A method for fusing double components of Rayleigh wave to prolong effective dispersion curve

Abstract

The application belongs to the field of seismic data processing and is a Rayleigh wave double-component fusion method capable of extending effective dispersion curves. The method comprises the following steps: acquiring continuous three-component background noise data of stations in a target area; calculating ZZ cross-correlation functions of all station pairs; calculating RR cross-correlation functions of all station pairs; calculating signal-to-noise ratios of the ZZ cross-correlation functions and the RR cross-correlation functions of each station pair; screening station pairs; calculating the ratio of the signal-to-noise ratio of the ZZ cross-correlation function to the signal-to-noise ratio of the RR cross-correlation function of each screened station pair; if the ratio of the station pair meets the similar condition, then the ZZ cross-correlation function and the RR cross-correlation function of the station pair are superimposed as the final effective signal; or the cross-correlation function with a larger signal-to-noise ratio is selected as the final effective signal. The application improves the signal-to-noise ratio of the fused signal through safe fusion, so that the data that would otherwise be eliminated can be effectively reused.

Description

Technical Field

[0001] This application belongs to the field of seismic data processing, specifically relating to a Rayleigh wave dual-component fusion method that can extend the effective dispersion curve. Background Technology

[0002] When using seismic background noise for surface wave imaging, Rayleigh wave signals mainly exist in two components: the vertical component (Z component) and the radial component (R component). Current mainstream methods typically process only the Z component data, which has a higher signal-to-noise ratio (SNR), while discarding the R component data, which has a lower SNR. This results in low data utilization and fails to fully exploit the potential of the dual-component data. Although the R component also carries a valid signal, practice shows that directly superimposing the Z and R components without discrimination can lead to noise in the low-quality signal contaminating the high-quality signal, thus degrading the results. Current technology lacks a method to intelligently evaluate the quality of the two component signals and achieve safe and effective fusion. This leads to a dilemma: either conservatively use only the Z component, resulting in data waste and potential signal loss; or risk attempting fusion, which risks degrading the SNR. Either choice limits the ability to extract high-quality Rayleigh wave signals from background noise, thus restricting the accuracy and reliability of the final surface wave imaging. To overcome this dilemma, the key technology lies in establishing a reliable criterion and method that can quantitatively evaluate the relationship between the quality of the two component signals and adaptively decide whether to fuse them. However, existing technologies have not yet provided clear theoretical guidance or an operational quantitative model for the core question of "under what specific conditions fusion can ensure performance gains rather than deterioration". Summary of the Invention

[0003] This application provides a Rayleigh wave dual-component fusion method that can extend the effective dispersion curve, overcoming the dilemma of low data utilization and risk of fusion deterioration faced by existing technologies due to the lack of effective criteria.

[0004] A Rayleigh wave dual-component fusion method for extending the effective dispersion curve, according to an embodiment of this application, includes the following steps: acquiring three-component background noise data (vertical, east-west, and north-south components) continuously collected by stations within the target area, and performing standardized preprocessing; using the preprocessed vertical component, calculating the ZZ cross-correlation function of all station pairs, where Z is the vertical component; using the preprocessed east-west and north-south components, calculating the RR cross-correlation function of all station pairs, where R is the radial component; and calculating the signal-to-noise ratio (SNR) of the ZZ cross-correlation function and the signal-to-noise ratio (SNR) of the RR cross-correlation function for each station pair. Signal-to-noise ratio (SNR); Station pairs whose SNR of both the ZZ cross-correlation function and the RR cross-correlation function is not lower than a preset threshold are selected; the ratio of the SNR of the ZZ cross-correlation function to the SNR of the RR cross-correlation function for each selected station pair is calculated; if the ratios within a station pair meet the similarity condition, the ZZ cross-correlation function and the RR cross-correlation function of that station pair are superimposed as the final valid signal; if the ratios within a station pair do not meet the similarity condition, the SNRs of the ZZ cross-correlation function and the RR cross-correlation function of that station pair are compared, and the cross-correlation function with the larger SNR is selected as the final valid signal.

[0005] Furthermore, using the preprocessed vertical components, the ZZ cross-correlation function for all station pairs is calculated, including:

[0006] Each station's continuous vertical component is divided into multiple time data segments;

[0007] For each time segment of data, calculate the ZZ cross-correlation function;

[0008] The ZZ cross-correlation functions of all data segments are linearly superimposed to obtain the final ZZ cross-correlation function for this station.

