CN111856555B - Underground detection method based on surface wave multi-scale window analysis - Google Patents

Underground detection method based on surface wave multi-scale window analysis Download PDF

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CN111856555B
CN111856555B CN202010565552.2A CN202010565552A CN111856555B CN 111856555 B CN111856555 B CN 111856555B CN 202010565552 A CN202010565552 A CN 202010565552A CN 111856555 B CN111856555 B CN 111856555B
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CN111856555A (en
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胡书凡
赵永辉
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Tongji University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/16—Receiving elements for seismic signals; Arrangements or adaptations of receiving elements
    • G01V1/20—Arrangements of receiving elements, e.g. geophone pattern
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/34—Displaying seismic recordings or visualisation of seismic data or attributes
    • G01V1/345—Visualisation of seismic data or attributes, e.g. in 3D cubes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/70—Other details related to processing
    • G01V2210/74—Visualisation of seismic data

Abstract

The invention relates to an underground detection method based on surface wave multi-scale window analysis, which comprises the following steps: s1, laying receiving arrays to observe and collect surface wave data; s2, extracting a local wave field from the surface wave data by adopting sliding space windows with different scales; s3, analyzing and processing the local wave field to obtain actually measured dispersion curves corresponding to all sliding space windows; s4, dispersing the underground model into grid cells, and assigning an initial value to the elastic parameter of each grid cell; s5, calculating theoretical dispersion curves corresponding to all sliding space windows; and S6, calculating the fitting degree of the actually measured frequency dispersion curve and the theoretical frequency dispersion curve, if not, correcting the parameters of each grid unit in the underground model, and repeating the steps S5-S6 until the set fitting precision is reached, thereby realizing the wave velocity imaging of the underground structure. Compared with the prior art, the method can ensure the quality of frequency dispersion data, has high transverse resolution and improves the accuracy of the detection result of the underground structure.

Description

Underground detection method based on surface wave multi-scale window analysis
Technical Field
The invention relates to the field of underground space development and detection, in particular to an underground detection method based on surface wave multi-scale window analysis.
Background
The surface wave analysis method is a nondestructive, efficient and economic geophysical detection method, and plays a very important role in the fields of various engineering surveys on the shallow earth surface, the research on the internal structure of the earth and the like. Currently, the commonly used Surface Wave Analysis methods include Surface Wave Analysis (SASW) and Multichannel Surface Wave Analysis (MASW).
In the surface wave spectrum analysis method, a surface wave dispersion curve is obtained by measuring the time difference or the phase difference of two receiving signals, and the surface wave phase velocity represented by the dispersion curve is the path average phase velocity, so that a one-dimensional shear wave velocity structure can be obtained by adopting an inversion algorithm based on the assumption of a horizontal lamellar model, and a two-dimensional shear wave velocity model with high transverse resolution can be obtained by changing the positions and the intervals of receiving points to carry out multiple measurements and combining a tomography algorithm. However, the accuracy of the dispersion curve extracted by using the surface wave spectrum analysis method is easily affected by the coupling of a detector, random noise, spatial aliasing and a surface wave high-order mode, so that the shear wave velocity reliability obtained by analyzing the method is low.
Compared with the method that only two receiving signals are used, the surface wave multichannel analysis method uses wave field transformation to transform the received time-space domain multichannel surface wave signals into another domain (such as a frequency-phase velocity domain) to perform frequency dispersion analysis, and can extract high-quality surface wave frequency dispersion data according to the strong energy characteristics of surface waves, but because the physical meaning of a frequency dispersion curve extracted by the surface wave multichannel analysis method in a transverse inhomogeneous medium is not clear, a horizontal lamellar model is also adopted to approximate the stratum below the whole arrangement under the condition that the observation arrangement has a certain receiving length, so that the transverse resolution of the shear wave velocity obtained by final inversion is low.
In general, the existing surface wave analysis method is severely limited by data quality or has the problem of low transverse resolution, and a method which can practically and effectively solve the actual transverse inhomogeneous medium model does not exist.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an underground detection method based on surface wave multi-scale window analysis, which can ensure the quality of frequency dispersion data and has high transverse resolution.
