Slope wave velocity change measurement method based on passive source space superposition

Abstract

The invention discloses a side slope wave velocity change measurement method based on passive source space superposition. The method comprises the following steps: preprocessing original seismic data of a seismic station; calculating a nine-component cross-correlation function between the station pairs; cross-correlation is taken as reference cross-correlation in five days before superposition; calculating a signal-to-noise ratio between a cross-correlation function tail wave and reference cross-correlation, extracting a tail wave signal with a relatively high signal-to-noise ratio for each component, and measuring wave velocity change through a straining method; dividing different sub-station network areas according to station layout, and superposing wave velocity changes obtained by each tail wave on different components of different rays; carrying out thermoelastic strain correction on the wave velocity change to obtain a wave velocity change diagram; calculating response coefficients of seismic waves with different frequencies at different depths; and obtaining an underground medium four-dimensional wave velocity change graph. According to the invention, new technical means and scientific basis are provided for constructing an underground shallow internal wave velocity change real-time monitoring system and researching and preventing landslide geological disasters.

Description

Technical Field

[0001] The invention relates to a method for measuring slope wave velocity changes based on passive source spatial superposition, and belongs to the technical field of slope geological disaster prevention and control and highway roadbed detection. Background Art

[0002] As seismic waves travel underground, they carry a wealth of information about underground media, making them a powerful tool for studying underground media. Changes in the velocity of seismic waves in rocks are very sensitive to the opening and closing of microcracks inside them, changes in stress or shear modulus, and fluctuations in pore water pressure. Therefore, by measuring the changes in the velocity of seismic waves in underground media, it is possible to continuously monitor relevant sensitive parameters in underground media, which will effectively reveal the inducing factors and mechanisms in slope activities. However, seismic waves are usually generated under conditions of earthquake events or artificial induction. Usually, the continuous waveforms recorded by the station are noise data, which makes it difficult to reflect the changes in underground geological conditions in the time domain by simply using traditional seismic signals for underground medium inversion.

[0003] In the research conducted at the beginning of this century, it was found that the cross-correlation calculation of the noise field between two stations can extract the empirical Green's function between them. This method proves that the noise signal can be used to invert the underground geological conditions similar to the seismic signal. What is more valuable is that the noise signal is continuous, so by measuring the time delay of the seismic wave, the internal changes of the underground medium can be monitored for a long time. The coda wave is located in the late stage of the earthquake record. Because it has a longer propagation path than the shear wave, the longitudinal wave, and the surface wave, it can more accurately capture the change of underground velocity. Therefore, by analyzing the phase change of the coda wave on the cross-correlation function, it is possible to accurately detect and monitor the tiny velocity changes as low as 0.1% in the underground medium. This has greatly facilitated human understanding of underground structural changes, including magma activity and stress changes in volcanic areas, damage and recovery mechanisms caused by earthquakes, glacial flow processes, groundwater exploration, engineering structure monitoring, and landslide monitoring.

[0004] However, the application of coda wave measurement currently faces many challenges. This is because it relies on the reconstruction of the Green's function, which relies on the moving superposition of a large amount of noise data to enhance the repeatability of the waveform. Therefore, the following technical problems need to be solved, including: (1) improving the temporal resolution of the measurement to ensure the capture of rapid changes related to the research content; (2) effectively distinguishing the regular daily and seasonal changes of the underground environment from 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; (4) reducing the error of measuring cross-correlation at high temporal resolution, so as to make the velocity change results more accurate. Summary of the invention

[0005] In order to overcome the defects in the prior art, the present invention aims to provide a slope wave velocity variation measurement method based on passive source spatial superposition.