[0009] Furthermore, using the preprocessed east-west and north-south components, the RR cross-correlation function for all station pairs is calculated, including: dividing the continuous east-west and north-south components of each station pair into multiple time data segments; for each time data segment, calculating the EE cross-correlation function, NN cross-correlation function, EN cross-correlation function, and NE cross-correlation function, where E represents the east-west component and N represents the north-south component; superimposing the cross-correlation function results of the same type of component pairs in all data segments to obtain the final EE cross-correlation function, NN cross-correlation function, EN cross-correlation function, and NE cross-correlation function for that station pair; based on the azimuth angle between station pairs, transforming the EE cross-correlation function, NN cross-correlation function, EN cross-correlation function, and NE cross-correlation function to a radial-tangential coordinate system to obtain the corresponding TT cross-correlation function, RR cross-correlation function, TR cross-correlation function, and RT cross-correlation function, where R represents radial and T represents tangential; and extracting the RR cross-correlation function from the transformation results.

[0010] Furthermore, the calculation of the signal-to-noise ratio (SNR) of the ZZ cross-correlation function and the SNR of the RR cross-correlation function includes:

[0011] Set a surface wave signal velocity window. The lower limit of the surface wave signal velocity window is the minimum velocity of the cross-correlation function, and the upper limit of the surface wave signal velocity window is the maximum velocity of the cross-correlation function.

[0012] Determine the signal arrival time window based on the surface wave signal velocity window;

[0013] Set the noise window to a duration of n seconds after the signal window;

[0014] The signal-to-noise ratio of the cross-correlation function is obtained by calculating the ratio of the maximum amplitude within the signal window to the root mean square amplitude within the noise window using the cross-correlation function.

[0015] Furthermore, the arrival time window of the signal is determined based on the surface wave signal velocity window, including:

[0016] Calculate the upper limit of the window when the signal arrives. for: , The distance between stations, The maximum velocity of the cross-correlation signal;

[0017] Calculate the lower limit of the window when the signal arrives. for: , The minimum velocity of the cross-correlated signal.

[0018] Furthermore, the preset threshold value is greater than 3.

[0019] Furthermore, similar conditions are: ,in This is the ratio of the signal-to-noise ratio (SNR) of the ZZ cross-correlation function to the SNR of the RR cross-correlation function. This is the tolerance threshold.

[0020] Furthermore, the tolerance threshold The value ranges from 0 to 0.5.

[0021] Compared with the prior art, the advantages of this application are as follows:

[0022] This application expands the sources of effective data. Traditional Rayleigh wave imaging methods primarily use the Z component due to a lack of secure fusion criteria, resulting in the discarding of R component (radial component) data. This application, by using signal-to-noise ratio similarity, securely incorporates qualified RR cross-correlation functions into the effective signal, thereby directly utilizing data resources that were not utilized in traditional methods.

[0023] This improves the usability of existing data. For a large amount of Z-component data with low signal-to-noise ratio that might be discarded under the screening criteria, when the signal-to-noise ratio of its R-component is similar, this application improves the signal-to-noise ratio of the fused signal through secure fusion, thereby enabling the effective reuse of this data that would otherwise be discarded.

[0024] By screening based on similarity in this application, for station pairs that meet the similarity criteria, the signal-to-noise ratio (SNR) of the fused signal is significantly improved compared to the single component with the higher SNR before fusion. Data from the embodiments of this application show an average improvement of 18.8%.

[0025] This application improves the signal-to-noise ratio of Rayleigh wave signals through fusion, enabling the extraction of effective signals with longer periods in subsequent dispersion analysis. Experimental analysis shows that the effective extraction lengths of both group velocity and phase velocity dispersion curves are significantly extended. Attached Figure Description

[0026] Figure 1 A flowchart illustrating the method provided in the embodiments of this application;

[0027] Figure 2 This application provides an embodiment of the cross-correlation function and signal-to-noise ratio of a certain station for ZZ, RR, and ZZ+RR.

[0028] Figure 3 The group velocity dispersion curves corresponding to the ZZ, RR, and ZZ+RR cross-correlation functions provided in the embodiments of this application are shown.

