Method and device for inversion of active and passive surface wave dispersion curves

Abstract

The present invention provides an active and passive surface wave dispersion curve inversion method and device, belonging to the field of surface engineering exploration technology, the method comprising: obtaining active surface wave seismic records and passive surface wave seismic records; generating active source dispersion curves based on the active surface wave seismic records, and generating passive source dispersion curves based on the passive surface wave seismic records; extracting high frequency band dispersion curves in the active source dispersion curves, extracting low frequency band dispersion curves in the passive source dispersion curves, and splicing the high frequency band dispersion curves and the low frequency band dispersion curves into active and passive surface wave dispersion curves; inverting the active and passive surface wave dispersion curves to obtain a one-dimensional shear wave velocity profile. The present invention solves the technical problems of strong multiple solutions and poor accuracy of inversion results in the inversion of active and passive surface wave dispersion curves in the prior art by jointly inverting the active source dispersion curves and the passive source dispersion curves.

Description

Technical Field

[0001] The invention relates to the technical field of surface engineering exploration, and in particular to an active and passive source surface wave dispersion curve inversion method and device. Background Art

[0002] The inversion of active surface wave dispersion curves can obtain high-resolution shear wave velocity profiles and has a wide range of applications in near-surface engineering exploration.

[0003] Currently, there are some applications of active and passive surface wave dispersion curve inversion, but their dispersion curves are often inverted independently, that is, the active and passive surface wave dispersion curves are extracted and inverted separately, and finally the shallow part of the active surface wave inversion result and the deep part of the passive surface wave inversion result are spliced ​​as the final inversion result. This independent inversion method does not fully utilize the high and low frequency dispersion information, the inversion has strong multiple solutions, and the inversion result is poor in accuracy. Summary of the invention

[0004] In view of this, it is necessary to provide a method and device for inverting active and passive surface wave dispersion curves to solve the technical problems of strong multi-solution inversion and poor accuracy of inversion results in the inversion of active and passive surface wave dispersion curves in the prior art.

[0005] In order to solve the above problems, on the one hand, the present invention provides a method for inverting active and passive surface wave dispersion curves, comprising:

[0006] Obtain active source surface wave seismic records and passive source surface wave seismic records;

[0007] Based on the active surface wave seismic record, an active source dispersion curve is generated, and based on the passive surface wave seismic record, a passive source dispersion curve is generated;

[0008] Extracting a high-frequency dispersion curve from the active source dispersion curve, extracting a low-frequency dispersion curve from the passive source dispersion curve, and splicing the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve;

[0009] Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained.

[0010] In a possible implementation, generating an active source dispersion curve based on the active source surface wave seismic record, and generating a passive source dispersion curve based on the passive source surface wave seismic record, includes:

[0011] Converting the active surface wave seismic record into a first dispersion energy spectrum, and converting the passive surface wave seismic record into a second dispersion energy spectrum;

[0012] An active source dispersion curve is generated based on the first dispersion energy spectrum, and a passive source dispersion curve is generated based on the second dispersion energy spectrum.

[0013] In a possible implementation, converting the active source surface wave seismic record into a first dispersion energy spectrum includes:

[0014] Performing Fourier transformation on the active surface wave seismic record in the time direction to obtain a first frequency domain signal;

[0015] The first frequency domain signal is integrated in a spatial direction to obtain a first dispersion energy spectrum.

[0016] In a possible implementation, converting the passive surface wave seismic record into a second dispersion energy spectrum includes:

[0017] Perform Fourier transformation on the passive surface wave seismic records in each direction in the time direction to obtain the second frequency domain signals in each direction;

[0018] The second frequency domain signals in each direction are spatially integrated and then superimposed to obtain a second dispersion energy spectrum.

[0019] In a possible implementation, obtaining active surface wave seismic records and passive surface wave seismic records includes:

[0020] Acquire multi-channel transient surface wave seismic records collected by active source surface wave observation devices arranged along a straight line in the survey area as active source surface wave seismic records;

[0021] The multi-channel long-duration micro-seismic records collected by the passive surface wave observation system arranged along a straight line in the survey area are obtained as passive surface wave seismic records.

[0022] In a possible implementation, the active surface wave observation device uses an iron hammer hitting an iron plate as a seismic source;

[0023] The passive surface wave observation system uses the vibration generated by the earth itself as the earthquake source.