The purpose of the invention can be realized by the following technical scheme:
an underground detection method based on surface wave multi-scale window analysis comprises the following steps:
s1, laying receiving arrays to observe and collect surface wave data;
s2, extracting a local wave field from the surface wave data by adopting sliding space windows with different scales;
s3, analyzing and processing the local wave field to obtain actually measured dispersion curves corresponding to all sliding space windows;
s4, dispersing the underground model into grid cells, and assigning an initial value to the elastic parameter of each grid cell;
s5, calculating theoretical dispersion curves corresponding to all sliding space windows;
and S6, calculating the fitting degree of the actually measured frequency dispersion curve and the theoretical frequency dispersion curve, if not, correcting the parameters of each grid unit in the underground model, and repeating the steps S5-S6 until the set fitting precision is reached, thereby realizing the wave velocity imaging of the underground structure.
Preferably, the receiving array in step S1 includes L detectors arranged at a track pitch Δ x.
Preferably, in step S1, an initial shot point is set when the surface wave data is observed and collected, the shot point is moved by a set distance along the survey line direction every time the shot point is excited, and a set of surface wave data is obtained after each excitation.
Preferably, step S2 is specifically: and sliding a sliding space window with different scales along the receiving arrangement direction for each group of surface wave data to obtain a local wave field.
Preferably, step S3 specifically includes:
s31, respectively generating a frequency dispersion image corresponding to each sliding space window for the local wave field extracted from each group of surface wave data;
s32, overlapping the obtained multiple scattered images for any sliding space window;
and S33, obtaining the actually measured frequency dispersion curve corresponding to each sliding space window according to the superposed frequency dispersion image.
Preferably, the scattered image in step S31 is obtained by a phase shift method or amplitude normalization bunching analysis, specifically:
Z(ω，v)＝|eH(ω，v)WS(ω)|
P(ω，v)＝Z(ω，v)2
wherein Z (omega, v) is an actually measured amplitude spectrum calculated by a phase shift method, P (omega, v) is an actually measured power spectrum obtained by amplitude normalization bunching analysis, omega is angular frequency and has the unit of rad/s, v is a test phase velocity and has the unit of m/s,
Figure BDA0002547712280000021
is a plane wave guide vector, where xiIs the offset distance of each channel in the sliding space window, and K is the total channel of the detector in the sliding space windowThe number of the first and second groups is,
Figure BDA0002547712280000031
for each signal in the local wavefield a column vector of fourier spectra,
Figure BDA0002547712280000032
the weighting matrix is normalized for amplitude and diag denotes a diagonal matrix.
Preferably, step S5 specifically includes:
s51, calculating a local dispersion curve of the layered model formed by combining each row of grid cells;
s52, calculating the phase difference between any two channels
Figure BDA0002547712280000033
Figure BDA0002547712280000034
Wherein k isp(ω) local spatial wavenumber, Δ x, determined for the elasticity parameter of the cells of the p-th column between the m and n trackspThe width of the P-th grid unit between m and n tracks is defined, and P is the total number of grid units between m and n tracks;
s53, substituting the phase difference into the following equation:
Figure BDA0002547712280000035
p' (ω, v) is the theoretical power spectrum, ω is the angular frequency in rad/s and v is the experimental phase velocity in m/s, xmnThe distance between m and n channels is defined, and K is the total channel number of the detector in the sliding space window;
and S54, calculating by using a local maximum search algorithm based on the theoretical power spectrum to obtain a theoretical dispersion curve corresponding to each sliding space window.
Preferably, step S6 of modifying the parameters of each grid cell in the subsurface model includes updating the elasticity parameters of the grid cells or both the elasticity parameters and the thicknesses of the grid cells.
Preferably, the elastic parameters of the grid cells include shear wave velocity, longitudinal wave velocity and density.
Preferably, said correction is performed by an inversion algorithm.
