CN109633742B - Full waveform inversion method and device - Google Patents
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Abstract
The invention provides a full waveform inversion method and a full waveform inversion device, wherein the method comprises the following steps: forward modeling the formation velocity model by using an optimal inversion frequency group; generating an earth surface receiving wave field by using the original single shot data and the forward result of the stratum velocity model; generating a basis function of the original single shot data by using a spectrum continuation method; recovering low-frequency data of the original single shot data by using the basis functions and the earth surface receiving wave field; generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data; inverting the stratum velocity model by using the frequency domain gradient field and the optimized step length; and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model. The method can recover the seismic data low-frequency data and can obtain an accurate full waveform inversion result by using the low-frequency data.
Description
Technical Field
The invention relates to the field of oil exploration, in particular to seismic data processing, and specifically relates to a full waveform inversion method and device.
Background
As seismic exploration continues to deepen, the faced geological problems become increasingly complex; short wavelength velocities can both improve the accuracy of depth domain migration and can be used to indicate small inhomogeneities, and thus geological targets with petrophysical differences such as sand, cracks, fracture zones, hyperbands, etc. The full waveform inversion method can invert short-wavelength speed change, can be applied to middle and shallow layer speed short-wavelength modeling, can also be combined with reverse migration to be used for reflected wave inversion, and is a method which is deeply researched and rapidly developed in the current international petroleum seismic exploration data processing method. The full waveform inversion has an obvious effect on constructing a high-resolution speed model, but low-frequency information is absent, so that the dependence on a high-precision initial model is increased, and an inaccurate initial model easily causes cycle skip under the condition of low-frequency absence and cannot well construct background speed, so that the full waveform inversion cannot obtain a good result. Meanwhile, when actual wavelets are missing, amplitude information is difficult to apply, full waveform inversion based on waveform subtraction is difficult to correct speed, and full waveform inversion based on travel time residual loses part of amplitude information.
Disclosure of Invention
Aiming at the problems in the prior art, the full waveform inversion method capable of recovering seismic data low-frequency information can be established, and the phenomenon that the inversion result is not converged to cause a local extreme value and the data lack low frequency to bring cycle skip can be avoided due to the 'ill-condition' of full waveform inversion, namely the decoupling of an initial stratum velocity model and seismic data.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a full waveform inversion method, comprising:
forward modeling the formation velocity model by using an optimal inversion frequency group;
generating an earth surface receiving wave field by using the original single shot data and the forward result of the stratum velocity model;
generating a basis function of original single shot data by using a spectrum continuation method;
recovering low-frequency data of original single shot data by using the basis functions and the earth surface receiving wave field;
generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data;
inverting the stratum velocity model by using the frequency domain gradient field and the optimized step length;
and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
In one embodiment, before forward modeling the formation velocity model using an optimal inversion frequency set, the method further comprises: and integrally transmitting the speed model data to a GPU video memory.
In one embodiment, before the integrally transmitting the speed model data to the GPU video memory, the method further includes:
selecting an optimal inversion frequency by using a Sirgue frequency optimization strategy;
and grouping the optimal inversion frequencies according to a preset frequency interval.
In one embodiment, forward modeling the velocity model using an optimal set of inversion frequencies comprises:
and performing seismic wave numerical simulation according to the optimal inversion frequency group and the stratum velocity model by using a 12-order space finite difference time domain propagation operator to generate a forward modeling result of the stratum velocity model.
In one embodiment, generating the surface receive wavefield using the raw single shot data and forward modeling of the formation velocity model comprises:
and generating a frequency domain earth surface receiving wave field of the optimal inversion frequency set by utilizing a discrete Fourier transform method according to the original single shot data and the forward modeling result of the stratum velocity model.
In one embodiment, generating a frequency domain gradient field of a preset number of single shot data sets from low frequency data comprises:
generating a residual wave field by using the low-frequency data and the original single-shot data;
a frequency domain gradient field is generated using the residual wave field.
In one embodiment, inverting the formation velocity model using the frequency domain gradient field and the optimized step size comprises:
calculating the sum of gradient fields of each frequency domain;
and carrying out inversion on the stratum velocity model by using the sum of the gradient fields of all frequency domains and the optimized step length.
