WO2019071515A1 - 面波勘探方法及终端设备 - Google Patents

面波勘探方法及终端设备 Download PDF

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WO2019071515A1
WO2019071515A1 PCT/CN2017/105837 CN2017105837W WO2019071515A1 WO 2019071515 A1 WO2019071515 A1 WO 2019071515A1 CN 2017105837 W CN2017105837 W CN 2017105837W WO 2019071515 A1 WO2019071515 A1 WO 2019071515A1
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surface wave
dispersion
spectrum
dispersion curve
algorithm
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PCT/CN2017/105837
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English (en)
French (fr)
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陈晓非
杨振涛
王建楠
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南方科技大学
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Priority to CN201780001187.9A priority Critical patent/CN109923440B/zh
Priority to JP2020520558A priority patent/JP6945895B2/ja
Priority to PCT/CN2017/105837 priority patent/WO2019071515A1/zh
Priority to US16/000,471 priority patent/US10739482B2/en
Publication of WO2019071515A1 publication Critical patent/WO2019071515A1/zh

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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
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • 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. for interpretation or for event detection
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/24—Recording seismic data
    • G01V1/247—Digital recording of seismic data, e.g. in acquisition units or nodes
    • 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. for interpretation or for event detection
    • G01V1/284—Application of the shear wave component and/or several components of the seismic signal
    • 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. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity

Definitions

  • the invention belongs to the technical field of geology and geophysical exploration, and particularly relates to a surface wave exploration method and terminal equipment.
  • the Rayleigh wave (Rayleigh wave) changes in phase velocity in a layered medium with a change in frequency, showing significant dispersion characteristics.
  • the Rayleigh wave in the horizontal layered medium is actually formed by the superposition of the longitudinal and transverse waves at various interfaces in the source region after complex reflection and transmission. It carries parameter information such as P wave velocity, S wave velocity and density of each layer of medium, and the speed mainly depends on the distribution of S wave velocity in the layered medium.
  • the variation characteristics of energy and velocity of Rayleigh wave in the process of propagation carry a large amount of information of the underground stratum, showing the dispersion characteristics, and indirectly reflecting some characteristics inherent in the layered medium itself. Therefore, the low-frequency Rayleigh wave dispersion in natural seismic waves can solve the deep geological structure problem; the higher frequency Rayleigh wave excited by artificial source can solve the shallow geology such as engineering survey, site and ground treatment evaluation, obstacle and cavity detection. problem.
  • the active source is the artificial excitation source to generate surface waves.
  • the name of the passive source surface wave is relative to the active source surface wave, that is, it does not require artificially active source of vibration, but the various vibrations generated by natural phenomena such as tides, winds, and volcanic activities in nature, and from Various kinds of human activities, such as vehicle driving, factory mechanical operation, human walking, etc., are used as seismic sources.
  • active source surface wave exploration requires artificial source, which will have certain impact on the environment, and has certain requirements on site conditions; detector observation systems need to be linearly arranged, and often cannot be implemented in complex urban areas; artificial sources The surface wave energy excited is limited, and the depth of exploration often cannot meet the needs of engineering survey. Therefore, the active source surface wave method has many limitations in urban engineering survey, so the passive source surface wave exploration method is introduced into the field of engineering geophysical exploration.
  • the main methods used to extract the passive source surface wave dispersion at this stage are: F-K method based on Fourier transform and spatial autocorrelation (SPAC) algorithm. These two methods have their own advantages and disadvantages.
  • the FK method can resolve the surface wave of higher-order modes, but the resolution is low, and a large number of receiving points are needed for simultaneous acquisition. Since the SPAC algorithm does not require a large number of receiving points, it has become The most popular passive source surface wave processing method. However, the SPAC algorithm can not extract the high-order modal surface wave dispersion curve. The algorithm can only extract the ground-level surface wave dispersion information, and the receiving points still have to be arranged according to a certain regular shape.
  • the embodiment of the present invention provides a surface wave exploration method and a terminal device to solve the problem that the high-order modal surface wave cannot be extracted in the passive source surface wave exploration, and the arrangement of the receiving point is demanding.
  • a first aspect of an embodiment of the present invention provides a surface wave exploration method, including:
  • the dispersion curve is inverted according to the initial formation model and the inversion algorithm.
  • a second aspect of the embodiments of the present invention provides a surface wave exploration apparatus, including:
  • An extraction module configured to calculate a dispersion spectrum according to the vector wavenumber transformation algorithm and the vibration data; extract a dispersion curve from the dispersion spectrum; the dispersion curve includes a base-order surface wave dispersion curve and a high order Surface wave dispersion curve;
  • the inversion module is configured to invert the dispersion curve according to the initial formation model and the inversion algorithm.
  • a third aspect of an embodiment of the present invention provides a surface wave exploration terminal device including a memory, a processor, and a computer program stored in the memory and operable on the processor, the processor executing the The steps of the method implemented in the computer program include:
  • the dispersion curve is inverted according to the initial formation model and the inversion algorithm.
  • a fourth aspect of the embodiments of the present invention provides a computer readable storage medium, where the computer readable storage medium stores a computer program, and the steps of the method implemented when the computer program is executed by the processor include:
  • the dispersion curve is inverted according to the initial formation model and the inversion algorithm.
  • the vector wave number transform algorithm is used to obtain the dispersion spectrum, and the dispersion curve including the base surface wave and the high-order surface wave is extracted in the dispersion spectrum, and an initial stratigraphic model is established, and the initial stratigraphic model and the inversion algorithm are used to match the frequency.
  • Inversion calculation of the scattered curve to achieve formation exploration high-order surface wave dispersion information can be extracted from the vibration data, and high-order surface wave frequency dispersion information can be extracted.
  • the information is added to the inversion of the stratum, thereby reducing the uncertainty of the inversion; by establishing the initial stratigraphic model, the computation time of the inversion algorithm can be reduced, and the instability of the inversion operation can be reduced; the vibration acquisition device can be arranged arbitrarily Reduce the requirements for the layout of the site and improve the site adaptability of surface wave exploration.
  • FIG. 1 is a flowchart of an implementation of a surface wave exploration method according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of an arrangement of an observation station of a conventional spatial autocorrelation algorithm according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of an arrangement of an observation station of a vector wave number conversion algorithm according to an embodiment of the present invention
  • FIG. 4 is a flowchart of realizing a dispersion spectrum obtained in a surface wave exploration method according to an embodiment of the present invention
  • FIG. 5 is a flowchart of realizing a mean value of cross-correlation function spectra in a surface wave exploration method according to an embodiment of the present invention
  • FIG. 6 is a flowchart of an implementation of establishing an initial formation model in a surface wave exploration method according to an embodiment of the present invention
  • FIG. 7 is a schematic diagram of a theoretical F-C dispersion spectrum (c) obtained by extracting an F-C dispersion spectrum (a), a frequency interval classification (b), and a Green's function kernel function according to an embodiment of the present invention
  • FIG. 8 is a schematic diagram of a dispersion spectrum and a formation model of a depth domain according to an embodiment of the present invention.
