record contains obvious surface wave signals propagating steadily (within the range of the red dashed line), which indicates that the high-speed train is gradually approaching the geophone array from the far side. This type of data segment belongs to the data segments that contribute more to passive source surface wave imaging. For the last three data segments ( Figure 5 , Figure 6 and Figure 7 ), it can be clearly seen that the wave field is relatively chaotic and there are signals in two directions. This indicates that the train has entered the range of the geophone array and the train source is close to the array. At this time, the surface waves have not fully developed. This type of data segment belongs to the data segments that contribute less to passive source surface wave imaging, but it contains stronger other energy. If such data segments are involved in the calculation during the interference superposition process, it will bring more noise interference to the imaging result, thereby reducing the quality of the extracted surface wave signals.

[0067] Step 2: Statistically analyze the RMS (root mean square value) of the segmented original record, and calculate the coefficient of variation of each data segment (used to reflect the characteristics of the original data wave field). The method of sampling statistics in the present invention is used to statistically analyze the RMS value of the original data to obtain the RMS value distribution in the time direction, smooth the discrete points to obtain the RMS curve, and then calculate the coefficient of variation of the curve. The coefficient of variation (degree of dispersion) obtained by statistics is used to reflect the discrete situation of the RMS curve, thereby judging the change situation of the corresponding original data wave field characteristics. The implementation process is as follows:

[0068] S2.1. Assume that each segment of the original data is data m (i, j), m represents dividing the original data into m segments, and i, j represent that each segment of data is a two-dimensional array of i rows * j columns. Calculate the root mean square value (RMS) of each row of the original record row by row (horizontally). The calculation formula is as follows:

[0069]

[0070] where: N is the number of data points in each row, x i,j is the data at the i-th row and j-th column, and RMS i represents the root mean square value at the i-th moment.

[0071] S2.2. Calculate the root mean square value of each row through the above formula. In this way, each segment of data can obtain i root mean square values. Then obtain the changing trend of the statistically obtained RMS values. Remove some noise or periodic fluctuations through smoothing to obtain the change situation of the RMS values. The specific calculation formula is as follows:

[0072]

[0073] Where: RMS smoothed,k represents the smoothed RMS value, k represents the current sliding window position, w represents the smoothing window size, represents the left and right half-widths (rounded down) of the window.

[0074] S2.3. After calculating and smoothing the RMS of the original noise data segment according to the above formula, the corresponding curve can be obtained. The characteristics of the smoothed RMS curve correspond to the wave field characteristics of the original data segment. In order to better reflect the wave field characteristics of different original data, the present invention uses the coefficient of variation of the RMS curve to describe the change of the wave field characteristics of the original data. First, calculate the standard deviation of the curve, which reflects the overall fluctuation size of the curve relative to the mean. The specific calculation formula is as follows:

[0075]

[0076] Where: μ is the mean of the curve, and n is the number of samples.

[0077] S2.4. According to the standard deviation reflecting the fluctuation of the curve, the greater the fluctuation, the more obvious the change of the wave field of the original data, and the smaller the fluctuation, the smaller the change of the wave field of the original data and the more stable the surface wave propagation. Although the standard deviation can reflect the fluctuation size of the curve, it is impossible to further quantify the change of the original data. The present invention further calculates its coefficient of variation on the basis of the standard deviation, that is, the ratio of the standard deviation to the mean. The size of the coefficient of variation is used to directly reflect the fluctuation of the curve. The specific calculation formula is as follows:

[0078]

[0079] Where: std is the standard deviation of the curve, and μ is the mean of the curve. If the CV value is larger, it means that the curve fluctuates stronger, that is, the wave field of the original data changes violently. If the CV value is smaller, it means that the curve fluctuates less, that is, the corresponding wave field of the original data changes less and the signal propagation is relatively stable.

[0080] Such as Figures 8 to 13 shown, the blue dots in the figure are the RMS of the original noise data segment calculated by formula 1, and the red curve is the RMS curve calculated and smoothed by formula 2. Figures 8 to 13 The relevant results of Figures 2 to 7 are calculated from the data segment of Figure 8 . Figure 9 and Figure 10 The RMS curves (red) in have relatively small fluctuations, and their coefficients of variation are: 0.080; 0.086; 0.089. The RMS is mainly concentrated in (1-4)*10 -3The corresponding original noise data segment Figure 2 、 Figure 3 and Figure 4 It can also be clearly seen that the surface wave propagation of this type of data segment is stable and other coherent noises are relatively small. And Fig.11 、 Figure 12 and Fig.13 have stronger fluctuations in the RMS curve, and the coefficient of variation values are: 0.289; 0.261; 0.124 respectively. The RMS is mainly concentrated in (6 - 10) * 10 -3 In the corresponding original data segment, it can also be seen that the wave field changes significantly and the amplitude energy changes violently within this type of data segment. At this time, the surface wave has not fully developed, but it has strong energy. If it is included in the interference superposition calculation, the signal-to-noise ratio of the superposition result will be seriously reduced.

