A method for measuring slope wave velocity changes based on passive source spatial superposition

Abstract

This invention discloses a method for measuring slope wave velocity variation based on passive source spatial superposition. The method includes preprocessing raw seismic data from seismic stations; calculating the nine-component cross-correlation function between station pairs; using the cross-correlation function from the first five days as a reference cross-correlation; calculating the signal-to-noise ratio (SNR) between the tail wave of the cross-correlation function and the reference cross-correlation; extracting tail wave signals with relatively high SNR for each component and measuring wave velocity variation using a stirring method; dividing the area into different sub-networks based on station layout; superimposing wave velocity variations obtained from tail waves of different components on different rays; performing thermoelastic strain correction on the wave velocity variations to obtain a wave velocity variation map; calculating the response coefficients of seismic waves of different frequencies at different depths; and obtaining a four-dimensional wave velocity variation graph of the subsurface medium. This invention provides new technical means and scientific basis for constructing a real-time monitoring system for wave velocity variations in shallow subsurface layers and for the research and prevention of landslide geological hazards.

Description

Technical Field

[0001] This invention relates to a method for measuring slope wave velocity variation based on passive source spatial superposition, belonging to the fields of slope geological disaster prevention and control and highway subgrade testing technology. Background Technology

[0002] Seismic waves, carrying rich information about the subsurface medium during their propagation, are a powerful tool for studying it. Changes in seismic wave velocity in rocks are highly sensitive to the opening and closing of internal microfractures, variations in stress or shear modulus, and fluctuations in pore water pressure. Therefore, measuring changes in seismic wave velocity in the subsurface medium allows for continuous monitoring of relevant sensitive parameters, effectively revealing the inducing factors and mechanisms of slope activity. However, seismic waves are typically generated only under conditions of seismic events or artificial induction. The continuous waveforms recorded by stations are usually noisy data, making it difficult to reflect the temporal changes in subsurface geological conditions using only traditional seismic signals for subsurface medium inversion.

[0003] Early 2000s research demonstrated that cross-correlation calculations of the noise fields between two stations could extract the empirical Green's function, proving that noise signals, similar to seismic signals, could be used to invert underground geological conditions. Even more valuable is the continuous nature of the noise signal; therefore, measuring the time delay of seismic waves allows for long-term monitoring of internal changes in the subsurface medium. The coma, located in the later stages of the seismic record, has a longer propagation path than shear waves, p-waves, and surface waves, thus enabling more accurate detection of changes in subsurface velocity. Therefore, by analyzing the phase changes of the coma in the cross-correlation function, minute velocity changes as low as 0.1% within the subsurface medium can be precisely detected and monitored. This greatly facilitates our understanding of subsurface structural changes, including magmatic activity and stress changes in volcanic areas, the damage and recovery mechanisms caused by earthquakes, glacial flow processes, groundwater exploration, engineering structure monitoring, and landslide monitoring.

[0004] However, the application of wake wave measurement currently faces many challenges. This is because it relies on the reconstruction of the Green's function, which in turn depends on the superposition of a large amount of noise data to enhance the repeatability of the waveform. Therefore, the following technical problems need to be solved: (1) improving the temporal resolution of the measurement to ensure the capture of rapid changes related to the research content; (2) effectively distinguishing between the regular diurnal and seasonal variations of the underground environment and the anomalies generated by the research content; (3) stably extracting the velocity changes at various locations in the research area to facilitate the identification of abnormal velocity changes through comparative analysis; and (4) reducing the error of cross-correlation measurement at high temporal resolution, thereby making the velocity change results more accurate. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the present invention aims to provide a method for measuring slope wave velocity variation based on passive source spatial superposition.

[0006] The technical solution provided by this invention to solve the above-mentioned technical problems is: a method for measuring slope wave velocity variation based on passive source spatial superposition, comprising the following steps:

[0007] Step 1: Obtain raw seismic data from seismic stations in the target area;

[0008] Step 2: Preprocess the raw seismic data from the seismic stations;

[0009] Step 3: Calculate the nine-component cross-correlation function between station pairs based on the preprocessed raw seismic data;

[0010] Step 4: Overlay the cross-correlation of the previous 5 days as a reference cross-correlation;

[0011] Step 5: Extract the individual tailwave signal envelope for each component, calculate the signal-to-noise ratio between the cross-correlation function tailwave and the reference cross-correlation, and remove tailwaves with a signal-to-noise ratio less than 4.

[0012] Step 6: Measure the wave velocity change using the strenching method, average the results after superimposing the wake wave, calculate the correlation coefficient between the superimposed wake wave and the reference, and delete wave velocity changes with a correlation coefficient less than 0.7.

[0013] Step 7: Using the linear relationship between the delayed wave velocity change curve and the air temperature, perform thermoelastic strain correction on the wave velocity change to obtain the wave velocity change diagram.

[0014] Step 8: Calculate the response coefficients of seismic waves of different frequencies at different depths using surface wave sensitive kernels;

[0015] Step 9: Calculate the wave velocity variation diagram of seismic waves at different frequencies and the response coefficient ratio at different depths. Multiply the wave velocity variation at each frequency by weight, and inversely obtain the wave velocity variation at different depths from the wave velocity variation at different frequencies. Finally, obtain the four-dimensional wave velocity variation diagram of the subsurface medium.