  • FIG. 9 is a schematic diagram of a surface wave exploration device according to an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of a surface wave exploration terminal device according to an embodiment of the present invention.
  • FIG. 1 is a flowchart of implementing surface wave exploration according to an embodiment of the present invention, which is described in detail as follows:
  • the vibration acquisition device includes, but is not limited to, an engineering seismograph or a detector, for example, a multi-channel wired connected engineering seismograph, or an independently wirelessly connected seismometer.
  • the detector can be a wide-band detector with a main frequency of no more than 4 hz, and the faster the acquisition bandwidth, the better the acquisition of surface waves of various frequencies.
  • the number of vibration collecting devices is greater than or equal to a preset number, for example, the number of detectors is greater than or equal to 12.
  • the sampling frequency of the vibration acquisition device should be full For the purpose of exploration, the sampling rate of engineering exploration is generally not less than 200hz.
  • the synchronous acquisition time of the vibration collecting device is greater than or equal to the preset time.
  • a dispersion spectrum is calculated according to a vector wave number transformation algorithm (English name: Vector Wavenumber Transform Method, English abbreviation: VWTM) and the vibration data; a dispersion curve is extracted from the dispersion spectrum; The dispersion curve includes a fundamental-order surface wave dispersion curve and a high-order surface wave dispersion curve.
  • a vector wave number transformation algorithm English name: Vector Wavenumber Transform Method, English abbreviation: VWTM
  • SPAC method The existing spatial autocorrelation algorithm (SPAC method) is a background noise research method proposed by geophysicist Aki in 1957.
  • the calculation formula is:
  • ⁇ ( ⁇ ,r) is the spatial autocorrelation coefficient
  • ⁇ is the angular frequency
  • ⁇ 2 ⁇ f
  • r is the distance between any two points
  • k is the wave number
  • k ⁇ /c
  • c is the wave velocity
  • J 0 is one A class-like zero-order Bessel function
  • c( ⁇ ) is the surface wave phase velocity. It can be seen that the spatial autocorrelation coefficient is a kind of zero-order Bessel function of surface wave phase velocity, frequency, and station spacing. Therefore, the surface wave phase velocity can be obtained by fitting the calculated spatial autocorrelation coefficient.
  • the SPAC method requires that the observing stations corresponding to the vibration collecting device must be arranged according to certain rules, such as linear, circular, L-shaped, etc. (as shown in Figure 2), which have certain requirements on the site.
  • the SPAC method approximates the actual signal. It is considered that the background noise signal of ground propagation is mainly composed of surface waves, and the fundamental surface wave is dominant.
  • the spatial autocorrelation coefficient is only the base surface wave. Spatial autocorrelation coefficient.
  • the SPAC method can only extract the fundamental surface wave dispersion curve from the passive source surface wave, and can not effectively extract the dispersion curve of the high-order surface wave. Theoretically speaking, it is very uncertain to rely on the fundamental surface wave dispersion curve to invert the stratigraphic structure. If the high-order surface wave dispersion curve can be extracted and used for inversion, the uncertainty of the inversion will be greatly reduced.
  • the embodiment of the invention proposes a vector wave number transformation algorithm (VWTM), which can extract the fundamental surface wave information and the high-order surface wave information from the vibration data, thereby using the high-order surface wave dispersion curve for the formation inversion and reducing the inverse The uncertainty of the performance.
  • VWTM vector wave number transformation algorithm
  • VWTM vector wave number conversion algorithm
  • A is a constant
  • ⁇ is the angular frequency
  • ⁇ 2 ⁇ f
  • f is the frequency
  • the fretting wave field is a time-space stable isotropic random wave field
  • the cross-correlation coefficient of the vertical component wave field of any two stations is spatially isotropic, namely:
  • the Fourier transform of the formula (3) can obtain the expression of the cross-correlation spectrum:
  • microseismic signals received by the two observing stations It is the Fourier transform of the microseismic signal.
  • Orthogonal properties according to Bessel functions Equation (8) can be simplified to:
  • VWTM Vector Wave Number Transformation Method
  • VWTM vector wave number conversion algorithm
  • the cross correlation of background noise data between any two stations can be calculated.
  • the values of the kernel functions g( ⁇ , k) of different wave numbers k are calculated, and the energy distributions of the surface waves of different modes in the frequency-wavenumber domain and the frequency-speed domain can be obtained.
  • the properties of the vibration wave are judged, and then the dispersion curve containing the higher-order mode is obtained.
  • the background noise is mainly the surface wave signal, and the body wave signal only appears in individual cases.
  • the surface wave dispersion curve inversion can be performed to obtain the transverse wave velocity structure of the subsurface medium.
  • VWTM vector wavenumber transform algorithm
  • the observation station corresponding to the vibration acquisition device does not need to be placed according to certain rules, and can be placed arbitrarily, of course, it can be placed in a linear, circular, L-shaped shape, etc. There is no requirement for the venue, as shown in Figure 3.
  • VWTM vector wave number conversion algorithm
  • the actual received background noise data consists of waves generated by various vibrations, including not only surface waves but also body waves.
  • the surface wave will have a dispersion phenomenon in the non-uniform medium, that is, the surface wave is composed of modes of different phase velocities. Therefore, by calculating the obtained dispersion spectrum, it is possible to separate the surface waves of different speeds (including the base order and the high order) and the components of the body wave.
  • calculating the dispersion spectrum according to the vector wave number conversion algorithm and the vibration data in S102 includes:
  • an observation station corresponding to any two of the vibration collecting devices is formed into an observation station group, and a station spacing corresponding to the observation station group is calculated; the station spacing is the observation station group. The distance between two observing stations.
  • all the observing stations are combined in two or two. If there are n observing stations, m station spacings r 1 , r 2 ... r m can be obtained.
  • each observing station group corresponds to a station spacing
  • the vibration data it is possible to calculate the cross-correlation of the two observing stations in the observing station group corresponding to any group of station spacing r 0 at any frequency ⁇ 0 .
  • the dispersion spectrum is calculated according to the station spacing, the cross-correlation spectrum corresponding to the station spacing, and the vector wave number conversion algorithm (VWTM).
