A novel polarization-based noise HV spectral ratio imaging method

Abstract

The present invention discloses a novel polarization-based noise HV spectral ratio imaging method, which collects background noise data within a predetermined monitoring area; preprocesses the collected background noise data, including data format conversion, data splicing, de-meaning, detrending, waveform clipping, and band-pass filtering; processes the preprocessed data using polarization analysis technology, separates the wave field by setting thresholds and analyzing the power spectrum, and distinguishes the background noise into linear body wave components and nonlinear surface wave components. The polarization-based noise HV spectral ratio imaging method of the present invention significantly improves the accuracy and efficiency of underground structure analysis through the precise separation of the wave field and the automated processing flow of the HV spectral ratio. The frequency and depth profiles automatically generated using polarization analysis-optimized data processing, as well as the automatically picked dominant frequencies and peaks, enable more accurate analysis of the underground velocity structure and wave impedance interface.

Description

Technical Field

[0001] The present invention relates to a new forming method, and particularly to a new noise HV spectral ratio imaging method based on polarization. Background Art

[0002] The microtremor HV spectral ratio method is based on the single-station three-component microtremor information on the Earth's surface. By appropriately averaging the horizontal components, the spectra of the horizontal and vertical components are obtained. This spectral ratio is a function of frequency and is called the HV spectral ratio or HVSR (Horizontal-to-Vertical Spectral Ratio). Even though there are disputes about the composition of the microtremor source, the HV spectral ratio method believes that the peak frequency of the HV spectral ratio curve corresponds to the resonance frequency of the S-wave of the site, or the dominant frequency. Through this frequency, the amplification factor and the thickness of the sedimentary layer of the site can be obtained. Due to the work of Nakamura (1989, 2000), the microtremor HV spectral ratio method has been popularized in the field of engineering seismology and is also called the Nakamura method. However, Nakamura himself prefers to call it the QTS (Quasi-Transfer Spectra) method (Nakamura, 2019). Since the microtremor HV spectral ratio method was proposed, some experiments (Lermo and Chávez-García, 1993; Gitterman et al., 1996; Seekins et al., 1996; et al., 1997) have shown that the HV spectral ratio method can be used to identify the fundamental resonance frequency of the sedimentary layer, and the good correspondence between the peak frequency of the microtremor HV spectral ratio and the fundamental resonance frequency of the SH-wave of the sedimentary layer has been confirmed by one-dimensional models. These studies use randomly distributed surface sources to simulate microtremor waveforms. When there is an obvious impedance ratio between the sedimentary layer and the bedrock, the HV spectral ratio generates a peak near the fundamental S-wave resonance frequency. For a single low-velocity layer on a half-space, Malischewsky and Scherbaum (2004) theoretically proved that the higher the impedance ratio between the low-velocity layer and the half-space, the better the correlation between the peak frequency of the microtremor HV spectral ratio and the fundamental resonance frequency of the S-wave of this layer. The HV spectral ratio method uses the HV peak to estimate the resonance frequency and is suitable for quickly studying the characteristics of the site. Compared with the method based on seismic records, the micro HV spectral ratio method has the advantages of simplicity, speed, economy, and less site restrictions. Although there are still some disputes in its theoretical interpretation, it has still been widely used in the field of engineering seismology, especially in places such as Japan. The HV spectral ratio method is also widely used in microzonation, that is, dividing, identifying, and mapping areas such as ground motion intensity, liquefaction, or landslide possibility.