[0016] A further technical solution is that the preprocessing in step two includes resampling, removing the mean, removing pinch-outs, and removing trends.

[0017] A further technical solution is that, in step two, the preprocessed raw seismic data is cut into data segments with smaller unit time intervals.

[0018] A further technical solution is that, in step three, cross-correlation calculations are performed on the three-component records of each station to obtain a nine-component cross-correlation function for a set of station pairs.

[0019] A further technical solution is that the formula for calculating the signal-to-noise ratio in step five is:

[0020]

[0021] Where: SNR is the signal-to-noise ratio of the reference cross-correlation; P s P represents the peak value of the microwave signal. n The average value of the signal envelope within the reference cross-correlation total measurement window.

[0022] A further technical solution is that the formula for calculating the correlation coefficient in step six is:

[0023]

[0024] In the formula: crr(ε) is the correlation coefficient between two cross-correlated variables; f ref For reference cross-correlation waveforms, The cross-correlation waveform for stretching or compression measurements.

[0025] A further technical solution is that the temperature correction in step seven is based on the following formula:

[0026] dv / v Corr =dv / v-KT delay＝nh

[0027] Where: dv / v and dv / v Corr The velocity change curves before and after removing the effect of temperature, T delay＝nh This represents a temperature change curve with an n-hour time delay, where K is the linear correlation coefficient between temperature and dv / v.

[0028] This invention offers the following advantages: It automatically extracts the wake component from high signal-to-noise ratio cross-correlation function data, then uses the "stretch-compression" method to calculate the correlation coefficient and velocity change with a reference cross-correlation function. The calculated velocity changes are then superimposed, and portions with a maximum correlation coefficient less than 0.7 are removed, resulting in more accurate velocity change results in the underground medium. Testing has shown a time resolution of up to 20 minutes. This allows for the inversion of four-dimensional internal structural deformation of slopes, the understanding of the impact of earthquakes and rainfall on landslide stability, and further insights into the opening and closing of landslide deformation fissures. This invention provides new technical means and scientific basis for constructing a real-time monitoring system for shallow underground wave velocity changes and for the research and prevention of landslide geological hazards. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the process of the present invention;

[0030] Figure 2 The lag time is used to match the reference cross-correlation results plot;

[0031] Figure 3 The measurement results are superimposed with a large number of wake waves;

[0032] Figure 4 A graph showing the wave velocity variations of seismic waves at different frequencies;

[0033] Figure 5 This is a diagram showing the four-dimensional wave velocity variation of the underground medium. Detailed Implementation

[0034] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] like Figure 1 As shown, the present invention provides a method for measuring slope wave velocity variation based on passive source spatial superposition, comprising the following steps:

[0036] Step 1: Obtain raw seismic data from seismic stations in the target area, such as data sampling rate, filter bandwidth, impedance gain, and energy spectrum analysis.

[0037] Step 2: Preprocess the raw seismic data from the seismic stations, including resampling, removing the mean, removing pinch-outs, and removing trends; then perform bandpass filtering within the effective signal frequency range obtained from the spectrum analysis; finally, cut the data into smaller unit time segments (1 day, 1 hour, 5 minutes).

[0038] Step 3: Calculate the nine-component cross-correlation function between station pairs based on the preprocessed raw seismic data;

[0039] Cross-correlation calculations were performed on the three-component records of each station to obtain a nine-component cross-correlation function for a set of station pairs;

[0040] Step 4: Overlay all available data from the previous 5 days for one component of each station pair to obtain the reference cross-correlation for each component in each station pair;

[0041] Step 5: Extract the individual tailwave signal envelope for each component, calculate the signal-to-noise ratio between the cross-correlation function tailwave and the reference cross-correlation, and remove tailwaves with a signal-to-noise ratio less than 4.

[0042] The signal-to-noise ratio (SNR) of the reference cross-correlation of a single-channel wake in the reference cross-correlation is calculated using the following formula:

[0043]

[0044] Where: SNR is the signal-to-noise ratio of the reference cross-correlation; P s P represents the peak value of the microwave signal. n The average value of the signal envelope within the total cross-correlation measurement window is used as a reference.

[0045] Within the tail wave time window of the nine-component cross-correlation function, a single-channel tail wave envelope with a signal-to-noise ratio greater than a certain threshold is automatically selected, and the minimum time window of the waveform under test is set to 3 times the period of the measured waveform.

[0046] Step 6: Measure wave velocity changes using the strenching method. Divide the sub-network areas according to the station layout, and average the results of each coda wave on different components of different rays in the sub-network. Calculate the correlation coefficient and wave velocity change of the cross-correlation between the superimposed coda wave and the reference, and delete wave velocity changes with a correlation coefficient less than 0.7.

[0047] The phase difference, i.e. wave velocity change, between the cross-correlation waveform and the reference cross-correlation waveform at different time intervals is measured using the "stretch-compression" method.

