CN111665553A - Acoustic parameter acquisition method for river and lake sediment detection - Google Patents
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Abstract
The invention relates to an acoustic parameter acquisition method for river and lake sediment detection, which comprises the following steps: acquiring a plurality of single shot data of seismic source vibration in a river and lake sediment detection area; intercepting direct waves, shallow reflected waves and shallow refracted waves in the single shot data by using a time window to obtain observation data; acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward simulation data; calculating a wave field residual error according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual error; utilizing wave field residual error back propagation to an acoustic parameter initial model space to obtain residual error back propagation data; respectively calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient of the error functional by utilizing forward modeling data and residual back propagation data; and respectively updating the acoustic parameter initial model by using each parameter gradient to obtain an accurate model corresponding to each acoustic parameter.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to an acoustic parameter acquisition method for river and lake sediment detection.
Background
The mineral products and the surrounding rock have obvious acoustic parameter difference, different objects have different acoustic parameter characteristics, the acoustic parameter values have certain ranges respectively, and the physical properties of the objects can be judged according to the acoustic parameter difference.
The method of first-arrival travel-time tomographic inversion based on ray is commonly used at present. Such methods are computationally simple, do not require accurate background fields, and apply complex surface conditions, and are therefore always common approaches to solving near-surface and shallow acoustic parameter modeling. But this method is based on the shortest path principle of high frequency hypothesis, and in the case of low-speed body development, the technology has a modeled 'dead zone'. In addition, the ray method is sensitive to ray density, when the speed changes violently, even if the speed is a high-speed abnormal body, the ray density is seriously influenced due to the occurrence of the full-emission phenomenon, and then the inversion precision is reduced. In practical data application, the first arrival picking workload is huge, errors exist in manual picking, and the first arrival picking is difficult to be accurate under the condition that a local table is complex.
Another new method for solving the problems is a full waveform inversion method, which is based on the wave equation and can truly simulate the wave propagation wave field, so that the method is not influenced by ray density. However, the implementation of this new method has many limitations, such as an observation system requiring a large offset, lack of low-frequency information, and the like. The practical application of full waveform inversion has a long distance, especially the practical application of land data.
Before dredging or cleaning silt in a river channel or a reservoir, the thickness and the volume of silt in the river channel or the lake bottom need to be known, and the top-bottom interface of the silt and the distribution of the silt and the silt along with the space are obtained by utilizing the sediment silt, the bedrock at the bottom of the river channel and the difference of the physical acoustic parameters of the silt and the water, so that the equivalent weight of the silt can be obtained. At present, a solution for obtaining acoustic parameters for river and lake sediment detection is lacked.
Disclosure of Invention
The invention aims to provide an acoustic parameter acquisition method for river and lake sediment detection aiming at the defects in the prior art.
In order to achieve the above object, in a first aspect, the present invention provides an acoustic parameter obtaining method for detecting river and lake sediments, including:
acquiring a plurality of single-shot data of seismic source vibration in a river and lake sediment detection area, wherein the single-shot data comprises single-shot single-channel data and single-shot multi-channel data;
intercepting direct waves, shallow reflected waves and shallow refracted waves in the single shot data by using a time window to obtain observation data;
acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward simulation data, wherein the acoustic parameters comprise a sound wave propagation speed, medium density, wave impedance, attenuation factors and Lame parameters;
calculating a wave field residual error according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual error;
utilizing the wave field residual error to perform back propagation to the acoustic parameter initial model space to obtain residual error back propagation data;
respectively calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data;
determining an optimal iteration step length and an iteration termination condition;
updating the acoustic parameter initial model according to the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the Lame parameter gradient and the optimal iteration step length;
and when the initial acoustic parameter model meets the iteration termination condition, obtaining an accurate model corresponding to each acoustic parameter.
Further, after the time window is used to intercept the direct wave, the shallow reflected wave, and the shallow refracted wave in the plurality of single shot data to obtain the observation data, the method further includes:
performing filtering processing on the observation data by utilizing wavelet transformation to obtain processed observation data;
and performing multiple suppression processing on the processed observation data.