[0003] The focus of the controversy over the microtremor HV spectral ratio method lies in which energy component in the microtremor record dominates. One view is that the main energy component of microtremors is body waves (Nakamura, 1989, 2000, 2009; Herak, 2008), especially the vertically incident SH wave. Under this assumption, the peak of the HV spectral ratio curve corresponds to the resonance frequency of the overlying soft soil layer, so the HV spectral ratio curve can be used to estimate the site amplification effect and the thickness of the sedimentary layer. Another view is that the main energy component in microtremors is Rayleigh waves, especially the fundamental mode Rayleigh wave. Under this assumption, the HV spectral ratio curve is mainly affected by the ellipticity of the fundamental Rayleigh wave, and there is no direct correlation with the actual site amplification effect in theory. In a clarification article by Nakamura (2000), this view does not seem to be recognized. It is considered that just because the shape of the HV spectral ratio curve is similar to the ellipticity curve of the fundamental Rayleigh wave does not mean that the HV spectral ratio curve corresponds to the ellipticity curve of the fundamental Rayleigh wave mode. Regardless of the influence degree of Rayleigh waves, the HV spectral ratio curve (QTS) can give the resonance frequency of SH waves to a certain extent, so as to infer the dominant frequency of the site and the site amplification factor.

[0004] In addition, in the frequency band outside the peak amplitude frequency of the microtremor HV spectral ratio curve, the contributions of different seismic phases are also different. Some authors believe that surface waves play a dominant role in the microtremor wave field, at least in the range above the fundamental resonance frequency of the sedimentary layer (Lunedei & Albarello, 2009; Albarello & Lunedei, 2011). Bonnefoy-Claudet et al. (2006b) simulated the microtremor HV spectral ratio curve using sources in bedrock and found that the peak of the HV spectral ratio curve is mainly caused by S-wave resonance. However, since the wave field around the array is mainly generated by human activities and is closer to surface waves, the actual HV spectral ratio may be mainly affected by the fundamental mode Rayleigh wave. In the research on the theoretical basis of the HV spectral ratio, the most common method is to model the HV spectral ratio curve using the ellipticity of Rayleigh waves and invert the medium structure using the HV spectral ratio curve. Due to the controversy over the microtremor wave formation components, some noise source models have also been developed currently, such as the DSS (Distributed Surface Sources) and DFA (Diffuse Field Approach) models.

[0005] Therefore, based on the viewpoints of different researchers and the development of seismic interferometry theory, there are clearly two branches in the study of ambient vibration HV spectral ratios (Lunedei & Malischewsky, 2015): The first branch is to study the Rayleigh wave ellipticity (or the ellipticity ZH amplitude ratio) by identifying the Rayleigh wave component in the ambient vibration records, and image the medium structure using the Rayleigh wave ellipticity or in combination with the Rayleigh wave dispersion curve. This method is not limited to seismic ambient noise and is also used for natural earthquake events, mainly in the field of regional or shallow subsurface seismic tomography. The theoretical basis of this direction is the Rayleigh wave theory in horizontally layered media. To distinguish it from the ambient vibration HV spectral ratio, the more explicit term "Rayleigh wave ZH amplitude ratio" is generally used. The second branch is to develop different ambient vibration source models by studying the wave mode components in the ambient vibration records, considering the contributions of different wave mode components, and explaining the measured HV spectral ratio curve. For example, the ambient vibration source model dominated by S waves, the diffused background noise model, etc. This direction has gradually developed along with the development of seismic interferometry theory. However, as Lunedei & Malischewsky (2015) pointed out, developing a theoretical model to explain the HV spectral ratio curve requires the model to be an actual model of the background noise wave field and should first be consistent with the findings of the actual background noise wave field, rather than just giving a possible way to generate and explain the HV spectral ratio curve.

[0006] Polarization analysis (Zebker et al., 2010) refers to a method of obtaining subsurface medium information by analyzing the polarization characteristics of seismic waveforms in seismic signal processing. Polarization analysis is a technique used in seismic signal processing that can help us understand the physical properties of the subsurface medium (Yilmaz et al, 2011; Dong et al, 2021). In HV spectral ratio imaging, polarization analysis can be used to optimize data processing and improve imaging accuracy. The applications of polarization analysis in HV spectral ratio imaging mainly include the following aspects:

[0007] (1) Improving the signal-to-noise ratio: By using polarization analysis, we can determine the polarization direction of the seismic signal and filter out the non-polarized signal part, thereby improving the signal-to-noise ratio of the data. This enables HV spectral ratio imaging to better capture the reflection information of the subsurface medium and improve the imaging quality.