[0048] This method compresses or stretches the hysteresis time of the cross-correlation to match the reference cross-correlation. Figure 2 The formula is as follows:

[0049]

[0050] Where ε is the stretching coefficient, and the correlation coefficient crr(ε) between two cross-correlation pairs is expressed as follows:

[0051]

[0052] In the formula: crr(ε) is the correlation coefficient between two cross-correlated variables; f ref For reference cross-correlation waveforms, The cross-correlation waveform for stretching or compression is given; here ε is the relative time shift.

[0053] The relationship between them and the change in velocity is as follows:

[0054]

[0055] After calculating the velocity change, the measurement results are averaged using the coherence of the travel time offsets of the wake waves in different directions and paths to enhance the stability of the measurement results. Figure 3 );

[0056] Step 7: Using the linear relationship between the delayed wave velocity change curve and air temperature, correct the influence of thermoelastic strain on the observed wave velocity change, and conduct a more detailed analysis on its correlation with earthquakes, landslides, and rainfall.

[0057] Temperature correction is based on the following formula:

[0058] dv / v Corr =dv / v-KT delay＝nh

[0059] Where dv / v and dv / v Corr The velocity change curves before and after removing the effect of temperature, T delay＝nh This represents a temperature change curve with an n-hour time delay, where K is the linear correlation coefficient between temperature and dv / v.

[0060] Step 8: Calculate the response coefficients of seismic waves of different frequencies at different depths using surface wave sensitive kernels;

[0061] Step 9: Calculate the ratio of response coefficients at different depths for the wave velocity variation diagrams of seismic waves at different frequencies. Use the following formula to weightedly multiply the wave velocity variation at each frequency, and inversely obtain the wave velocity variation at depth from the wave velocity variation at different frequencies, ultimately obtaining a four-dimensional wave velocity variation diagram of the subsurface medium. Figure 5 );

[0062]

[0063] Where α is the sensitive kernel weighting coefficient for wave velocity change at a certain frequency at a certain depth;

[0064] Finally, it can reflect the impact of rainfall and earthquakes on slopes or roadbeds, detect crack expansion caused by sliding, and help identify abnormal signals.

[0065] The above description is not intended to limit the present invention in any way. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall fall within the scope of the present invention.

Claims

1. A method for measuring slope wave velocity variation based on passive source spatial superposition, characterized in that, Includes the following steps: Step 1: Obtain raw seismic data from seismic stations in the target area; Step 2: Preprocess the raw seismic data from the seismic stations; Step 3: Calculate the nine-component cross-correlation function between station pairs based on the preprocessed raw seismic data; Step 4: Overlay the cross-correlation of the previous 5 days as a reference cross-correlation; Step 5: Extract the individual tailwave signal envelope for each component, calculate the signal-to-noise ratio between the cross-correlation function tailwave and the reference cross-correlation, and remove tailwaves with a signal-to-noise ratio less than 4. Step 6: Measure the wave velocity change using the stretch-compression method, average the results after superimposing the wake wave, calculate the correlation coefficient between the superimposed wake wave and the reference, and delete wave velocity changes with a correlation coefficient less than 0.

7. Step 7: Using the linear relationship between the delayed wave velocity change curve and the air temperature, perform thermoelastic strain correction on the wave velocity change to obtain the wave velocity change diagram. Step 8: Calculate the response coefficients of seismic waves of different frequencies at different depths using surface wave sensitive kernels; Step 9: Calculate the ratio of response coefficients at different depths for the wave velocity variation diagrams of seismic waves at different frequencies, multiply the wave velocity variation at each frequency by weight, and inversely obtain the wave velocity variation at depth from the wave velocity variation at different frequencies, finally obtaining a four-dimensional wave velocity variation diagram of the subsurface medium.

2. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1, characterized in that, The preprocessing in step two includes resampling, removing the mean, removing sharpening, and removing trend.

3. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1, characterized in that, In step two, the preprocessed raw seismic data is cut into smaller time-segment data.

4. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1, characterized in that, In step three, cross-correlation calculations are performed on the three-component records of each station to obtain a nine-component cross-correlation function for a set of station pairs.

5. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1, characterized in that, The formula for calculating the signal-to-noise ratio in step five is as follows: Where: SNR is the signal-to-noise ratio of the reference cross-correlation; P s P represents the peak value of the microwave signal. n The average value of the signal envelope within the reference cross-correlation total measurement window.

6. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1, characterized in that, The formula for calculating the correlation coefficient in step six is ​​as follows: In the formula: crr(ε) is the correlation coefficient between two cross-correlated variables; f ref For reference cross-correlation waveforms, The cross-correlation waveform for stretching or compression measurements.

7. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1, characterized in that, The temperature correction in step seven is based on the following formula: dv / v Corr =dv / v-KT delay＝nh In the formula: dv / v is the velocity change curve before the effect of temperature is removed; dv / v Corr The velocity change curve after removing the effect of temperature, T delay＝nh This represents a temperature change curve with an n-hour time delay, where K is the linear correlation coefficient between temperature and dv / v.

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