CN111665551B - Acoustic parameter acquisition method for bridge substrate detection - Google Patents
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Abstract
The invention relates to an acoustic parameter acquisition method for bridge substrate detection, which comprises the following steps: acquiring a plurality of single shot data of vibration of a seismic source in a detection area of a bridge substrate; intercepting direct waves, shallow reflected waves and shallow refraction waves in the plurality of single shot data by utilizing a time window to obtain observation data; 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; calculating wave field residual errors according to the observation data and the forward modeling data, and constructing error functional according to the wave field residual errors; back-propagating the wave field residual errors 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 pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual counter-propagation data; and updating the acoustic parameter initial model by utilizing each parameter gradient respectively 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 bridge substrate detection.
Background
The mineral products and the surrounding rock have obvious acoustic parameter differences, different objects have different acoustic parameter characteristics, acoustic parameter values have a certain range, and the physical properties of the objects can be judged according to the acoustic parameter differences of the different objects.
The method based on ray-based first arrival time tomographic inversion is more commonly used at present. The method is simple in calculation, does not require an accurate background field, and applies complex earth surface conditions, so that the method is a common method for solving near-surface and shallow acoustic parameter modeling. But this approach is based on the principle of shortest path on the assumption of high frequencies, and in the case of low-speed volume development, the technique has a modeled 'dead zone'. In addition, the ray method is sensitive to ray density, and when the speed change is severe, even if the speed is high, the ray density is seriously affected due to the occurrence of the full emission phenomenon, so that the inversion accuracy is reduced. In addition, in the actual data application, the first arrival pickup workload is huge, errors exist in manual pickup, and under the condition of complex local surface, the first arrival is difficult to pick accurately.
Another new approach to solving this problem is full waveform inversion, which is based on wave equation, and thus can simulate the wave field of a wave more truly, and is not affected by the ray density. However, this new method has many limitations, such as the requirement of a large offset observation system, low frequency information loss, etc. The full waveform inversion has long distance to practical use, especially the practical use of land data.
When constructing tunnel and bridge foundation, the foundation material and strength need to be known, the physical property difference between the crack (containing gas or water) and the rock is utilized to detect the crack, and different construction methods are needed for different rocks. There is currently a lack of a solution to acquire acoustic parameters for bridge foundation exploration.
Disclosure of Invention
The invention aims at overcoming the defects in the prior art and provides an acoustic parameter acquisition method for bridge substrate detection.
To achieve the above object, in a first aspect, the present invention provides an acoustic parameter acquisition method for detecting a bridge substrate, including:
acquiring a plurality of single shot data of vibration of a vibration source in a bridge substrate detection area, wherein the single shot data comprise single shot single channel data and single shot multi-channel data;
intercepting direct waves, shallow reflected waves and shallow refraction waves in the plurality of single shot data by utilizing a time window to obtain initial observation data;
filtering and multiple wave suppression processing are carried out on the initial observed data to obtain observed data;
acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward modeling data, wherein the acoustic parameters comprise acoustic wave propagation speed, medium density, wave impedance, attenuation factor and pull Mei Canshu;
calculating a wave field residual according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual;
back-propagating the wave field residual error to the acoustic parameter initial model space to obtain residual error back-propagation data;
respectively calculating the speed gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual counter-propagation data;
determining optimal iteration step length and iteration termination conditions;
updating the acoustic parameter initial model according to the speed gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient and the optimal iteration step length;
and when the initial model of the acoustic parameters meets the iteration termination condition, obtaining an accurate model corresponding to each acoustic parameter.
Further, the forward modeling of the source waveform based on the acoustic parameter initial model specifically includes:
performing time domain dispersion on the wave equation of the seismic source waveform by using an interleaved grid finite difference method to obtain a dispersed wave equation;
and determining the wave field value of the spatial distribution of each moment of the staggered grid according to the discretized wave equation and the acoustic parameter initial model.