  • the scan frequency sequence may be generated according to a preset scan frequency range and a preset frequency interval.
  • a scan speed sequence is generated according to a preset scan speed range and a preset speed interval.
  • the frequency wave and velocity scanning are performed according to the equation (2) by using the vector wave number conversion algorithm (VWTM), and the dispersion spectrum is calculated.
  • the dispersion spectrum can be a frequency-speed dispersion spectrum, that is, an f-c dispersion spectrum.
  • the method further includes:
  • the station spacing corresponding to each observing station group can be sorted from small to large, and the cross-correlation spectrum corresponding to the observing station group with equal station spacing is averaged as the cross correlation corresponding to the station spacing.
  • the spectrum can thus make full use of the vibration data of the observation station group with equal station spacing, so that the calculated cross-correlation spectrum is more accurate, thereby enhancing the effectiveness of the extracted dispersion spectrum and improving the accuracy of formation exploration.
  • the transient surface wave exploration method in engineering application only uses the energy maximum value in the frequency-speed dispersion spectrum, manually or automatically connects the dispersion curve, and inverts the formation depth according to the zigzag feature in the dispersion curve. And thickness.
  • the transient surface wave exploration method in engineering application only uses the energy maximum value in the frequency-speed dispersion spectrum, manually or automatically connects the dispersion curve, and inverts the formation depth according to the zigzag feature in the dispersion curve. And thickness.
  • Phenomenon when there are low-speed layers or high-speed layers in the local layer, not only the energy distribution of each Rayleigh wave mode changes, but also the speed of each mode changes with frequency, which often results in “mode kissing”. Phenomenon, this will bring great difficulty to the judgment of high-order modal dispersion curves.
  • the high-order surface wave has more energy than the base surface wave, which means that it cannot be obtained by the current method in a certain frequency range.
  • the fundamental surface wave but only the high-order surface wave.
  • the real stratum is not an ideal horizontal layered isotropic structure, which results in Rayleigh's imaging quality in the high-order modes of the dispersion spectrum is usually not high, all of which restrict the use of these factors.
  • the high-order dispersion curve is inverted.
  • the Rayleigh wave energy in the frequency range corresponding to the buried depth of the interlayer is from the base order to the first-order or higher-order mode when there is a low-speed or high-speed interlayer in the local layer.
  • the frequency intervals are classified according to the relationship of the energy distributions of the modes in the different frequency intervals in the frequency-velocity spectrum, thereby rapidly establishing a simple layered stratigraphic model as a subsequent accurate inversion. Initial model.
  • S103 may include:
  • the frequency interval is classified according to an energy distribution of a surface wave mode of each frequency interval in the dispersion spectrum.
  • the initial stratigraphic model is established according to the corresponding relationship between the classified frequency interval and the stratum.
  • Fig. 7a is the FC dispersion spectrum, in which the dotted line is the theoretical surface wave dispersion curve; Fig. 7b) is the discrete dispersion point obtained from the dispersion spectrum energy extraction, and according to the distribution characteristics The frequency interval is divided into four categories; Fig. 7c) is the theoretical FC dispersion spectrum obtained by the Green's function kernel function.
  • the dispersion point in the frequency-velocity domain is converted to the depth-velocity domain according to the half-wavelength theory, as shown in Fig. 8.
  • Figure 8a) is the dispersion spectrum of the depth domain
  • Figure 8b) is the formation model. It can be seen that the points on the 1st and 3rd points on the dispersion spectrum are points on the fundamental dispersion curve, and the points on the 2nd and 4th points are points on the higher-order dispersion curve.
  • the third layer (low-velocity layer) with a displacement of 20-40m is basically the same as the distribution of the fourth-order dispersion point in the depth-velocity profile; the first layer and depth with a displacement of 0-10m -
  • the distribution of the 2nd dispersion point in the velocity profile is also basically the same. In this way, we can see that there is a one-to-one correspondence between the distribution of higher-order dispersion curves and the formation. It is thus proved that the idea of establishing initial modeling is correct by classifying the dispersion points in the frequency domain and layering the formations.
  • the dispersion curve is inverted according to the initial formation model and the inversion algorithm.
  • the inversion algorithm such as a simulated annealing (Simulated Annealing) algorithm or a genetic algorithm may be used to invert the dispersion curve to obtain velocity information of the formation information and/or the vibration wave.
  • a simulated annealing Simulated Annealing
  • a genetic algorithm may be used to invert the dispersion curve to obtain velocity information of the formation information and/or the vibration wave.
  • formation depth information and velocity profiles can be obtained to achieve exploration of the formation structure.
  • High-order surface wave dispersion information can be extracted from the passive source surface wave signal.
  • the initial model is established, and then the inversion algorithm is used for stratigraphic inversion, which greatly reduces the computation time of the inversion algorithm and greatly reduces the instability of the inversion. .
  • the observation station can be arranged arbitrarily, there is almost no requirement for the site, the acquisition time can be about 15 minutes to 30 minutes, and the calculation time is about 10 minutes for the ordinary laptop (i5, 8g memory).
  • the time required for the inversion operation is small. Moreover, it does not need a source, and does not have any impact on the environment. It can be collected by ordinary engineering seismographs (such as transient surface wave seismographs), which is efficient and economical.
  • the vector wave number transform algorithm is used to obtain the dispersion spectrum, and the dispersion curve including the base surface wave and the high-order surface wave is extracted in the dispersion spectrum, and an initial stratigraphic model is established, and the initial stratigraphic model and the inversion algorithm are used to match the frequency.
  • Inversion calculation of the scattered curve to achieve formation exploration high-order surface wave dispersion information can be extracted from the vibration data, and high-order surface wave dispersion information is added to the inversion of the formation, thereby reducing the uncertainty of the inversion.
  • the computation time of the inversion algorithm can be reduced, and the instability of the inversion operation can be reduced; the vibration acquisition device can be arbitrarily arranged, the requirements on the layout site can be reduced, and the site adaptability of the surface wave exploration can be improved.
  • FIG. 9 shows a schematic diagram of a surface wave exploration device provided by an embodiment of the present invention. For the convenience of explanation, only the parts related to the present embodiment are shown.
  • the apparatus includes an acquisition module 91, an extraction module 92, a construction module 93, and an inversion module 94.
  • the obtaining module 91 is configured to acquire vibration data collected by the vibration collecting device.
  • An extraction module 92 configured to calculate a dispersion spectrum according to the vector wave number transformation algorithm and the vibration data; extract a dispersion curve from the dispersion spectrum; the dispersion curve includes a base plane wave dispersion curve and a height Step wave dispersion curve.