[0008] (2) Identifying abnormal media: Abnormalities in the subsurface medium may cause differences in the polarization signals in different directions. By using polarization analysis, we can detect these abnormalities and provide more information for HV spectral ratio imaging.

[0009] (3) Determine physical properties: When seismic waves pass through a medium, polarization rotation occurs. We can use polarization analysis to infer the physical properties of the subsurface medium, such as compressibility, shearability, and density. This helps to interpret the HV spectral ratio imaging results and improve the understanding of the subsurface structure.

[0010] The applications of polarization analysis in seismology are very extensive and mainly include the following aspects:

[0011] (1) Earthquake location: Earthquake location refers to the process of determining the depth and location of the earthquake source. Polarization analysis can be used to determine the depth and location of the earthquake source. Since the polarization of seismic signals changes during propagation, earthquake sources at different locations and depths will result in different polarization characteristics. This property can be used for earthquake location.

[0012] (2) Subsurface medium structure analysis: Polarization analysis can be used to study the physical properties and structure of the subsurface medium, such as the change in the water table, fractures and faults in rocks, etc. Seismic waves are affected by the medium structure when passing through the subsurface, and different structures will result in different polarization characteristics. By analyzing the polarization characteristics, the structure and physical properties of the subsurface medium can be inferred.

[0013] (3) Seismic imaging: Seismic imaging refers to the process of constructing a three-dimensional model of the subsurface medium using seismic data. Polarization analysis can be used for seismic imaging. In seismic data processing, polarization characteristics can be extracted to determine the physical properties and structure of the subsurface medium, and then a three-dimensional model of the subsurface medium can be constructed.

[0014] (4) Rock mechanics research: Polarization analysis can be used to study the mechanical properties of rocks, such as the stress state of rocks, fractures and faults in rocks, etc. Seismic waves will undergo polarization changes when passing through rocks. By analyzing the polarization characteristics, the mechanical properties and structure of rocks can be inferred.

[0015] (5) Sediment research: Polarization analysis can be used to study the physical properties of sediments, such as the thickness, density, and porosity of sediments, etc. Since the physical properties of sediments are related to the waveform polarization characteristics, the physical properties and structure of sediments can be inferred by analyzing the polarization characteristics.

[0016] The existing background noise HV spectral ratio method is a method for calculating the Fourier spectral ratio of the horizontal and vertical components of the data recorded by a three-component seismograph. It is generally agreed that the peak frequency of the HV spectral ratio curve is consistent with or close to the resonance frequency in the loose sediment layer, and research shows that when the wave impedance interface ratio is large, near-surface noise sources will produce sharp peaks. Therefore, the HV spectral ratio curve can reflect the underground velocity structure, and whether there is a strong wave impedance interface underground and calculate the interface depth can be judged according to whether there is a peak. However, this method still has some disadvantages. Currently, the research on HV spectral ratio is based on three directions, namely body waves, surface waves, and mixed wave fields. When we cannot further determine the wave field that plays a decisive role in the HV spectral ratio curve, problems such as multiple peaks, inability to judge the peaks, and inability to extract the dominant frequency will occur. Summary of the Invention

[0017] To solve the above problems, the present invention provides a novel noise HV spectral ratio imaging method based on polarization, which solves the problems of multiple peaks and unclear peaks when calculating the HV spectral ratio due to the mixing of wave fields in the noise HV spectral ratio.

[0018] The present invention is realized through the following technical solutions: A novel noise HV spectral ratio imaging method based on polarization, including the following specific steps:

[0019] Step 1: Collect background noise data in a predetermined monitoring area;

[0020] Step 2: Preprocess the collected background noise data, including data format conversion, data splicing, de-meaning, detrending, waveform clipping, and band-pass filtering;

[0021] Step 3: Use polarization analysis technology to process the preprocessed data, separate the wave fields by setting thresholds and analyzing the power spectrum, and distinguish the background noise into linear body wave components and non-linear surface wave components;

[0022] Step 4: Conduct single-station HV spectral ratio analysis on the separated linear body waves and non-linear surface waves, and calculate their respective HV spectral ratios;

[0023] Step 5: Use the obtained HV spectral ratios to perform profile imaging in the frequency domain according to the relative positions of the stations;

[0024] Step 6: Use the obtained HV spectral ratios to perform profile imaging in the depth domain according to the relative positions of the stations.