Further, the forward modeling the seismic source waveform based on the acoustic parameter initial model to obtain forward modeling data specifically includes:
performing time domain dispersion on the wave equation of the seismic source waveform by using a staggered grid finite difference method to obtain a dispersed wave equation;
and determining the wave field value of the spatial distribution of the staggered grid at each moment according to the dispersed wave equation and the acoustic parameter initial model.
Further, the constructing an error functional according to the wave field residual specifically includes:
according to the formula
Calculating an error functional, wherein E (m) is the error functional, b (m) is a linear function representing the result data of the forward modeling, dobsFor observation data, b (m) -dobsAs wave field residual, CDAs a data covariance matrix, CMIs a covariance matrix of the model, m is a model parameter of the initial model of the acoustic parameters, mpriorIs a prior information model, and lambda is a prior information specific gravity parameter.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data specifically includes:
according to the formula
Calculating a velocity gradient of the error functional; wherein the content of the first and second substances,
k＝ρVp 2，Pffor forward modeling of data, PbResidual back-propagation data, ω frequency, VPAnd k and rho are initial model parameters, and E is an error functional.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data specifically includes:
according to the formula
Calculating a density gradient of the error functional; it is composed ofIn (1),
k＝ρVp 2；Pffor forward modeling of data, PbResidual back propagation data, ω is frequency, ρ is density, k is modulus, E is error functional, VpIs the velocity.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data specifically includes:
according to the formula
Calculating a wave impedance gradient of the error functional; wherein the content of the first and second substances,
Pffor forward modeling of data, PbResidual back-propagation data, ω frequency, IPAnd k and rho are initial model parameters, and E is an error functional.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data specifically includes:
according to the formula
Calculating an attenuation factor gradient of the error functional; wherein the content of the first and second substances,
e is an error functional, QjFor the attenuation factor, ω is the frequency, ωrIs the resonance frequency, ρ is the density, vjIs speed, PfFor forward modeling of data, PbResidual counterpropagation data.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data specifically includes:
according to the formula
Calculating the Lame parameter gradient of the error functional, wherein m is an initial model parameter, u is a forward simulation broadcast field, B is a forward operator, delta d is a wave field residual error, and B is-1tIs residual backpropagated data.
The invention provides an acoustic parameter acquisition method for river and lake sediment detection, which is used for acquiring a plurality of single-shot data of seismic source vibration in a river and lake sediment detection area, wherein the single-shot data comprises single-shot single-channel data and single-shot multi-channel data; intercepting direct waves, shallow reflected waves and shallow refracted waves in the single shot data by using a time window to obtain observation data; acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward simulation data, wherein the acoustic parameters comprise acoustic wave propagation speed, medium density, wave impedance, attenuation factors and Lame parameters; calculating a wave field residual error according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual error; utilizing wave field residual error back propagation to an acoustic parameter initial model space to obtain residual error back propagation data; respectively calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient of the error functional by utilizing forward modeling data and residual back propagation data; and respectively updating the acoustic parameter initial model by using the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient to obtain an accurate model corresponding to each acoustic parameter. According to the method provided by the invention, waveform inversion is completed by using the kinematics and the dynamics characteristics of a wave field in a period of time after the first arrival according to the geological task requirements, and high-precision acoustic parameter modeling of a shallow layer is realized.
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Fig. 1 is a flow chart of an acoustic parameter acquisition method for detecting river and lake sediment according to an embodiment of the present invention;
FIG. 2 is a Gaussian function graph provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a grid configuration of physical quantities and acoustic parameters of a two-dimensional sound wave according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a grid configuration of physical quantities and acoustic parameters of a three-dimensional sound wave according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the difficulty of shallow layer modeling, particularly under the conditions of severe speed change and low-speed orbit body development, the invention avoids the weakness based on the ray theory, comprehensively utilizes wave field information in a period of time from the first arrival to the later to realize the high-precision shallow layer modeling and provides reliable support for the middle-deep layer modeling and the offset imaging.