[0029] Figure 4 The phase velocity dispersion curves corresponding to the ZZ, RR, and ZZ+RR cross-correlation functions provided in the embodiments of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] See Figure 1 As shown, a Rayleigh wave dual-component fusion method that can extend the effective dispersion curve includes:

[0032] Data acquisition and preprocessing: Acquire three-component background noise data, including vertical, east-west and north-south components, continuously collected by stations within the target area, and perform standardized preprocessing on the three-component background noise data.

[0033] A seismic station refers to a fixed observation point used for earthquake monitoring, equipped with seismographs, data acquisition devices, communication equipment, etc., to acquire continuous background noise data in three components: vertical (Z), north-south (N), and east-west (E). The background noise data undergoes standardized preprocessing, including resampling, bandpass filtering, time-domain normalization, and spectral whitening.

[0034] ZZ cross-correlation function calculation: Using data collected by the stations, calculate the ZZ cross-correlation function for all station pairs, where Z is the vertical component;

[0035] A station pair refers to pairing up all stations. For example, if there are four stations, ABCD, the possible station pairs are AB, AC, AD, BC, BD, and CD. Calculating the ZZ cross-correlation function for all station pairs means calculating the cross-correlation function of the vertical components of the data collected by the two stations in each pair, and obtaining the vertical component cross-correlation function, which is represented by the ZZ cross-correlation function.

[0036] RR cross-correlation function calculation: Using the preprocessed east-west and north-south components, the RR cross-correlation function of all station pairs is calculated, where R is the radial component;

[0037] The RR cross-correlation function cannot be obtained by directly taking the cross-correlation of the collected vertical (Z), north-south (N), and east-west (E) components. Instead, it is obtained by taking the cross-correlation of the north-south (N) and east-west (E) components, applying the coordinate rotation formula, transforming to the radial-tangential coordinate system, and then extracting the RR cross-correlation function from the cross-correlation function.

[0038] Signal-to-noise ratio calculation and initial screening:

[0039] Calculate the signal-to-noise ratio (SNR) of the ZZ cross-correlation function and the SNR of the RR cross-correlation function for each station pair; select station pairs whose SNR of both the ZZ cross-correlation function and the RR cross-correlation function is not lower than a preset threshold; this step aims to initially select station pairs with basic quality by setting a preset threshold for the SNR.

[0040] Signal-to-noise ratio (SNR) calculation: Calculate the ratio of the SNR of each selected station to the ZZ cross-correlation function to the SNR of the RR cross-correlation function;

[0041] An adaptive fusion decision is set up to obtain the final effective signal: if the ratio within a station pair meets the similarity condition, the ZZ cross-correlation function and the RR cross-correlation function of the station pair are superimposed as the final effective signal; if the ratio within a station pair does not meet the similarity condition, the signal-to-noise ratio of the ZZ cross-correlation function and the RR cross-correlation function of the station pair are compared, and the cross-correlation function with the larger signal-to-noise ratio is selected as the final effective signal.

[0042] The process of setting adaptive fusion decision is to adaptively decide whether to perform two-component fusion or select a single component for each station pair, aiming to systematically improve signal quality and completely avoid the risk of fusion degradation.

[0043] In one embodiment, the ZZ cross-correlation function for all station pairs is calculated using the preprocessed vertical components, where Z is the vertical component, including:

[0044] The continuous data of the vertical component (Z) of each station pair is divided into multiple time data segments; for each time data segment, its ZZ cross-correlation function is calculated; the ZZ cross-correlation functions of all time data segments are linearly superimposed to obtain the final ZZ cross-correlation function of the station pair. That is, it is the ZZ cross-correlation function of the vertical component of one station within a station pair with the vertical component of another station within the same time data segment, obtained by superimposing multiple time data segments.

[0045] In one embodiment, the RR cross-correlation function for all station pairs is calculated, including:

[0046] The data from each station for the continuous east-west component (E) and north-south component (N) are divided into multiple time data segments;

[0047] For each time data segment, the EE, NN, EN, and NE cross-correlation functions are calculated, where E represents the east-west component and N represents the north-south component. The cross-correlation function results of the same type of component pairs in all time data segments are superimposed to obtain the final EE, NN, EN, and NE cross-correlation functions for that station pair. Based on the azimuth angle between station pairs, the EE, NN, EN, and NE cross-correlation functions are transformed to a radial-tangential coordinate system to obtain the corresponding TT, RR, TR, and RT cross-correlation functions, where R represents radial and T represents tangential. The RR cross-correlation function is extracted from the transformation results.