[0025] As a preferred technical solution, in step 2, the band-pass filtering is adjusted for a specific frequency range to optimize the response to a specific frequency and enhance the signal-to-noise ratio of the target frequency.

[0026] As a preferred technical solution, in step three, the polarization analysis further includes using multi-dimensional data processing technology based on time window and frequency selection to ensure the accuracy and efficiency of wave field separation.

[0027] As a preferred technical solution, in step four, the HV spectral ratio analysis includes applying an adaptive algorithm to reduce noise in the analysis and optimize the accuracy of the HV spectral ratio curve.

[0028] As a preferred technical solution, the frequency domain and depth domain profile imaging step includes using high-resolution image processing technology to clearly reveal the velocity structure and wave impedance interface underground.

[0029] The data processing system of the present invention, the system includes:

[0030] A data acquisition module for acquiring background noise in the monitoring area;

[0031] A data processing module configured with software for performing data preprocessing, polarization analysis, HV spectral ratio calculation, and profile imaging;

[0032] A display module for displaying the results of profile imaging and providing an interactive operation interface.

[0033] As a preferred technical solution, the data processing module includes at least one microprocessor and related storage devices, and the storage devices store instructions and algorithms for executing the above steps.

[0034] The beneficial effects of the present invention are: By adopting the polarization-based noise HV spectral ratio imaging method, the present invention significantly improves the accuracy and efficiency of underground structure analysis, optimizes the data processing process, allows for the automatic generation of frequency and depth profiles, and further improves the simplicity of operation and the reliability of results;

[0035] By automatically picking the excellent frequencies and peaks of the HV spectral ratio, the present invention can more accurately analyze and identify the wave impedance interface and velocity structure underground, which is of great significance for the prevention of geological disasters and the exploration of resources. The automated processing not only reduces human errors but also speeds up the data processing speed, making the analysis of seismic data more efficient and accurate.

[0036] The present invention significantly optimizes the processing and analysis of seismic data by using polarization analysis, improves the imaging accuracy, enhances the application value of the data, and brings innovative technological progress to the fields of seismology and geological exploration. Brief Description of the Drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0038] Figure 1 It is a comparison diagram of the numerical simulation waveform polarization of the present invention before and after processing;

[0039] Figure 2 It is a comparison diagram of the vector end diagrams of the present invention's numerical simulation waveform before and after polarization;

[0040] Figure 3 It is a comparison diagram of the actual station waveform polarization of the present invention before and after processing;

[0041] Figure 4 It is a comparison diagram of the single-station HV spectral ratio curve polarization of the present invention before and after processing;

[0042] Figure 5 It is a sectional view of the HV spectral ratio in the frequency domain of the present invention;

[0043] Figure 6 It is a sectional view of the HV spectral ratio in the depth domain of the present invention. Specific embodiments

[0044] All features disclosed in this specification, or all steps in the disclosed methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

[0045] Any feature disclosed in this specification (including any additional claims, abstract, and drawings), unless specifically stated, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically stated, each feature is only an example in a series of equivalent or similar features.

[0046] A novel noise HV spectral ratio imaging method based on polarization of the present invention, based on the idea of wave field separation, utilizes the polarization characteristics of the wave field, adopts the polarization method to separate the background noise in the wave field, and uses different thresholds and wave field quality control to separate the background noise of the full wave field into relatively pure body waves and surface waves. Then, further calculate the HV spectral ratio value, obtain the HV spectral ratio values in different wave field conditions, and thus improve the imaging resolution and imaging quality, including the following specific steps:

[0047] Step 1: Collect background noise data in a predetermined monitoring area;

[0048] Step 2: Preprocess the collected background noise data, including data format conversion, data splicing, mean removal, detrending, waveform clipping, and band-pass filtering.