Fig. 1 is a flow chart of an acoustic parameter acquisition method for river and lake sediment detection according to an embodiment of the present invention. As shown in fig. 1, the method specifically comprises the following steps:
the seismic prospecting instrument is arranged in a river and lake sediment detection area, single-shot data of one shot are collected when each shot is placed, and the single-shot data comprise single-shot single-channel data and single-shot multi-channel data. And transmitting multiple guns to acquire multiple groups of single gun data according to specific conditions and construction requirements. The seismic exploration instrument is a 408ULS cable instrument and the like.
The single-shot data are shallow profile data, collected single-shot multi-channel data are processed into single-shot single-channel data by a Gaussian function, half of the Gaussian function is taken to cover the whole measuring line, then corresponding discrete points are used as weight coefficients, and the weight coefficients are normalized to enable the sum to be 1. And (4) sorting the single-shot multi-channel data into a super-channel set as shown in a formula (1), so that the single-shot multi-channel data is processed into single-shot single-channel data.
Where μ is the shot point, i is the demodulator probe, and σ is the Gaussian window factor.
And (3) selecting an x-axis positive half-axis part in a Gaussian function graph (shown in FIG. 2), wherein the abscissa corresponds to a demodulator probe, and the coefficient of each point is a Gaussian coefficient M, as shown in a formula (2).
Step 102, intercepting direct waves, shallow reflected waves and shallow refracted waves in a plurality of single shot data by using a time window to obtain observation data;
specifically, the time window control is carried out in the process of sound wave propagation, and the waveform information of near offset distance propagating on the near-surface and the medium-shallow layer is obtained. And intercepting direct waves, shallow reflected waves and shallow refracted waves in the single shot data by using a moving time window with fixed time length or non-fixed time length to obtain observation data.
The intercepted wave field is not specific to a certain type of wave, and contains information of many waves, such as direct waves, first-cast waves, transmitted waves, refracted waves, and the like. The wave field intercepted by the dynamic time window is used for carrying out high-precision speed modeling on the low-speed body development area.
The method comprises the following steps of intercepting a direct wave, a shallow reflected wave and a shallow refracted wave in single shot data by using a time window to obtain observation data, and preprocessing the observation data, wherein the method specifically comprises the following steps: carrying out filtering processing on the observation data by utilizing wavelet transformation to obtain processed observation data; and performing multiple suppression processing on the processed observation data.
Specifically, the wavelet transformation is utilized to carry out frequency division and denoising on the observation data, the wavelet transformation can be infinitely subdivided and mutually orthogonal, the observation data containing coherent interference is subjected to frequency division, and denoising processing can be carried out only in a narrow frequency band, so that the loss of effective waves after denoising is reduced to the maximum extent, and the frequency leakage phenomenon of Fourier transformation does not exist.
And performing multiple suppression processing on the processed observation data by adopting a common central point superposition method. The common center point superposition method is to superpose the signals from different excitation points of the same underground reflection point received by different receiving points after dynamic correction according to the difference of residual time difference between primary wave and multiple wave after dynamic correction, and to suppress the multiple wave. The velocity of the primary wave is used for actuating correction, the same phase axis of the primary wave is leveled, the multiple waves have residual time difference, and the primary wave is enhanced and the multiple waves are weakened through superposition.
In addition, the multiple suppression can be carried out by adopting two-dimensional filtering methods such as dip filtering, velocity filtering, fan-shaped filtering and the like, and the multiple is filtered out and the primary wave is reserved.
103, acquiring an acoustic parameter initial model, and forward modeling the seismic source waveform based on the acoustic parameter initial model to obtain forward modeling data;
the acoustic parameters comprise sound wave propagation speed, medium density, wave impedance, attenuation factor and Lame parameters.
Specifically, the underground is subjected to meshing subdivision, the size of the model is m × n, m represents the number of grid points in the horizontal direction, n represents the number of grid points in the longitudinal direction, the grid interval is h, the size of the acoustic parameter initial model is m × h meters in the horizontal direction, and n × h meters in the longitudinal direction. The matrix form of the initial model of the acoustic parameters is shown in equation (3):
the wave equation of the sound wave is shown in equation (4):
wherein p is a pressure field,vxAnd vzTransverse and longitudinal velocity fields, respectively; k is ρ v2；tsIs the acoustic travel time.
