Passive source multi-channel surface wave analysis advantage noise source distribution scanning and stacking correction method and system

Abstract

This invention relates to a method and system for scanning and superimposing correction of dominant noise source distribution in passive source multichannel surface wave analysis. The method first determines the stable frequency band and velocity scanning range. Then, within the stable frequency band, at each scanning velocity, a frequency-azimuth domain energy spectrum scan is performed, incorporating the azimuth of the two-dimensional observation station. Next, based on the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each velocity is selected, resulting in the final azimuth range of the dominant noise source distribution. Finally, the dispersion spectrum after azimuth correction of the dominant noise source distribution is obtained. This invention considers the azimuth of the observation station pair and can be applied to any two-dimensional observation array. Furthermore, through the reasonable determination of the stable frequency band and velocity scanning range, the analysis of the dominant noise source azimuth range, and the weighted superposition of the azimuth-corrected dispersion spectrum, the calculation quality of the dispersion spectrum under non-uniform noise source distribution conditions is effectively improved, and it has high fault tolerance.

Description

Technical Field

[0001] This invention relates to passive source surface wave imaging technology for underground space in the field of urban underground space development and utilization, and particularly to a method and system for scanning and superimposing correction of dominant noise source distribution in passive source multichannel surface wave analysis. Background Technology

[0002] Passive-source surface wave (SSW) methods extract and invert surface wave dispersion information from micro-motion signals to obtain subsurface shear wave velocity structures, making them an important method for fine imaging of subsurface space. Research on passive-source SSW imaging has a history of nearly 70 years. Akamatu (1956) discovered that spatially randomly distributed noise sources possess coherence, and Aki (1957) proposed the Spatial Autocorrelation (SPAC) method, ushering in an era of imaging subsurface structures using seismic background noise. To overcome the limitations of the SPAC method, which requires a strictly circular observation system, Ling and Okada (1993) proposed the Extended Spatial Autocorrelation (ESPAC) method, applicable to observation arrays of arbitrary shapes (Ohori et al., 2002; Okada, 2003; Asten et al., 2018; Cho et al., 2021). Chávez-García et al. (2005) found that time averaging can, to some extent, replace spatial averaging, further enhancing the practicality of the SPAC method. Hayashi (2002) introduced the concept of common-center imaging from active-source surface wave exploration into passive-source surface wave imaging, establishing the CMP-SPAC method (Hayashi et al., 2004, 2015), which improved the efficiency of three-dimensional subsurface space imaging. Campillo and Paul (2003) demonstrated that cross-correlation of seismic background noise data from two observation locations can construct an excitation-received wavefield. Schuster et al. (2004), based on the concept of interferometry in optics, named this method seismic interferometry. Louie (2001) based on... - The concept of transformation led to the development of the ReMi (Re-Motion Refraction) method, which requires careful consideration when dealing with directional noise distributions (Strobbia and Cassiani, 2011). Strobbia et al. (2015) proposed a ReMi method using a two-dimensional array composed of two orthogonal linear arrays, broadening its applicability. Xu et al. (2017) combined seismic interferometry with ReMi, demonstrating the ability to obtain more accurate surface wave dispersion energy. Wang et al. (2019) proposed the Frequency Bessel Transform (FJ) method, which can better extract multi-order surface wave dispersion information (Pan et al., 2019; Li Xueyan, 2020).

[0003] In addition, there is another research approach in passive source surface wave methods that incorporates the concept of active source multichannel surface wave analysis (MASW) into passive source surface wave exploration. This is known as passive source multichannel surface wave analysis, such as the roadside passive source multichannel surface wave analysis (PMASW) proposed by Park and Miller (2008). Cheng et al. (2016) proposed a cross-correlation-based passive source surface wave multichannel analysis (MAPS) method, which effectively combines the roadside passive source method with seismic interferometry, resulting in higher quality dispersive energy spectra (Pan et al., 2016; Xia et al., 2017). Pan et al. (2019) analyzed MAPS imaging under different noise windows based on noise records from adjacent railways and proposed a signal-to-noise ratio-based data filtering technique in the time domain, which can effectively solve the interference of non-steady-state windows and effectively improve the imaging effect of the MAPS method.

[0004] However, traditional passive source surface wave methods assume uniform noise source distribution. In urban environments, due to complex human activities, noise source distribution is sometimes non-uniform, or even directional, which greatly affects the quality of surface wave dispersion curve extraction in passive source multichannel surface wave analysis. Park et al. (2004), based on the MASW method, used a cross-shaped survey line to scan the noise distribution of each frequency in all directions, and then superimposed it along the azimuth angle to obtain the dispersion spectrum, which effectively reduced the influence of non-uniform noise sources. Park and Miller (2008) used the azimuth of the strongest energy in a certain time period as the azimuth of the main noise source, ignoring the influence of other noise, which effectively improved the imaging quality of the dispersion spectrum. Feuvre et al. (2014) effectively combined the cross-correlation algorithm and the beamforming algorithm, and determined the azimuth of passive source noise through beamforming analysis, which improved the accuracy of passive source surface wave dispersion analysis. Weaver and Yoritomo (2018) used time-domain weighted superposition to screen out cross-correlation functions with high signal-to-noise ratios for time-varying noise fields, mitigating the influence of destructive noise sources in the unstable phase region. Liu et al. (2020) proposed a pseudo-linear array passive source surface wave analysis (PLAS) method based on a clustering analysis algorithm. By adding detectors in the longitudinal direction of a one-dimensional linear array, a pseudo-linear array was constructed, which accurately determined the azimuth range of the noise source. Ning L (2022) selected time-segment data whose peak results from clustering analysis conformed to the Fresnel angle and velocity range, correlated and superimposed them to generate a virtual shot gather, and used MASW to calculate the dispersion spectrum, improving the quality of dispersion spectrum calculation. Cheng (2023) analyzed the artifacts caused by directional noise sources in the dispersion spectrum imaging of a linear receiver array.