In a second aspect, the present invention provides a full waveform inversion apparatus comprising:
the forward modeling unit is used for forward modeling the formation velocity model by utilizing an optimal inversion frequency group;
the earth surface receiving wave field generating unit is used for generating an earth surface receiving wave field by utilizing the original single shot data and the forward modeling result of the stratum velocity model;
the base function generating unit is used for generating a base function of the original single shot data by using a spectrum continuation method;
the low-frequency data recovery unit is used for recovering the low-frequency data of the original single-shot data by using the basis function and the earth surface receiving wave field;
the frequency domain gradient field generating unit is used for generating frequency domain gradient fields of a preset number of single shot data sets according to the low-frequency data;
the inversion unit is used for inverting the stratum velocity model by utilizing the frequency domain gradient field and the optimized step length;
and the iteration unit is used for executing iteration operation, and performing forward modeling on the inverted stratum velocity model by using another optimal inversion frequency group to obtain a final inversion result of the stratum velocity model.
In one embodiment, the full waveform inversion apparatus further comprises: and the GPU transmission unit is used for integrally transmitting the speed model data to the GPU video memory.
In one embodiment, the full waveform inversion apparatus further comprises: the optimal inversion frequency selecting unit is used for selecting the optimal inversion frequency by utilizing a Sirgue frequency optimal selection strategy;
and the optimal inversion frequency grouping unit is used for grouping the optimal inversion frequencies according to the preset frequency interval.
In one embodiment, the forward unit is specifically configured to: and performing seismic wave numerical simulation according to the optimal inversion frequency group and the stratum velocity model by using a 12-order space finite difference time domain propagation operator to generate a forward modeling result of the stratum velocity model.
In one embodiment, the surface receive wavefield generation unit is specifically configured to: and generating a frequency domain earth surface receiving wave field of the optimal inversion frequency set by utilizing a discrete Fourier transform method according to the original single shot data and the forward modeling result of the stratum velocity model.
In one embodiment, the frequency domain gradient field generating unit includes:
the residual wave field generating module is used for generating a residual wave field by utilizing the low-frequency data and the original single-shot data;
a frequency domain gradient field generation module for generating a frequency domain gradient field using the residual wave field.
In one embodiment, the inversion unit comprises:
the frequency domain gradient field summation module is used for calculating the sum of the frequency domain gradient fields;
and the stratum velocity model inversion module is used for inverting the stratum velocity model by using the sum of the gradient fields of all frequency domains and the optimized step length.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the full waveform inversion method when executing the program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a full waveform inversion method.
From the above description, the present invention provides a full waveform inversion method and apparatus, which can select an optimal inversion frequency, forward the initial formation velocity model by using one frequency or a group of frequencies in the optimal inversion frequency, generate a surface receiving wave field by combining a single shot data in the current frequency and the forward result, generate a basis function of the single shot data by a spectrum continuation method, the basis function and the surface receiving wave field can recover low frequency data in the single shot data, the low frequency data is critical to full wavelength inversion, generate a frequency domain gradient field of the single shot data based on the low frequency data, and so on, generate a gradient field of all single shot data in the frequency or the frequency group, forward the formation velocity model by using the gradient field and an optimization step length, and then use the forward result as the initial formation velocity model of the optimal inversion frequency group of the next optimal inversion frequency, iteration is carried out until an optimal inversion result is obtained, or all the optimal inversion frequencies or the optimal inversion frequency groups are completely iterated, the method starts from actual data, frequency spectrum reconstruction is carried out on the actual data by using different basis functions through a frequency spectrum continuation principle (modulation) and by using the frequency shifting characteristics of signals, the low-frequency part in the seismic data is recovered, the low-frequency data can reduce the nonlinear problem of inversion, the target function of the low-frequency data is relatively smooth and can be easily converged to a global minimum point, the dependence degree of the low-frequency data on an initial model is relatively low, and the method has the greatest characteristic that the obtained basis functions are dynamic, so that the forward data can be well matched with the actual data when the band is dropped each time. And in the initial stage of inversion, relatively accurate smooth background speed and large-scale structure are obtained by using relatively low frequency data, and on the basis, fine structure is carved by using high frequency data, so that the inversion stability can be improved, the target function is gradually converged near the global minimum value, and a relatively good inversion result can be obtained.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a full waveform inversion method in an embodiment of the invention;
FIG. 2 is a schematic flow chart of step 500 of a full waveform inversion method in an embodiment of the invention;
FIG. 3 is a schematic flow chart of step 600 of a full waveform inversion method in an embodiment of the invention;
FIG. 4 is a schematic illustration of a seismic recording of raw data in an embodiment of the present invention;
FIG. 5 is a schematic illustration of a seismic record after low frequency information is recovered from the raw data in an embodiment of the invention;
FIG. 6 is a diagram illustrating the result of the 190 th trace of raw data spectral analysis according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a result of spectrum analysis of the 190 th data after low-frequency information of the original data is restored in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a block A of a true formation velocity model according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of an A-block initial formation velocity model in accordance with an embodiment of the present invention;
FIG. 10 is a schematic diagram of the inversion results of the block A with only low frequency data in the original data according to an embodiment of the present invention;
FIG. 11 is a schematic diagram illustrating the inversion result of the block A with only high frequency data in the original data according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of the result of the A-block inversion with full band data in the raw data according to an embodiment of the present invention;
FIG. 13 is a schematic structural diagram of a full waveform inversion apparatus in an embodiment of the invention;
fig. 14 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
An embodiment of the present invention provides a specific implementation of a full waveform inversion method, which specifically includes the following steps, with reference to fig. 1:
step 100: and forward modeling the formation velocity model by utilizing an optimal inversion frequency group.
