Method, device, equipment, storage medium and product for extracting high-frequency surface wave group velocity

Abstract

The present application discloses a method, device, equipment, storage medium and product for extracting high-frequency surface wave group velocity, which relates to the field of geophysical exploration technology. The method for extracting high-frequency surface wave group velocity comprises: determining a data processing flow according to a data source, processing station data according to the data processing flow, and obtaining surface wave data; performing multiple filtering on the surface wave data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map; and determining the high-frequency surface wave group velocity according to the dispersion energy map. The present application can maximize the use of limited data to extract a more reliable surface wave group velocity.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration technology, and in particular to a method, device, equipment, storage medium and product for extracting high-frequency surface wave group velocity. Background Art

[0002] For shallow geophysical exploration or inversion of formation parameters at engineering scale, close-range target positioning and other applications, the extraction of surface wave group velocity is a key step. The existing methods for extracting surface wave group velocity have their applicable scenarios and limitations. The selection of an appropriate method usually depends on the quality of the data, the available computing resources and the specific research needs. For small-scale applications ranging from a few hundred meters to a few kilometers, surface wave signals usually appear at a few hertz to tens of hertz, which is different from the long-period surface waves (several seconds to hundreds of seconds) excited by natural earthquakes used at the global or regional scale. Therefore, the specific parameters used in extracting surface wave group velocity are also different; in addition, when the number of stations is small (only a few), how to quickly and effectively extract surface wave group velocity through data obtained from active sources or passive sources (such as background noise) is of great practical significance for preliminary exploration of shallow underground structures and close-range target positioning.

[0003] Traditionally, the most commonly used method for extracting surface wave group velocity is the multiple filtering method. In order to reduce the error of surface wave dispersion measurement, it is generally believed that the Gaussian filter parameters used in the multiple filtering method should be adjusted according to the epicentral distance (or station spacing) and the filter frequency; however, the Gaussian filter parameters given in the existing literature are given for natural earthquakes or large-scale applications, and there is no discussion on how to design the Gaussian filter in the frequency band range of small-scale high-frequency surface waves. On the other hand, with the rise of background noise imaging, people use short-period dense arrays to observe the study area. Since the number of surface wave dispersions to be extracted is very large, manual extraction is time-consuming and inefficient. Therefore, the existing technology has produced some methods for automatically extracting surface wave dispersion curves based on machine learning. This type of method requires a large number of stations because a certain number of samples are required for training. Therefore, it is necessary to specifically consider the technology of extracting surface wave group velocity from a small number of station records for small-scale applications. This is very necessary for those who want to use limited resources (cost, number of stations, etc.) to have a preliminary understanding of the underground structure information of the target area and to locate the target of the monitored sound source information. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, equipment, storage medium and product for extracting high-frequency surface wave group velocity, aiming to solve the technical problem of how to extract the group velocity of high-frequency surface waves in small-scale applications from a small number of station records.

[0005] To achieve the above object, the present application proposes a method for extracting high-frequency surface wave group velocity, the method for extracting high-frequency surface wave group velocity comprising:

[0006] Determine a data processing flow according to the data source, process the station data according to the data processing flow, and obtain surface wave data;

[0007] Performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map;

[0008] A high frequency surface wave group velocity is determined based on the dispersion energy diagram.

[0009] Optionally, the step of determining a data processing flow according to a data source, and processing the station data according to the data processing flow to obtain surface roll data includes:

[0010] When the data source is a single station method, obtaining shot point information and station location;

[0011] determining a group velocity range based on the shot point information and the station location;

[0012] Determine the time window where the surface wave signal is located according to the group velocity range, pre-process the station data, and obtain pre-processed station data;

[0013] The preprocessed station data are cut according to the time window to obtain surface roll data.

[0014] Optionally, the step of determining a data processing flow according to a data source, and processing the station data according to the data processing flow to obtain surface roll data includes:

[0015] When the data source is the dual-station method, determine the data source of the station data;

[0016] When the data source is a preset data source, selecting a source-station pair that meets the preset conditions;

[0017] determining a surface roll window based on the source-station pair;

[0018] Preprocessing the station data to obtain preprocessed dual-station data;

[0019] The dual-station data are cross-correlated, and surface wave window interception is performed on the cross-correlated dual-station data according to the surface wave window to obtain surface wave data.

[0020] Optionally, the step of selecting a source-station pair that meets preset conditions includes:

[0021] Determine the first angle between the earthquake source and the line connecting the two stations;

[0022] Determining a second angle between a line from the earthquake source to the first station and a line between the stations;

[0023] A source-station pair that meets preset conditions is selected according to the first angle and the second angle.