Further, the constructing an error functional from the wavefield residual specifically includes:
according to the formula
Calculating an error functional, wherein E (m) is the error functional, b (m) is the linear function representing the result data of the forward modeling, d obs For observing data, b (m) -d obs For wave field residual, C D As a data covariance matrix, C M Is covariance matrix of model, m is model parameter of initial model of acoustic parameter, m prior For the prior information model, λ is the prior information specific gravity parameter.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual back propagation data respectively specifically includes:
according to the formula
Calculating the speed gradient of the error functional; wherein,
k＝ρV p 2 ，P f p is forward modeling data b Residual back propagation data, ω is frequency, V P For speed, k, ρ are initial model parameters and E is the error functional.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual back propagation data respectively specifically includes:
according to the formula
Calculating the density gradient of the error functional; wherein,
k＝ρV p 2 ；P f p is forward modeling data b Residual back propagation data, ω is frequency, ρ is density, k is modulus, E is error functional, V p Is the speed.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual back propagation data respectively specifically includes:
according to the formula
Calculating the wave impedance gradient of the error functional; wherein (1)>
P f P is forward modeling data b Residual back propagation data, ω is frequency, I P For wave impedance, k, ρ are initial model parameters and E is the error functional.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual back propagation data respectively specifically includes:
according to the formula
Calculating the attenuation factor gradient of the error functional; wherein (1)>
E is the error functional, Q j Is an attenuation factor, ω is a frequency, ω r Is the resonance frequency, ρ is the density, v j For speed, P f P is forward modeling data b The residual back propagates the data.
Further, the calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual back propagation data respectively specifically includes:
according to the formula
Calculating a pull Mei Canshu gradient of the error functional, wherein m is an initial model parameter, u is a forward modeling broadcast field, B is a forward operator, Δd is a wave field residual, and B -1t Data is back-propagated for the residual.
According to the acoustic parameter acquisition method for detecting the bridge substrate, provided by the invention, a plurality of single shot data of vibration of a seismic source in a detection area of the bridge substrate are acquired, wherein the single shot data comprise single shot single channel data and single shot multi-channel data; intercepting direct waves, shallow reflected waves and shallow refraction waves in the plurality of single shot data by utilizing a time window to obtain observation data; acquiring an acoustic parameter initial model, and forward modeling the source waveform based on the acoustic parameter initial model to obtain forward modeling data, wherein the acoustic parameters comprise acoustic wave propagation speed, medium density, wave impedance, attenuation factors and pull Mei Canshu; calculating wave field residual errors according to the observation data and the forward modeling data, and constructing error functional according to the wave field residual errors; back-propagating the wave field residual errors 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 pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual counter-propagation data; and updating the initial model of the acoustic parameters by using the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the pull Mei Canshu gradient respectively to obtain an accurate model corresponding to each acoustic parameter. According to the method provided by the invention, waveform inversion is completed by utilizing the kinematics and dynamics characteristics of the wave field in a period of time after the first arrival according to geological task requirements, and high-precision acoustic parameter modeling of a shallow layer is realized.
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FIG. 1 is a flowchart of an acoustic parameter acquisition method for detecting a bridge substrate according to an embodiment of the present invention;
FIG. 2 is a Gaussian function diagram provided by an embodiment of the invention;
FIG. 3 is a diagram of a grid configuration of physical quantities and acoustic parameters of a two-dimensional acoustic 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 acoustic 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 more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Aiming at the difficulty of shallow modeling, particularly under the conditions of severe speed change and low-speed coiling body development, the invention avoids the weakness based on the ray theory, comprehensively utilizes wave field information in a period of time after the first arrival to realize shallow high-precision modeling, and provides reliable support for middle-deep modeling and offset imaging.
Fig. 1 is a flowchart of an acoustic parameter acquisition method for detecting a bridge substrate 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 bridge substrate detection area, and each shot is put to collect single shot data of one shot, wherein the single shot data comprises single shot single channel data and single shot multi-channel data. And according to specific situations and construction requirements, transmitting multiple cannons to acquire multiple groups of single-cannon data. The seismic prospecting instrument is specifically a 408ULS cable instrument and the like.
The method comprises the steps of processing acquired single-shot multi-channel data into single-shot single-channel data by using a Gaussian function, taking half of the Gaussian function to cover the whole measuring line, taking corresponding discrete points as weight coefficients, and normalizing the weight coefficients to be 1. And (3) sorting the single-shot multi-channel data into a super-channel set as shown in a formula (1), thereby processing the single-shot multi-channel data into single-shot single-channel data.
Wherein μ is a shot point, i is a detector point, and σ is a Gaussian window factor.
The positive half-axis part of the X-axis in the Gaussian function chart (shown in figure 2) is selected, the abscissa corresponds to the detection point, and the coefficient of each point is a Gaussian coefficient M, as shown in a formula (2).
102, intercepting direct waves, shallow reflected waves and shallow refraction waves in a plurality of single shot data by utilizing a time window to obtain initial observation data;
specifically, time window control is applied in the acoustic wave propagation process, so that waveform information of near offset propagation in the near surface and middle shallow layers is obtained. And intercepting a direct wave, a shallow reflection wave and a shallow refraction wave in the single shot data by using a moving time window with fixed time length or non-fixed time length to obtain initial observation data.