Step 100 specifically comprises: and establishing a stratum velocity model according to the existing data (such as measurement data, well drilling data and the like), and solving the corresponding seismic response by using the model and the optimal inversion frequency set.
Step 200: and generating an earth surface receiving wave field by using the original single shot data and the forward modeling result of the stratum velocity model.
As can be appreciated, the raw shot data, which contains a plurality of individual shot data, first enters the shot loop, reads the individual shot data, determines the reception range of the individual shots, and generates the surface receive wavefield.
Step 300: and generating a basis function of the original single shot data by using a spectrum continuation method.
It is understood that the concept of spectrum extension in step 300 is derived from communication principles, the essence of spectrum extension is modulation, the frequency shift in the time domain is the convolution of the frequency domain, i.e. the product of the time domain, and the two signals are multiplied point by point, thereby widening the spectrum of the information.
Step 400: and recovering low-frequency data of the original single-shot data by using the basis functions and the surface receiving wave field.
It will be appreciated that the basis functions of step 400 are dynamic and that the low frequency data in the original single shot data can be recovered using the basis functions and the surface receive wavefields generated in step 200, thereby providing data support for the full waveform inversion.
Step 500: and generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data.
The step 500 is specifically: calculating a corresponding frequency domain gradient field of the current shot record under the current frequency or frequency group by using the low-frequency data; and simultaneously accumulating the frequency domain gradient field of the shot-point-sharing gather under the current frequency until all the shot points are circulated.
Step 600: and carrying out inversion on the formation velocity model by using the frequency domain gradient field and the optimized step length.
It is understood that step 600 is to estimate the structural morphology inside the formation and the characteristic change inside the formation by using the frequency domain gradient field and the optimization step size.
Step 700: and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
It is to be understood that step 700 describes an iterative process from step 100 to step 600, where the last formation velocity model update resulted in the initial formation velocity model for the next optimal frequency set, and it is noted that the formation velocity model was the initial formation velocity model when step 100 was first performed.
From the above description, the present invention provides a full waveform inversion method, which can select an optimal inversion frequency, forward the initial formation velocity model by using one frequency or a group of frequencies in the optimal inversion frequency, generate a surface receiving wave field by combining a single shot data in the current frequency and a forward result, generate a basis function of the single shot data by using a spectrum continuation method, the basis function and the surface receiving wave field can recover low frequency data in the single shot data, the low frequency data is critical to full wavelength inversion, generate a frequency domain gradient field of the single shot data based on the low frequency data, and so on, generate a gradient field of all single shot data in the frequency or the frequency group, forward the formation velocity model by using the gradient field and an optimization step length, and then use the forward result as the initial formation velocity model of the next optimal inversion frequency group, iteration is carried out until an optimal inversion result is obtained, or all the optimal inversion frequencies or the optimal inversion frequency groups are completely iterated, the method starts from actual data, frequency spectrum reconstruction is carried out on the actual data by using different basis functions through a frequency spectrum continuation principle (modulation) and by using the frequency shifting characteristics of signals, the low-frequency part in the seismic data is recovered, the low-frequency data can reduce the nonlinear problem of inversion, the target function of the method is relatively smooth, the target function can be easily converged to a global minimum point, and the dependence degree of the method on an initial model is relatively low. And in the initial stage of inversion, relatively accurate smooth background speed and large-scale structure are obtained by using relatively low frequency data, and on the basis, fine structure is carved by using high frequency data, so that the inversion stability can be improved, the target function is gradually converged near the global minimum value, and a relatively good inversion result can be obtained.