[0024] Optionally, the step of performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map includes:

[0025] Performing time-frequency analysis on the surface roll data to determine the frequency band range of the surface roll signal;

[0026] Performing multiple filtering on the surface roll data according to the preset Gaussian filter and the frequency band range to obtain a time domain waveform;

[0027] Performing Hilbert transform on the time domain waveform to obtain an analytical signal;

[0028] A two-dimensional dispersion energy map is determined based on the analytical signal.

[0029] The step of performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map comprises:

[0030] Get the preset frequency parameters and set the Gaussian function cutoff threshold;

[0031] Determine a Gaussian filter parameter according to the preset frequency parameter and the Gaussian function cutoff threshold;

[0032] The surface roll data is subjected to multiple filtering using a preset Gaussian filter based on the Gaussian filter parameters to obtain a two-dimensional dispersion energy map.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for extracting high-frequency surface wave group velocity, the device for extracting high-frequency surface wave group velocity comprising:

[0034] A surface roll data determination module is used to determine a data processing flow according to a data source, and to process the station data according to the data processing flow to obtain surface roll data;

[0035] A multiple filtering module, used for performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map;

[0036] The high-frequency surface wave group velocity determination module is used to determine the high-frequency surface wave group velocity according to the dispersion energy diagram.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for extracting high-frequency surface wave group velocity, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for extracting high-frequency surface wave group velocity as described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for extracting high-frequency surface wave group velocity as described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the method for extracting high-frequency surface wave group velocity as described above are implemented.

[0040] The present application determines the data processing flow according to the data source, processes the station data according to the data processing flow, and obtains the surface wave data; performs multiple filtering on the surface wave data based on the preset Gaussian filter to obtain a two-dimensional dispersion energy map; and determines the high-frequency surface wave group velocity according to the dispersion energy map. The present application can make the most of limited data to extract a more reliable surface wave group velocity, which is an effective supplement to the surface wave group velocity dispersion extraction technology currently involved in large-scale applications or background noise imaging using dense arrays. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the method for extracting high-frequency surface wave group velocity in this application;

[0044] Figure 2 A schematic diagram of a flow chart provided for the second embodiment of the method for extracting high-frequency surface wave group velocity in this application;

[0045] Figure 3 A schematic diagram of the location of the source-station pair provided in Example 2 of the method for extracting high-frequency surface wave group velocity of the present application;

[0046] Figure 4 A waveform diagram provided for the second embodiment of the method for extracting high-frequency surface wave group velocity in this application;

[0047] Figure 5 A schematic diagram of Rayleigh wave group velocity extraction provided in Embodiment 2 of the method for extracting high-frequency surface wave group velocity of the present application;

[0048] Figure 6 A schematic diagram of extracting the Love wave group velocity provided in Embodiment 2 of the method for extracting the high-frequency surface wave group velocity of the present application;

[0049] Figure 7 A schematic diagram of Rayleigh wave group velocity on a dispersion energy diagram provided in Example 2 of the method for extracting high-frequency surface wave group velocity in this application;

[0050] Figure 8 A schematic diagram of station arrangement provided for Embodiment 2 of the method for extracting high-frequency surface wave group velocity in this application;

[0051] Fig. 9 A schematic diagram of group velocity values ​​provided in Example 2 of the method for extracting high-frequency surface wave group velocity in this application;

[0052] Fig.10 A schematic diagram of waveform, amplitude spectrum, and time-frequency spectrum provided in Example 2 of the method for extracting high-frequency surface wave group velocity in this application;

[0053] Fig.11 A schematic diagram of group velocity extraction results provided in Example 2 of the method for extracting high-frequency surface wave group velocity in this application;

[0054] Fig.12 A schematic diagram of the module structure of a device for extracting high-frequency surface wave group velocity according to an embodiment of the present application;

[0055] Fig.13 Schematic diagram of the device structure of the hardware operating environment involved in the method for extracting high-frequency surface wave group velocity in the embodiment of the present application.

[0056] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0058] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of the embodiment of the present application is: determine the data processing flow according to the data source, process the station data according to the data processing flow to obtain surface wave data; perform multiple filtering on the surface wave data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map; determine the high-frequency surface wave group velocity according to the dispersion energy map. The present application can make the most of limited data to extract a more reliable surface wave group velocity, which is an effective supplement to the surface wave group velocity dispersion extraction technology currently used for large-scale applications or background noise imaging using dense arrays.

[0060] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a device for extracting high-frequency surface wave group velocity, etc. The following takes the device for extracting high-frequency surface wave group velocity as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, the present application embodiment provides a method for extracting high-frequency surface wave group velocity, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for extracting high-frequency surface wave group velocity in the present application.