  • the building module 93 is configured to establish an initial formation model according to the dispersion spectrum.
  • the inversion module 94 is configured to invert the dispersion curve according to the initial formation model and the inversion algorithm.
  • the calculation formula of the vector wave number conversion algorithm is:
  • A is a constant
  • ⁇ is the angular frequency
  • ⁇ 2 ⁇ f
  • f is the frequency
  • g( ⁇ ,k) is the kernel function of the vertical component of the Green's function.
  • the calculation process of the vector wave number transformation algorithm is specifically:
  • microseismic signals received by the two observing stations a Fourier transform of the microseismic signal
  • G( ⁇ ,r) is the vertical component of the Green's function.
  • J 0 is a class of zero order Bessel function;
  • the extraction module 92 is configured to:
  • the observation stations corresponding to any two of the vibration collecting devices are formed into an observation station group, and the station spacing corresponding to the observation station group is calculated; the station spacing is two observations in the observation station group The distance between stations;
  • the extraction module 92 is further configured to:
  • the cross-correlation spectra corresponding to the observation station groups having the same station spacing are superimposed and averaged, and the obtained average value is used as the cross-correlation spectrum corresponding to the station spacing.
  • the building block 93 is used to:
  • the initial stratigraphic model is established according to the corresponding relationship between the classified frequency interval and the stratum.
  • the vector wave number transform algorithm is used to obtain the dispersion spectrum, and the dispersion curve including the base surface wave and the high-order surface wave is extracted in the dispersion spectrum, and an initial stratigraphic model is established, and the initial stratigraphic model and the inversion algorithm are used to match the frequency.
  • the inversion curve is used for inversion calculation to achieve formation exploration. Since the high-order surface wave dispersion information can be extracted from the vibration data, and the high-order surface wave dispersion information is added to the inversion operation of the formation, the uncertainty of the inversion is greatly reduced; the initial formation model can be reduced by establishing the initial formation model. Invert the algorithm's operation time and reduce the instability of the inversion operation; enable the vibration acquisition device to be arbitrarily arranged, reduce the requirements on the layout site, and improve the site adaptability of the surface wave exploration.
  • FIG. 10 is a schematic diagram of a surface wave exploration terminal device according to an embodiment of the present invention.
  • the surface wave exploration terminal device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and operable on the processor 100, such as a surface wave. Exploration program.
  • the processor 100 executes the computer program 102, the steps in the embodiments of the various surface wave exploration methods described above are implemented, such as steps 101 to 104 shown in FIG.
  • the processor 100 when executing the computer program 102, implements the functions of the modules/units in the various apparatus embodiments described above, such as the functions of the modules 91-94 shown in FIG.
  • the computer program 102 can be partitioned into one or more modules/units that are stored in the memory 101 and executed by the processor 100 to complete this invention.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing a particular function, the instruction segments being used to describe the execution of the computer program 102 in the surface wave exploration terminal device 10.
  • the computer program 102 can be divided into an acquisition module, an extraction module, a construction module, and an inversion module, and the specific functions of each module are as follows:
  • An extraction module configured to calculate a dispersion spectrum according to the vector wavenumber transformation algorithm and the vibration data; extract a dispersion curve from the dispersion spectrum; the dispersion curve includes a base-order surface wave dispersion curve and a high order Surface wave dispersion curve;
  • the inversion module is configured to invert the dispersion curve according to the initial formation model and the inversion algorithm.
  • the surface wave exploration terminal device 10 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the surface wave exploration terminal device may include, but is not limited to, the processor 100 and the memory 101. It will be understood by those skilled in the art that FIG. 10 is merely an example of the surface wave exploration terminal device 10 and does not constitute a limitation to the surface wave exploration terminal device 10, and may include more or less components than those illustrated, or may combine some components. , or different components, such as the surface wave exploration terminal device, may also include a display, an input and output device, a network access device, a bus, and the like.
  • the processor 100 may be a central processing unit (CPU), or may be other Processor, digital signal processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 101 may be an internal storage unit of the surface wave exploration terminal device 10, such as a hard disk or memory of the surface wave exploration terminal device 10.
  • the memory 101 may also be an external storage device of the surface wave exploration terminal device 10, for example, a plug-in hard disk equipped with the surface wave exploration terminal device 10, a smart memory card (SMC), and a secure digital number. (Secure Digital, SD) card, flash card, etc.
  • SMC smart memory card
  • secure digital number Secure Digital, SD
  • the memory 101 may also include both an internal storage unit of the surface wave exploration terminal device 10 and an external storage device.
  • the memory 101 is used to store the computer program and other programs and data required by the surface wave exploration terminal device.
  • the memory 101 can also be used to temporarily store data that has been output or is about to be output.
  • each functional unit and module in the foregoing system may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be implemented by hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application.
  • the disclosed apparatus/terminal device and method may be implemented in other manners.
  • the device/terminal device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division.
  • there may be another division manner for example, multiple units.
  • components may be combined or integrated into another system, or some features may be omitted or not performed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented when the program is executed by the processor.
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
  • the computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM). , random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media It does not include electrical carrier signals and telecommunication signals.