In a specific embodiment, the present invention further provides a specific embodiment of a full waveform inversion method, which specifically includes the following steps:
step 001: and selecting the optimal inversion frequency by using a Sirgue frequency optimization strategy.
Specifically, the optimal inversion frequency can be selected according to the Sirgue frequency preference strategy (Sirgue and Pratt were proposed in 2004).
Step 002: and grouping the optimal inversion frequencies according to a preset frequency interval.
Step 002 may beTo thereby enable
To compare the 100 optimal inversion frequencies selected in step 001 and group the frequencies, it is understood that each group may contain one or more optimal inversion frequencies.
Step 003: and integrally transmitting the speed model data to a GPU video memory.
Step 100: and forward modeling the formation velocity model by utilizing an optimal inversion frequency group.
Step 100 specifically comprises: and for a preset stratum velocity model (established by using logging data, drilling data and other geological data), calculating corresponding seismic response according to the optimal inversion frequency set and the stratum velocity model by using a 12-order space finite difference time domain propagation operator, and generating a forward result of the stratum velocity model. The step adopts two-dimensional forward modeling to calculate and synthesize the seismic section.
Step 200: and generating an earth surface receiving wave field by using the original single shot data and the forward modeling result of the stratum velocity model.
The step 200 specifically comprises: and generating a frequency domain earth surface receiving wave field of an optimal inversion frequency group by using a discrete Fourier transform method according to the original single shot data and the forward modeling result of the stratum velocity model.
Preferably, the surface received wave field at each time instant of forward motion can be recorded, and the frequency domain forward motion wave field of the corresponding frequency is extracted by using a discrete Fourier transform method, namely, the wave field contribution of the time instant to the current frequency domain wave field is calculated for the wave field snapshot at each time instant, and the wave field contributions are accumulated.
Step 300: and generating a basis function of the original single shot data by using a spectrum continuation method.
It is understood that the method used in step 300 is the principle of spectrum extension in communication signal processing, the essence of spectrum extension is modulation, the frequency shift in the time domain is the convolution of the frequency domain, i.e. the product of the time domain, and the two signals are multiplied point by point, thereby widening the spectrum of the information, specifically: two time domain signals f1(t)f2(t) in the frequency domain of F1(ω)F2(ω), then the convolution of the frequency domain can be expressed as:
F1(u)*F2and (omega-u) realizes frequency shifting, performs a product in a time domain, realizes frequency shifting, selects a proper time domain function, and can realize data spectrum broadening.
Step 400: and recovering low-frequency data of the original single-shot data by using the basis functions and the surface receiving wave field.
Step 500: and generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data. Referring to fig. 2, step 500 may include:
step 501: generating a residual wave field by using the low-frequency data and the original single-shot data;
step 502: a frequency domain gradient field is generated using the residual wave field.
The formula that may be used in step 502 is:
wherein Grad is a frequency domain gradient field, ufIs a seismic source forward wave field, ubFor residual back-propagation wavefields, v is the velocity of the subsurface medium, xsAnd xrThe horizontal positions of the source and detector, respectively, and ω is the frequency of the current inversion.
Step 600: and carrying out inversion on the formation velocity model by using the frequency domain gradient field and the optimized step length.
In one embodiment, referring to fig. 3, step 600 may comprise:
step 601: and calculating the sum of the gradient fields of the frequency domains.
Step 601 is specifically to calculate the current optimal inversion frequency or the sum of the gradient fields of all single shot data under the current optimal inversion frequency group by using the formula (2).
Step 602: and carrying out inversion on the stratum velocity model by using the sum of the gradient fields of all frequency domains and the optimized step length.
It is understood that step 602 is to estimate the structural morphology inside the formation and the change of the internal features of the formation by using the sum of the frequency domain gradient fields calculated in step 601 and the optimization step size.
Step 700: and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
It can be understood that the formation velocity model is continuously updated through forward-calculated parameters, and then the updated formation velocity model is used as the initial formation velocity model of the next optimal frequency group, and the steps are repeated to obtain the final inversion result of the formation velocity model.