[0006] The present invention provides a technical solution to solve the above technical problems: a slope wave velocity change measurement method based on passive source spatial superposition, comprising the following steps:

[0007] Step 1: Obtain the original seismic data of the seismic stations in the target area;

[0008] Step 2: preprocessing the original seismic data of the seismic station;

[0009] Step 3, calculating the nine-component cross-correlation function between station pairs based on the preprocessed original seismic data;

[0010] Step 4: superimpose the cross-correlations of the previous 5 days as reference cross-correlations;

[0011] Step 5: extract the separate coda wave signal envelope for each component, calculate the signal-to-noise ratio between the coda wave of the cross-correlation function and the reference cross-correlation, and remove the coda wave with a signal-to-noise ratio less than 4;

[0012] Step 6: Use the strenching method to measure the wave velocity changes, superimpose the coda waves, average the results, calculate the correlation coefficient between the superimposed coda waves and the reference, and delete the wave velocity changes with correlation coefficients 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 a wave velocity change graph;

[0014] Step 8: Obtain the response coefficients of seismic waves of different frequencies at different depths by calculating the surface wave sensitivity kernel;

[0015] Step nine, obtain the response coefficient ratio of the velocity variation diagram of seismic waves of different frequencies at different depths, perform weighted multiplication of the velocity variation at each frequency, invert the velocity variation at depth from the velocity variation at different frequencies, and finally obtain a four-dimensional velocity variation diagram of the underground medium.

[0016] A further technical solution is that the preprocessing in step 2 includes resampling, removing the mean value, de-pinching, and de-trending.

[0017] A further technical solution is that in step 2, the pre-processed original seismic data is cut into fragment data of smaller unit time.

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

[0019] A further technical solution is that the calculation formula of the signal-to-noise ratio in step 5 is:

[0020]

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

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

[0023]

[0024] Where: crr(ε) is the correlation coefficient between two cross-correlations; f ref is the reference cross-correlation waveform, Cross-correlation waveform for stretch 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 Corr and dv / v are the velocity variation curves before and after removing the temperature effect, respectively, and represents the temperature variation curve with a time delay of n hours. is the linear correlation coefficient between temperature and dv / v.

[0028] The present invention has the following beneficial effects: the present invention automatically picks up the high signal-to-noise ratio data of the cross-correlation function to extract the coda wave part, then uses the "stretch-compression" method to calculate the correlation coefficient and velocity change with the reference cross-correlation, superimposes the velocity change obtained and removes the part with the maximum correlation coefficient less than 0.7, so as to obtain a more accurate result of the velocity change of the underground medium, and the time resolution can reach 20 minutes after testing. This makes it possible to invert the four-dimensional internal structural deformation of the slope, obtain the influence of earthquakes and rainfall on the stability of the landslide, and further understand the opening and closing of the landslide deformation cracks; the present invention provides new technical means and scientific basis for constructing a real-time monitoring system for the internal wave velocity change of the underground shallow layer and the research and prevention of landslide geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0030] Figure 2 is the lag time to match the reference cross-correlation result graph;

[0031] Figure 3 This is a graph of measurement results with a large number of coda waves superimposed;

[0032] Figure 4 This is a graph showing the velocity changes of seismic waves of different frequencies;

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

[0034] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] like Figure 1 As shown, a slope wave velocity change measurement method based on passive source spatial superposition of the present invention comprises the following steps:

[0036] Step 1: Obtain the original seismic data of the seismic stations in the target area, such as data sampling rate, filter band, impedance gain, energy spectrum analysis, etc.;

[0037] Step 2: Preprocess the original seismic data from the seismic station, including resampling, removing the average value, de-sharpening, and de-trending; then perform bandpass filtering within the effective signal frequency range obtained in the spectrum analysis, and finally cut the data into smaller unit time segments (1 day, 1 hour, 5 minutes);

[0038] Step 3, calculating the nine-component cross-correlation function between station pairs based on the preprocessed original seismic data;

[0039] The three-component records of each station are cross-correlated, and a nine-component cross-correlation function of a set of station pairs is obtained;

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

[0041] Step 5: extract the separate coda wave signal envelope for each component, calculate the signal-to-noise ratio between the coda wave of the cross-correlation function and the reference cross-correlation, and remove the coda wave 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 coda wave in the reference cross-correlation is calculated using the formula:

[0043]

[0044] Where: SNR is the signal-to-noise ratio of the reference cross-correlation; P sis the peak value of the microwave signal; P n is the average value of the signal envelope within the total measurement window of the reference cross-correlation;

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

[0046] Step 6: Use the strenching method to measure the wave velocity change. Divide the sub-network area according to the station layout, superimpose the coda waves on different rays and components of the sub-network, average the results, calculate the correlation coefficient and wave velocity change of the coda wave after superposition and the reference cross-correlation, and delete the wave velocity changes with a correlation coefficient less than 0.7;

[0047] The “stretch-compression” method is used to measure the phase difference, that is, the wave velocity change, between the cross-correlation of different time periods and the reference cross-correlation.