[0062] In this embodiment, the method for extracting high-frequency surface wave group velocity includes steps S10 to S30:

[0063] Step S10, determining a data processing flow according to the data source, and processing the station data according to the data processing flow to obtain surface roll data;

[0064] It should be noted that the data sources include the single-station method and the dual-station method. The station data is the data collected by the station. Specifically, when the data source is the single-station method, the data processing flow is to estimate the time window where the surface wave signal is located based on the distance between the shot point and the station (i.e., the epicentral distance) and the group velocity range (obtained based on experience); after the station data is detrended, demeaned, de-instrumented, downsampled, and band-pass filtered, the waveform is cut according to the estimated time window of the surface wave signal to obtain data containing the surface wave signal.

[0065] Further, the step S10 may include: when the data source is a single station method, obtaining shot point information and station location;

[0066] Determine the time window where the surface wave signal is located according to the shot point information, the station location and the group velocity range;

[0067] Preprocessing the station data to obtain preprocessed station data;

[0068] The preprocessed station data are cut according to the time window to obtain surface roll data.

[0069] It should be noted that the shot point information includes the time of earthquake occurrence and the shot point location. The station location is the location information of a single station. The group velocity range can be determined based on test results and previous experience or with reference to an engineering geology manual. The time window where the surface wave signal is located according to the shot point information, the station location and the group velocity range can be determined by determining the distance information according to the shot point information and the station location, and determining the time window of the surface wave signal according to the distance information and the group velocity range. The preprocessing of the station data to obtain the preprocessed station data includes: performing detrending, demeaning, deinstrument response, downsampling and bandpass filtering on the station data to obtain the preprocessed station data. Then, the preprocessed station data is cut according to the estimated time window of the surface wave signal (i.e., the time window where the above-mentioned surface wave signal is located) to obtain data containing the surface wave signal, i.e., the surface wave data.

[0070] When the data source is the dual-station method, the data processing flow needs to be determined according to the generation method of the station data. When the station data is generated by artificial or natural earthquake excitation, a source-station pair that meets the preset conditions is selected; according to the source-station pair, the surface wave windows of the two station data can be determined respectively by the given group velocity range; the station data is preprocessed, and the dual-station data are intercepted respectively according to the surface wave window to obtain the preprocessed data; the preprocessed dual-station data are cross-correlated, and the surface wave signal window can be estimated according to the distance between the two stations (i.e., the station spacing), and the surface wave window is intercepted on the cross-correlated dual-station data according to the surface wave window to obtain the surface wave data. The preprocessing of the station data specifically includes: detrending, demeaning, de-instrument response, downsampling, bandpass filtering, and selecting a surface wave window. The step of selecting a source-station pair that meets preset conditions includes: determining a first angle between the source and the line between the two stations; determining a second angle between the line from the source to the nearest station and the line between the stations; and selecting the source-station pair based on the conditions that the first angle does not exceed 0.5° and the second angle does not exceed 2°.

[0071] When the station data is generated in the form of background noise, it is necessary to perform preprocessing on the data of the two stations respectively by removing trends, removing means, removing instrument responses, downsampling, bandpass filtering, data cutting, time domain normalization, and frequency domain spectrum whitening. After that, the results of the preprocessing of the two station data are cross-correlated and superimposed. At the same time, the waveform is cut by the surface wave window obtained by the distance between the two stations and the given group velocity range to obtain data containing surface wave signals.

[0072] Step S20, performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map;

[0073] It should be noted that the preset Gaussian filter is a filter with pre-designed parameters for performing multiple filtering on surface roll data. Specifically, the step of performing multiple filtering on the surface roll data based on the preset Gaussian filter to obtain a two-dimensional dispersion energy map includes:

[0074] Get the preset frequency parameters and set the Gaussian function cutoff threshold;

[0075] Determine a Gaussian filter parameter according to the preset frequency parameter and the Gaussian function cutoff threshold;

[0076] The surface roll data is subjected to multiple filtering using a preset Gaussian filter based on the Gaussian filter parameters to obtain a two-dimensional dispersion energy map.