To further illustrate the present solution, the following will illustrate the effects of the present invention with a specific application example as follows:
the method of the embodiment of the invention is utilized to carry out full waveform inversion on a certain oil field A block in China (the real stratum velocity model of the A block is known). The initial formation velocity model size is 3840mx1220m, and the spacing Δ x- Δ y-10 m. The frequency domain full waveform inversion is adopted, the step length selection adopts a step length attenuation method, the inversion algorithm adopts a Hessian matrix method, 10 cannons are adopted totally, the time sampling interval is 1ms, the sampling length is 1s, and the boundary strip adopts 15 layers of pml absorption boundaries.
For comparison, two sets of data are respectively used for inversion, one set is original data with unrecovered low frequency, the other set is original data with restored low frequency, and then an inversion result of the original data with unrecovered low frequency is compared with an inversion result of the original data with restored low frequency, and the specific application example comprises the following steps:
s1: and selecting the optimal inversion frequency by using a Sirgue frequency optimization strategy.
S2: to be provided with
For equal-ratio comparison of optimal inversion frequencyThe rates are grouped.
S3: and integrally transmitting the preset speed model data to a GPU video memory.
It will be appreciated that the initial formation velocity model previously created using the logging data, drilling data and other geological data is transmitted in its entirety to the GPU memory.
S4: and solving a corresponding seismic response according to the optimal inversion frequency group (or the optimal inversion frequency) and the stratum velocity model by using a 12-order space finite difference time domain propagation operator to generate a two-dimensional forward result of the stratum velocity model.
S5: and generating a frequency domain earth surface receiving wave field of an optimal inversion frequency group by using a discrete Fourier transform method according to the original single shot data and a two-dimensional forward result of the stratum velocity model.
S6: and generating a basis function of the original single shot data by using a spectrum continuation method.
S6: the low frequency data of the original single shot data is recovered using the basis functions and the surface receive wavefield, see figures 4 through 7,
comparing fig. 4 and fig. 5, it is apparent that the low frequency information in the original data is better recovered by the spectrum continuation method (modulation).
Comparing fig. 6 and fig. 7, it is obvious that the low frequency information in the 190 data spectrum analysis in the original data is better recovered by the spectrum continuation method (modulation), and particularly, the frequency below 20Hz is obviously recovered.
S7: and generating a residual wave field by using the low-frequency data and the original single-shot data.
S8: the frequency domain gradient field is generated using equation (2).
S9: and calculating the sum of the current optimal inversion frequency or the gradient fields of all single shot data under the current optimal inversion frequency group.
S10: obtaining optimized step length by using step length attenuation method
S11: and carrying out inversion on the stratum velocity model by using the sum of the gradient fields of all frequency domains and the optimized step length.
S12: executing iteration operation, performing forward modeling on the inverted stratum velocity model by using another optimal inversion frequency group to obtain a final inversion result of the stratum velocity model, respectively inverting only low-frequency data, only high-frequency data and full-frequency-band data in original data by using the method provided by the embodiment, and comparing the inverted stratum velocity model with an A-block stratum velocity model (a real stratum velocity model) and an initial model, so that the background velocity and the shallow part can be recovered to a certain extent under the condition that only low-frequency data exists in the original data, and the difference between the original model and the initial model is not large under the condition that only high-frequency data exists, because the first iteration is trapped in local minimum, iteration updating cannot be performed, and cycle jump is easily generated and trapped in local minimum; and the most accurate inversion result can be obtained by recovering the full-band data after the low-frequency data, as shown in fig. 8 to 12.