[0024] In a possible implementation, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve, including:

[0025] Calculating point by point the phase velocity difference between the high-frequency band dispersion curve and the low-frequency band dispersion curve;

[0026] Determine the frequency corresponding to the minimum phase velocity difference as the optimal splicing point frequency;

[0027] Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve.

[0028] In a possible implementation, based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve, including:

[0029] Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​to generate an initial active and passive surface wave dispersion curve;

[0030] The initial active and passive surface wave dispersion curves are smoothed by the sliding average method to obtain the final active and passive surface wave dispersion curves.

[0031] In a possible implementation, based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained, including:

[0032] Determine the optimal position of a single particle in the particle swarm optimization algorithm, as well as the optimal position of a particle swarm;

[0033] Determine a particle increment in a particle swarm optimization algorithm, update an optimal position of a single particle and an optimal position of a particle swarm based on the particle increment, and obtain an updated optimal position of the single particle and an updated optimal position of the particle swarm;

[0034] Determine the inversion objective function based on the updated optimal position of the single particle and the updated optimal position of the particle group, and the active and passive source surface wave dispersion curves;

[0035] A plurality of particles with the smallest inversion objective function value are determined as the initial model, and the damped least squares method inversion is performed on the initial model. The inversion result is updated to the particle swarm optimization algorithm, and a one-dimensional shear wave velocity profile is obtained by iteration.

[0036] On the other hand, the present invention also provides an active and passive surface wave dispersion curve inversion device, comprising:

[0037] A seismic record acquisition module, used for acquiring active source surface wave seismic records and passive source surface wave seismic records;

[0038] A dispersion curve generating module, used for generating an active source dispersion curve based on the active source surface wave seismic record, and generating a passive source dispersion curve based on the passive source surface wave seismic record;

[0039] A dispersion curve splicing module is used to extract the high-frequency dispersion curve in the active source dispersion curve, extract the low-frequency dispersion curve in the passive source dispersion curve, and splice the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve;

[0040] The dispersion curve inversion module is used to obtain a one-dimensional shear wave velocity profile based on the active and passive surface wave dispersion curve inversion.

[0041] The beneficial effect of adopting the above implementation mode is: the active and passive surface wave dispersion curve inversion method and device provided by the present invention, after generating active source dispersion curve and passive source dispersion curve based on active source surface wave seismic record and passive source surface wave seismic record, extracts the high frequency band dispersion curve in the active source dispersion curve, and extracts the low frequency band dispersion curve in the passive source dispersion curve, and splices the high frequency band dispersion curve and the low frequency band dispersion curve into the active and passive surface wave dispersion curve, so that in the subsequent dispersion curve inversion process, the active source dispersion curve and the passive source dispersion curve are jointly inverted, rather than inverting the active source dispersion curve and the passive source dispersion curve separately in an independent manner, and in the process of joint inversion, the high and low frequency dispersion information of the active and passive surface wave dispersion curves is fully utilized, so as to solve the technical problems of strong multi-solution inversion and poor accuracy of inversion results in the inversion of active and passive surface wave dispersion curves in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A flow chart of an embodiment of the method for inverting the active and passive surface wave dispersion curves provided by the present invention;

[0044] Figure 2 A schematic diagram of an active surface wave observation device provided by the present invention;

[0045] Figure 3 A schematic diagram of a passive surface wave observation device provided by the present invention;

[0046] Figure 4 A schematic diagram of splicing dispersion curves provided by the present invention;

[0047] Figure 5 The flow chart of the PSO-DLS algorithm provided by the present invention;

[0048] Figure 6 A flow chart of another embodiment of the method for inverting active and passive surface wave dispersion curves provided by the present invention;

[0049] Figure 7 A principle block diagram of an embodiment of the active and passive surface wave dispersion curve inversion device provided by the present invention;

[0050] Figure 8 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0052] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.

[0053] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or equipment comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or equipment.

[0054] The naming or numbering of the steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0055] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] The present invention provides a method and device for inverting active and passive surface wave dispersion curves, which are respectively described below.

[0057] like Figure 1 As shown, the present invention provides a method for inverting active and passive surface wave dispersion curves, comprising:

[0058] S101, obtaining active source surface wave seismic records and passive source surface wave seismic records;

[0059] S102, generating an active source dispersion curve based on the active source surface wave seismic record, and generating a passive source dispersion curve based on the passive source surface wave seismic record;

[0060] S103, extracting a high-frequency dispersion curve from the active source dispersion curve, extracting a low-frequency dispersion curve from the passive source dispersion curve, and splicing the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve;

[0061] S104. Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained.