[0005] In summary, the existing methods have the following main problems:

[0006] Existing methods for calculating the dispersion spectrum of passive source multichannel surface wave analysis are usually only applicable to linear observation arrays, without considering the azimuth distribution of the stations in the observation array, and cannot adapt to the noise source distribution correction of the micro-motion detection of arbitrary two-dimensional observation arrays.

[0007] Existing methods are highly dependent on the accuracy of scanning speed, have low fault tolerance, and usually only extract the azimuth angle of a single noise source distribution for correction, which cannot comprehensively correct the impact of noise source distribution on the dispersion spectrum calculation, thus limiting the accuracy of dispersion spectrum calculation.

[0008] Currently, no method has been found for calculating the dispersion spectrum of micro-motion detectors that can be applied to any two-dimensional observation array and can effectively correct within the azimuth range of the dominant noise source. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing methods and propose a passive source multichannel surface wave analysis method and system for dominant noise source distribution scanning and superposition correction. This method effectively solves the technical obstacle of existing passive source multichannel surface wave analysis, which does not consider the azimuth distribution of the observation array and cannot adapt to the noise source distribution correction of micro-motion detection of arbitrary two-dimensional observation arrays. This method also simultaneously solves the technical problem of using a single noise source distribution azimuth correction in existing technologies. Specifically, the technical solution of this invention proposes frequency-azimuth domain energy spectrum scanning considering the azimuth of two-dimensional observation arrays, making the technical solution applicable to arbitrary two-dimensional arrays. Furthermore, this method utilizes the energy distribution of the frequency-azimuth domain dispersion spectrum to effectively identify the azimuth range of dominant noise sources and fully utilizes the azimuth of dominant noise sources to comprehensively correct the influence of noise source distribution on dispersion spectrum calculation. Thus, through a superposition strategy, it significantly improves the quality and reliability of dispersion spectrum calculation under non-uniform noise distribution and has high fault tolerance, providing technical support for high-resolution detection imaging of urban underground spaces.

[0010] To achieve the above objectives, the present invention provides a method for scanning and superimposing dominant noise source distribution in passive source multichannel surface wave analysis, comprising the following steps:

[0011] Step S1: Obtain the stable frequency band range and velocity scan range;

[0012] Step S2: For each scanning velocity in the velocity scanning range, within the stable frequency band, a frequency-azimuth domain energy spectrum scan is performed by introducing the azimuth factor from the two-dimensional observation station to obtain the frequency-azimuth domain dispersion energy spectrum corresponding to each scanning velocity.

[0013] Step S3: Determine the azimuth range of the dominant noise source distribution;

[0014] Based on the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each scanning speed is picked out, and then the final azimuth range of the dominant noise source distribution is obtained statistically.

[0015] Step S4: Correct each noise azimuth angle in the range of the dominant noise source distribution to obtain the dispersion energy spectrum after correction of each azimuth angle, and fuse the dispersion energy spectra after correction of each azimuth angle to obtain the dispersion energy spectrum after correction of the dominant noise source distribution azimuth angle.

[0016] Furthermore, in step S2, when the frequency-azimuth domain energy spectrum scan of the azimuth factor is performed by introducing a two-dimensional observation station, the corresponding model is expressed as:

[0017] ;

[0018] In the formula, Scanning speed within the stated speed scanning range The corresponding frequency-azimuth domain dispersion energy spectrum; For the first Daohedi Causal branch of frequency domain cross-correlation function for railway station pairs; For the first Daohedi The frequency domain cross-correlation function of the railway station pair has a non-causal branch (a commonly used technical term in this field; any related calculation has both causal and non-causal branches). e is the imaginary unit, and e is the natural base. For frequencies within a stable frequency band; For the first Tao and the First The distance between the Daotai stations; The noise azimuth angle; For the first Tao and the First The angle between the station and the 0° azimuth line represents the azimuth angle of the two-dimensional observation station; N is the number of stations in the observation array.