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Abstract

本方案适用于地质勘探技术领域，提供了一种面波勘探方法及终端设备。该方法包括：获取振动采集装置采集到的振动数据（S101）；根据矢量波数变换算法和振动数据计算得到频散谱；从频散谱中提取出频散曲线；频散曲线包括基阶面波频散曲线和高阶面波频散曲线（S102）；根据频散谱建立初始地层模型（S103）；根据初始地层模型和反演算法，对频散曲线进行反演（S104）。本方案能够从振动数据中提取出高阶面波频散信息，降低反演的不确定性；通过建立初始地层模型能够降低反演算法的运算时间，并且降低反演运算的不稳定性；使振动采集装置能够任意布置，降低对布置场地的要求，提高面波勘探的场地适应性。

Description

面波勘探方法及终端设备 技术领域
本发明属于地质、地球物理勘探技术领域，尤其涉及一种面波勘探方法及终端设备。
背景技术
从二十世纪五十年代，科学家发现Rayleigh波(瑞雷波)在层状介质中相速度随频率改变而改变，呈现明显的频散特性。水平层状介质中的Rayleigh波实际上是纵波和横波在震源区域内各界面处经过复杂的反射、透射后相互干涉叠加而成。它携带了各层介质的P波速度、S波速度、密度等参数信息，且速度主要取决于层状介质中S波速度的分布。Rayleigh波在传播过程中能量和速度的变化特征携带了大量地下地层的信息，呈现出的频散特征，也间接反映了层状介质本身所固有的一些特征。由此研究天然地震波中的低频Rayleigh波频散可以解决深部地质构造问题；研究人工震源激发的较高频率的Rayleigh波可以解决工程勘察、场地和地基处理评价、障碍物和空洞探测等浅层地质问题。
面波勘探是工程物探领域应用最广泛的物探方法，按震源的不同可分为两类：主动源法和被动源法。其中主动源就是人工激发震源产生面波。被动源面波顾名思义这一称呼是相对于主动源面波的，即不需要人工主动制造震源，而是将自然界中的潮汐、风、火山活动等自然现象所产生的各种震动，以及来自于人类的各种活动，如车辆行驶、工厂机械运行、人类走动等产生的各种震动作为震源。
在城市工程物探中，主动源面波勘探需要人工震源，会对环境造成一定影响，且对场地条件有一定的要求；检波器观测系统需要线性排列，在城市复杂区域探测经常无法实施；人工震源激发的面波能量有限，勘探深度常常无法满足工程勘察的需要。所以主动源面波法在城市工程勘察中存在诸多局限性，因此被动源面波勘探方法被引入到工程物探领域。
被动源面波勘探的关键问题之一是如何从采集到的面波数据中提取频散曲线。现阶段用于提取被动源面波频散的主要方法有：基于傅立叶变换的F-K法和空间自相关(SPAC)算法。这两种方法各有优缺点，其中F-K法虽然能分辨高阶模态的面波，但分辨率低，且需大量接收点同时采集；由于SPAC算法对接收点个数要求也不高，现已成为最流行的被动源面波处理方法。但SPAC算法并不能提取高阶模态面波频散曲线，算法只能提取到基阶面波频散信息，且接收点仍须按一定规则形状布置。
技术问题
有鉴于此，本发明实施例提供了一种面波勘探方法及终端设备，以解决目前被动源面波勘探中不能提取高阶模态面波，且对接收点排布要求苛刻的问题。
技术解决方案
本发明实施例的第一方面提供了一种面波勘探方法，包括：
获取振动采集装置采集到的振动数据；
根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
根据所述频散谱建立初始地层模型；
根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
本发明实施例的第二方面提供了一种面波勘探装置，包括：
获取模块，用于获取振动采集装置采集到的振动数据；
提取模块，用于根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
构建模块，用于根据所述频散谱建立初始地层模型；
反演模块，用于根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
本发明实施例的第三方面提供了一种面波勘探终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现的方法的步骤包括：
获取振动采集装置采集到的振动数据；
根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
根据所述频散谱建立初始地层模型；
根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
本发明实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现的方法的步骤包括：
获取振动采集装置采集到的振动数据；
根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
根据所述频散谱建立初始地层模型；
根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
有益效果
本发明实施例利用矢量波数变换算法得到频散谱，在频散谱提取出包括基阶面波和高阶面波的频散曲线，建立初始地层模型，通过初始地层模型和反演算法对频散曲线进行反演运算，实现地层勘探，能够从振动数据中提取出高阶面波频散信息，将高阶面波频散信 息加入到地层的反演运算中，从而降低反演的不确定性；通过建立初始地层模型能够降低反演算法的运算时间，并且降低反演运算的不稳定性；使振动采集装置能够任意布置，降低对布置场地的要求，提高面波勘探的场地适应性。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的面波勘探方法的实现流程图；
图2是本发明实施例提供的现有的空间自相关算法的观测台站布置示意图；
图3是本发明实施例提供的矢量波数变换算法的观测台站布置示意图；
图4是本发明实施例提供的面波勘探方法中得到频散谱的实现流程图；
图5是本发明实施例提供的面波勘探方法中互相关函数谱取均值的实现流程图；
图6是本发明实施例提供的面波勘探方法中建立初始地层模型的实现流程图；
图7是本发明实施例提供的提取到的F-C频散谱(a)、频率区间分类(b)和格林函数核函数得到的理论F-C频散谱(c)的示意图；
图8是本发明实施例提供的深度域的频散谱和地层模型的示意图；
图9是本发明实施例提供的面波勘探装置的示意图；
图10是本发明实施例提供的面波勘探终端设备的示意图。
本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本发明实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本发明。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本发明的描述。
为了说明本发明所述的技术方案，下面通过具体实施例来进行说明。
图1为本发明实施例提供的面波勘探的实现流程图，详述如下：
在S101中，获取振动采集装置采集到的振动数据。
在本实施例中，振动采集装置包括但不限于工程地震仪或检波器，例如，可以采用多道有线连接的工程地震仪，或者独立无线连接的地震仪。优选地，检波器可以为主频不高于4hz的宽频带检波器，采集带宽越快越有利于各种频率的面波的采集。振动采集装置的个数大于等于预设个数，例如检波器的数量大于等于12个。振动采集装置的采样频率应满 足勘探目的，工程勘探采样率一般不低于200hz。振动采集装置的同步采集时间大于等于预设时间。
在S102中，根据矢量波数变换算法(英文命名为:Vector Wavenumber Transform Method，英文缩写：VWTM)和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线。
现有的空间自相关算法(SPAC法)是1957年地球物理学家Aki提出的一种背景噪音研究方法，计算式为：
Figure PCTCN2017105837-appb-000001