[0048] The conversion formula is:

[0049] ,

[0050] in the formula This indicates the azimuth angle between the first and second stations relative to the center of the station. This indicates the azimuth angle from the second station to the first station.

[0051] In one embodiment, the signal-to-noise ratio (SNR) of the ZZ cross-correlation function and the signal-to-noise ratio (SNR) of the RR cross-correlation function are calculated for all station pairs, and a preset threshold SNR_th = 5 is set for initial screening. Only station pairs that simultaneously satisfy SNR(ZZ) ≥ SNR_th and SNR(RR) ≥ SNR_th are retained. The purpose is to exclude data without cross-correlation signals.

[0052] In one embodiment, the calculation process of the signal-to-noise ratio of the ZZ cross-correlation function and the signal-to-noise ratio of the RR cross-correlation function includes: setting a surface wave signal velocity window. The lower limit of the surface wave signal velocity window The minimum velocity of the cross-correlation function, and the upper limit of the surface wave signal velocity window. The maximum velocity of the cross-correlation function;

[0053] Determine the signal arrival time window based on the surface wave signal velocity window. ;

[0054] After setting the noise window to the signal window for a certain period of time seconds, in one example,

[0055] , , The station spacing is set; the noise window is set to a duration of 150 seconds after the signal window, i.e. .

[0056] The signal-to-noise ratio of the cross-correlation function is obtained by calculating the ratio of the maximum amplitude within the signal window to the root mean square amplitude within the noise window using the cross-correlation function.

[0057] For example, calculating the signal-to-noise ratio of the ZZ cross-correlation function, and the velocity window of the surface wave signal. Lower limit of the velocity window for mid-surface wave signals for The lower limit of the surface wave signal velocity window The minimum velocity of the ZZ cross-correlation function, and the upper limit of the velocity window for the surface wave signal. The maximum velocity of the ZZ cross-correlation function is the upper limit of the velocity window for the surface wave signal. The maximum velocity of the ZZ cross-correlation function is used to calculate the signal-to-noise ratio of the RR cross-correlation function using the same method.

[0058] In one embodiment, the ratio of the signal-to-noise ratio (SNR) of the ZZ cross-correlation function to the SNR of the RR cross-correlation function for each station pair is calculated. Based on this ratio, a adaptive decision is made whether to perform two-component fusion or selectively choose a single component for each station pair, using comparisons with similar conditions. This ratio quantifies the relative quality of the Z component and the R component (radial component).

[0059] Set a tolerance threshold δ, and based on this, form a range of conditions with similar signal-to-noise ratios: Determine if the ratio satisfies this condition: that is:

[0060] ,

[0061] This represents the ratio of the signal-to-noise ratio (SNR) of the ZZ cross-correlation function to the SNR of the RR cross-correlation function.

[0062] In a preferred embodiment, based on statistical optimization of typical data from the study area, the tolerance threshold δ is set to 0.35. In this case, the above judgment condition is specified as follows: .

[0063] Based on the judgment result, different operation paths are executed:

[0064] If similar conditions are met, that is Falling within the above-mentioned range Internal: The ZZ cross-correlation function and the RR cross-correlation function are linearly superimposed to generate the fused ZZ+RR cross-correlation function, and this fused signal is used as the final effective signal of the station pair.

[0065] If the similarity conditions are not met, that is Falling within the above-mentioned range External: Compare the values ​​of SNR(ZZ) and SNR(RR), and select the single-component cross-correlation function (ZZ cross-correlation function or RR cross-correlation function) corresponding to the higher value as the final effective signal of the station pair.

[0066] If the ZZ cross-correlation function and the RR cross-correlation function are superimposed indiscriminately, when the signal-to-noise ratio (SNR) difference between the two components is significant, the noise in the low SNR component will contaminate the effective signal of the high SNR component, causing the overall SNR to decrease rather than increase after fusion. Therefore, the method in this application sets a reasonable decision criterion, allowing fusion only when the signal quality is similar, to ensure the benefits and safety of the operation.

[0067] The tolerance threshold δ for similar conditions is a key parameter for controlling the above trade-offs. If the value of δ is too small, there will be too few station pairs that can meet the fusion conditions, and the data potential will not be fully explored; if the value of δ is too large, some data pairs with large differences in signal-to-noise ratio will enter the fusion process, introducing the aforementioned risk of deterioration.