[0019] Furthermore, the implementation process of step S4 is as follows:

[0020] Calculate the azimuth range of the dominant noise source distribution. The dispersion spectrum after correction for each noise azimuth angle;

[0021] By weighted superposition and averaging of all azimuth-corrected dispersive energy spectra, the azimuth-corrected dispersive energy spectrum of the dominant noise source distribution is obtained:

[0022] ;

[0023] ;

[0024] This is the final superimposed dispersion spectrum, i.e., the dispersion spectrum after azimuth correction of the dominant noise source distribution; The azimuth range of the dominant noise source distribution The Middle Noise azimuth angle The corresponding weighting coefficients; The azimuth range of the dominant noise source distribution Number of noise azimuth angles within; Noise azimuth angle Corrected dispersion spectrum, To stabilize frequencies within the frequency band, The scanning speed represents any scanning speed within the scanning range.

[0025] Among them, when the noise source is evenly distributed or the azimuth angle of the main noise is located in the Fresnel zone angle. When the noise is inside, only the noise azimuth angle needs to be calculated. =0 As ; ; For surface wave wavelength, The distance between the station and the platform.

[0026] Furthermore, weighting coefficients The azimuth range of the dominant noise source distribution The Middle Noise azimuth angle The noise source energy is located within the azimuth range of the dominant noise source distribution. The percentage of total energy.

[0027] Furthermore, the stable frequency band range mentioned in step S1 is determined based on the inflection point of the peak energy of the initial dispersion spectrum of the micro-motion and high-frequency interference.

[0028] In urban environments, the frequency of seismic background noise is relatively high. Furthermore, in practical passive source multichannel surface wave analysis applications, the imaging quality of the low-frequency portion of the dispersive energy spectrum is often low, while the quality of the dispersive energy spectrum in the high-frequency band, especially the frequency band where the high-frequency phase velocity tends to be stable (referred to as the stable frequency band), is higher. Therefore, the technical solution of this invention utilizes the scanning of the main azimuth distribution range of noise sources in passive source multichannel surface wave analysis within the stable frequency band to obtain more stable and accurate noise source distribution results.

[0029] Furthermore, the velocity scanning range in step S1 is determined based on prior information about the high-frequency phase velocity or surface velocity of the initial dispersion energy spectrum of the micro-motion.

[0030] Since the stable frequency band is mainly distributed in the high-frequency phase velocity stable region, this velocity scanning range can ensure that the noise source distribution scan is focused on the effective signal frequency band, avoiding interference from irrelevant velocity intervals on the scanning results. Generally, based on the a priori information of high-frequency phase velocity or surface velocity from the initial dispersion spectrum results of micro-motion, the velocity scanning center value is determined, and the velocity scanning range is determined by setting a certain upper and lower fluctuation ratio according to the actual situation.

[0031] Furthermore, the implementation process of step S3 is as follows:

[0032] First, set the bandwidth for picking up the dominant azimuth angle;

[0033] Secondly, based on the dominant azimuth pickup bandwidth, and according to the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each scanning speed is picked out. Among them, the azimuth range of the dominant noise source distribution The corresponding energy value is the highest;

[0034] Finally, based on the probability statistics of the azimuth angle range of the dominant noise source distribution corresponding to all scanning speeds, the one with the highest probability will be selected. As the ultimate advantage, the azimuth range of noise source distribution .

[0035] Furthermore, the present invention also provides a system based on the above method, comprising:

[0036] The preprocessing module is used to obtain the stable frequency band range and velocity scan range;

[0037] The scanning module is used to perform a frequency-azimuth domain energy spectrum scan for each scanning speed in the velocity scanning range within the stable frequency band, introducing the azimuth factor of the two-dimensional observation station, to obtain the frequency-azimuth domain dispersion energy spectrum corresponding to each scanning speed.

[0038] The dominant noise source distribution azimuth angle determination module is used to determine the range of dominant noise source distribution azimuth angles.

[0039] Based on the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each scanning speed is picked out, and then the final azimuth range of the dominant noise source distribution is obtained statistically.

[0040] The correction module is used to synthesize the dispersion energy spectrum of each noise azimuth angle within the range of the dominant noise source distribution azimuth angle to obtain the dispersion energy spectrum after the dominant noise source distribution azimuth angle correction.

[0041] The present invention also provides a computer device, comprising: one or more processors and a memory storing one or more computer programs;

[0042] The processor invokes a computer program to achieve the following:

[0043] Steps of the passive source multichannel surface wave analysis method for noise source distribution scanning and superposition correction.

[0044] The present invention also provides a computer-readable storage medium storing a computer program, which is invoked by a processor to implement the following:

[0045] Steps of the passive source multichannel surface wave analysis method for noise source distribution scanning and superposition correction.

[0046] The beneficial effects of this invention are as follows:

[0047] This invention proposes a novel method for scanning and superimposing correction of noise source distribution in passive source multichannel surface wave analysis. This method overcomes the shortcomings of existing passive source multichannel surface wave analysis methods, such as the inability to adapt noise source distribution correction for arbitrary two-dimensional observation array micro-motion detection, high dependence on scanning speed accuracy, low fault tolerance, and the inability to comprehensively correct the impact of noise source distribution on dispersion spectrum calculation by only extracting the azimuth angle of a single noise source distribution, which limits the accuracy of dispersion spectrum calculation.