其中，ρ(ω,r)为空间自相关系数，ω为角频率，ω＝2πf，r为任意两点间的距离，k为波数，k＝ω/c，c为波速，J0为一类零阶贝塞尔函数，c(ω)为面波相速度。可以看出，空间自相关系数是面波相速度、频率，以及台站间距的一类零阶贝塞尔函数。因此，可以通过拟合计算得到的空间自相关系数，求出面波相速度。
现有的SPAC法存在以下缺点：
1.SPAC法要求振动采集装置对应的观测台站必须按一定规则进行布置，如线性、圆形、L型等(如图2所示)，对场地有一定要求。
2.SPAC法对实际信号进行了很大的近似，认为地面传播的背景噪声信号主要由面波组成，而且由基阶面波占主导，求出的空间自相关系数仅为基阶面波的空间自相关系数。
3.SPAC法仅能从被动源面波中提取基阶面波频散曲线，并不能有效的提取高阶面波的频散曲线。从理论上来讲，仅依靠基阶面波频散曲线反演地层结构具有很大的不确定性。如果高阶面波频散曲线能被提取并用于反演，那么反演的不确定性将大大降低。
本发明实施例提出了矢量波数变换算法(VWTM)，能够从振动数据中提取到基阶面波信息和高阶面波信息，从而将高阶面波频散曲线用于地层反演，降低反演的不确定性。
作为本发明的一个实施例，所述矢量波数变换算法(VWTM)的计算式为：
Figure PCTCN2017105837-appb-000002
其中，
Figure PCTCN2017105837-appb-000003
为互相关谱，A为常量，
Figure PCTCN2017105837-appb-000004
为两个观测台站之间的距离，ω为角频率，ω＝2πf，f为频率，g(ω,k)为格林函数垂向分量的核函数，
Figure PCTCN2017105837-appb-000005
为波数，k＝ω/c。
具体地，
Figure PCTCN2017105837-appb-000006
为任意两观测台站间互相关的频谱。
下面对提出的矢量波数变换算法的计算过程进行阐述。
在水平层状地表某个测点
Figure PCTCN2017105837-appb-000007
接收到的微震垂直分量的记录可以表示为
Figure PCTCN2017105837-appb-000008
则位于地 表某两处
Figure PCTCN2017105837-appb-000009
和
Figure PCTCN2017105837-appb-000010
的台站的背景噪音信号的时间域互相关定义为：
Figure PCTCN2017105837-appb-000011
其中，
Figure PCTCN2017105837-appb-000012
由于微动波场是时间-空间稳定的各向同性的随机波场，故任意两个台站微震垂向分量波场的互相关系数是空间各向同性的，即：
Figure PCTCN2017105837-appb-000013
其中
Figure PCTCN2017105837-appb-000014
根据卷积定理，对公式(3)做傅里叶变换可以得到互相关谱的表达式：
Figure PCTCN2017105837-appb-000015
其中，
Figure PCTCN2017105837-appb-000016
和
Figure PCTCN2017105837-appb-000017
分别为两个观测台站接收到的微震信号，
Figure PCTCN2017105837-appb-000018
为微震信号的傅里叶变换。
根据
Figure PCTCN2017105837-appb-000019
正比于波动方程的格林函数的虚部，即
Figure PCTCN2017105837-appb-000020
其中，A为常量，G(ω,r)为格林函数的垂向分量，可以表示为
Figure PCTCN2017105837-appb-000021
我们对
Figure PCTCN2017105837-appb-000022
做矢量波数变换，并利用公式
Figure PCTCN2017105837-appb-000023
得：
Figure PCTCN2017105837-appb-000024
将公式(5)、(6)代入
Figure PCTCN2017105837-appb-000025
进行矢量波数变换，得到如下中间算式：
Figure PCTCN2017105837-appb-000026
根据贝塞尔函数的正交性质
Figure PCTCN2017105837-appb-000027
公式(8)可简化为：
Figure PCTCN2017105837-appb-000028
如图7(c)所示，k＝kn(ω),n＝1,2,3,....是核函数Im{g(ω,k)}的极点，Im{g(ω,k)}在kn(ω)(n＝1,2,3…)处趋于无穷。在实际应用过程中因为用有限求和代替了积分，所以在kn(ω)(n＝1,2,3…)有极大值。至此得到了一种新的提取频散曲线的方法，我们称之为矢量波数变换方法(VWTM)。
根据矢量波数变换算法(VWTM)的计算式，计算任意两个台站之间背景噪声数据的互相关可得到
Figure PCTCN2017105837-appb-000029
再按公式(9)进行处理，计算不同波数k的核函数g(ω,k)的值，就可以得到不同模的面波在频率-波数域、频率-速度域的能量分布。根据波的能量分布形态， 判断振动波的性质，进而得到含有高阶模态的频散曲线。需要注意的是，在实际探测中背景噪声主要是面波信号，体波信号只是在个别情况下出现。之后可以进行面波频散曲线反演得到地下介质的横波速度结构。
提出的矢量波数变换算法(VWTM)具有以下优点：
1.采用矢量波数变换算法(VWTM)，振动采集装置对应的观测台站无需要按一定规则进行摆放，可任意摆放，当然也可按线性、圆形，L型等形状摆放，对场地无要求，如图3所示。
2.实际接收的背景噪声数据由各种震动产生的波组成，不仅包含面波，也包含了体波。而且面波在非均匀介质中会发生频散现象，即面波由不同相速度的模态组成。所以通过计算得到的频散谱就能分离出由不同速度的面波(包含了基阶和高阶)和体波的组分。
作为本发明的一个实施例，S102中的根据矢量波数变换算法和所述振动数据计算得出频散谱包括：
在S401中，将任意两个所述振动采集装置对应的观测台站组成观测台站组，计算得到所述观测台站组对应的台站间距；所述台站间距为所述观测台站组中两个观测台站之间的距离。
在本实施例中，将所有观测台站进行两两组合，若共有n个观测台站，则可以得到m个台站间距r1，r2...rm，其中
Figure PCTCN2017105837-appb-000030
在S402中，根据所述振动数据，计算得到所述观测台站组对应的互相关谱。
其中，每个观测台站组对应一个台站间距，根据振动数据，可以计算任一组台站间距r0对应的观测台站组中两个观测台站在任一频率ω0下的互相关的谱值
Figure PCTCN2017105837-appb-000031
在S403中，根据所述台站间距、所述台站间距对应的互相关谱和所述矢量波数变换算法(VWTM)计算得到所述频散谱。
具体地，可以根据预设的扫描频率范围和预设的频率间隔，生成扫描频率序列。根据预设的扫描速度范围和预设的速度间隔，生成扫描速度序列。根据扫描频率序列和扫描速度序列，利用矢量波数变换算法(VWTM)，按照式(2)进行频率和速度扫描，计算得到频散谱。频散谱可以为频率-速度频散谱，即f-c频散谱。
作为本发明的一个实施例，在S401之后，S402之前，还包括：
在S501中，对比各个所述观测站台组对应的台站间距。
在S502中，将所述台站间距相等的观测站台组对应的互相关谱进行叠加平均，并将得到的平均值作为所述台站间距对应的互相关谱。
在本实施例中，可以将各个观测站台组对应的台站间距从小到大进行排序，将台站间距相等的观测站台组对应的互相关谱取平均值，作为该台站间距对应的互相关谱，由此能够充分利用台站间距相等的观测站台组的振动数据，使计算得到的互相关谱更为准确，从而增强提取到的频散谱的有效性，进而提高地层勘探的准确度。
在S103中，根据所述频散谱建立初始地层模型。
