Method for extracting surface wave dispersion of vehicle seismic source signals under conditions of low road smoothness

Abstract

The invention discloses a method for extracting surface wave dispersion of vehicle source signals under the condition of low road flatness, and relates to the technical field of vehicle source signals. Each channel of a power spectrum function calculated from a vehicle source signal is multiplied by an offset distance r to obtain a new function. A spatial Fourier transform of the new function with respect to the offset distance r is performed to obtain a frequency wavenumber spectrum. The frequency wavenumber spectrum F(ω, k') is interpolated and superimposed to obtain the method. Based on the F‑J method, the invention improves the calculation method through the characteristics of equidistant linearly arranged detectors used in vehicle source signal acquisition, and improves the efficiency and accuracy of dispersion curve extraction.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle seismic source signals, and in particular to a method for extracting surface wave dispersion of vehicle seismic source signals under the condition of low road smoothness. Background Art

[0002] Vehicle source signals are a type of passive source signal that is widely present in cities and is easy to collect and has good correlation. They have gradually attracted the attention of scholars as a potential urban near-surface exploration source (Behm et al., 2014; Quiros et al., 2016; Cheng et al., 2019; Pang et al., 2019). At present, except for large underground buildings, urban exploration is mainly concentrated around roads, and subway tunnels often pass under major roads. Therefore, the acquisition and processing of vehicle signals has great application potential in the exploration and monitoring of the overlying layers of underground tunnels. The dispersion data extracted from the signal can be used to invert the near-surface geological structure. Yan et al. (2021) explained the nature of the Doppler effect and proved that the dispersion curve extracted from the vehicle signal is not affected by the vehicle speed and is authentic and reliable, providing a theoretical basis for the application of vehicle source signals.

[0003] At present, the approximate implementation scheme is seismic interferometry. Zhang et al. (2018) collected and processed vehicle signal data in Singapore through seismic interferometry and proposed a more efficient collection method. Zhang et al. (2020) compared the near-surface structure obtained by active source signals and vehicle signals, which demonstrated the accuracy of near-surface detection by vehicle signals. Liu et al. (2021) proposed and verified a real-time monitoring method for highway and railway subgrades based on seismic interferometry and recurrent neural network (RNN). In addition, the frequency-Bessel function method (FJ method) proposed by Chen Xiaofei et al. can also be used for passive source dispersion data extraction.

[0004] At present, the seismic interferometry method obtains pseudo-track gathers by performing autocorrelation and cross-correlation operations between each track and the first track, which has low information utilization and low resolution of the obtained dispersion data. Although the FJ method can obtain high-resolution dispersion data between any two detectors, it has poor effect when arranged in equidistant straight lines. Summary of the invention

[0005] In order to solve the above technical problems, the present invention discloses a method for extracting surface wave dispersion of vehicle source signals under the condition of low road flatness. The method can greatly improve the utilization rate of information through cross-correlation calculation between any two detectors. On the basis of the FJ method, according to the characteristics of equidistant linearly arranged detectors used in vehicle source signal acquisition, the calculation method is improved, and the efficiency and accuracy of dispersion curve extraction are improved.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The method for extracting surface wave dispersion of vehicle source signals under the condition of low road roughness comprises the following steps:

[0008] Step a, the power spectrum function calculated from the vehicle source signal Multiply each trace by the offset r to get the new function

[0009] Step b, for the new function Perform spatial Fourier transform about the offset r to obtain the frequency wavenumber spectrum

[0010] Step c, interpolate and superimpose the frequency wavenumber spectrum F(ω,k') to obtain

[0011] Further, in step a, in the layered model, the dispersion property is obtained by the following equation (1):

[0012]

[0013] in, is the cross-correlation function between any two passive source seismic records, J0(kr) is a zero-order first-class Bessel function, r is the distance between the detectors, and r is an arithmetic progression in the case of linear equidistant detector arrangement.