[0048] Specifically, the method of this invention considers the azimuth of the observation station pair (the azimuth of the station pair is the distribution angle of the line connecting the station pairs) in the azimuth correction of the noise source distribution, and designs a frequency-azimuth domain energy spectrum scanning model, making the method applicable to any two-dimensional observation array. Furthermore, the method of this invention also effectively improves the calculation quality of the dispersion energy spectrum of passive source multichannel surface wave analysis under non-uniform noise source distribution by reasonably determining the stable frequency band and velocity scanning range, analyzing the azimuth range of the dominant noise source, and weighting the azimuth correction dispersion energy spectrum. It also has high fault tolerance and is more conducive to practical applications. The technical solution of this invention provides methodological support for high-precision micro-motion detection of underground space velocity structures in complex noise environments such as cities. Attached Figure Description

[0049] Figure 1 A flowchart of a passive source multichannel surface wave analysis dominant noise source distribution scanning and superposition correction method provided in one embodiment of the present invention;

[0050] Figure 2 This is a diagram showing the given model parameters and noise source distribution in one embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of determining the stable frequency band range based on the initial dispersion energy spectrum in one embodiment of the present invention. The horizontal axis represents frequency, and the vertical axis represents phase velocity.

[0052] Figure 4 This is a schematic diagram showing the azimuth relationship between the noise source (blue dot) and the observation station pair (green triangle) in one embodiment of the present invention;

[0053] Figure 5 This is a schematic diagram of the azimuth scanning results of the noise source distribution at each scanning speed in one embodiment of the present invention (only a partial speed slice is shown).

[0054] Figure 6The following are the results of extracting the azimuth range of the dominant noise source distribution according to an embodiment of the present invention: (a) the result when the azimuth range of the dominant noise source distribution is not extracted in the stable frequency band, (b) the azimuth range of the dominant noise source distribution obtained when the azimuth picking bandwidth of the dominant noise source is 30°, and (c) the azimuth range of the dominant noise source distribution obtained when the azimuth picking bandwidth of the dominant noise source distribution is 10°.

[0055] Figure 7 Comparison of the superimposed dispersion energy spectrum effect of the azimuth correction of the advantageous noise source distribution in one embodiment of the present invention: (a) dispersion energy spectrum without azimuth correction of the noise source, (b) dispersion energy spectrum with azimuth correction of the noise source at 45° center azimuth, (c) dispersion energy spectrum with azimuth correction of the noise source at 37° center azimuth, (d) dispersion energy spectrum with azimuth correction of the advantageous noise source distribution.

[0056] Figure 8 The following are the superimposed dispersion spectrum imaging results when there is a deviation in the azimuth range and energy distribution of the advantageous noise source in one embodiment of the present invention: (a) average superposition of azimuth angles from 30° to 60°, (b) weighted superposition of azimuth angles from 25° to 55°, and (c) weighted superposition of azimuth angles from 35° to 65°.

[0057] Figure 9 This is a fragment of measured micro-motion data from an embodiment of the present invention.

[0058] Figure 10 This is an azimuth scan result of the noise source distribution according to an embodiment of the present invention;

[0059] Figure 11 The measured data dispersion spectrum calculation results of one embodiment of the present invention are as follows: (a) ESPAC method, (b) traditional passive source multichannel surface wave azimuth correction method, and (c) the method of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0061] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0063] like Figure 1 As shown, this invention provides a method for scanning and superimposing the distribution of dominant noise sources in passive source multichannel surface wave analysis. The technical idea of ​​this method is as follows:

[0064] Step S1: Obtain the stable frequency band range and velocity scanning range; Step S2: For each scanning velocity in the velocity scanning range, within the stable frequency band range, consider the azimuth angle of the two-dimensional observation station and perform a frequency-azimuth domain energy spectrum scan to obtain the frequency-azimuth domain dispersion energy spectrum corresponding to each scanning velocity; Step S3: Determine the azimuth range of the dominant noise source distribution; Step S4: Combine the dispersion energy spectrum of each noise azimuth angle corrected in the azimuth range of the dominant noise source distribution to obtain the dispersion energy spectrum after azimuth angle correction of the dominant noise source distribution.

[0065] The key improvement of this invention lies in the frequency-azimuth domain energy spectrum scanning process, which considers the azimuth angle of the two-dimensional observation station, overcoming existing technical limitations. Furthermore, unlike existing technologies that only extract the azimuth angle of a single noise source for correction, this invention proposes a technical solution for identifying the azimuth angle range of dominant noise sources and a strategy for superimposing dispersive energy spectrum imaging. The technical solution of this invention will be described in detail below with reference to specific embodiments.

[0066] Example 1

[0067] like Figure 1 As shown in the figure, the passive source multichannel surface wave analysis dominant noise source distribution scanning and superposition correction method provided by the present invention includes the following steps:

[0068] Step 1: Determine the stable frequency band range based on the inflection point of the peak energy of the initial dispersion spectrum of the micro-motion and the high-frequency interference.