[0068] To scientifically determine the key parameter of this application, namely the tolerance threshold δ, this embodiment selected the cross-correlation functions of 19 representative station pairs from the study area as a test set. These station pairs cover different station spacings, azimuth angles, and typical signal-to-noise ratio ranges, effectively reflecting the general characteristics of the data in this area. Based on this test set, system tests were conducted using different tolerance threshold values ​​δ (from 0.10 to 1.60), and the following key indicator data are statistically analyzed as shown in Table 1:

[0069] Table 1 shows the fusion performance statistics under different tolerance thresholds δ:

[0070]

[0071] Table 1 shows the number of station pairs that can be merged, i.e., those that meet the requirements. The number of stations under the given conditions.

[0072] Convergence ratio of stations: The percentage of stations that meet the criteria out of the total number of stations.

[0073] Fusion success rate: The proportion of station pairs whose signal-to-noise ratio (SNR) after fusion is positively improved compared to the better component's SNR before fusion. Average SNR improvement: The average relative percentage improvement in the final signal SNR of all 19 station pairs obtained after processing them using the method of this application, based on the current tolerance threshold δ and performing fusion or optimization decisions for each station pair, compared to the traditional baseline method (i.e., using only the Z component signal for all station pairs). This indicator comprehensively reflects the overall performance gain of the method of this application under different tolerance thresholds δ.

[0074] Two conclusions can be drawn from the data in Table 1:

[0075] (1) Determination of the optimal parameter δ=0.35:

[0076] When δ=0.35, the method of this application achieved the highest overall average signal-to-noise ratio improvement of +7.90%, while maintaining a fusion success rate of 100%. This indicates that under this parameter, the system can maximize the signal-to-noise ratio improvement while completely avoiding the risk of signal degradation caused by the fusion operation.

[0077] (2) Data validation and rationality analysis of "similarity conditions":

[0078] As the δ value continues to increase from 0.35, the data reveals a clear risk-reward trade-off curve: the "average signal-to-noise ratio improvement" begins to decline monotonically from its peak of 7.90%, while the "fusion success rate" drops rapidly from 100%.

[0079] When δ=1.6 (i.e., near-unconditional forced fusion), the fusion success rate plummeted to 42.10%, and the overall improvement turned to -2.75%. This result empirically demonstrates the risk of signal deterioration caused by blind fusion, as pointed out in the background technology.

[0080] The preferred value of δ=0.35 in this application is not an empirical guess, but rather revealed through system parameter scanning, enabling the application to simultaneously achieve optimal boundary values ​​in terms of gain, security, and data utilization. Those skilled in the art can adjust it within a reasonable range centered on 0.35 based on actual data characteristics.

[0081] See Figure 2 As shown, taking a certain station as an example, the three cross-correlation signals—ZZ component, RR component, and ZZ+RR component fused by the method of this application—were compared. The signal-to-noise ratios (SNRs) were: SNR(ZZ) 16.7, SNR(RR) 12.7, and SNR(ZZ+RR) 18.6. From Figure 2 As can be seen, the fused ZZ+RR waveform is more prominent than the single-component ZZ or RR waveform, and its signal-to-noise ratio (SNR) is the highest. The average SNR improvement of the fused signal compared to the single component with the higher SNR before fusion is 11.4%.

[0082] To verify the improvement of dispersion curves achieved by this application, four representative sets of data were selected from the station pairs. For each set of data, dispersion curves were extracted and compared using the ZZ+RR fused cross-correlation function, the ZZ cross-correlation function, and the RR cross-correlation function (RR cross-correlation function).

[0083] To verify the effectiveness of this application in extracting group velocity dispersion curves, see [link to relevant documentation]. Figure 3 As shown:

[0084] Group 1 ( Figure 4 (Left side): The extractable effective period range is extended from 27.0 seconds (ZZ cross-correlation function) to 35.7 seconds (ZZ+RR cross-correlation function), an extension of 7.7 seconds.

[0085] Group 2 ( Figure 4 (Right side): The extractable effective period range is extended from 25.6 seconds (ZZ cross-correlation function) to 31.3 seconds (ZZ+RR cross-correlation function), an extension of 5.7 seconds. To verify the phase velocity dispersion curve extraction effect of this application, see [reference needed]. Figure 4 As shown: Group 3: The effective period range that can be extracted is extended from 25 seconds (ZZ cross-correlation function) to 27 seconds (ZZ+RR cross-correlation function), which is extended by 2 seconds.