[0077] It should be noted that the preset frequency parameter may be a frequency parameter suitable for shallow seismic exploration, and in this embodiment may be: [1, 5, 10, 20, 30, 50, 80, 100, 120, 150] Hz. The Gaussian filter parameter is determined according to the preset frequency parameter. , the absolute bandwidth is given by the Gaussian filter parameter ,in The center frequency is Gaussian filter The actual calculation needs to truncate the Gaussian function. After testing, the Gaussian function truncation threshold can be used in this implementation. Set it to 2.5 to achieve a better final filtering effect. is the relative bandwidth, is the lower limit frequency of the passband, is the upper limit frequency of the passband, let Since the Gaussian filter parameters The value of has a greater impact on long-period surface waves, while this embodiment is aimed at high-frequency (short-period) surface waves. When the frequencies are 1, 5, and 10 Hz, the Gaussian filter parameters are selected. , and the corresponding absolute bandwidths can be calculated to be 0.22294, 1.1147, and 2.2294 Hz; for the above frequency parameters [20, 30, 50, 80, 100, 120, 150] Hz, the absolute bandwidth can be uniformly set to 2.5 Hz, as given by The corresponding Gaussian filter parameters can be obtained. If the frequency of the surface roll signal (i.e., the surface roll data) is very close to the frequency of the interference signal (such as noise, body wave, etc.), the absolute bandwidth can be set to be smaller, which can be set according to the actual scenario. Based on the above frequency parameters and the corresponding absolute bandwidth parameters, the absolute bandwidth parameters corresponding to other frequency points within and outside the frequency range [1,150] Hz can be obtained by piecewise cubic Hermite interpolation or extrapolation, and then the corresponding Gaussian filter parameters can be obtained. Then, based on the solved Gaussian filter parameters, the surface roll data is multi-filtered using a Gaussian filter to obtain a two-dimensional dispersion energy map.

[0078] Furthermore, the step S20 may include: performing time-frequency analysis on the surface roll data to determine the frequency band range of the surface roll signal;

[0079] Performing multiple filtering on the surface roll data according to the preset Gaussian filter and the frequency band range to obtain a time domain waveform;

[0080] Performing Hilbert transform on the time domain waveform to obtain an analytical signal;

[0081] A two-dimensional dispersion energy map is determined based on the analytical signal.

[0082] In the specific implementation, the surface wave data is first subjected to time-frequency analysis to determine the frequency band range of the surface wave data, and a series of frequency points can be set accordingly. The above-mentioned preset frequency parameters can be referred to, and for each frequency point, a Gaussian filter is applied to perform narrow-band pass filtering on the surface wave data, and then the amplitude envelope of the filtered waveform is obtained, and the energy values ​​corresponding to a series of velocity values ​​within the group velocity range are obtained, so that a 2D frequency-velocity dispersion energy diagram can be drawn. That is, the dispersion energy diagram. Among them, for the amplitude envelope of the waveform, the time domain waveform after Gaussian filtering is subjected to Hilbert transformation, with the signal before the transformation as the real part and the signal after the transformation as the imaginary part, to form an analytical signal, so that the envelope of the filtered time domain waveform is the modulus of the analytical signal, and the square of the modulus is taken as the natural logarithm as the energy value corresponding to different frequency points and different velocity points.

[0083] Step S30, determining the high-frequency surface wave group velocity according to the dispersion energy diagram.

[0084] It should be noted that the determination of the high-frequency surface wave group velocity according to the dispersion energy diagram can be performed by using the dispersion energy diagram as the background, narrow-band filtering the surface wave data with a Gaussian filter of a certain center frequency, and then obtaining the amplitude envelope of the filtered waveform, and obtaining the time corresponding to the maximum point of the amplitude envelope by interpolation as the arrival time of the group velocity, and dividing the epicenter distance or the station distance by the arrival time to obtain the group velocity corresponding to the center frequency or instantaneous frequency, that is, the high-frequency surface wave group velocity. Among them, the instantaneous frequency is the partial derivative of the instantaneous phase with respect to time, and the instantaneous phase is obtained by the inverse tangent of the ratio of the imaginary part to the real part of the analytical signal. In order to obtain a more accurate instantaneous frequency, spline interpolation can be performed on the instantaneous frequency returned by the amplitude envelope obtained above and the corresponding time point to obtain the instantaneous frequency point corresponding to the arrival time of the group velocity.

[0085] This embodiment determines the data processing flow according to the data source, processes the station data according to the data processing flow, and obtains the surface wave data; performs multiple filtering on the surface wave data based on the preset Gaussian filter to obtain a two-dimensional dispersion energy map; and determines the high-frequency surface wave group velocity according to the dispersion energy map. This embodiment can make the most of limited data to extract a more reliable surface wave group velocity, which is an effective supplement to the surface wave group velocity dispersion extraction technology involved in current large-scale applications or background noise imaging using dense arrays.

[0086] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2 The flowchart of the second embodiment of the method for extracting high-frequency surface wave group velocity provided by the present application, the step S10 further includes the following steps:

[0087] Step S101: when the data source is the dual-station method, determining the data source of the station data;

[0088] It should be noted that the data source of the station data is the method of generating the station data, and the station data includes natural earthquake data, data obtained by artificial stimulation, and background noise data.

[0089] Step S102: when the data source is a preset data source, selecting a source-station pair that meets preset conditions;

[0090] It should be noted that the preset data source may be natural earthquake data and artificially stimulated data. The selection of the source-station pair that meets the preset conditions may be a source-station pair in which the angle between the line connecting the source and the two stations does not exceed a first preset threshold, and the angle between the line connecting the source to the nearest station and the line connecting the stations does not exceed a second preset threshold. The first preset threshold and the second preset threshold may be pre-set angle thresholds.