[0069] Figure 2 Given model parameters and noise source distribution map, 500 noise sources are non-uniformly distributed within an azimuth angle of 30° to 60°, of which 400 noise sources are uniformly distributed within an azimuth angle of 30° to 60° and 100 noise sources are uniformly distributed within an azimuth angle of 30° to 50°. Figure 3This diagram illustrates the determination of the stable frequency band based on the dispersive energy spectrum. The imaging quality of the low-frequency portion of the dispersive energy spectrum is often lower, while the high-frequency band, especially the frequency band where the high-frequency phase velocity tends to stabilize (referred to as the stable frequency band), exhibits higher and more stable dispersive energy spectrum quality. Based on a comprehensive judgment of the inflection point of the peak energy of the dispersive energy spectrum and the high-frequency interference situation, and considering the relatively weak energy of the high-frequency noise source, the stable frequency band is determined to be 15Hz-40Hz in some embodiments. It should also be understood that in other feasible embodiments, the stable frequency band can be determined arbitrarily according to the actual application situation; this invention does not limit its value range.

[0070] Step 2: Determine the velocity scanning range using prior information on the high-frequency phase velocity or surface velocity from the initial dispersion spectrum of the micro-motion.

[0071] The specific implementation process is as follows: Based on the high-frequency phase velocity or surface velocity prior information from the initial dispersion energy spectrum results of microtremor surveys (a professional term in the field of microtremor surveys, involving surface wave dispersion analysis in seismology and engineering geophysics), the velocity scan center value is determined, and the velocity scan range is determined by setting a certain upper and lower fluctuation ratio according to the actual situation. For example, based on... Figure 2 The model parameters are set with a surface shear wave velocity of 200 m / s, which fluctuates by 20% in this embodiment, and the velocity scanning range is set to 160-240 m / s.

[0072] Since the stable frequency band is mainly distributed in the high-frequency phase velocity stable region, the velocity scanning range determined based on the high-frequency phase velocity can ensure that the noise source distribution scanning is focused on the effective signal frequency band. By proposing velocity range scanning, the problem of inaccurate noise source orientation scanning results caused by inaccurate velocity during single velocity scanning can be avoided.

[0073] Step 3: For each scanning velocity, within the stable frequency band, consider the azimuth angle of the two-dimensional observation station and perform a frequency-azimuth domain energy spectrum scan.

[0074] Figure 4 This is a schematic diagram showing the azimuth relationship between the noise source (blue dot) and the observation station pair (green triangle). Figure 5 The azimuth scan results for the noise source distribution at each scan rate are shown (only a partial velocity slice is displayed). Within the stable frequency band determined in step 1, each scan rate within the velocity scan range set in step 2 is analyzed. The scanning speed is obtained by performing a frequency-azimuth domain scan. The corresponding dispersion energy spectrum.

[0075]

[0076] ——No. Frequency-azimuth domain dispersion energy spectrum corresponding to each scanning speed;

[0077] ——No. Daohedi Causal branch of frequency domain cross-correlation function for railway station pairs;

[0078] ——No. Daohedi The frequency domain cross-correlation function of the railway station pair is non-causal.

[0079] —Imaginary unit;

[0080] —Frequencies within a stable frequency band;

[0081] ——No. Tao and the First The distance between the Daotai stations;

[0082] —Noise azimuth angle;

[0083] ——No. Tao and the First The angle between the railway station and the 0° azimuth line;

[0084] —The first in the velocity scan range Each scan speed value is selected.

[0085] N represents the number of stations in the observation array.

[0086] Step 4: Set the dominant azimuth angle picking bandwidth. Based on the energy distribution of the frequency-azimuth angle domain dispersion spectrum, pick the dominant noise source distribution azimuth angle range corresponding to each velocity. The final dominant noise source distribution azimuth angle range is obtained through probability statistics.

[0087] In step 4, the dominant azimuth angle pickup bandwidth is set, that is, based on the noise source distribution scanning results, the azimuth angle width for picking the dominant noise source distribution is set (e.g., a range width of 30 degrees). Based on this range width, according to the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth angle range of the dominant noise source distribution corresponding to each velocity is picked, and the final azimuth angle range of the dominant noise source distribution is obtained through probability statistics.

[0088] Set the dominant azimuth pickup bandwidth parameter, and based on the energy distribution of the frequency-azimuth domain dispersion spectrum, pick up the azimuth range of the dominant noise source distribution corresponding to each velocity within the velocity scanning range. Within this azimuth range, the energy value of the frequency-azimuth domain dispersion spectrum is the highest. Based on the probability statistics of the azimuth range of the dominant noise source distribution corresponding to all scanning velocities, the one with the highest probability will appear... As the ultimate advantage, the azimuth range of noise source distribution . Figure 6 The image shows the extraction results of the azimuth range of the dominant noise source distribution. Among them, Figure 6 Figure a shows the result when the dominant noise source distribution azimuth range is extracted outside the stable frequency band. Due to low-frequency energy interference, an incorrect dominant noise source distribution azimuth range is obtained. Figure b shows the dominant noise source distribution azimuth range obtained when the dominant noise source azimuth pickup bandwidth is 30°, with a result of 30° to 60° and a center azimuth of 45°. Figure c shows the dominant noise source distribution azimuth range obtained when the dominant noise source distribution azimuth pickup bandwidth is 10°, with a result of 32° to 42° and a center azimuth of 37°.