In the field of seismic exploration, the idea of staggered grids of acoustic wave equations is to configure different wave field components and subsurface acoustic parameters on different grid nodes, and time stepping is also time staggered stepping. The grid configuration of each physical quantity and acoustic parameter of the two-dimensional sound wave adopted by the invention is shown in fig. 3, and the grid configuration of each physical quantity and acoustic parameter of the three-dimensional sound wave is shown in fig. 4.
Before the sound wave equation is dispersed, Taylor expansion method is firstly adopted to deduce high-precision approximation of the spatial derivative of the sound wave field in a regular grid and a staggered grid.
Assuming u (x) has a derivative of order 2N +1, the Taylor expansion of order 2N +1 at x0 +. DELTA.x and x 0-DELTA.x of u (x) is:
(5) and (6) obtaining a formula (7) by subtracting the two formulas:
the same principle is as follows:
then the calculation formula of any 2N order precision center finite difference coefficient is:
order to
Then there are:
simplifying to obtain:
and similarly, any 2N-order precision finite difference format and difference coefficient calculation formula of the staggered grid can be derived.
According to the Taylor expansion:
simplifying to obtain:
wherein, anIs a difference coefficient
Therefore, with the staggered grid finite difference, the three-dimensional first-order velocity-stress acoustic wave equation can be discretized into:
where Δ x, Δ y, Δ z, Δ t are the spatial and temporal sampling intervals, respectively, p is the stress wavefield at each time, v represents the displacement wavefield at each time, x, y, z represent different directions, respectively, ρ represents density, and f represents the source function.
When initial and edge conditions are given, the spatial distribution of the wavefield at each time instant can be recursively derived using the difference format described above.
In the forward modeling process of the finite difference wave equation, in order to avoid numerical noise and instability, the grid size and the time step length of the finite difference need to respectively satisfy the dispersion relation and the stability condition for a given frequency bandwidth. The technical scheme of the invention adopts the finite difference dispersion relation to meet the requirement that each minimum wavelength needs at least 5 grid points, namely the space sampling interval for avoiding grid dispersion needs to meet the formula (29):
where Δ x is the spatial grid size, λminIs the shortest wavelength, vpminIs the minimum longitudinal wave velocity, fmaxIs the maximum frequency.
After the spatial sampling grid size is determined, a proper time sampling size needs to be selected to satisfy the finite difference numerical stability condition:
where Δ t is the time sampling interval, vpmaxIs the maximum longitudinal wave velocity.
104, calculating a wave field residual error according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual error;
specifically, the observed data and the forward modeling data are subtracted to obtain a wave field residual error. And (3) subtracting data on corresponding points in the two-dimensional array by using the observation data and the forward modeling data which are both two-dimensional arrays to obtain wave field residual errors.
Constructing an error functional according to equation (31):
wherein E (m) is an error functional, b (m) is a linear function representing the result data of the forward modeling, dobsFor observation data, b (m) -dobsAs wave field residual, CDAs a data covariance matrix, CMIs a covariance matrix of the model, m is a model parameter of the initial model of the acoustic parameters, mpriorIs a prior information model, lambda is a prior information specific gravity parameter,used to adjust the specific gravity of the model term and the prior information term.
105, reversely propagating the wave field residual error to an acoustic parameter initial model space to obtain residual error reverse propagation data;
and (4) acting the counter-propagation operator on the wave field residual error to obtain residual error counter-propagation data of the initial velocity model space.
And 104, obtaining wave field residual errors at the positions of the wave detection points, loading the wave field residual errors at the points into a time domain forward modeling process as a seismic source, and performing time reverse propagation to obtain residual reverse propagation data.
the gradient computation is a key part of parameter inversion and represents the updating direction of the model, and the gradient guiding inversion method searches the iterative updating direction through the derivative of the target functional to the model parameters so as to update the model. The method carries out gradient calculation based on an adjoint state method, and uses the data residual error of a forward wave field and a backward wave field as a new seismic source to carry out forward modeling so as to calculate the gradient of a target function to a model.