[0062] It can be understood that the present invention uses a vertical geophone to receive seismic wave field propagation information, and the surface wave described in the present invention is a Rayleigh wave. Active source surface wave exploration often uses a hammer hitting an iron plate as a source, and the energy of the excited seismic wave field is weak and the frequency is high. The geophone arrangement is usually short and the detection depth is small. Passive source surface waves use the weak vibrations generated by the earth itself as the source. Through specific observation devices and dispersion imaging methods, low-frequency surface wave dispersion information can be obtained, and the detection depth is large. The present invention provides an active and passive source surface wave dispersion curve inversion method, which aims to make the inversion have a higher shallow resolution and a larger detection depth.

[0063] In order to ensure that the surface wave dispersion curve inversion obtains a one-dimensional shear wave velocity profile with high shallow resolution and large detection depth, the present invention provides a joint inversion method for active and passive surface waves, wherein the methods for obtaining the active and passive surface wave dispersion energy spectra are essentially phase shift methods, which greatly weakens the impact of different extraction methods. The present invention adopts an automatic splicing algorithm for active and passive source dispersion curves, which reduces human subjectivity and improves the efficiency of dispersion curve extraction. The present invention adopts the PSO-DLS algorithm to carry out dispersion curve inversion. The algorithm does not need to set an initial model, is not easy to fall into local extreme values, has high inversion efficiency, and has accurate inversion results.

[0064] In some embodiments, generating an active source dispersion curve based on the active source surface wave seismic record, and generating a passive source dispersion curve based on the passive source surface wave seismic record comprises:

[0065] Converting the active surface wave seismic record into a first dispersion energy spectrum, and converting the passive surface wave seismic record into a second dispersion energy spectrum;

[0066] An active source dispersion curve is generated based on the first dispersion energy spectrum, and a passive source dispersion curve is generated based on the second dispersion energy spectrum.

[0067] It can be understood that the methods for active surface wave dispersion imaging include: fk transformation, τ-p transformation, phase shift transformation, high-resolution linear Radon transformation, frequency-Bessel transformation (FJ); the methods for passive surface wave dispersion imaging include: spatial autocorrelation (SPAC), extended spatial autocorrelation (ESPAC), refraction micromotion (REMI), phase shift method, frequency-Bessel transformation (FJ). The detector arrangement of active surface waves is usually linear, and there are many detector arrangements for passive surface waves, such as linear, circular, multiple circular, linear, cross, etc. Different detector arrangements and dispersion imaging methods will result in certain differences in the dispersion energy spectra. In order to reduce their impact, the present invention adopts a linear detection arrangement for both active and passive surface waves, and the dispersion imaging method adopts a phase shift method.

[0068] In some embodiments, converting the active surface wave seismic record into a first dispersive energy spectrum comprises:

[0069] Performing Fourier transformation on the active surface wave seismic record in the time direction to obtain a first frequency domain signal;

[0070] The first frequency domain signal is integrated in a spatial direction to obtain a first dispersion energy spectrum.

[0071] In some embodiments, converting the passive surface wave seismic record into a second dispersive energy spectrum comprises:

[0072] Perform Fourier transformation on the passive surface wave seismic records in each direction in the time direction to obtain the second frequency domain signals in each direction;

[0073] The second frequency domain signals in each direction are spatially integrated and then superimposed to obtain a second dispersion energy spectrum.

[0074] In some embodiments, acquiring active surface wave seismic records and passive surface wave seismic records includes:

[0075] Acquire multi-channel transient surface wave seismic records collected by active source surface wave observation devices arranged along a straight line in the survey area as active source surface wave seismic records;

[0076] The multi-channel long-duration micro-seismic records collected by the passive surface wave observation system arranged along a straight line in the survey area are obtained as passive surface wave seismic records.

[0077] The active surface wave observation device uses a hammer hitting an iron plate as a seismic source;

[0078] The passive surface wave observation system uses the vibration generated by the earth itself as the earthquake source.

[0079] In some embodiments, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve, including:

[0080] Calculating point by point the phase velocity difference between the high-frequency band dispersion curve and the low-frequency band dispersion curve;

[0081] Determine the frequency corresponding to the minimum phase velocity difference as the optimal splicing point frequency;

[0082] Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve.