[0089] Step 5: Calculate the dispersion spectrum of each azimuth correction, and perform weighted average based on the proportion of the corresponding noise source energy in the total noise energy within the dominant azimuth to obtain the dispersion spectrum of the dominant noise source distribution after azimuth correction.

[0090] Figure 7 A comparison of the effects of superimposed dispersive energy spectrum imaging on azimuth correction of dominant noise source distribution is presented. To improve noise source distribution correction, a superimposed dispersive energy spectrum imaging strategy is proposed. The azimuth range of the dominant noise source distribution is defined. Every angle Dispersion spectrum calculations were performed on all dispersion spectra. By performing a weighted average, the superimposed dispersion energy spectrum after azimuth correction of the final noise source distribution is obtained. The weighting coefficient is the first... The proportion of noise source energy in each azimuth angle to the total energy in the azimuth angle range of the dominant noise source distribution.

[0091]

[0092] ;

[0093] —Final superposition dispersion energy spectrum;

[0094] —and the The weighting coefficients for the linear positive correlation of energy in each azimuth slice;

[0095] —Azimuth range of dominant noise source distribution Number of azimuth angles within;

[0096] ——No. The dispersion spectrum of each noise source distribution after azimuth correction.

[0097] When the noise source is evenly distributed or the main noise location is within the Fresnel zone angle When inside, only the azimuth angle needs to be calculated. =0 ;

[0098]

[0099] — Surface wave wavelength;

[0100] —Distance between stations.

[0101] Figure 7 Figure a shows the dispersion spectrum without noise source azimuth correction; Figure b shows the dispersion spectrum with noise source correction at a 45° center azimuth angle; Figure c shows the dispersion spectrum with noise source correction at a 37° center azimuth angle; and Figure d shows the dispersion spectrum of the dominant noise source distribution with azimuth correction and superposition. The dispersion curves extracted from the dispersion spectrum obtained by passive source multichannel surface wave analysis without noise source azimuth correction show a generally higher systematic error compared to the theoretical dispersion curves. Figure 7 Figure a). Azimuth correction was performed using the center azimuth angle of the dominant noise source distribution at 45° to obtain the dispersion spectrum. Compared to the theoretical dispersion curve, the overall higher systematic error of the extracted dispersion curve was significantly improved, but a large error still exists. Figure 7 (See Figure b). This is because the noise source distribution is not uniform in the azimuth range of 30° to 60°. Azimuth correction was performed using the center azimuth angle of the dominant noise source distribution (37°) to obtain the dispersion spectrum. The extracted dispersion curve showed good agreement with the theoretical dispersion curve. Figure 7 (Figure c). Figure 7 Figures b and c show that calculating the dispersive energy spectrum based on the azimuth angle of a single noise source requires high accuracy of the azimuth angle; when the azimuth angle accuracy is low, non-negligible errors will occur. The dispersive energy spectrum obtained using the superposition correction dispersive energy spectrum imaging strategy has the highest quality, and the extracted dispersive curve shows a further improved agreement with the theoretical dispersive curve. Figure 7 The d-figure illustrates that the method proposed in this invention can improve the accuracy of dispersion curve extraction. Figure 7 In the diagram, the black dots represent the extracted dispersion curves, and the white dots represent the theoretical dispersion curves.

[0102] Figure 8The results of stacked dispersive energy spectrum imaging when there are deviations in the azimuth range and energy distribution of the dominant noise source are as follows: (a) average stacking at azimuth angles of 30°–60°; (b) weighted stacking at azimuth angles of 25°–55°; (c) weighted stacking at azimuth angles of 35°–65°. In actual passive source surface wave exploration, due to the complexity of measured data, the calculated azimuth range of the dominant noise source distribution may not be very accurate, and the fault tolerance of the method is very important for practical applications. In order to analyze the adaptability of the stacked dispersive energy spectrum imaging strategy, it is assumed that the energy of the noise source is consistent within the dominant azimuth range of 30°–60°. Although the dispersion curve picked up by the average stacked dispersive energy spectrum has a lower degree of agreement with the theoretical dispersion curve than that of the weighted stacking, it still has high accuracy. Figure 8 As shown in Figure a), it also proves that the average superposition method of the present invention is feasible in other feasible embodiments. By using a velocity with a large error to perform noise source azimuth scanning, the dominant noise source azimuth angle ranges of 25°–55° and 35°–65° are obtained respectively. The weighted superposition strategy then yields the following... Figure 8 The dispersion spectra shown in Figures b and c demonstrate that the extracted dispersion curves still fit the theoretical dispersion curves well. This indicates that the weighted stacking dispersion spectrum imaging strategy can still obtain high-quality dispersion spectra even when there are certain deviations in the obtained azimuth range and energy distribution of the dominant noise source, proving that the present invention has strong fault tolerance.