[0049] Step 3: Process the preprocessed data using polarization analysis technology, separate the wave field by setting thresholds and analyzing the power spectrum, and distinguish the background noise into linear body wave components and nonlinear surface wave components.

[0050] Step 4: Conduct single-station HV spectral ratio analysis on the separated linear body waves and nonlinear surface waves, and calculate their respective HV spectral ratios.

[0051] Step 5: Use the obtained HV spectral ratios to perform cross-sectional imaging in the frequency domain based on the relative positions of the stations.

[0052] Step 6: Use the obtained HV spectral ratios to perform cross-sectional imaging in the depth domain based on the relative positions of the stations.

[0053] Among them, in Step 2, the band-pass filtering is adjusted for a specific frequency range to optimize the response to a specific frequency and enhance the signal-to-noise ratio of the target frequency.

[0054] In Step 3, the polarization analysis further includes using a multi-dimensional data processing technology based on time window and frequency selection to ensure the accuracy and efficiency of wave field separation.

[0055] In Step 4, the HV spectral ratio analysis includes applying an adaptive algorithm to reduce noise in the analysis and optimize the accuracy of the HV spectral ratio curve.

[0056] Among them, the cross-sectional imaging steps in the frequency domain and depth domain include using high-resolution image processing technology to clearly reveal the velocity structure and wave impedance interface underground.

[0057] The data processing system of the present invention, which includes:

[0058] A data acquisition module for collecting background noise in the monitoring area;

[0059] A data processing module configured with software for performing data preprocessing, polarization analysis, HV spectral ratio calculation, and cross-sectional imaging;

[0060] A display module for presenting the results of cross-sectional imaging and providing an interactive operation interface.

[0061] The data processing module includes at least one microprocessor and related storage devices, and the storage devices store instructions and algorithms for performing the above steps.

[0062] To study the feasibility of polarization analysis in HV spectral ratio imaging, we first conducted numerical simulations to verify the feasibility of polarization analysis in wavefield separation. Then, we performed polarization processing using the actually deployed stations. Finally, we analyzed the results before and after the processing. The actual data had a station spacing of 5 m, 94 stations, an effective acquisition duration of 4 hours, and a sampling frequency of 1000 Hz.

[0063] Figure 1 This is the three-component numerical simulation for a single station, with a total of 1200 samples. Among them, samples 1 - 600 are linear motion (body waves), and samples 601 - 1200 are planar elliptical motion (surface waves). Figure 1 In [Figure], a and b are the waveform comparisons before and after processing. We can see that when the threshold T0 is 0.8, we can well separate the two wavefields.

[0064] Figure 2 This is the comparison of waveforms and hodographs before and after polarization of the numerical simulation waveforms. Among them Figure 2 in [Figure], a is the original numerical simulation waveform, and b is the hodograph corresponding to the original numerical simulation waveform. From Figure 2 a and b in [Figure], it can be seen that the original data is divided into surface wave components of inverse-path ellipses and body wave components of linear motion; Figure 2 c and d in [Figure] are the surface wave components after polarization processing, Figure 2 e and f in [Figure] are the body wave components after polarization processing; it can be seen from the comparison that in terms of the angle of the wavefield, polarization can well separate surface waves and body waves.

[0065] Figure 3 This is the comparison diagram of the three-component short-period station waveforms before and after polarization of the actually deployed stations. Among them Figure 3 in [Figure], a and b are the waveform comparisons before and after polarization processing. We can see that when the threshold R0 is 0.2, we can relatively well separate body waves and surface waves. Figure 3 c and d in [Figure] are continuous waveforms over a longer time. The waveforms after polarization have relatively clearer relative amplitudes and more distinct energies.