[0083] In some embodiments, based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve, including:

[0084] Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​to generate an initial active and passive surface wave dispersion curve;

[0085] The initial active and passive surface wave dispersion curves are smoothed by the sliding average method to obtain the final active and passive surface wave dispersion curves.

[0086] It is understandable that, although there are currently a small number of applications for the joint inversion of active surface wave dispersion curves, the dispersion curves are often spliced ​​manually, which has low splicing efficiency, is greatly affected by human subjectivity, and is not convenient for large-scale, automated data processing.

[0087] By using the dispersion curve splicing method provided in this embodiment, automatic data processing can be achieved, thereby improving the splicing efficiency of dispersion curves.

[0088] In some embodiments, based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained, including:

[0089] Determine the optimal position of a single particle in the particle swarm optimization algorithm, as well as the optimal position of a particle swarm;

[0090] Determine a particle increment in a particle swarm optimization algorithm, update an optimal position of a single particle and an optimal position of a particle swarm based on the particle increment, and obtain an updated optimal position of the single particle and an updated optimal position of the particle swarm;

[0091] Determine the inversion objective function based on the updated optimal position of the single particle and the updated optimal position of the particle group, and the active and passive source surface wave dispersion curves;

[0092] A plurality of particles with the smallest inversion objective function value are determined as the initial model, and the damped least squares method inversion is performed on the initial model. The inversion result is updated to the particle swarm optimization algorithm, and a one-dimensional shear wave velocity profile is obtained by iteration.

[0093] It is understandable that general dispersion curve inversion methods can be divided into two categories: local optimization inversion methods based on gradient descent and global optimization inversion methods based on random evolution. Common local optimization inversion methods include: damped least squares (DLS), OCCAM algorithm, quasi-Newton method, Gauss-Newton method and conjugate gradient method, etc. Such methods usually have high convergence efficiency and good inversion stability, but they are highly dependent on the initial model and the inversion results are of low accuracy; common global optimization inversion methods include: neighborhood algorithm, genetic algorithm, fast simulated annealing method, particle swarm optimization algorithm (PSO), etc. Such methods usually have low dependence on the initial model and high accuracy of inversion results, but they have low convergence efficiency and poor inversion stability.

[0094] The present invention proposes a particle swarm-least squares joint inversion algorithm (PSO-DLS), which selects some particles as the initial model to carry out DLS inversion under the framework of the PSO algorithm, and updates the DLS inversion results to the PSO results. The PSO-DLS algorithm has the characteristics of the PSO algorithm not relying on the initial model and accurate inversion results, and the DLS algorithm has high convergence efficiency and good stability.

[0095] In some embodiments, the active and passive surface wave dispersion curve inversion method provided by the present invention adopts the following algorithm:

[0096] 1. Phase shift method

[0097] Multi-channel active surface wave observation device Figure 2 As shown, the seismic record that records the surface wave propagation information is recorded as d ( x, t ).

[0098] right d ( x, t ) is transformed in the time direction to obtain :

[0099] (1.1)

[0100] It can be expressed as the product of two terms:

[0101] (1.2)

[0102] Integrate equation (1.2) in the spatial direction to obtain :

[0103] (1.3)

[0104] When equation (1.4) is satisfied, for a given and , the dispersion energy spectrum can be obtained from formula (1.5): :

[0105] (1.4)

[0106] (1.5)

[0107] 2. Omnidirectional phase shift method

[0108] In the active surface wave observation device, the source and the detector are arranged in a straight line. Passive surface waves use the weak vibration of the earth itself as the source, and the direction of the source and the relationship between the arrangement of the detectors cannot be known. The source can propagate at any angle, and the microvibration source is often located deep in the earth, far enough away from the arrangement of the detectors. Assume that the angle between the propagation direction of the source wave field and the arrangement direction of the detectors is θ ,but θ The actual value is -π~π. Figure 3 shown.

[0109] The omnidirectional phase shift method requires calculating the dispersed energy spectra in all directions and superimposing them, as shown in equations (2.1) to (2.2).

[0110] (2.1)

[0111] (2.2)

[0112] When processing measured passive surface wave data, when the dispersed energy in a certain direction is significantly dominant, using the dispersed energy spectrum in that direction may have better results. When processing measured data, it should be reasonably selected based on actual conditions, as shown in formula (2.3).