[0103] The technical solution of this invention is illustrated below with specific measured data examples.

[0104] Figure 9 This is a segment of measured micromotion data. In passive source surface wave detection in a coal mine goaf, due to environmental constraints, a linear array was used with a station spacing of 5 m, a number of 25 stations, and an array length of 120 m. The time acquisition interval was 0.004 s, and the acquisition duration was 40 min. Short-period seismic stations were deployed using the buried method to obtain measured micromotion data (…). Figure 9 ). Figure 10 This is the azimuth scanning result of the noise source distribution in this invention. Based on prior information, the velocity scanning range is set to 200-300 m / s. The azimuth scanning result of the noise source distribution at all scanning velocities is obtained using the noise source distribution azimuth scanning strategy of this invention. Figure 10 According to probability statistics, the azimuth range of the dominant noise source distribution is 38° to 58°. Figure 11The results of the dispersion spectrum calculation based on measured data are presented: (a) ESPAC method, (b) traditional passive source multichannel surface wave azimuth correction method, and (c) the method of this invention. It can be seen that the ESPAC dispersion spectrum imaging results have the most severe interference contamination. The quality is relatively good in the low-to-mid frequency band below 25 Hz, but due to the non-uniformity of noise source distribution, the phase velocity corresponding to the peak energy in the dispersion spectrum is generally too high. The dispersion spectrum imaging results obtained by the traditional passive source multichannel surface wave azimuth correction method show that it successfully corrects the high phase velocity, but the continuity of the peak energy in the dispersion spectrum is poor. The dispersion spectrum imaging results obtained by the method of this invention have the highest overall quality, correcting the overall high phase velocity phenomenon caused by the non-uniform distribution of noise sources, and also improving the continuity of the peak energy in the dispersion spectrum. Figure 11 ).

[0105] In summary, this invention proposes a novel method for scanning and superimposing correction of dominant noise source distribution in passive source multichannel surface wave analysis. It considers the azimuth angles of observation pairs and is applicable to any two-dimensional observation array. By rationally determining the stable frequency band and velocity scanning range, analyzing the azimuth angle range of dominant noise sources, and weighting the azimuth-corrected dispersion spectrum, the method effectively improves the dispersion spectrum calculation quality of passive source multichannel surface wave analysis under non-uniform noise source distribution conditions. Furthermore, it possesses high fault tolerance and is more conducive to practical applications.

[0106] In some embodiments, the present invention also provides a system for applying the above method, including a preprocessing module, a scanning module, a dominant noise source distribution azimuth angle determination module, and a correction module that are interconnected or sequentially connected.

[0107] The preprocessing module is used to obtain the stable frequency band range and velocity scanning range; the scanning module is used to perform frequency-azimuth domain energy spectrum scanning for each scanning velocity in the velocity scanning range, considering the azimuth angle of the two-dimensional observation station, within the stable frequency band range, to obtain the frequency-azimuth domain dispersion energy spectrum corresponding to each scanning velocity; the dominant noise source distribution azimuth angle determination module is used to determine the dominant noise source distribution azimuth angle range; the correction module is used to synthesize the dispersion energy spectrum corrected for each noise azimuth angle in the dominant noise source distribution azimuth angle range to obtain the dominant noise source distribution azimuth angle corrected dispersion energy spectrum.

[0108] It should also be understood that the specific implementation process of each module is described in the above method. This invention will not repeat it here. The above division of functional modules is only for illustrative purposes. In some embodiments, some functional modules can be combined and some functional modules can be separated. Each functional module can be implemented in software, hardware, or a combination of software and hardware. The software and hardware devices include, but are not limited to, general-purpose computer equipment, programmable gate arrays, digital signal processors, microprocessors and their corresponding programming or burning software.

[0109] In some embodiments, the present invention also provides a computer device, including: one or more processors and a memory storing one or more computer programs; wherein the processor invokes the computer programs to implement the steps of a passive source multichannel surface wave analysis dominant noise source distribution scanning and superposition correction method. Specifically, steps S1-S4 are executed; or steps 1-4.

[0110] Please refer to the explanation of the method above for the specific implementation process of each step.

[0111] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0112] In some embodiments, the present invention also provides a computer-readable storage medium storing a computer program that is invoked by a processor to implement the steps of a passive source multichannel surface wave analysis dominant noise source distribution scanning and superposition correction method.

[0113] Please refer to the preceding explanation for the specific implementation process of each step. For example, execute steps S1-S4; or steps 1-4.