[0066] Figure 4 This is the comparison diagram of the single-station HV spectral ratio curves before and after polarization. Among them, the blue and red are respectively the comparison of the single-station HV spectral ratio curves before and after polarization processing. We can see that the HV spectral ratio curve after polarization processing has a larger ratio, more wave peaks, clearer curve changes, and carries more medium information than before the processing. This is very beneficial for subsequent data processing such as imaging, inversion, extraction of dominant frequencies, and estimation of sediment layer thickness.

[0067] Figure 5Among them, a and b are respectively the waveform in the frequency domain and the variable area HV spectrum ratio profile, and 0-20Hz and 20-100Hz are respectively displayed at equal intervals. We can clearly see that there is a clear in-phase axis in the range of 0-20Hz, spreading from west to east and from deep to shallow. An obvious convex structure appears between channels 80-90, which is speculated to be the uplift of underground rock layers, related to the migration of groundwater and formation movement. There is a continuous energy axis in the range of 20-100Hz, with a frequency range of 50-70Hz. Calculated according to the empirical formula, the depth range is roughly about 20-30cm, which should be the bluestone slab and asphalt layer on the surface.

[0068] Figure 6 The depth domain profile obtained by depth conversion through the empirical formula Depth = 108f in the HV spectrum ratio frequency domain. From the figure, we can see that there is a continuous in-phase axis in the depth range of 5-15m. Among them, there are three in-phase axes between channels 1-40, one continuous in-phase axis between channels 40-70, and one shallower continuous in-phase axis between channels 70-90. Obvious offsets occur between channels 38-44 and between channels 70-75 in the whole structure, and it is speculated that hidden faults occur at these two positions. -1.551 As mentioned above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any change or replacement that can be thought of without creative work should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope defined by the claims.

[0069] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any change or replacement that can be thought of without creative work should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope defined by the claims.

Claims

1. A novel polarization-based noise HV spectrum ratio imaging method, characterized in that: The specific steps include: Step 1: Collect background noise data in a predetermined monitoring area; Step 2: preprocessing the collected background noise data, including data format conversion, data splicing, de-averaging, de-trending, waveform pinching out, and bandpass filtering; Step 3: Use polarization analysis technology to process the preprocessed data, separate the wave field by setting thresholds and analyzing power spectra, and distinguish the background noise into linear body wave components and nonlinear surface wave components; Step 4: Perform HV spectrum ratio analysis on the separated linear body waves and nonlinear surface waves, and calculate their respective HV spectrum ratios; Step 5: Use the obtained HV spectrum ratio to perform profile imaging in the frequency domain according to the relative positions of the stations; Step 6: Use the obtained HV spectrum ratio to perform profile imaging in the depth domain according to the relative positions of the stations.

2. The novel polarization-based noise HV spectrum ratio imaging method according to claim 1, characterized in that: In step 2, the bandpass filter is adjusted for a specific frequency range to optimize the response to the specific frequency and enhance the signal-to-noise ratio of the target frequency.

3. The novel polarization-based noise HV spectrum ratio imaging method according to claim 1, characterized in that: In step three, the polarization analysis further includes using a multi-dimensional data processing technology based on time window and frequency selection to ensure the accuracy and efficiency of wave field separation.

4. The novel polarization-based noise HV spectrum ratio imaging method according to claim 1, characterized in that: In step 4, the HV spectrum ratio analysis includes applying an adaptive algorithm to reduce noise in the analysis and optimize the accuracy of the HV spectrum ratio curve.

5. The novel polarization-based noise HV spectrum ratio imaging method according to claim 1, characterized in that: The frequency domain and depth domain profile imaging steps include using high-resolution image processing technology to clearly reveal the velocity structure and wave impedance interface under the surface.

6. A data processing system for executing the method according to any one of claims 1 to 5, characterized in that: The system includes: Data acquisition module, used to collect background noise in the monitoring area; Data processing module, equipped with software for data preprocessing, polarization analysis, HV spectrum ratio calculation and profile imaging; The display module is used to display the results of profile imaging and provide an interactive operation interface.

7. The data processing system according to claim 6, characterized in that: The data processing module includes at least one microprocessor and a related storage device, and the storage device stores instructions and algorithms for executing the above steps.

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