[0113] (2.3)

[0114] 3. Dispersion curve stitching algorithm

[0115] The high frequency band of the dispersion curve mainly reflects shallow information and has a small overall contribution to the inversion; the low frequency band mainly reflects deep information and has a large overall contribution to the inversion. When extracting the dispersion curve, it is advisable to increase the frequency points of the low frequency band and reduce the high frequency points. The method for selecting the frequency points is not fixed and can be selected according to formula (3.1) to formula (3.2).

[0116] (3.1)

[0117] (3.2)

[0118] in, f down is the lower limit of frequency, f up is the upper frequency limit, and N is the number of frequency points.

[0119] The circular frequency can be expressed as ω Convert to frequency f , for each frequency point f i is within the phase velocity range, and its phase velocity spectrum is searched I The maximum value of , and finally a series of frequency points about frequency and phase velocity are obtained, which is the dispersion curve.

[0120] (3.3)

[0121] (3.4)

[0122] in, C min and C max are the minimum and maximum phase velocities respectively.

[0123] The schematic diagram of active and passive source dispersion curve is as follows Figure 4 As shown, the active source surface wave extracts the mid-high frequency band ( f 1~ f up) dispersion curve, passive surface wave extraction in the low and medium frequency bands ( f down~ f 2) Dispersion curve, f 1 is less than f 2, that is, the active and passive surface wave dispersion curves are f 1~ f The two frequency bands overlap, and the dispersion curves in the overlapping area often do not overlap. The phase velocity difference of the active and passive source dispersion curves must be calculated point by point using formula (3.5). The frequency corresponding to the minimum is the optimal splicing point frequency f c.

[0124] (3.5)

[0125] (3.6)

[0126] In the formula, A ( fi )and P ( fi ) are the active and passive surface wave dispersion curves, abs represents the absolute value function, fc Indicates the optimal splicing point frequency.

[0127] The spliced ​​dispersion curve can be smoothed by the sliding average method. The sliding average formula is shown in formula (3.7), where and Respectively represent i The original phase velocity and sliding average phase velocity of each frequency point; W is the sliding window length, usually an odd number, and is preferably set to 3 or 5 in this solution. W / 2 is rounded down.

[0128] (3.7)

[0129] 4. PSO-DLS algorithm

[0130] 4.1 PSO Algorithm

[0131] Particle Swarm Optimization (PSO) is a typical swarm intelligence random evolution algorithm. The algorithm generates multiple random solutions at one time, with a single solution recorded as a particle and all solutions recorded as a swarm. It continuously searches for the global optimal solution through the historical best solutions of single particles and particle swarms.

[0132] In the PSO algorithm, the solution corresponding to each particle is called position (Position, p), the position increment is called velocity (Velocity, v), and the position update strategy is as follows:

[0133] (4.1)

[0134] (4.2)

[0135] In the formula, i represents the particle number; k Indicates the number of iterations; p represents the position of the particle; Indicates i Particles passing through k The historical best position of the iteration; Represents the historical optimal position of the entire particle swarm after k iterations; ω is the weight of the previous position; and It is i The position and position increment of a particle at the kth iteration; and represents local and global weight factors; and is a random number in the interval (0, 1).

[0136] In the inversion of surface wave dispersion curves, the parameters to be solved are usually the shear wave velocity and layer thickness of the formation. The initial model of the PSO algorithm is randomly generated by equations (4.3) and (4.4), and the update of the solution contains two random numbers. Therefore, the initial model of the algorithm has low dependence and is not easy to fall into local extreme values. The inversion results often have global optimality.

[0137] (4.3)

[0138] (4.4)

[0139] 4.2 DLS Algorithm

[0140] The Damped Least Square Method (DLS) uses the first-order derivative to establish a linear equation system. By continuously adjusting the model parameters, the error between the model forward data and the observed data tends to be minimized in the sense of least squares. The main solution process of the DLS algorithm is as follows:

[0141] (4.5)

[0142] (4.6)

[0143] In the formula is the solution increment vector, Y is the measured data vector, f is the dispersion equation, A is the partial derivative vector, I is a unit vector, υ is the damping coefficient. f As an implicit function, it is impossible to directly derive it, so we use the idea of ​​difference instead of differentiation to convert f The derivative is approximately:

[0144] (4.7)

[0145] 4.3 PSO-DLS algorithm

[0146] The PSO algorithm has low dependence on the initial model and accurate inversion results, but the amount of calculation is huge and the convergence efficiency is low; the DLS algorithm has a small amount of calculation and high inversion efficiency, but the accuracy of the inversion result is highly dependent on the initial model. The present invention proposes a PSO and DLS joint inversion algorithm, the core idea of ​​which is to carry out DLS inversion with a small number of excellent particles as the initial model under the framework of the PSO algorithm, and update the DLS inversion results to the PSO results. The PSO-DLS algorithm has the random evolution characteristics of the PSO algorithm, does not need to set the initial model, is not easy to fall into the local extreme value, and also has the efficient local optimal solution search capability of the DLS algorithm, and the convergence efficiency is much higher than that of the PSO algorithm.