Compared with the prior art, the invention has the following advantages:
(1) according to the method, the sliding space windows with different scales are adopted to extract the frequency dispersion information of the surface waves, a plurality of frequency dispersion curves corresponding to the sliding space windows exist at each central point, and the redundancy in the surface wave observation data is fully excavated, so that the change of the underground elastic parameters can be better restrained, and the underground detection accuracy is improved (the underground detection result is displayed through underground structure wave velocity imaging);
(2) the method provided by the invention properly considers the transverse deformation of the underground medium in the process of calculating the theoretical frequency dispersion curve of each sliding space window, and compared with the assumption that a horizontal layered model is closer to the actual situation, the theoretical frequency dispersion curve calculated by the method provided by the invention is more consistent with the meaning of an actually measured frequency dispersion curve, so that the corrected underground model has high transverse resolution;
(3) according to the method, the two-dimensional geological model is directly subjected to inversion interpretation in the underground model correction process, and the two-dimensional response of the underground medium is fully considered in the iterative correction process, so that a more reasonable and accurate result can be obtained;
(4) the invention can not only extract high-quality frequency dispersion data from surface wave observation data, but also can quickly and efficiently acquire the elastic parameter structure with high transverse resolution below the receiving array, thereby providing an effective solution for underground space development and shallow surface engineering investigation and detection.
Drawings
FIG. 1 is a block flow diagram of a subsurface detection method based on surface wave multi-scale window analysis in accordance with the present invention;
FIG. 2 is a schematic view of a sequence of dispersion analysis based on a local wavefield of the present invention, wherein ^ represents a geophone and ■ represents a dispersion curve;
FIG. 3 is a dispersion curve obtained by using a small-scale sliding spatial window according to the present invention;
FIG. 4 is a dispersion curve obtained by using a large-scale sliding spatial window according to the present invention;
FIG. 5 is a two-dimensional shear wave velocity profile obtained by the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, a subsurface detection method based on surface wave multi-scale window analysis includes the following steps:
step S1: the method comprises the steps of laying a receiving array for observing and collecting surface wave data, wherein the receiving array comprises L detectors which are arranged at a certain track interval delta x, setting an initial shot point when observing and collecting the surface wave data, moving the shot point along the measuring line direction for a set distance every time of excitation, and obtaining a group of surface wave data after each time of excitation.
Step S2: and extracting the local wave field from the surface wave data by adopting sliding space windows with different scales, and sliding the sliding windows with different scales along the receiving arrangement direction for each group of surface wave data to obtain the local wave field.
Step S3: analyzing and processing the local wave field to obtain actually measured dispersion curves corresponding to all sliding space windows, which specifically comprises the following steps:
step S31: respectively generating a frequency dispersion image corresponding to each sliding space window for the local wave field extracted from each group of surface wave data, wherein the frequency dispersion image is obtained by a phase shift method or amplitude normalization bunching analysis, and specifically comprises the following steps:
Z(ω，v)＝|eH(ω，v)WS(ω)|
P(ω，v)＝Z(ω，v)2
wherein Z (omega, v) is an actually measured amplitude spectrum calculated by a phase shift method, P (omega, v) is an actually measured power spectrum obtained by amplitude normalization bunching analysis, omega is angular frequency and has the unit of rad/s, v is a test phase velocity and has the unit of m/s,
Figure BDA0002547712280000051
is a plane wave guide vector, where xiIs the offset distance of each channel in the sliding space window, K is the total channel number of the detector in the sliding space window,
Figure BDA0002547712280000052
for each signal in the local wavefield a column vector of fourier spectra,
Figure BDA0002547712280000053
the weighting matrix is normalized by amplitude, and diag represents a diagonal matrix;
step S32: for any one sliding space window, overlapping the obtained multiple scattered images;
step S33: and obtaining an actually measured frequency dispersion curve corresponding to each sliding space window according to the superposed frequency dispersion image.