目前工程应用中瞬态面波勘探方法只在频率-速度频散谱中按能量极大值，手动或自动连接频散曲线，根据频散曲线中的“之”字型特征来反演地层深度和厚度。以上利用高阶面波的反演，必须对所利用的高阶面波的阶数有个一个准确的判断。但当地层存在低速层或高速层时，不但瑞雷波各个模态的能量分布发生变化，各个模态的速度随频率的变化也会发生改变，从而经常会产生“模式接吻(mode kissing)”现象，这样就会对高阶模态频散曲线的判断带来很大困难。而且当在高频范围内在水平层状的地层模型中存在软弱夹层时，高阶面波比基阶面波具有更大的能量，这就意味着在一定频率范围内通过目前的方法是无法得到基阶面波的，而仅能得到高阶面波。在实际勘探中，真实地层并不是理想的水平层状各向同性的结构，从而导致瑞雷波(Rayleigh)在频散谱波高阶模态的成像质量通常不高，以上这些因素都制约了利用高阶频散曲线进行反演。
为了避免在上述采用高阶频散曲线反演遇到的问题，我们发现当地层存在低速或高速夹层时，夹层埋深对应的频率范围区间内Rayleigh波能量从基阶向一阶或更高阶模态阶跃，从而导致基阶和高阶面波频散曲线出现只在某一频率范围内连续，在实际数据中成像质量可能更差。我们认为频率—速度谱中，各个模态能量的分布也与地层结构有着密切的联系。因此，在本实施例中，根据在频率—速度谱中各个不同频率区间各模态的能量分布的关系，对频率区间进行分类，从而迅速建立简单的层状地层模型，作为后续精确反演的初始模型。
作为本发明的一个实施例，S103可以包括：
在S601中，根据所述频散谱中各个频率区间的面波模态的能量分布，对所述频率区间进行分类。
在S602中，根据分类后的频率区间与地层的对应关系建立所述初始地层模型。
如图7所示，图7a)为F-C频散谱，图中点线为理论面波频散曲线；图7b)为根据频散谱能量提取得到的离散的频散点，并根据分布特性将频率区间分为4类；图7c)为格林函数核函数得到的理论F-C频散谱。
将频率-速度域的频散点，根据半波长理论转换到深度-速度域，见图8。图8a)深度域的频散谱，图8b)为地层模型。可以看到，频散谱上1号和3号点线上的点为基阶频散曲线上的点，2号和4号点线上的点为高阶频散曲线上的点。将地层模型与得到的深度域的 频散曲线进行对比，可以看到位移20-40m埋深的第三层(低速层)与深度-速度剖面中的4号频散点的分布基本一致；位移0-10m的第一层与深度-速度剖面中的2号频散点的分布也基本一致。这样我们可以看到高阶频散曲线的分布与地层确实存在一一对应的关系。由此证明，通过在频率域将频散点进行分类，对地层进行分层，建立初始建模的思路是正确的。
在S104中，根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
在本实施例中，可以采用模拟退火(Simulated Annealing)算法、遗传算法等反演算法，对所述频散曲线进行反演，得到地层信息和/或振动波的速度信息。例如，可以得到地层深度信息和速度剖面，从而实现对地层结构的勘探。
本发明实施例提出的面波勘探具有以下优点：
1.能从被动源面波信号中提取出高阶面波频散信息。
2.通过在频率域分类的方法对地层分层，建立初始模型，然后再采用反演算法进行地层反演，这样大大降低了反演算法的运算时间，并大大降低了反演的不稳定性。
3.在被动源信号采集时，观测台站可任意布置，对场地几乎无任何要求，采集时间可以在15分钟-30分钟左右，后期计算时间普通笔记本电脑(i5，8g内存)约为10分钟，反演运算所需的时间少。而且无需震源，对环境不造成任何影响，并可采用普通工程地震仪(如瞬态面波地震仪)进行采集，集高效、经济等优点。
本发明实施例利用矢量波数变换算法得到频散谱，在频散谱提取出包括基阶面波和高阶面波的频散曲线，建立初始地层模型，通过初始地层模型和反演算法对频散曲线进行反演运算，实现地层勘探，能够从振动数据中提取出高阶面波频散信息，将高阶面波频散信息加入到地层的反演运算中，从而降低反演的不确定性；通过建立初始地层模型能够降低反演算法的运算时间，并且降低反演运算的不稳定性；使振动采集装置能够任意布置，降低对布置场地的要求，提高面波勘探的场地适应性。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本发明实施例的实施过程构成任何限定。
对应于上文实施例所述的面波勘探方法，图9示出了本发明实施例提供的面波勘探装置的示意图。为了便于说明，仅示出了与本实施例相关的部分。
参照图9，该装置包括获取模块91、提取模块92、构建模块93和反演模块94。
获取模块91，用于获取振动采集装置采集到的振动数据。
提取模块92，用于根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线。
构建模块93，用于根据所述频散谱建立初始地层模型。
反演模块94，用于根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
优选地，所述矢量波数变换算法的计算式为：
Figure PCTCN2017105837-appb-000032
其中，
Figure PCTCN2017105837-appb-000033
为互相关谱，A为常量，
Figure PCTCN2017105837-appb-000034
为两个观测台站之间的距离，ω为角频率，ω＝2πf，f为频率，g(ω,k)为格林函数垂向分量的核函数，
Figure PCTCN2017105837-appb-000035
为波数。
优选地，所述矢量波数变换算法的计算过程具体为：
建立互相关谱的表达式：
Figure PCTCN2017105837-appb-000036
其中，
Figure PCTCN2017105837-appb-000037
和
Figure PCTCN2017105837-appb-000038
分别为两个观测台站接收到的微震信号，
Figure PCTCN2017105837-appb-000039
为微震信号的傅里叶变换；
将互相关谱近似为格林函数，并进行矢量波数变换，得到中间计算式：
Figure PCTCN2017105837-appb-000040
其中，G(ω,r)为格林函数的垂向分量，
Figure PCTCN2017105837-appb-000041
J0为一类零阶贝塞尔函数；
根据所述中间计算式和贝塞尔函数的正交性质，得到所述矢量波数变换算法的计算式：
Figure PCTCN2017105837-appb-000042
优选地，所述提取模块92用于：
将任意两个所述振动采集装置对应的观测台站组成观测台站组，计算得到所述观测台站组对应的台站间距；所述台站间距为所述观测台站组中两个观测台站之间的距离；
根据所述振动数据，计算得到所述观测台站组对应的互相关谱；
根据所述台站间距、所述台站间距对应的互相关谱和所述矢量波数变换算法计算得到所述频散谱。
优选地，所述提取模块92还用于：
对比各个所述观测站台组对应的台站间距；
将所述台站间距相等的观测站台组对应的互相关谱进行叠加平均，并将得到的平均值作为所述台站间距对应的互相关谱。
优选地，所述构建模块93用于：
根据所述频散谱中各个频率区间的面波模态的能量分布，对所述频率区间进行分类；
根据分类后的频率区间与地层的对应关系建立所述初始地层模型。
本发明实施例利用矢量波数变换算法得到频散谱，在频散谱提取出包括基阶面波和高阶面波的频散曲线，建立初始地层模型，通过初始地层模型和反演算法对频散曲线进行反演运算，实现地层勘探。由于能够从振动数据中提取出高阶面波频散信息，将高阶面波频散信息加入到地层的反演运算中，反演的不确定性被大大降低；通过建立初始地层模型能够降低反演算法的运算时间，并且降低反演运算的不稳定性；使振动采集装置能够任意布置，降低对布置场地的要求，提高面波勘探的场地适应性。
图10是本发明一实施例提供的面波勘探终端设备的示意图。如图10所示，该实施例的面波勘探终端设备10包括：处理器100、存储器101以及存储在所述存储器101中并可在所述处理器100上运行的计算机程序102，例如面波勘探程序。所述处理器100执行所述计算机程序102时实现上述各个面波勘探方法实施例中的步骤，例如图1所示的步骤101至104。或者，所述处理器100执行所述计算机程序102时实现上述各装置实施例中各模块/单元的功能，例如图9所示模块91至94的功能。