From the above description, the present invention provides a full waveform inversion method, which can select an optimal inversion frequency, forward the initial formation velocity model by using one frequency or a group of frequencies in the optimal inversion frequency, generate a surface receiving wave field by combining a single shot data in the current frequency and a forward result, generate a basis function of the single shot data by using a spectrum continuation method, the basis function and the surface receiving wave field can recover low frequency data in the single shot data, the low frequency data is critical to full wavelength inversion, generate a frequency domain gradient field of the single shot data based on the low frequency data, and so on, generate a gradient field of all single shot data in the frequency or the frequency group, forward the formation velocity model by using the gradient field and an optimization step length, and then use the forward result as the initial formation velocity model of the next optimal inversion frequency group, iteration is carried out until an optimal inversion result is obtained, or all the optimal inversion frequencies or the optimal inversion frequency groups are completely iterated, the method starts from actual data, frequency spectrum reconstruction is carried out on the actual data by using different basis functions through a frequency spectrum continuation principle (modulation) and by using the frequency shifting characteristics of signals, the low-frequency part in the seismic data is recovered, the low-frequency data can reduce the nonlinear problem of inversion, the target function of the method is relatively smooth, the target function can be easily converged to a global minimum point, and the dependence degree of the method on an initial model is relatively low. And in the initial stage of inversion, relatively accurate smooth background speed and large-scale structure are obtained by using relatively low frequency data, and on the basis, fine structure is carved by using high frequency data, so that the inversion stability can be improved, the target function is gradually converged near the global minimum value, and a relatively good inversion result can be obtained.
Based on the same inventive concept, the present application further provides a full waveform inversion apparatus, which can be used to implement the methods described in the foregoing embodiments, as described in the following embodiments. Because the principle of the full waveform inversion device for solving the problems is similar to that of the full waveform inversion method, the full waveform inversion method can be implemented in the implementation of the full waveform inversion device, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
An embodiment of the present invention provides a specific implementation of a full waveform inversion apparatus, and referring to fig. 13, the full waveform inversion apparatus specifically includes the following contents:
the forward modeling unit 10 is used for forward modeling the formation velocity model by using an optimal inversion frequency group;
the earth surface receiving wave field generating unit 20 is used for generating an earth surface receiving wave field by utilizing the original single shot data and the forward modeling result of the stratum velocity model;
a basis function generating unit 30, configured to generate a basis function of the original single shot data by using a spectrum continuation method;
a low-frequency data recovery unit 40, configured to recover the low-frequency data of the original single-shot data by using the basis functions and the surface received wave field;
a frequency domain gradient field generating unit 50, configured to generate a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data;
an inversion unit 60, configured to invert the formation velocity model by using the frequency domain gradient field and the optimized step length;
and the iteration unit 70 is configured to perform an iteration operation, and forward the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
In one embodiment, the full waveform inversion apparatus further comprises: and the GPU transmission unit is used for integrally transmitting the speed model data to the GPU video memory.
In one embodiment, the full waveform inversion apparatus further comprises: the optimal inversion frequency selecting unit is used for selecting the optimal inversion frequency by utilizing a Sirgue frequency optimal selection strategy;
and the optimal inversion frequency grouping unit is used for grouping the optimal inversion frequencies according to the preset frequency interval.
In one embodiment, the forward unit is specifically configured to: and performing seismic wave numerical simulation according to the optimal inversion frequency group and the stratum velocity model by using a 12-order space finite difference time domain propagation operator to generate a forward modeling result of the stratum velocity model.
In one embodiment, the surface receive wavefield generation unit is specifically configured to: and generating a frequency domain earth surface receiving wave field of the optimal inversion frequency set by utilizing a discrete Fourier transform method according to the original single shot data and the forward modeling result of the stratum velocity model.
In one embodiment, the frequency domain gradient field generating unit includes:
the residual wave field generating module is used for generating a residual wave field by utilizing the low-frequency data and the original single-shot data;
a frequency domain gradient field generation module for generating a frequency domain gradient field using the residual wave field.
In one embodiment, the inversion unit comprises:
the frequency domain gradient field summation module is used for calculating the sum of the frequency domain gradient fields;
and the stratum velocity model inversion module is used for inverting the stratum velocity model by using the sum of the gradient fields of all frequency domains and the optimized step length.