[0114] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the hardware and software device described in any of the foregoing embodiments, such as the hard drive or memory of the controller. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart MediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both internal storage units and external storage devices of the controller. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0115] Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application refers to flowchart illustrations and / or instructions executed by a processor of a method, apparatus (system), and computer program product according to embodiments of this application to create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams. These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowchart illustrations and / or one or more block diagrams. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0117] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for scanning and superimposing the distribution of dominant noise sources in passive source multichannel surface wave analysis, characterized in that: The method includes: Step S1: Obtain the stable frequency band range and velocity scan range; Step S2: For each scanning velocity in the velocity scanning range, within the stable frequency band, a frequency-azimuth domain energy spectrum scan is performed by introducing the azimuth factor from the two-dimensional observation station to obtain the frequency-azimuth domain dispersion energy spectrum corresponding to each scanning velocity. Step S3: Determine the azimuth range of the dominant noise source distribution; Based on the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each scanning speed is picked out, and the final azimuth range of the dominant noise source distribution is obtained statistically. Step S4: Correct each noise azimuth angle in the range of the dominant noise source distribution to obtain the dispersion energy spectrum after correction of each azimuth angle, and fuse the dispersion energy spectra after correction of each azimuth angle to obtain the dispersion energy spectrum after correction of the dominant noise source distribution azimuth angle.

2. The method according to claim 1, characterized in that: When performing a frequency-azimuth domain energy spectrum scan of the azimuth factor using a two-dimensional observation station in step S2, the corresponding model of the obtained frequency-azimuth domain dispersion energy spectrum is expressed as follows: ； In the formula, Scanning speed within the stated speed scanning range The corresponding frequency-azimuth domain dispersion energy spectrum; For the first Daohedi Causal branch of frequency domain cross-correlation function for railway station pairs; For the first Daohedi The frequency domain cross-correlation function of the railway station pair is non-causal. e is the imaginary unit, and e is the natural base. For frequencies within a stable frequency band; For the first Tao and the First The distance between the Daotai stations; The noise azimuth angle; For the first Tao and the First The angle between the station pair and the 0° azimuth line represents the azimuth angle of the two-dimensional observation station pair; N is the number of stations in the observation array.

3. The method according to claim 2, characterized in that: The implementation process of step S4 is as follows: Calculate the azimuth range of the dominant noise source distribution. The dispersion spectrum after correction for each noise azimuth angle; By weighted superposition and averaging of all azimuth-corrected dispersive energy spectra, the azimuth-corrected dispersive energy spectrum of the dominant noise source distribution is obtained: ； ； This is the final superimposed dispersion spectrum, i.e., the dispersion spectrum after azimuth correction of the dominant noise source distribution; The azimuth range of the dominant noise source distribution The Middle Noise azimuth angle The corresponding weighting coefficients; The azimuth range of the dominant noise source distribution Number of noise azimuth angles within; Noise azimuth angle Corrected dispersion spectrum, To stabilize frequencies within the frequency band, The scanning speed represents any scanning speed within the scanning range. Among them, when the noise source is evenly distributed or the azimuth angle of the main noise is located in the Fresnel zone angle. When the noise is inside, only the noise azimuth angle needs to be calculated. =0 ; ; For surface wave wavelength, The distance between the station and the platform.

4. The method according to claim 3, characterized in that: Weighting coefficients The azimuth range of the dominant noise source distribution The Middle Noise azimuth angle The noise source energy is located within the azimuth range of the dominant noise source distribution. The percentage of total energy.

5. The method according to claim 1, characterized in that: The stable frequency band range mentioned in step S1 is determined based on the inflection point of the peak energy of the initial dispersion spectrum of the micro-motion and high-frequency interference.

6. The method according to claim 1, characterized in that: The velocity scanning range mentioned in step S1 is determined based on the prior information of the high-frequency phase velocity or surface velocity of the initial dispersion energy spectrum of the micro-motion.

7. The method according to claim 1, characterized in that: The implementation process of step S3 is as follows: First, set the bandwidth for picking up the dominant azimuth angle; Secondly, based on the dominant azimuth pickup bandwidth, and according to the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each scanning speed is picked out. Among them, the azimuth range of the dominant noise source distribution The corresponding energy value is the highest; Finally, based on the probability statistics of the azimuth angle range of the dominant noise source distribution corresponding to all scanning speeds, the one with the highest probability will be selected. As the ultimate advantage, the azimuth range of noise source distribution .

8. A system based on the method of any one of claims 1-7, characterized in that: include: The preprocessing module is used to obtain the stable frequency band range and velocity scan range; The scanning module is used to perform a frequency-azimuth domain energy spectrum scan for each scanning speed in the velocity scanning range within the stable frequency band, introducing the azimuth factor of the two-dimensional observation station, to obtain the frequency-azimuth domain dispersion energy spectrum corresponding to each scanning speed. The dominant noise source distribution azimuth angle determination module is used to determine the range of dominant noise source distribution azimuth angles. Based on the energy distribution of the frequency-azimuth domain dispersion spectrum, the azimuth range of the dominant noise source distribution corresponding to each scanning speed is picked out, and then the final azimuth range of the dominant noise source distribution is obtained statistically. The correction module is used to synthesize the dispersion energy spectrum of each noise azimuth angle within the range of the dominant noise source distribution azimuth angle to obtain the dispersion energy spectrum after the dominant noise source distribution azimuth angle correction.

9. A computer device, characterized in that: include: One or more processors; And a memory that stores one or more computer programs; The processor invokes a computer program to achieve the following: The steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer program is stored and is invoked by the processor to implement: The steps of the method according to any one of claims 1-7.

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