Specifically, the velocity gradient of the error functional is calculated according to the formula (32);
wherein the content of the first and second substances,
k＝ρVp 2(35)，Pffor forward modeling of data, PbResidual back-propagation data, ω frequency, VPAnd k and rho are initial model parameters, and E is an error functional.
Calculating a density gradient of the error functional according to equation (36);
wherein the content of the first and second substances,
k＝ρVp 2(39)；Pffor forward modeling of data, PbResidual back propagation data, ω is frequency, ρ is density, k is modulus, E is error functional, VpIs the velocity.
Calculating a wave impedance gradient of the error functional according to formula (40);
wherein the content of the first and second substances,
Pffor forward modeling of data, PbResidual back-propagation data, ω frequency, IPAnd k and rho are initial model parameters, and E is an error functional.
Calculating the attenuation factor gradient of the error functional according to equation (44);
wherein the content of the first and second substances,
e is an error functional, QjFor the attenuation factor, ω is the frequency, ωrIs the resonance frequency, ρ is the density, vjIs speed, PfFor forward modeling of data, PbResidual counterpropagation data.
Calculating a Lame parameter gradient of the error functional according to a formula (46);
wherein m is an initial model parameter, u is a forward simulation broadcast field, B is a forward operator, and delta d is a wave field residual error,B-1tis residual backpropagated data.
And step 107, updating the acoustic parameter initial model by respectively utilizing the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient to obtain an accurate model corresponding to each acoustic parameter.
In particular, let the gradient function
And obtaining a disturbance model, wherein the final accurate model is the sum of the initial model and the disturbance model.
And determining the optimal iteration step size according to a fastest descent method, a conjugate gradient method and a quasi-Newton method LBFGS. Updating the acoustic parameter initial model according to the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the Lame parameter gradient and the optimal iteration step length; and setting an iteration termination condition, and obtaining an accurate model corresponding to each acoustic parameter when the initial acoustic parameter model meets the iteration termination condition.
Model update is performed using equation (48):
mupdate＝mbefore-αkdk(48)
wherein, αkFor the optimal iteration step size of step k, dkThe gradient of the model at step k.
The iteration termination condition is shown in equation (49):
equation (9) specifies an iterative convergence rule of the acoustic parameter m, and if the nth update amount is smaller than a certain proportion of n-1 iterations, for example, 0.001, the iterations converge and the inversion is terminated; otherwise, the updating result is used as input to carry out the next iteration.
The invention provides an acoustic parameter acquisition method for river and lake sediment detection, which is used for acquiring a plurality of single-shot data of seismic source vibration in a river and lake sediment detection area, wherein the single-shot data comprises single-shot single-channel data and single-shot multi-channel data; intercepting direct waves, shallow reflected waves and shallow refracted waves in the single shot data by using a time window to obtain observation data; acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward simulation data, wherein the acoustic parameters comprise acoustic wave propagation speed, medium density, wave impedance, attenuation factors and Lame parameters; calculating a wave field residual error according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual error; utilizing wave field residual error back propagation to an acoustic parameter initial model space to obtain residual error back propagation data; respectively calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient of the error functional by utilizing forward modeling data and residual back propagation data; and respectively updating the acoustic parameter initial model by using the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient to obtain an accurate model corresponding to each acoustic parameter. According to the method provided by the invention, waveform inversion is completed by using the kinematics and the dynamics characteristics of a wave field in a period of time after the first arrival according to the geological task requirements, and high-precision acoustic parameter modeling of a shallow layer is realized.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for acquiring acoustic parameters for detecting river and lake sediment is characterized by comprising the following steps:
acquiring a plurality of single-shot data of seismic source vibration in a river and lake sediment detection area, wherein the single-shot data comprises single-shot single-channel data and single-shot multi-channel data;
intercepting direct waves, shallow reflected waves and shallow refracted waves in the single shot data by using a time window to obtain observation data;
acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward simulation data, wherein the acoustic parameters comprise a sound wave propagation speed, medium density, wave impedance, attenuation factors and Lame parameters;
calculating a wave field residual error according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual error;
utilizing the wave field residual error to perform back propagation to the acoustic parameter initial model space to obtain residual error back propagation data;
respectively calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the Lame parameter gradient of the error functional by using the forward modeling data and the residual back propagation data;
determining an optimal iteration step length and an iteration termination condition;
updating the acoustic parameter initial model according to the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the Lame parameter gradient and the optimal iteration step length;
and when the initial acoustic parameter model meets the iteration termination condition, obtaining an accurate model corresponding to each acoustic parameter.