[0091] Furthermore, the step of selecting a source-station pair that meets the preset conditions includes:

[0092] Determine the first angle between the earthquake source and the line connecting the two stations;

[0093] Determining a second angle between a line from the earthquake source to the first station and a line between the stations;

[0094] A source-station pair that meets preset conditions is selected according to the first angle and the second angle.

[0095] It should be noted that, please refer to Figure 3 , Figure 3 The schematic diagram of the position of the source-station pair provided in the second embodiment of the method for extracting high-frequency surface wave group velocity in this application, the first angle between the source E and the line connecting the two stations A and B can be Figure 3 In , the distances from the earthquake source E to stations A and B are , , the angle between the earthquake source and the line connecting the two stations (great circle arc) No more than 0.5° can be a pre-set selection condition, and the second angle between the line from the source to the nearest station A and the line between stations A and B can be Figure 3 In , Not exceeding 2° may be a pre-set selection condition.

[0096] Step S103: determining a surface wave window according to the source-station pair;

[0097] It should be noted that determining the surface roll window according to the source-station pair may be determining the station distances from the source to two stations respectively according to the source-station pair, and determining the surface roll window according to the station distances and the group velocity range.

[0098] Step S104: preprocessing the station data to obtain preprocessed dual-station data;

[0099] It should be noted that the preprocessing of the station data may be detrending, demeaning, de-instrument response, downsampling, and bandpass filtering of the station data, wherein each processing may refer to the relevant processing method, and this embodiment will not be repeated here. The preprocessed dual station data is obtained, that is, the station data of the two stations are included.

[0100] Step S105: cross-correlating the dual-station data, and performing surface roll window interception on the cross-correlated dual-station data according to the surface roll window to obtain surface roll data.

[0101] It should be noted that the cross-correlation of the dual-station data may be performed using an existing cross-correlation method to obtain the cross-correlated dual-station data, and then the cross-correlated dual-station data may be subjected to surface roll window interception according to the surface roll window to obtain surface roll data.

[0102] Furthermore, if the data source is not the preset data source, that is, background noise data, it is necessary to preprocess the station data of the two stations respectively by removing trends, removing means, removing instrument responses, downsampling, bandpass filtering, data cutting, time domain normalization, and frequency domain spectrum whitening. After that, the preprocessed station data of the two stations are cross-correlated and superimposed, and the surface wave time window is selected at the same time (refer to the above step of determining the time window) to obtain data containing surface wave signals, that is, surface wave data.

[0103] In the specific implementation, synthetic data is first used to test the filtering effect of the Gaussian filter given in this embodiment. The synthetic signal is composed of a sinusoidal signal with a frequency of 40 Hz and an amplitude of 1, a sinusoidal signal with a frequency of 41 Hz and an amplitude of 2, and a Gaussian white noise with a standard deviation of 1. The sampling frequency is 1000 Hz, and the number of sampling points is 1000. In order to retain the 40 Hz sinusoidal signal, conventional bandpass filtering is performed, and the passband range is [40-0.6, 40+0.6] Hz. In contrast, the Gaussian filter in this embodiment is used for narrowband filtering. In order to avoid interference from the adjacent 41 Hz signal, the absolute bandwidth should be set smaller. Therefore, the Gaussian filter parameter is taken as 5000. Please refer to Figure 4 , Figure 4 A waveform diagram provided for the second embodiment of the method for extracting high-frequency surface wave group velocity in this application; Figure 4 The original waveform, the waveform after conventional filtering, the waveform after Gaussian filtering, and their respective amplitude spectra are displayed. It can be seen that the waveform obtained after Gaussian filtering is a pure target signal (i.e., station signal), and the amplitude is close to the theoretical value; while the waveform after conventional bandpass filtering still cannot obtain the expected target signal due to the influence of filtering capacity, noise, etc., and the amplitude of the signal after filtering is far lower than the theoretical value.

[0104] Secondly, the Gaussian filter parameters constructed in this embodiment can also reflect a high degree of accuracy for the group velocity extraction of long-period surface waves of natural earthquakes. The CPS program package provided by Herrmann and Ammon was used to synthesize the surface wave waveform with an epicenter distance of 2500km using the vibration mode superposition method. The group velocities of Rayleigh waves and Love waves were extracted for the vertical component and the tangential component respectively, and the extracted results (marked with a plus sign "+") were superimposed on the theoretical group velocity dispersion curve (black solid line), which can be referred to Figure 5 , Figure 6 as well as Figure 7 , Figure 5 A schematic diagram of Rayleigh wave group velocity extraction provided in Embodiment 2 of the method for extracting high-frequency surface wave group velocity of the present application; Figure 6 A schematic diagram of extracting the Love wave group velocity provided in Embodiment 2 of the method for extracting the high-frequency surface wave group velocity of the present application; Figure 7 A schematic diagram of Rayleigh wave group velocity on a dispersion energy diagram provided in Embodiment 2 of the method for extracting high-frequency surface wave group velocity in this application; Figure 5 and Figure 6 As shown. The figure also includes the extraction results of the CPS program (marked by black dots) for comparison. It can be seen that this embodiment can also give relatively satisfactory results for long-period surface waves of natural seismic data (large-scale applications). Figure 7 The extracted Rayleigh wave group velocity overlaid on the dispersion energy diagram is given.