[0147] PSO-DLS algorithm flow chart Figure 5 , Figure 5 middle M is the number of particle groups, N is the number of particles for DLS inversion, K is the maximum number of iterations of the PSO-DLS algorithm, L is the maximum number of iterations of the DLS algorithm.

[0148] Figure 5 middle Φ is the objective function of dispersion curve inversion, obtained by equation (4.8). Q is the number of frequency points of the dispersion curve, and Respectively i Theoretical and measured phase velocities at each frequency point.

[0149] (4.8)

[0150] In some embodiments, Figure 6 As shown, the active and passive surface wave dispersion curve inversion method proposed in the present invention is as follows:

[0151] 1. Obtain active and passive surface wave seismic records.

[0152] Active and passive surface wave observation systems are arranged along a straight line in the survey area. The active surface wave observation device uses a hammer hitting an iron plate as a source to collect multiple transient surface wave seismic records; the passive surface wave uses the weak vibrations generated by the earth itself as a source to collect multiple long-term micro-seismic records. To ensure that the inversion has a high shallow resolution, the interval between active surface wave detectors should not be greater than 2 meters, and the number of detectors should not be less than 24; to ensure that the inversion has sufficient depth, the number of passive surface wave detectors should not be less than 12, and the arrangement length should not be less than 110 meters.

[0153] 2. Obtain the active and passive surface wave dispersion energy spectrum.

[0154] (1) Using the phase shift method (see Equations (1.1) to (1.5)), the multi-channel active surface wave seismic records are converted into a dispersive energy spectrum;

[0155] (2) Using the omnidirectional phase shift method (see Equations (2.1) to (2.3)), multi-channel passive surface wave seismic records are converted into a dispersive energy spectrum.

[0156] 3. Obtain active and passive surface wave dispersion curves.

[0157] (1) The frequency of the dispersion curve is shown in equations (3.1) to (3.2), and the upper frequency limit is f upShould not be less than 70Hz, the lower frequency limit f down It should be no higher than 1Hz, with denser frequencies in the high frequency band and sparser frequencies in the low frequency band;

[0158] (2) Based on the energy maximum method (see formula (3.4)), the active source Rayleigh wave dispersion curve is extracted. The active source dispersion curve only needs to extract the medium and high frequency bands, and the lower frequency limit f 1 should not be greater than 4Hz;

[0159] (3) Based on the energy maximum method (see formula (3.4)), the passive source Rayleigh wave dispersion curve is extracted. The passive source dispersion curve only needs to extract the low and medium frequency bands, and the upper frequency limit f 2 should not be greater than 10Hz;

[0160] (4) Based on the adaptive algorithm (see equations (3.5) to (3.6)), the dispersion curves are spliced ​​and the final dispersion curve is obtained based on the multi-point averaging method (see equation (3.7)).

[0161] 4. Obtain one-dimensional shear wave velocity profile by inverting the dispersion curve.

[0162] Based on the PSO-DLS algorithm (see Equations (4.1) to (4.7)), the obtained dispersion curve is inverted to obtain a one-dimensional shear wave velocity formation model.

[0163] The active and passive surface wave dispersion curve inversion method provided by the present invention has the following beneficial effects:

[0164] 1. A comprehensive extraction and analysis method of multi-directional source dispersion energy spectrum is proposed to obtain more accurate Rayleigh wave dispersion information;

[0165] 2. An automatic splicing algorithm for active and passive source Rayleigh wave dispersion curves is proposed to automate dispersion curve splicing and reduce the influence of human subjectivity;

[0166] 3. A PSO-DLS dispersion curve inversion method is proposed, which makes the inversion process have the global optimality of the PSO algorithm and the high efficiency of the DLS algorithm.