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

[0049]

[0050] where ε is the stretch factor, and the correlation coefficient crr(ε) between two cross correlations is expressed as follows:

[0051]

[0052] Where: crr(ε) is the correlation coefficient between two cross-correlations; f ref is the reference cross-correlation waveform, is the measured cross-correlation waveform of stretching or compression; here ε is the relative time shift.

[0053] The relationship between the speed change is as follows:

[0054]

[0055] After calculating the velocity change, the coherence of the travel time offset of the coda wave in different directions and paths is used to average the measurement results 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 the air temperature, the observed wave velocity changes are corrected for the influence of thermoelastic strain, and a more detailed analysis is performed on the correlation with earthquakes, landslide activities and rainfall;

[0057] The temperature correction is based on the following formula:

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

[0059] Where dv / v Corr and dv / v are the velocity variation curves before and after removing the temperature effect, respectively, and represent the temperature variation curve with a time delay of n hours, and is the linear correlation coefficient between temperature and dv / v

[0060] Step 8: Obtain the response coefficients of seismic waves of different frequencies at different depths by calculating the surface wave sensitivity kernel;

[0061] Step 9. Obtain the response coefficient ratio of the velocity variation diagram of seismic waves of different frequencies at different depths, and use the following formula to weight and multiply the velocity variation at each frequency. Invert the velocity variation at depth from the velocity variation at different frequencies, and finally obtain the four-dimensional velocity variation diagram of the underground medium ( Figure 5 );

[0062]

[0063] Where α is the sensitive kernel weight coefficient of the 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 highway subgrades, 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 form. Although the present invention has been disclosed through the above embodiments, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are within the scope of the technical solution of the present invention.

Claims

1. A method for measuring slope wave velocity variation based on passive source spatial superposition, characterized in that: The following steps are involved: Step 1: Obtain the original seismic data of the seismic stations in the target area; Step 2: preprocessing the original seismic data of the seismic station; Step 3, calculating the nine-component cross-correlation function between station pairs based on the preprocessed original seismic data; Step 4: superimpose the cross-correlations of the previous 5 days as reference cross-correlations; Step 5: extract the separate coda wave signal envelope for each component, calculate the signal-to-noise ratio between the coda wave of the cross-correlation function and the reference cross-correlation, and remove the coda wave with a signal-to-noise ratio less than 4; Step 6: Use the strenching method to measure the wave velocity changes, superimpose the coda waves, average the results, calculate the correlation coefficient between the superimposed coda waves and the reference, and delete the wave velocity changes with correlation coefficients 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 a wave velocity change graph; Step 8: Obtain the response coefficients of seismic waves of different frequencies at different depths by calculating the surface wave sensitivity kernel; Step 9. Obtain the response coefficient ratio of the velocity variation diagram of seismic waves of different frequencies at different depths, perform weighted multiplication of the velocity variation at each frequency, invert the velocity variation at depth from the velocity variation at different frequencies, and finally obtain a four-dimensional velocity variation diagram of the underground medium.

2. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1 is characterized in that: The preprocessing in step 2 includes resampling, removing the mean value, de-pinching, and de-trending.

3. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1 is characterized in that: In the step 2, the pre-processed original seismic data is cut into smaller time unit segment data.

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

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

6. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1 is characterized in that: The calculation formula of the correlation coefficient in step 6 is: Where: crr(ε) is the correlation coefficient between two cross-correlations; f ref is the reference cross-correlation waveform, Cross-correlation waveform for stretch or compression measurements.

7. The method for measuring slope wave velocity variation based on passive source spatial superposition according to claim 1 is characterized in that: The temperature correction in step 7 is based on the following formula: dv / v Corr =dv / v-KT delay =nh Where: dv / v Corr and dv / v are the velocity variation curves before and after removing the temperature effect, respectively, and represents the temperature variation curve with a time delay of n hours. is the linear correlation coefficient between temperature and dv / v.

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