In this embodiment, as shown in fig. 2, the minimum window WminHas a size of 6 channels and a maximum window WmaxIs 13, after obtaining the frequency dispersion image, extracting to obtain V corresponding to each sliding space windowobsThe ω -curve, i.e. the measured dispersion curve, is extracted using 8 windows as shown in fig. 3, and extracted using 13 windows as shown in fig. 4, and the measured dispersion curves of all spatial windows are integrated together to obtain the observed data dobs＝[Vobs(Wi，ωj)，i＝1，2，...，I；j＝1，2，...J]TWherein I and J are the number of the space window and the frequency point respectively.
Step S4: dispersing the underground model into grid units, and assigning initial values to the elastic parameters of each grid unit, wherein the elastic parameters of the grid units comprise shear wave velocity and longitudinal velocityWave velocity and density. In this embodiment, the initial width and thickness of each grid cell are 1m and 0.25m, the initial shear wave velocity of each grid cell is assigned to the maximum phase velocity of 118.5m/s, the longitudinal wave velocity is assigned to the shear wave velocity of 2 times, and the density is 1900kg/m3。
Step S5: calculating theoretical dispersion curves corresponding to all sliding windows, specifically, the method comprises the following steps:
step S51: calculating a local frequency dispersion curve of a layered model formed by combining each row of grid units;
step S52: calculating the phase difference between any two channels
Figure BDA0002547712280000054
Figure BDA0002547712280000055
Wherein k isp(ω) local spatial wavenumber, Δ x, determined for the elasticity parameter of the cells of the p-th column between the m and n trackspThe width of the P-th grid unit between m and n tracks is defined, and P is the total number of grid units between m and n tracks;
step S53: substituting the phase difference into the following equation:
Figure BDA0002547712280000061
p' (ω, v) is the theoretical power spectrum, ω is the angular frequency in rad/s and v is the experimental phase velocity in m/s, xmnThe distance between m and n channels is defined, and K is the total channel number of the detector in the sliding space window;
step S54: calculating by using a local maximum search algorithm based on a theoretical power spectrum to obtain a theoretical dispersion curve V corresponding to each sliding space windowcalω, taken together to obtain theoretical data dcal＝[Vcal(Wi，ωj)，i＝1，2，...，I；j＝1，2，...J]TWhere I and J are the spatial window and frequency point, respectivelyThe number of (2).
Step S6: calculating the fitting degree of the actually measured frequency dispersion curve and the theoretical frequency dispersion curve, if not, correcting the parameters of each grid unit in the underground model through an inversion algorithm, and repeating the steps S5-S6 until the preset fitting precision is reached to obtain the underground shear wave distribution, wherein the step of correcting the parameters of each grid unit in the underground model comprises updating and correcting the elastic parameters of the grid units or simultaneously updating and correcting the elastic parameters and the thicknesses of the grid units, and the elastic parameters of the grid units comprise shear wave speed, longitudinal wave speed and density.
The wave velocity imaging of the subsurface structure in the embodiment can be shown by a two-dimensional shear wave velocity profile shown in fig. 5, which can be reflected by fig. 5: a low shear wave velocity region having a velocity of less than 105m/s exists in the underground, and the low shear wave velocity region corresponds to a range of 5.5 to 17.5m in the horizontal direction and a range of 2.5 to 5.5m in the depth direction. The shear wave velocity model structure obtained by the method is well matched with the known information, and the reliability of the method is fully demonstrated. It should be noted that only two-dimensional shear wave velocity is shown in the wave velocity imaging of the underground structure in this embodiment, the method of the present invention is used for underground exploration, which is not limited to the detection of shear wave velocity, but also can obtain parameters such as longitudinal wave velocity, attenuation factor, etc., and an effective solution is provided for underground space development and shallow surface engineering exploration and detection.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.