[0167] like Figure 7 As shown, the present invention also provides an active and passive surface wave dispersion curve inversion device 700, comprising:

[0168] A seismic record acquisition module 701 is used to acquire active source surface wave seismic records and passive source surface wave seismic records;

[0169] A dispersion curve generating module 702, configured to generate an active source dispersion curve based on the active source surface wave seismic record, and to generate a passive source dispersion curve based on the passive source surface wave seismic record;

[0170] The dispersion curve splicing module 703 is used to extract the high-frequency dispersion curve in the active source dispersion curve, extract the low-frequency dispersion curve in the passive source dispersion curve, and splice the high-frequency dispersion curve and the low-frequency dispersion curve into the active and passive surface wave dispersion curves;

[0171] The dispersion curve inversion module 704 is used to obtain a one-dimensional shear wave velocity profile based on the active and passive surface wave dispersion curve inversion.

[0172] The active and passive surface wave dispersion curve inversion device provided in the above embodiment can realize the technical solution described in the above active and passive surface wave dispersion curve inversion method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above active and passive surface wave dispersion curve inversion method embodiment, which will not be repeated here.

[0173] like Figure 8 As shown, the present invention also provides an electronic device 800. The electronic device 800 includes a processor 801, a memory 802 and a display 803. Figure 8 Only some components of the electronic device 800 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0174] In some embodiments, the memory 802 may be an internal storage unit of the electronic device 800, such as a hard disk or memory of the electronic device 800. In other embodiments, the memory 802 may also be an external storage device of the electronic device 800, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 800.

[0175] Furthermore, the memory 802 may include both an internal storage unit of the electronic device 800 and an external storage device. The memory 802 is used to store application software installed in the electronic device 800 and various data.

[0176] In some embodiments, the processor 801 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 802, such as the active and passive surface wave dispersion curve inversion method of the present invention.

[0177] In some embodiments, the display 803 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 803 is used to display information on the electronic device 800 and to display a visual user interface. The components 801-803 of the electronic device 800 communicate with each other via a system bus.

[0178] In some embodiments of the present invention, when the processor 801 executes the active and passive surface wave dispersion curve inversion program in the memory 802, the following steps may be implemented:

[0179] Obtain active source surface wave seismic records and passive source surface wave seismic records;

[0180] Based on the active surface wave seismic record, an active source dispersion curve is generated, and based on the passive surface wave seismic record, a passive source dispersion curve is generated;

[0181] Extracting a high-frequency dispersion curve from the active source dispersion curve, extracting a low-frequency dispersion curve from the passive source dispersion curve, and splicing the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve;

[0182] Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained.

[0183] It should be understood that, when the processor 801 executes the active and passive surface wave dispersion curve inversion program in the memory 802, in addition to the above functions, other functions may also be implemented, for details, see the description of the corresponding method embodiments above.

[0184] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 800 mentioned, and the electronic device 800 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 800 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0185] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for inverting the active and passive surface wave dispersion curves provided by the above methods is implemented, and the method comprises:

[0186] Obtain active source surface wave seismic records and passive source surface wave seismic records;

[0187] Based on the active surface wave seismic record, an active source dispersion curve is generated, and based on the passive surface wave seismic record, a passive source dispersion curve is generated;

[0188] Extracting a high-frequency dispersion curve from the active source dispersion curve, extracting a low-frequency dispersion curve from the passive source dispersion curve, and splicing the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve;

[0189] Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained.

[0190] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0191] The active and passive surface wave dispersion curve inversion method and device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for inverting active and passive surface wave dispersion curves, characterized in that: include: Obtain active source surface wave seismic records and passive source surface wave seismic records; Based on the active surface wave seismic record, an active source dispersion curve is generated, and based on the passive surface wave seismic record, a passive source dispersion curve is generated; Extracting a high-frequency dispersion curve from the active source dispersion curve, extracting a low-frequency dispersion curve from the passive source dispersion curve, and splicing the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve; Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained; Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained, including: Determine the optimal position of a single particle in the particle swarm optimization algorithm, as well as the optimal position of a particle swarm; Determine a particle increment in a particle swarm optimization algorithm, update an optimal position of a single particle and an optimal position of a particle swarm based on the particle increment, and obtain an updated optimal position of the single particle and an updated optimal position of the particle swarm; Determine the inversion objective function based on the updated optimal position of the single particle and the updated optimal position of the particle group, and the active and passive source surface wave dispersion curves; A plurality of particles with the smallest inversion objective function value are determined as the initial model, and the damped least squares method is used to invert the initial model. The inversion result is updated to the particle swarm optimization algorithm, and a one-dimensional shear wave velocity profile is obtained by iteration.