[0081] Step 3: Set a threshold according to the coefficient of variation of the RMS curve to screen data segments. According to Figures 8 to 13 The calculation results show that when the wave field of the original data propagates smoothly, the coefficient of variation of this type of data segment is relatively small. When the wave field changes greatly and the amplitude decays violently, the coefficient of variation of the RMS curve is relatively large at this time, and the RMS value is also large. Using the difference in the coefficient of variation, setting a reasonable threshold can distinguish the data segments beneficial to imaging from the interference data segments, so as to improve the imaging quality. Specifically, it is expressed as follows:

[0082]

[0083] where Data i is the data segment retained after screening, CV is the coefficient of variation of each data segment with respect to the RMS curve. T is the threshold, and the specific threshold value is selected according to the signal-to-noise ratio of the actual data.

[0084] Step 4: Interference superposition calculation. Seismic interferometry is a new method widely used in ambient noise imaging in recent years. This method reconstructs the empirical Green's function between two points by performing interference processing on the noise records of two receivers (seismic traces), so as to obtain effective signals. There are three commonly used seismic interferometry methods: cross-correlation, deconvolution, and cross-coherence. In this invention, the cross-coherence interferometry method is mainly used this time, which can eliminate the influence of the source wavelet. At the same time, by normalizing the amplitudes of each frequency, the effective frequency band is broadened. The specific expression formula is as follows:

[0085]

[0086] where: u(A, s, ω) represents the frequency-domain signal excited by the source at S and received by the receiver at A.

[0087] u(B, s, ω) represents the frequency-domain signal excited by the source at S and received by the receiver at B.

[0088] Among them H AB (ω) represents the surface wave signal propagating from point A to point B extracted through mutual interference.

[0089] The original data segment ( Figures 2 to 7 ) to perform interference calculation and obtain the interference result ( Figures 14 to 19 ).like Figures 14 to 16 We can see that the surface wave signal has a very good signal-to-noise ratio, which is consistent with the previous analysis of the original noise data segment. Figures 2 to 4 ) It can be seen that the wave field characteristics of stable propagation are obvious. The RMS curve calculated in step 2 ( Figures 8 to 10 ) is also small. From the original data to the interference results, it is also proved that this type of data segment belongs to the data segment that is effective for surface wave imaging. By setting a threshold value by the coefficient of variation, it can be screened and retained. Figures 17 to 19 The interference result shows that in addition to a small amount of surface wave signals, there is more noise interference. The fundamental reason for this result is that the original data segment ( Figures 5 to 7 ) have complex wavefield characteristics, with surface waves not yet fully developed, yet the noise energy is strong. From the perspective of interferometric stacking, incorporating such signals into the stacking calculation will result in a poor signal-to-noise ratio, making subsequent dispersion curve calculation and inversion difficult. The coefficient of variation calculated from the RMS curve of the raw data also shows a high coefficient of variation for such data segments, indicating drastic wavefield fluctuations. Similarly, such data segments can be filtered out by setting a corresponding threshold according to Step 3.

[0090] Figure 20 This is the result of the traditional method for processing noise examples. This method does not analyze the wave field characteristics of the noise data, but directly and crudely performs segmented interference superposition on the data. It can be seen from the results that the extracted surface wave signal has a poor signal-to-noise ratio, strong noise interference, and poor imaging results. According to the passive source surface wave data screening and imaging method based on noise wave field characteristic analysis provided by the present invention, the original data can be processed and analyzed first. Specifically, the data is first segmented, and then the RMS calculation of the segmented data is performed using mathematical statistics methods, and the coefficient of variation of the RMS curve is calculated. According to the statistical results, the brushing threshold is set to 0.010. This method can be used to eliminate data segments with obvious changes in the wave field characteristics of the original data segment and large noise content. Finally, a surface wave signal with a good signal-to-noise ratio is obtained by mutual interference superposition. According to the processing results ( Figure 21 ) shows that the signal-to-noise ratio of the surface wave signal is greatly improved and the noise is relatively small. The improved method of the present invention can significantly improve the signal-to-noise ratio and imaging quality of the processing result.