Claims (8)

1. An underground detection method based on surface wave multi-scale window analysis is characterized by comprising the following steps:
s1, laying receiving arrays to observe and collect surface wave data;
s2, extracting a local wave field from the surface wave data by adopting sliding space windows with different scales;
s3, analyzing and processing the local wave field to obtain actually measured dispersion curves corresponding to all sliding space windows;
s4, dispersing the underground model into grid cells, and assigning an initial value to the elastic parameter of each grid cell;
s5, calculating theoretical dispersion curves corresponding to all sliding space windows;
s6, calculating the fitting degree of the actually measured frequency dispersion curve and the theoretical frequency dispersion curve, if not, correcting the parameters of each grid unit in the underground model, repeating the steps S5-S6 until the set fitting precision is reached, and realizing the wave velocity imaging of the underground structure;
receiving and arranging detectors comprising L detectors arranged at a certain track interval delta x in the step S1;
step S5 specifically includes:
s51, calculating a local dispersion curve of the layered model formed by combining each row of grid cells;
s52, calculating the phase difference between any two channels
Figure FDA0003015861620000011
Figure FDA0003015861620000012
Wherein k isp(ω) local spatial wavenumber, Δ x, determined for the elasticity parameter of the cells of the p-th column between the m and n trackspThe width of the P-th grid unit between m and n tracks is defined, and P is the total number of grid units between m and n tracks;
s53, substituting the phase difference into the following equation:
Figure FDA0003015861620000013
p' (ω, v) is the theoretical power spectrum, ω is the angular frequency in rad/s and v is the experimental phase velocity in m/s, xmnThe distance between m and n channels is defined, and K is the total channel number of the detector in the sliding space window;
and S54, calculating by using a local maximum search algorithm based on the theoretical power spectrum to obtain a theoretical dispersion curve corresponding to each sliding space window.
2. The underground detection method based on the surface wave multi-scale window analysis according to claim 1, wherein the step S1 is to set an initial shot point when observing and collecting the surface wave data, the shot point is moved a set distance along the survey line direction every time of excitation, and a set of surface wave data is obtained after each excitation.
3. A subsurface detection method based on surface wave multi-scale window analysis as claimed in claim 2, wherein the step S2 specifically comprises: and sliding a sliding space window with different scales along the receiving arrangement direction for each group of surface wave data to obtain a local wave field.
4. A subsurface detection method based on surface wave multi-scale window analysis as claimed in claim 3, wherein the step S3 specifically includes:
s31, respectively generating a frequency dispersion image corresponding to each sliding space window for the local wave field extracted from each group of surface wave data;
s32, overlapping the obtained multiple scattered images for any sliding space window;
and S33, obtaining the actually measured frequency dispersion curve corresponding to each sliding space window according to the superposed frequency dispersion image.
5. The underground detection method based on the surface wave multi-scale window analysis of claim 4, wherein the scattered images in step S31 are obtained by phase shift method or amplitude normalization bunching analysis, specifically:
Z(ω，v)＝|eH(ω，v)WS(ω)|
P(ω，v)＝Z(ω，v)2
wherein Z (omega, v) is an actually measured amplitude spectrum calculated by a phase shift method, P (omega, v) is an actually measured power spectrum obtained by amplitude normalization bunching analysis, omega is angular frequency and has the unit of rad/s, v is a test phase velocity and has the unit of m/s,
Figure FDA0003015861620000021
as a plane wave guide vector, eH(ω, v) is the conjugate transpose of e (ω, v), where xiIs the offset distance of each channel in the sliding space window, K is the total channel number of the detector in the sliding space window,
Figure FDA0003015861620000022
for each signal in the local wavefield a column vector of fourier spectra,
Figure FDA0003015861620000023
the weighting matrix is normalized for amplitude and diag denotes a diagonal matrix.
6. The method of claim 1, wherein the step S6 of modifying the parameters of each grid cell in the subsurface model comprises updating and modifying the elastic parameters of the grid cells or both the elastic parameters and the thicknesses of the grid cells.
7. A subsurface detection method based on surface wave multi-scale window analysis as claimed in claim 6, wherein the elastic parameters of the grid cells include shear wave velocity, longitudinal wave velocity and density.
8. A subsurface detection method based on surface wave multi-scale window analysis as claimed in claim 1 wherein said correction is performed by an inversion algorithm.
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