The intercepted wave field is not specific to a particular type of wave and contains many waves of information such as direct wave, first-cast wave, transmitted wave, return wave, refracted wave, etc. The waveform information of the waveform information contains rich information of the speed anomaly in the area without reflection interface transformation, so the invention uses the wave field intercepted by the dynamic time window to carry out high-precision speed modeling on the area where the low-speed body develops.
Intercepting direct waves, shallow reflected waves and shallow refraction waves in single shot data by utilizing a time window, and then preprocessing the initial observation data, wherein the method specifically comprises the following steps of: filtering the initial observation data by utilizing wavelet transformation to obtain the processed initial observation data; and performing multiple wave suppression treatment on the treated initial observation data to obtain observation data.
Specifically, the initial observation data is subjected to frequency division denoising by utilizing wavelet transformation, wavelet changes can be infinitely subdivided and mutually orthogonal, the observation data containing coherent interference can be subjected to frequency division, denoising processing can be limited to be performed in a very narrow frequency band, so that loss of effective waves after denoising is furthest reduced, and the frequency leakage phenomenon of Fourier transformation does not exist.
And adopting a common center point superposition method to perform multiple suppression treatment on the initial observation data after treatment. The common center point superposition method is to superpose signals received by different receiving points from different excitation points of the same reflecting point underground according to the difference of residual time difference between the primary wave and the multiple wave after dynamic correction, and to press the multiple wave after dynamic correction. The velocity of the primary wave is used for action correction, the in-phase axis of the primary wave is leveled, the secondary wave still has residual time difference, and the primary wave is enhanced and the secondary wave is weakened through superposition.
In addition, the method can also adopt two-dimensional filtering methods such as dip angle filtering, speed filtering, fan filtering and the like to carry out multiple suppression, and filter the multiple to retain the primary wave.
acoustic parameters include, among others, acoustic wave propagation speed, dielectric density, wave impedance, attenuation factor, and pull Mei Canshu.
Specifically, meshing subdivision is performed on the underground, the size of the model is m×n, m represents grid points in the horizontal direction, n represents grid points in the longitudinal direction, the grid distance 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 acoustic parameters is shown in formula (3):
the wave equation of the acoustic wave is shown in formula (4):
wherein p is the pressure field, v x and vz Transverse and longitudinal velocity fields, respectively; k=ρv 2 ；t s Is the acoustic travel time.
In the field of seismic exploration, the idea of a staggered grid of acoustic wave equations is to configure different wave field components and subsurface acoustic parameters on different grid nodes, and time stepping is also performed by time staggered stepping. The grid configuration of each physical quantity and acoustic parameter of the two-dimensional acoustic 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 acoustic wave is shown in fig. 4.
Before the acoustic wave equation is discretized, a taylor expansion method is firstly adopted to deduce the high-precision approximation of the spatial derivative of the acoustic wave field in the regular grid and the staggered grid.
Let u (x) have 2N+1 th order derivatives, then u (x) has 2N+1 th order Taylor expansion at x0+. DELTA.x, and x 0-. DELTA.x as:
(5) And (3) obtaining a formula (7) by taking the difference between the two formulas:
the same principle is as follows:
then the calculation formula of the finite difference coefficient of the arbitrary 2N-order precision center is:
order the
Then there are:
simplifying and obtaining:
similarly, the finite difference format and the differential coefficient calculation formula of the staggered grid with arbitrary 2N-order precision can be deduced.
According to the taylor expansion there are:
simplifying and obtaining:
wherein ,an Is a difference coefficient
Thus, with the staggered grid finite difference, the three-dimensional first-order velocity-stress acoustic wave equation can be discretized into:
wherein, deltax, deltay, deltaz and Deltat are respectively the space and time sampling intervals, p is the stress wave field at each moment, v is the displacement wave field at each moment, x, y and z are respectively different directions, ρ is the density, and f is the seismic source function.
When the initial value and the boundary value conditions are given, the spatial distribution of wave fields at each moment can be recursively obtained by using the differential format.
In the forward modeling of the finite difference wave equation, in order to avoid numerical noise and instability, the finite difference grid size and time step size need to satisfy the dispersion relation and stability conditions, respectively, for a given frequency bandwidth. The technical scheme of the invention adopts a finite difference frequency dispersion relation to meet the requirement that at least 5 grid points are needed for each minimum wavelength, namely, the space sampling interval of the grid dispersion is required to meet the formula (29):
where Δx is the spatial grid size, λ min Is the shortest wavelength of the light that is to be emitted,
for minimum longitudinal wave velocity, f max Is the maximumFrequency.
After the spatial sampling grid size is determined, a suitable temporal sampling size is also selected to satisfy the finite differential numerical stability condition:
where deltat is the time sampling interval,
is the maximum longitudinal wave velocity.