Claims

1. A passive source surface wave data screening and imaging method based on noise wavefield feature analysis, characterized in that It includes the following steps: Step 1: Preprocess the original passive source noise record, and then cut and segment the processed original data; Step 2: Statistically calculate the root mean square value RMS of the segmented original record, and calculate the coefficient of variation of each data segment; Using the statistical method to statistically obtain the RMS value distribution in the time direction for the RMS value of the original data, smooth the discrete points to obtain the RMS curve, and then calculate the coefficient of variation of the curve; use the statistically obtained coefficient of variation to reflect the discrete situation of the RMS curve, thereby judging the change situation of the corresponding original data wave field characteristics; Step 3: Set a threshold according to the coefficient of variation of the RMS curve for screening data segments; Using the difference in the coefficient of variation and setting a reasonable threshold will help distinguish the data segments conducive to imaging from the interference data segments and improve the imaging quality; Step 4: Perform interference superposition calculation to obtain the effective surface wave signal; By performing interference processing on the noise records of two receivers to reconstruct the empirical Green's function between two points, thereby obtaining an effective signal.

2. The passive source surface wave data screening and imaging method based on noise wavefield feature analysis according to claim 1, wherein Step 2 specifically includes the following sub-steps: S2.

1. Let each piece of original data be , where m represents dividing the original data into m segments, and i, j represent that each segment of data is a two-dimensional array of i rows * j columns; calculate the root mean square value RMS of each row of the original record horizontally by row, and its calculation formula is as follows: ， Where: N is the number of data points in each row, is the data at the j-th column of the i-th row, represents the root mean square value at the i-th moment; S2.

2. Calculate the root mean square value of each row, and obtain i root mean square values for each data segment; then obtain the change trend of the statistically obtained RMS value; remove some noise or short-term fluctuations through smoothing to obtain the change situation of the RMS value. The specific calculation formula is as follows: ， Wherein: represents the smoothed RMS value, k represents the current sliding window position, and w represents the smoothing window size, represents the left and right half-widths of the window, rounded down; S2.

3. Calculate and smooth the RMS of the original noise data segment to obtain the corresponding curve. The characteristics of the smoothed RMS curve correspond to the wave field characteristics of the original data segment; use the coefficient of variation of the RMS curve to describe the change situation of the wave field characteristics of the original data. Calculate the standard deviation of the curve to reflect the overall fluctuation size of the curve relative to the mean value; the specific calculation formula is as follows: ， where: std is the standard deviation of the curve, μ is the mean value of the curve, and n is the number of samples; S2.

4. According to the standard deviation reflecting the fluctuation situation of the curve, the greater the fluctuation, the more obvious the change in the wave field of the original data, and the smaller the fluctuation, the smaller the change in the wave field of the original data and the more stable the surface wave propagation; calculate the coefficient of variation based on the standard deviation, that is, the ratio of the standard deviation to the mean value; use the size of the coefficient of variation to directly reflect the fluctuation situation of the curve. The specific calculation formula is as follows: ， where: CV is the coefficient of variation of each data segment with respect to the RMS curve, std is the standard deviation of the curve, and μ is the mean value of the curve; if the CV value is large, it indicates that the curve fluctuates strongly, that is, the wave field of the original data changes violently, and if the CV value is small, it indicates that the curve fluctuates less, that is, the corresponding wave field of the original data changes less and the signal propagation is relatively stable.

3. The passive-source surface wave data screening and imaging method based on noise wavefield feature analysis according to claim 1, wherein Step 3 is specifically expressed as follows: ， Among them is the data segment selected and retained. CV is the coefficient of variation of each data segment with respect to the RMS curve; T is the threshold value.

4. The passive source surface wave data screening and imaging method based on noise wavefield feature analysis according to claim 1, wherein Step 4 adopts the cross-correlation interference method and simultaneously normalizes the amplitudes of each frequency, which broadens the effective frequency band; the specific expression formula is as follows: ， Wherein: represents the frequency-domain signal excited by the seismic source at S and received by the receiver at A; Indicates the frequency-domain signal excited at the source S and received at the receiver B; Among them It means that the surface wave signal propagated from point A to point B is extracted through mutual interference.

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