[0086] Group 4: The effective period range that can be extracted is extended from 22 seconds (ZZ cross-correlation function) to 25 seconds (ZZ+RR cross-correlation function), an extension of 3 seconds.

[0087] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A Rayleigh wave dual-component fusion method that can extend the effective dispersion curve, characterized in that, The process includes the following steps: acquiring three-component background noise data (vertical, east-west, and north-south) continuously collected from stations within the target area, and performing standardized preprocessing; using the preprocessed vertical component, calculating the ZZ cross-correlation function for all station pairs, where Z is the vertical component. Using the preprocessed east-west and north-south components, the RR cross-correlation function of all station pairs is calculated, where R is the radial component. The signal-to-noise ratio (SNR) of the ZZ cross-correlation function and the SNR of the RR cross-correlation function are calculated for each station pair. Station pairs whose SNR of both the ZZ and RR cross-correlation functions is not lower than a preset threshold are selected. The ratio of the SNR of the ZZ cross-correlation function to the SNR of the RR cross-correlation function for each selected station pair is calculated. If the ratio within a station pair meets the similarity condition, the ZZ and RR cross-correlation functions of that station pair are superimposed as the final valid signal. If the ratio within a station pair does not meet the similarity condition, the SNR of the ZZ and RR cross-correlation functions of that station pair is compared, and the cross-correlation function with the larger SNR is selected as the final valid signal.

2. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 1, characterized in that, Using the preprocessed vertical components, the ZZ cross-correlation function for all station pairs was calculated, including: Each station divides its continuous vertical components into multiple time data segments; For each time segment of data, calculate the ZZ cross-correlation function; The ZZ cross-correlation functions of all data segments are linearly superimposed to obtain the final ZZ cross-correlation function for this station.

3. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 1, characterized in that, Using the preprocessed east-west and north-south components, the RR cross-correlation function for all station pairs is calculated, including: dividing the continuous east-west and north-south components of each station pair into multiple time data segments; for each time data segment, calculating the EE cross-correlation function, NN cross-correlation function, EN cross-correlation function, and NE cross-correlation function, where E represents the east-west component and N represents the north-south component; superimposing the cross-correlation function results of the same type of component pairs in all data segments to obtain the final EE cross-correlation function, NN cross-correlation function, EN cross-correlation function, and NE cross-correlation function for that station pair; based on the azimuth angle between station pairs, transforming the EE cross-correlation function, NN cross-correlation function, EN cross-correlation function, and NE cross-correlation function to a radial-tangential coordinate system to obtain the corresponding TT cross-correlation function, RR cross-correlation function, TR cross-correlation function, and RT cross-correlation function, where R represents radial and T represents tangential; and extracting the RR cross-correlation function from the transformation results.

4. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 1, characterized in that, The calculation of the signal-to-noise ratio (SNR) of the ZZ cross-correlation function and the RR cross-correlation function includes: Set a surface wave signal velocity window. The lower limit of the surface wave signal velocity window is the minimum velocity of the cross-correlation function, and the upper limit of the surface wave signal velocity window is the maximum velocity of the cross-correlation function. Determine the signal arrival time window based on the surface wave signal velocity window; Set the noise window to a duration of n seconds after the signal window; The signal-to-noise ratio of the cross-correlation function is obtained by calculating the ratio of the maximum amplitude within the signal window to the root mean square amplitude within the noise window using the cross-correlation function.

5. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 4, characterized in that, The arrival time window of the signal is determined based on the surface wave signal velocity window, including: Calculate the upper limit of the window when the signal arrives. for: , The distance between stations, The maximum velocity of the cross-correlation signal; Calculate the lower limit of the window when the signal arrives. for: , The minimum velocity of the cross-correlated signal.

6. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 1, characterized in that, The preset threshold value is greater than 3.

7. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 1, characterized in that, Similar conditions are: ,in This is the ratio of the signal-to-noise ratio (SNR) of the ZZ cross-correlation function to the SNR of the RR cross-correlation function. This is the tolerance threshold.

8. The Rayleigh wave dual-component fusion method for extending the effective dispersion curve according to claim 7, characterized in that, The tolerance threshold The value ranges from 0 to 0.5.

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