104, calculating a wave field residual according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual;
specifically, the observed data and the forward modeling data are subjected to difference to obtain a wave field residual error. The observed data and the forward modeling data are two-dimensional arrays, and the data on the corresponding points in the two-dimensional arrays are subtracted to obtain wave field residual errors.
Constructing an error functional according to formula (31):
wherein E (m) is an error functional, b (m) is a linear function representing result data of forward modeling, d obs For observing data, b (m) -d obs For wave field residual, C D As a data covariance matrix, C M Is covariance matrix of model, m is model parameter of initial model of acoustic parameter, m prior For the prior information model, λ is a prior information specific gravity parameter used to adjust the specific gravity of the model item and the prior information item.
and applying an anti-propagation operator to the wave field residual error to obtain residual error anti-propagation data of the initial velocity model space.
gradient calculation is a key part of parameter inversion and represents the updating direction of a model, and the gradient guidance inversion method searches for the iterative updating direction through the derivative of a target functional to model parameters so as to realize the updating of the model. The invention performs gradient calculation based on an accompanying state method, and takes the data residual errors of a forward wave field and a reverse wave field as a new seismic source to perform forward modeling so as to calculate the gradient of an objective function to a model.
Specifically, a velocity gradient of the error functional is calculated according to formula (32);
wherein ,
k＝ρV p 2 (35)，P f p is forward modeling data b Residual back propagation data, ω is frequency, V P For speed, k, ρ are initial model parameters and E is the error functional.
Calculating a density gradient of the error functional according to formula (36);
wherein ,
k＝ρV p 2 (39)；P f p is forward modeling data b Residual back propagation data, ω is frequency, ρ is density, k is modulusQuantity E is error functional, V p Is the speed.
Calculating the wave impedance gradient of the error functional according to formula (40);
wherein ,
P f p is forward modeling data b Residual back propagation data, ω is frequency, I P For wave impedance, k, ρ are initial model parameters and E is the error functional.
Calculating the attenuation factor gradient of the error functional according to the formula (44);
wherein ,
e is the error functional, Q j Is an attenuation factor, ω is a frequency, ω r Is the resonance frequency, ρ is the density, v j For speed, P f P is forward modeling data b The residual back propagates the data.
Calculating a pull Mei Canshu gradient of the error function according to equation (46);
wherein m is an initial model parameter, u is a forward modeling broadcast field, B is a forward operator, Δd is a wave field residual error, B -1t Data is back-propagated for the residual.
And 107, updating the initial model of the acoustic parameters by using the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the pull Mei Canshu gradient respectively to obtain an accurate model corresponding to each acoustic parameter.
Specifically, let the gradient function
And obtaining a disturbance model, wherein a final accurate model is the sum of the initial model and the disturbance model.
And determining the optimal iteration step according to the fastest descent method, the conjugate gradient method and the Newton-like method LBFGS. Updating the acoustic parameter initial model according to the speed gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient and the optimal iteration step length; setting iteration termination conditions, and obtaining an accurate model corresponding to each acoustic parameter when the initial model of the acoustic parameter meets the iteration termination conditions.
Model update using equation (48):
m update ＝m before -α k d k (48)
wherein ,αk For the optimal iteration step length of the kth step, d k Is the gradient of the kth model.
The iteration termination condition is shown in formula (49):
equation (9) specifies an iterative convergence rule for acoustic parameter m, where the n-th update is less than a certain proportion of n-1 iterations, e.g., ε=0.001, and the iteration converges and the inversion terminates; otherwise, taking the updated result as input, and performing the next iteration.