[0105] Finally, the present embodiment was tested using experimental data of artificial excitation (active source) and background noise data (passive source). Figure 8 As shown, Figure 8 The schematic diagram of station arrangement provided for the second embodiment of the method for extracting high-frequency surface wave group velocity in this application: 8 stations are arranged in a rectangular area of ​​300m*100m, with a total of 100 shot points. Select the source-station that meets the conditions to process the data, and finally directly give the group velocity values ​​of several discrete frequency points from the strongest energy point in the dispersion energy diagram. Fig. 9 shown. Fig. 9 Schematic diagram of group velocity values ​​provided in Example 2 of the method for extracting high-frequency surface wave group velocity in this application; for the passive source data test, two hours of background noise data recorded by two seabed OBN nodes with a horizontal distance of 660m were taken, and the waveform, amplitude spectrum, and time spectrum obtained after preprocessing, cross-correlating, and intercepting the surface wave window of the two stations were respectively Fig.10 As shown, Fig.10 The waveform, amplitude spectrum, and time-frequency spectrum provided in the second embodiment of the method for extracting high-frequency surface wave group velocity in this application are shown in FIG. Fig.11 shown. Fig.11Schematic diagram of group velocity extraction results provided by Example 2 of the method for extracting high-frequency surface wave group velocity in this application; since the signal-to-noise ratio of the sea trial data is relatively low, Fig.11 Can get Fig.10 The group velocity range of the strong medium signal frequency band of 6~10Hz is 88~116m / s, based on which a rough range of the shear wave velocity of the seabed sediment can be inverted.

[0106] This embodiment proposes a specific parameter selection scheme for the Gaussian filter in the multiple filtering method for extracting the group velocity of high-frequency surface waves in small-scale applications from a small number of station records: including providing the best Gaussian function cutoff threshold; by giving Gaussian filter parameters or absolute bandwidth parameters of several specific frequency points (and then deriving Gaussian filter parameters), the Gaussian filter parameters corresponding to different central frequency points are obtained by interpolation or extrapolation. The method is simple and easy to implement, and the calculation accuracy is high. In the prior art, either Gaussian filter parameters of different periods and epicenter distances are given for natural earthquakes and surface waves in large-scale applications, which are not applicable to the parameters required for extracting the group velocity of high-frequency surface waves in small-scale in this embodiment, which is different from the present embodiment that can simply and conveniently calculate the Gaussian filter parameters corresponding to any frequency point.

[0107] In this embodiment, when the data source is the dual-station method, the data source of the station data is determined; when the data source is a preset data source, a source-station pair that meets the preset conditions is selected; a surface wave window is determined according to the source-station pair; the station data is preprocessed to obtain preprocessed dual-station data; the dual-station data is cross-correlated, and the surface wave window is intercepted according to the surface wave window to obtain the surface wave data. The method given in this embodiment for extracting high-frequency surface wave group velocity for small-scale applications is universal, that is, the single-station method or the dual-station method can be selected according to the actual number of stations or experimental conditions. For the latter, regardless of whether the data is natural earthquake data, artificially excited data, or background noise data, corresponding data processing can be performed according to different situations. This embodiment can make full use of limited station records to extract group velocity information of the structure below the station, which has important guiding significance for resource exploration, target positioning, etc.

[0108] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for extracting high-frequency surface wave group velocity in the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0109] The present application also provides a device for extracting high-frequency surface wave group velocity, please refer to Fig.12 , the device for extracting high-frequency surface wave group velocity comprises:

[0110] The surface roll data determination module 10 is used to determine the data processing flow according to the data source, and process the station data according to the data processing flow to obtain the surface roll data;

[0111] A multiple filtering module 20, configured to perform multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map;

[0112] The high-frequency surface wave group velocity determination module 30 is used to determine the high-frequency surface wave group velocity according to the dispersion energy diagram.

[0113] This embodiment determines the data processing flow according to the data source, processes the station data according to the data processing flow, and obtains the surface wave data; performs multiple filtering on the surface wave data based on the preset Gaussian filter to obtain a two-dimensional dispersion energy map; and determines the high-frequency surface wave group velocity according to the dispersion energy map. This embodiment can make the most of limited data to extract a more reliable surface wave group velocity, which is an effective supplement to the surface wave group velocity dispersion extraction technology involved in current large-scale applications or background noise imaging using dense arrays.