2. The method of claim 1, wherein after the intercepting the direct wave, the shallow reflected wave, and the shallow refracted wave in the plurality of single shot data by using the time window to obtain the observation data, the method further comprises:
performing filtering processing on the observation data by utilizing wavelet transformation to obtain processed observation data;
and performing multiple suppression processing on the processed observation data.
3. The method of claim 1, wherein forward modeling the seismic source waveform based on the initial model of acoustic parameters to obtain forward simulation data specifically comprises:
performing time domain dispersion on the wave equation of the seismic source waveform by using a staggered grid finite difference method to obtain a dispersed wave equation;
and determining the wave field value of the spatial distribution of the staggered grid at each moment according to the dispersed wave equation and the acoustic parameter initial model.
4. The method according to claim 1, wherein said constructing an error functional from said wavefield residual specifically comprises:
according to the formula
Calculating an error functional, wherein E (m) is the error functional, b (m) is a linear function representing the result data of the forward modeling, dobsFor observation data, b (m) -dobsAs wave field residual, CDAs a data covariance matrix, CMIs a covariance matrix of the model, and m is a model parameter of an acoustic parameter initial model，mpriorIs a prior information model, and lambda is a prior information specific gravity parameter.
5. The method according to claim 1, wherein the calculating a velocity gradient, a density gradient, a wave impedance gradient, an attenuation factor gradient, and a Lamei parameter gradient of the error functional using the forward modeling data and the residual back propagation data respectively comprises:
according to the formula
Calculating a velocity gradient of the error functional; wherein the content of the first and second substances,
k＝ρVp 2，Pffor forward modeling of data, PbResidual back-propagation data, ω frequency, VPAnd k and rho are initial model parameters, and E is an error functional.
6. The method according to claim 1, wherein the calculating a velocity gradient, a density gradient, a wave impedance gradient, an attenuation factor gradient, and a Lamei parameter gradient of the error functional using the forward modeling data and the residual back propagation data respectively comprises:
according to the formula
Calculating a density gradient of the error functional; wherein the content of the first and second substances,
k＝ρVp 2；Pffor forward modeling of data, PbResidual back propagation data, ω is frequency, ρ is density, k is modulus, E is error functional, VpIs the velocity.
7. The method according to claim 1, wherein the calculating a velocity gradient, a density gradient, a wave impedance gradient, an attenuation factor gradient, and a Lamei parameter gradient of the error functional using the forward modeling data and the residual back propagation data respectively comprises:
according to the formula
Calculating a wave impedance gradient of the error functional; wherein the content of the first and second substances,
Pffor forward modeling of data, PbResidual back-propagation data, ω frequency, IPAnd k and rho are initial model parameters, and E is an error functional.
8. The method according to claim 1, wherein the calculating a velocity gradient, a density gradient, a wave impedance gradient, an attenuation factor gradient, and a Lamei parameter gradient of the error functional using the forward modeling data and the residual back propagation data respectively comprises:
according to the formula
Calculating an attenuation factor gradient of the error functional; wherein the content of the first and second substances,
e is an error functional, QjFor the attenuation factor, ω is the frequency, ωrIs the resonance frequency, ρ is the density, vjIs speed, PfFor forward modeling of data, PbResidual counterpropagation data.
9. The method according to claim 1, wherein the calculating a velocity gradient, a density gradient, a wave impedance gradient, an attenuation factor gradient, and a Lamei parameter gradient of the error functional using the forward modeling data and the residual back propagation data respectively comprises:
according to the formula
Calculating the Lame parameter gradient of the error functional, wherein m is an initial model parameter, u is a forward simulation broadcast field, B is a forward operator, delta d is a wave field residual error, and B is-1tIs residual backpropagated data.
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