示例性的，所述计算机程序102可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器101中，并由所述处理器100执行，以完成本发明。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序102在所述面波勘探终端设备10中的执行过程。例如，所述计算机程序102可以被分割成获取模块、提取模块、构建模块和反演模块，各模块具体功能如下：
获取模块，用于获取振动采集装置采集到的振动数据；
提取模块，用于根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
构建模块，用于根据所述频散谱建立初始地层模型；
反演模块，用于根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
所述面波勘探终端设备10可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述面波勘探终端设备可包括，但不仅限于，处理器100、存储器101。本领域技术人员可以理解，图10仅仅是面波勘探终端设备10的示例，并不构成对面波勘探终端设备10的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述面波勘探终端设备还可以包括显示器、输入输出设备、网络接入设备、总线等。
所称处理器100可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通 用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器101可以是所述面波勘探终端设备10的内部存储单元，例如面波勘探终端设备10的硬盘或内存。所述存储器101也可以是所述面波勘探终端设备10的外部存储设备，例如所述面波勘探终端设备10上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器101还可以既包括所述面波勘探终端设备10的内部存储单元也包括外部存储设备。所述存储器101用于存储所述计算机程序以及所述面波勘探终端设备所需的其他程序和数据。所述存储器101还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
在本发明所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。 另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。
以上所述实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种面波勘探方法，其特征在于，包括：
    获取振动采集装置采集到的振动数据；
    根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
    根据所述频散谱建立初始地层模型；
    根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
  2. 如权利要求1所述的面波勘探方法，其特征在于，所述矢量波数变换算法的计算式为：
    Figure PCTCN2017105837-appb-100001
    其中，
    Figure PCTCN2017105837-appb-100002
    为互相关谱，A为常量，
    Figure PCTCN2017105837-appb-100003
    为两个观测台站之间的距离，ω为角频率，ω＝2πf，f为频率，g(ω，k)为格林函数垂向分量的核函数，
    Figure PCTCN2017105837-appb-100004
    为波数。
  3. 如权利要求1所述的面波勘探方法，其特征在于，所述矢量波数变换算法的计算过程具体为：
    建立互相关谱的表达式：
    Figure PCTCN2017105837-appb-100005
    其中，
    Figure PCTCN2017105837-appb-100006
    分别为两个观测台站接收到的微震信号，
    Figure PCTCN2017105837-appb-100007
    为微震信号的傅里叶变换；
    将互相关谱近似为格林函数，并进行矢量波数变换，得到中间计算式：
    Figure PCTCN2017105837-appb-100008
    其中，G(ω，r)为格林函数的垂向分量，
    Figure PCTCN2017105837-appb-100009
    J0为一类零阶贝塞尔函数；
    根据所述中间计算式和贝塞尔函数的正交性质，得到所述矢量波数变换算法的计算式：
    Figure PCTCN2017105837-appb-100010
  4. 如权利要求1所述的面波勘探方法，其特征在于，所述根据矢量波数变换算法和所述振动数据计算得出频散谱包括：
    将任意两个所述振动采集装置对应的观测台站组成观测台站组，计算得到所述观测台站组对应的台站间距；所述台站间距为所述观测台站组中两个观测台站之间的距离；
    根据所述振动数据，计算得到所述观测台站组对应的互相关谱；
    根据所述台站间距、所述台站间距对应的互相关谱和所述矢量波数变换算法计算得到所述频散谱。
  5. 如权利要求4所述的面波勘探方法，其特征在于，在所述计算得到所述观测台站组对应的台站间距之后，所述根据所述振动数据，计算得到所述观测台站组对应的互相关谱之前，还包括：
    对比各个所述观测站台组对应的台站间距；
    将所述台站间距相等的观测站台组对应的互相关谱进行叠加平均，并将得到的平均值作为所述台站间距对应的互相关谱。
  6. 如权利要求1所述的面波勘探方法，其特征在于，所述根据所述频散谱建立初始地层模型包括：
    根据所述频散谱中各个频率区间的面波模态的能量分布，对所述频率区间进行分类；
    根据分类后的频率区间与地层的对应关系建立所述初始地层模型。
  7. 一种面波勘探装置，其特征在于，包括：
    获取模块，用于获取振动采集装置采集到的振动数据；
    提取模块，用于根据矢量波数变换算法和所述振动数据计算得到频散谱；从所述频散谱中提取出频散曲线；所述频散曲线包括基阶面波频散曲线和高阶面波频散曲线；
    构建模块，用于根据所述频散谱建立初始地层模型；
    反演模块，用于根据所述初始地层模型和反演算法，对所述频散曲线进行反演。
  8. 如权利要求7所述的面波勘探装置，其特征在于，所述矢量波数变换算法的计算式为：
    Figure PCTCN2017105837-appb-100011
    其中，
    Figure PCTCN2017105837-appb-100012
    为互相关谱，A为常量，
    Figure PCTCN2017105837-appb-100013
    为两个观测台站之间的距离，ω为角频率，ω＝2πf，f为频率，g(ω，k)为格林函数垂向分量的核函数，
    Figure PCTCN2017105837-appb-100014
    为波数。
  9. 一种面波勘探终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至6任一项所述方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征 在于，所述计算机程序被处理器执行时实现如权利要求1至6任一项所述方法的步骤。
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