[0114] The device for extracting high-frequency surface wave group velocity provided by the present application adopts the method for extracting high-frequency surface wave group velocity in the above-mentioned embodiment, and can solve the technical problem of how to extract the group velocity of high-frequency surface waves in small-scale applications from a small number of station records. Compared with the prior art, the beneficial effects of the device for extracting high-frequency surface wave group velocity provided by the present application are the same as the beneficial effects of the method for extracting high-frequency surface wave group velocity provided by the above-mentioned embodiment, and other technical features in the device for extracting high-frequency surface wave group velocity are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0115] The present application provides a device for extracting high-frequency surface wave group velocity, and the device for extracting high-frequency surface wave group velocity includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for extracting high-frequency surface wave group velocity in the above-mentioned embodiment 1.

[0116] Reference below Fig.13, which shows a schematic diagram of the structure of a device for extracting high-frequency surface wave group velocity suitable for implementing the embodiment of the present application. The device for extracting high-frequency surface wave group velocity in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.13 The device for extracting high-frequency surface wave group velocity shown is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.

[0117] like Fig.13 As shown, the device for extracting high-frequency surface wave group velocity may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the device for extracting high-frequency surface wave group velocity are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the device for extracting high-frequency surface wave group velocity to communicate wirelessly or wired with other devices to exchange data. Although the device for extracting high-frequency surface wave group velocity having various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0118] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0119] The device for extracting high-frequency surface wave group velocity provided by the present application adopts the method for extracting high-frequency surface wave group velocity in the above-mentioned embodiment, and can solve the technical problem of how to extract the group velocity of high-frequency surface waves in small-scale applications from a small number of station records. Compared with the prior art, the beneficial effects of the device for extracting high-frequency surface wave group velocity provided by the present application are the same as the beneficial effects of the method for extracting high-frequency surface wave group velocity provided by the above-mentioned embodiment, and other technical features in the device for extracting high-frequency surface wave group velocity are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0120] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0122] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for extracting high-frequency surface wave group velocity in the above-mentioned embodiment.

[0123] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0124] The computer-readable storage medium may be included in the device for extracting high-frequency surface wave group velocity; or may exist independently without being assembled into the device for extracting high-frequency surface wave group velocity.

[0125] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the device for extracting high-frequency surface wave group velocity, the device for extracting high-frequency surface wave group velocity executes the method for extracting high-frequency surface wave group velocity.

[0126] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0129] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for extracting the group velocity of high-frequency surface waves, and can solve the technical problem of how to extract the group velocity of high-frequency surface waves in small-scale applications from a small number of station records. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method for extracting the group velocity of high-frequency surface waves provided in the above-mentioned embodiment, and will not be repeated here.

[0130] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for extracting high-frequency surface wave group velocity when executed by a processor.

[0131] The computer program product provided by the present application can solve the technical problem of how to extract the group velocity of high-frequency surface waves in small-scale applications from a small number of station records. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the method for extracting the group velocity of high-frequency surface waves provided by the above-mentioned embodiment, and will not be described in detail here.

[0132] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for extracting high-frequency surface wave group velocity, characterized in that: The method for extracting high-frequency surface wave group velocity comprises the following steps: Determine a data processing flow according to the data source, process the station data according to the data processing flow, and obtain surface wave data; Performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map; The preset Gaussian filter is a filter with parameters designed in advance for performing multiple filtering on surface roll data; the step of performing multiple filtering on the surface roll data based on the preset Gaussian filter to obtain a two-dimensional dispersion energy map includes: Get the preset frequency parameters and set the Gaussian function cutoff threshold; Determine a Gaussian filter parameter according to the preset frequency parameter and the Gaussian function cutoff threshold; Based on the Gaussian filter parameters, the surface roll data is subjected to multiple filtering using a preset Gaussian filter to obtain a two-dimensional dispersion energy map; The preset frequency parameter is: [1, 5, 10, 20, 30, 50, 80, 100, 120, 150] Hz, and the Gaussian filter parameter is determined according to the preset frequency parameter , the absolute bandwidth is given by the Gaussian filter parameter ,in The center frequency is Gaussian filter The filter parameters, the Gaussian function needs to be truncated in the calculation, the Gaussian function truncation threshold Set to 2.5, is the relative bandwidth, is the lower limit frequency of the passband, is the upper limit frequency of the passband, let , for frequencies of 1, 5, and 10 Hz, select the Gaussian filter parameters , so the corresponding absolute bandwidths can be calculated to be 0.22294, 1.1147, and 2.2294 Hz. For the above frequency parameters [20, 30, 50, 80, 100, 120, 150] Hz, the absolute bandwidth is uniformly set to 2.5 Hz, as given by The corresponding Gaussian filter parameters can be obtained. From the above frequency parameters and the corresponding absolute bandwidth parameters, the absolute bandwidth parameters corresponding to other frequency points within and outside the frequency range [1, 150] Hz can be obtained by piecewise cubic Hermite interpolation or extrapolation, and then the corresponding Gaussian filter parameters can be obtained. Then, based on the obtained Gaussian filter parameters, the surface wave data are multi-filtered using a Gaussian filter to obtain a two-dimensional dispersion energy map. A high frequency surface wave group velocity is determined based on the dispersion energy diagram.