According to the acoustic parameter acquisition method for detecting the bridge substrate, provided by the invention, a plurality of single shot data of vibration of a seismic source in a detection area of the bridge substrate are acquired, wherein the single shot data comprise single shot single channel data and single shot multi-channel data; intercepting direct waves, shallow reflected waves and shallow refraction waves in the plurality of single shot data by utilizing a time window to obtain observation data; acquiring an acoustic parameter initial model, and forward modeling the source waveform based on the acoustic parameter initial model to obtain forward modeling data, wherein the acoustic parameters comprise acoustic wave propagation speed, medium density, wave impedance, attenuation factors and pull Mei Canshu; calculating wave field residual errors according to the observation data and the forward modeling data, and constructing error functional according to the wave field residual errors; back-propagating the wave field residual errors 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 pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual counter-propagation data; and updating the initial model of the acoustic parameters by using the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the pull Mei Canshu gradient respectively to obtain an accurate model corresponding to each acoustic parameter. According to the method provided by the invention, waveform inversion is completed by utilizing the kinematics and dynamics characteristics of the wave field in a period of time after the first arrival according to 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 elements 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 elements and steps are described above generally in terms of function in order to clearly illustrate the 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 solution. 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, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed 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 foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. An acoustic parameter acquisition method for bridge foundation exploration, the method comprising:
acquiring a plurality of single shot data of vibration of a vibration source in a bridge substrate detection area, wherein the single shot data comprise single shot single channel data and single shot multi-channel data;
intercepting direct waves, shallow reflected waves and shallow refraction waves in the plurality of single shot data by utilizing a time window to obtain initial observation data;
filtering and multiple wave suppression processing are carried out on the initial observed data to obtain observed data;
acquiring an acoustic parameter initial model, and forward modeling a seismic source waveform based on the acoustic parameter initial model to obtain forward modeling data, wherein the acoustic parameters comprise acoustic wave propagation speed, medium density, wave impedance, attenuation factor and pull Mei Canshu;
calculating a wave field residual according to the observation data and the forward modeling data, and constructing an error functional according to the wave field residual;
back-propagating the wave field residual error to the acoustic parameter initial model space to obtain residual error back-propagation data;
respectively calculating the speed gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient and the pull Mei Canshu gradient of the error functional by using the forward modeling data and the residual counter-propagation data;
determining optimal iteration step length and iteration termination conditions;
updating the acoustic parameter initial model according to the speed gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient and the optimal iteration step length;
when the initial model of the acoustic parameters meets the iteration termination condition, obtaining an accurate model corresponding to each acoustic parameter;
the forward modeling method for obtaining forward modeling data specifically includes:
performing time domain dispersion on the wave equation of the seismic source waveform by using an interleaved grid finite difference method to obtain a dispersed wave equation;
determining wave field values of spatial distribution of each moment of the staggered grid according to the discretized wave equation and the acoustic parameter initial model;
the discrete wave equation is:
wherein Δx, Δy, Δz, Δt are the spatial and temporal sampling intervals, respectively, p is the stress wave field at each time, v is the displacement wave field at each time, x, y, z are different directions, ρ is the density, f is the source function, a is the differential coefficient, and κ=ρv 2 。
2. The method of claim 1, wherein constructing an error functional from the wavefield residual comprises:
according to the formula
Calculating an error functional, wherein E (m) is the error functional, b (m) is the linear function representing the result data of the forward modeling, d obs For observing data, b (m) -d obs For wave field residual, C D As a data covariance matrix, C M Is covariance matrix of model, m is model parameter of initial model of acoustic parameter, m prior For the prior information model, λ is the prior information specific gravity parameter.
3. The method according to claim 1, wherein calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient of the error functional using the forward modeling data and the residual back propagation data, respectively, specifically comprises:
according to the formula
Calculating the speed gradient of the error functional; wherein,
k＝ρV p 2 ，P f p is forward modeling data b Residual back propagation data, ω is frequency, V P For speed, k, ρ are initial model parameters and E is the error functional.
4. The method according to claim 1, wherein calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient of the error functional using the forward modeling data and the residual back propagation data, respectively, specifically comprises:
according to the formula
Calculating the density gradient of the error functional; wherein,
k＝ρV p 2 ；P f p is forward modeling data b Residual back propagation data, ω is frequency, ρ is density, k is modulus, E is error functional, V p Is the speed.
5. The method according to claim 1, wherein calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient of the error functional using the forward modeling data and the residual back propagation data, respectively, specifically comprises:
according to the formula
Calculating the wave impedance gradient of the error functional; wherein (1)>
P f P is forward modeling data b Residual back propagation data, ω is frequency, I P For wave impedance, k, ρ are initial model parameters and E is the error functional. />
6. The method according to claim 1, wherein calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient of the error functional using the forward modeling data and the residual back propagation data, respectively, specifically comprises:
according to the formula
Attenuation factor ladder for calculating error functionalA degree; wherein (1)>
E is the error functional, Q j Is an attenuation factor, ω is a frequency, ω r Is the resonance frequency, ρ is the density, v j For speed, P f P is forward modeling data b The residual back propagates the data.
7. The method according to claim 1, wherein calculating the velocity gradient, the density gradient, the wave impedance gradient, the attenuation factor gradient, the pull Mei Canshu gradient of the error functional using the forward modeling data and the residual back propagation data, respectively, specifically comprises:
according to the formula
Calculating a pull Mei Canshu gradient of the error functional, wherein m is an initial model parameter, u is a forward modeling broadcast field, B is a forward operator, Δd is a wave field residual, and B -1t Data is back-propagated for the residual. />
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