2. The method for inverting active and passive surface wave dispersion curves according to claim 1, characterized in that: Generating an active source dispersion curve based on the active source surface wave seismic record, and generating a passive source dispersion curve based on the passive source surface wave seismic record, comprising: Converting the active surface wave seismic record into a first dispersion energy spectrum, and converting the passive surface wave seismic record into a second dispersion energy spectrum; An active source dispersion curve is generated based on the first dispersion energy spectrum, and a passive source dispersion curve is generated based on the second dispersion energy spectrum.

3. The method for inverting active and passive surface wave dispersion curves according to claim 2, characterized in that: Converting the active source surface wave seismic record into a first dispersion energy spectrum comprises: Performing Fourier transformation on the active surface wave seismic record in the time direction to obtain a first frequency domain signal; The first frequency domain signal is integrated in a spatial direction to obtain a first dispersion energy spectrum.

4. The method for inverting active and passive surface wave dispersion curves according to claim 2, characterized in that: Converting the passive surface wave seismic record into a second dispersion energy spectrum comprises: Perform Fourier transformation on the passive surface wave seismic records in each direction in the time direction to obtain the second frequency domain signals in each direction; The second frequency domain signals in each direction are spatially integrated and then superimposed to obtain a second dispersion energy spectrum.

5. The method for inverting active and passive surface wave dispersion curves according to claim 1, characterized in that: Acquire active surface wave seismic records and passive surface wave seismic records, including: Acquire multi-channel transient surface wave seismic records collected by active source surface wave observation devices arranged along a straight line in the survey area as active source surface wave seismic records; The multi-channel long-duration micro-seismic records collected by the passive surface wave observation system arranged along a straight line in the survey area are obtained as passive surface wave seismic records.

6. The method for inverting active and passive surface wave dispersion curves according to claim 5, characterized in that: The active source surface wave observation device uses an iron hammer hitting an iron plate as a seismic source; The passive surface wave observation system uses the vibration generated by the earth itself as the earthquake source.

7. The method for inverting active and passive surface wave dispersion curves according to claim 1, characterized in that: The high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve, including: Calculating point by point the phase velocity difference between the high-frequency band dispersion curve and the low-frequency band dispersion curve; Determine the frequency corresponding to the minimum phase velocity difference as the optimal splicing point frequency; Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve.

8. The method for inverting active and passive surface wave dispersion curves according to claim 7, characterized in that: Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​into an active and passive surface wave dispersion curve, including: Based on the optimal splicing point frequency, the high-frequency band dispersion curve and the low-frequency band dispersion curve are spliced ​​to generate an initial active and passive surface wave dispersion curve; The initial active and passive surface wave dispersion curves are smoothed by the sliding average method to obtain the final active and passive surface wave dispersion curves.

9. An active and passive surface wave dispersion curve inversion device, characterized in that: include: A seismic record acquisition module, used for acquiring active source surface wave seismic records and passive source surface wave seismic records; A dispersion curve generating module, used for generating an active source dispersion curve based on the active source surface wave seismic record, and generating a passive source dispersion curve based on the passive source surface wave seismic record; A dispersion curve splicing module is used to extract the high-frequency dispersion curve in the active source dispersion curve, extract the low-frequency dispersion curve in the passive source dispersion curve, and splice the high-frequency dispersion curve and the low-frequency dispersion curve into an active and passive surface wave dispersion curve; A dispersion curve inversion module, used for obtaining a one-dimensional shear wave velocity profile based on the active and passive surface wave dispersion curve inversion; Based on the inversion of the active and passive surface wave dispersion curves, a one-dimensional shear wave velocity profile is obtained, including: Determine the optimal position of a single particle in the particle swarm optimization algorithm, as well as the optimal position of a particle swarm; Determine a particle increment in a particle swarm optimization algorithm, update an optimal position of a single particle and an optimal position of a particle swarm based on the particle increment, and obtain an updated optimal position of the single particle and an updated optimal position of the particle swarm; Determine the inversion objective function based on the updated optimal position of the single particle and the updated optimal position of the particle group, and the active and passive source surface wave dispersion curves; A plurality of particles with the smallest inversion objective function value are determined as the initial model, and the damped least squares method is used to invert the initial model. The inversion result is updated to the particle swarm optimization algorithm, and a one-dimensional shear wave velocity profile is obtained by iteration.

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