[0014] Furthermore, in step b, for the new function Perform spatial Fourier transform to simplify the calculation process;

[0015] According to the definition of Bessel function:

[0016]

[0017] Substituting into (1), we get:

[0018]

[0019] make but Substituting the above relationship into (3) and exchanging the order of integration, we obtain:

[0020]

[0021] because Therefore Substituting into (4), we get

[0022]

[0023] make So we get:

[0024]

[0025] The beneficial effect of the present invention is that, compared with the prior art,

[0026] (1) Based on the existing frequency-Bessel method, this method takes into account the characteristics of vehicle source signal acquisition along the road or railway line, and improves the dispersion extraction algorithm;

[0027] (2) This method is suitable for situations where the road is uneven and the vehicle source signal energy is strong. The acquisition array is parallel to the direction of vehicle movement, which can quickly and efficiently perform dispersion extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The distribution diagram of multiple vehicle sources and detectors in the present invention;

[0029] Figure 2 Schematic diagram of the extraction of dispersion curve of synthetic data in the present invention, (a) synthetic seismic record; (b) wave field reconstruction and pseudo gather extraction based on actual signal; (c) FK spectrum; (d) dispersion data extraction, where the black line is the theoretical dispersion curve. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0031] Aiming at the problem that the signal utilization rate is low when the existing seismic interferometry method is used to process vehicle source signals, and the frequency-Bessel method has poor dispersion extraction effect in linear equidistant arrangement, the present invention provides a method for extracting surface wave dispersion of vehicle source signals under the condition of low road smoothness.

[0032] A method for extracting surface wave dispersion of vehicle source signals under the condition of low road smoothness comprises the following steps:

[0033] (1) The power spectrum function calculated from the vehicle source signal Multiply each trace by the offset r to get the new function

[0034] According to Wang et al. (2015), in the layered model, the dispersion property is obtained by the following equation (1):

[0035]

[0036] in, is the cross-correlation function between any two passive source seismic records, J0(kr) is a zero-order first-class Bessel function, r is the distance between the detectors, and r is an arithmetic progression in the case of linear equidistant detector arrangement.

[0037] For new functions Perform spatial Fourier transform about the offset r to obtain the frequency wavenumber spectrum

[0038] For new functions Perform spatial Fourier transform to simplify the calculation process;

[0039] According to the definition of Bessel function:

[0040]

[0041] Substituting into formula (1), we can get:

[0042]

[0043] make but Substituting the above relationship into formula (3) and exchanging the order of integration, we get:

[0044]

[0045] (3) By interpolating and superimposing the frequency wavenumber spectrum F(ω,k'), we can obtain

[0046] because Therefore Substituting into formula (4), we get

[0047]

[0048] make So we get:

[0049]

[0050] Based on the dispersion extraction of vehicle source signals under linear equidistant arrangement, this method discloses an algorithm improvement under linear equidistant arrangement based on the frequency-Bessel method when the road roughness is high, that is, the vehicle source energy is strong. This can effectively reduce the amount of calculation, extract high-order dispersion data, and improve the accuracy of urban geological near-surface parameter inversion.

[0051] Compared with the existing speed pickup technology, the present invention has the following three advantages:

[0052] (1) Applicable to linear equidistant seismic signal acquisition system arranged along the road;

[0053] (2) Applicable to situations where road roughness is low;

[0054] (3) Fast and efficient, it can realize the extraction of high-order dispersion information.

[0055] The present invention adopts an algorithm improvement based on the frequency-Bessel method, and can also use seismic interferometry and the like to reconstruct the wave field, but the information utilization rate is low.

[0056] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for extracting surface wave dispersion of vehicle source signals under low road roughness conditions, characterized in that: The following steps are involved: Step a, the power spectrum function calculated from the vehicle source signal Multiply each trace by the offset r to get the new function Step b, for the new function Perform spatial Fourier transform about the offset r to obtain the frequency wavenumber spectrum Step c, interpolate and superimpose the frequency wavenumber spectrum F(ω,k') to obtain 2. The method for extracting surface wave dispersion of vehicle source signals under low road roughness as claimed in claim 1, characterized in that: Step a, in the layered model, the dispersion properties are obtained by the following equation (1): in, is the cross-correlation function between any two passive source seismic records, J0(kr) is a zero-order first-class Bessel function, r is the offset distance, and r is an arithmetic progression in the case of a linear equidistant detector arrangement.

3. The method for extracting surface wave dispersion of vehicle source signals under low road roughness as claimed in claim 2, characterized in that: Step b, for the new function Perform spatial Fourier transform to simplify the calculation process; According to the definition of Bessel function: Substituting into (1), we get: make but Substituting the above relationship into (3) and exchanging the order of integration, we obtain: because Therefore Substituting into (4), we get make So we get:

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