2. The method for extracting high-frequency surface wave group velocity according to claim 1, characterized in that: The step of determining a data processing flow according to the data source, and processing the station data according to the data processing flow to obtain surface wave data includes: When the data source is a single station method, obtaining shot point information and station location; determining a group velocity range based on the shot point information and the station location; Determine the time window where the surface wave signal is located according to the group velocity range, pre-process the station data, and obtain pre-processed station data; The preprocessed station data are cut according to the time window to obtain surface roll data.

3. The method for extracting high-frequency surface wave group velocity according to claim 1, characterized in that: The step of determining a data processing flow according to the data source, and processing the station data according to the data processing flow to obtain surface wave data includes: When the data source is the dual-station method, determine the data source of the station data; When the data source is a preset data source, selecting a source-station pair that meets the preset conditions; determining a surface roll window based on the source-station pair; Preprocessing the station data to obtain preprocessed dual-station data; The dual-station data are cross-correlated, and surface wave window interception is performed on the cross-correlated dual-station data according to the surface wave window to obtain surface wave data.

4. The method for extracting high-frequency surface wave group velocity according to claim 3, characterized in that: The step of selecting a source-station pair that meets the preset conditions comprises: Determine the first angle between the earthquake source and the line connecting the two stations; Determining a second angle between a line from the earthquake source to the first station and a line between the stations; A source-station pair that meets preset conditions is selected according to the first angle and the second angle.

5. A device for extracting high-frequency surface wave group velocity, characterized in that: The device for extracting high-frequency surface wave group velocity comprises: A surface roll data determination module is used to determine a data processing flow according to a data source, and to process the station data according to the data processing flow to obtain surface roll data; A multiple filtering module, used for performing multiple filtering on the surface roll data based on a preset Gaussian filter to obtain a two-dimensional dispersion energy map; A high-frequency surface wave group velocity determination module, used to determine the high-frequency surface wave group velocity according to the dispersion energy diagram; The preset Gaussian filter is a filter with parameters designed in advance for performing multiple filtering on surface wave data; The multiple filtering module is further used to: obtain a preset frequency parameter and set a Gaussian function cutoff threshold; Determine a Gaussian filter parameter according to the preset frequency parameter and the Gaussian function cutoff threshold; Based on the Gaussian filter parameters, the surface roll data is subjected to multiple filtering using a preset Gaussian filter to obtain a two-dimensional dispersion energy map; The preset frequency parameter is: [1, 5, 10, 20, 30, 50, 80, 100, 120, 150] Hz, and the Gaussian filter parameter is determined according to the preset frequency parameter , the absolute bandwidth is given by the Gaussian filter parameter ,in The center frequency is Gaussian filter The filter parameters, the Gaussian function needs to be truncated in the calculation, the Gaussian function truncation threshold Set to 2.5, is the relative bandwidth, is the lower limit frequency of the passband, is the upper limit frequency of the passband, let , for frequencies of 1, 5, and 10 Hz, select the Gaussian filter parameters , so the corresponding absolute bandwidths can be calculated to be 0.22294, 1.1147, and 2.2294 Hz. For the above frequency parameters [20, 30, 50, 80, 100, 120, 150] Hz, the absolute bandwidth is uniformly set to 2.5 Hz, as given by The corresponding Gaussian filter parameters can be calculated. From the above frequency parameters and the corresponding absolute bandwidth parameters, the absolute bandwidth parameters corresponding to other frequency points within and outside the frequency range of [1, 150] Hz can be calculated through piecewise cubic Hermite interpolation or extrapolation, and then the corresponding Gaussian filter parameters can be calculated. Then, based on the solved Gaussian filter parameters, the surface roll data are multi-filtered using a Gaussian filter to obtain a two-dimensional dispersion energy map.

6. A device for extracting high-frequency surface wave group velocity, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for extracting high-frequency surface wave group velocity according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for extracting high-frequency surface wave group velocity according to any one of claims 1 to 4 are implemented.

8. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for extracting high-frequency surface wave group velocity according to any one of claims 1 to 4 are implemented.

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