From the above description, the present invention provides a full waveform inversion apparatus, which can select an optimal inversion frequency, forward the initial formation velocity model by using one frequency or a group of frequencies in the optimal inversion frequency, generate a surface receiving wave field by combining a single shot data in the current frequency and the forward result, generate a basis function of the single shot data by using a spectrum continuation method, the basis function and the surface receiving wave field can recover low frequency data in the single shot data, the low frequency data is critical to full wavelength inversion, generate a frequency domain gradient field of the single shot data based on the low frequency data, and so on, generate a gradient field of all single shot data in the frequency or the frequency group, forward the formation velocity model by using the gradient field and an optimized step length, and then use the forward result as the initial formation velocity model of the next optimal inversion frequency group, iteration is carried out until an optimal inversion result is obtained, or all the optimal inversion frequencies or the optimal inversion frequency groups are completely iterated, the method starts from actual data, frequency spectrum reconstruction is carried out on the actual data by using different basis functions through a frequency spectrum continuation principle (modulation) and by using the frequency shifting characteristics of signals, the low-frequency part in the seismic data is recovered, the low-frequency data can reduce the nonlinear problem of inversion, the target function of the method is relatively smooth, the target function can be easily converged to a global minimum point, and the dependence degree of the method on an initial model is relatively low. And in the initial stage of inversion, relatively accurate smooth background speed and large-scale structure are obtained by using relatively low frequency data, and on the basis, fine structure is carved by using high frequency data, so that the inversion stability can be improved, the target function is gradually converged near the global minimum value, and a relatively good inversion result can be obtained.
An embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all steps in the full waveform inversion method in the foregoing embodiment, and referring to fig. 14, the electronic device specifically includes the following contents:
a processor (processor)1201, a memory (memory)1202, a communication interface 1203, and a bus 1204;
the processor 1201, the memory 1202 and the communication interface 1203 complete mutual communication through the bus 1204; the communication interface 1203 is configured to implement information transmission between related devices, such as a server-side device, a detection device, a client device, and the like;
the processor 1201 is configured to call a computer program in the memory 1202, and the processor implements all the steps of the full waveform inversion method in the above embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:
step 100: and forward modeling the formation velocity model by utilizing an optimal inversion frequency group.
Step 200: and generating an earth surface receiving wave field by using the original single shot data and the forward modeling result of the stratum velocity model.
Step 300: and generating a basis function of the original single shot data by using a spectrum continuation method.
Step 400: and recovering low-frequency data of the original single-shot data by using the basis functions and the surface receiving wave field.
Step 500: and generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data.
Step 600: and carrying out inversion on the formation velocity model by using the frequency domain gradient field and the optimized step length.
Step 700: and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
As can be seen from the above description, the electronic device in the embodiment of the present application may select an optimal inversion frequency, forward the initial formation velocity model by using one frequency or a group of frequencies in the optimal inversion frequency, generate a surface receiving wavefield by combining one single shot data in the current frequency and a forward result, generate a basis function of the single shot data by using a spectrum continuation method, where the basis function and the surface receiving wavefield may recover low frequency data in the single shot data, the low frequency data is critical to full-wavelength inversion, generate a frequency domain gradient field of the single shot data based on the low frequency data, and so on, generate a gradient field of all the single shot data in the frequency or the frequency group, forward the formation velocity model by using the gradient field and an optimized step length, and then use the forward result as the initial formation velocity model of the next optimal inversion frequency group of the optimal inversion frequency, iteration is carried out until an optimal inversion result is obtained, or all the optimal inversion frequencies or the optimal inversion frequency groups are completely iterated, the method starts from actual data, frequency spectrum reconstruction is carried out on the actual data by using different basis functions through a frequency spectrum continuation principle (modulation) and by using the frequency shifting characteristics of signals, the low-frequency part in the seismic data is recovered, the low-frequency data can reduce the nonlinear problem of inversion, the target function of the method is relatively smooth, the target function can be easily converged to a global minimum point, and the dependence degree of the method on an initial model is relatively low. And in the initial stage of inversion, relatively accurate smooth background speed and large-scale structure are obtained by using relatively low frequency data, and on the basis, fine structure is carved by using high frequency data, so that the inversion stability can be improved, the target function is gradually converged near the global minimum value, and a relatively good inversion result can be obtained.
Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the full waveform inversion method in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when the computer program is executed by a processor, the computer program implements all steps of the full waveform inversion method in the above embodiments, for example, the processor implements the following steps when executing the computer program:
step 100: and forward modeling the formation velocity model by utilizing an optimal inversion frequency group.
Step 200: and generating an earth surface receiving wave field by using the original single shot data and the forward modeling result of the stratum velocity model.
Step 300: and generating a basis function of the original single shot data by using a spectrum continuation method.
Step 400: and recovering low-frequency data of the original single-shot data by using the basis functions and the surface receiving wave field.
Step 500: and generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data.
Step 600: and carrying out inversion on the formation velocity model by using the frequency domain gradient field and the optimized step length.
Step 700: and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
As can be seen from the above description, the computer-readable storage medium in the embodiment of the present application may select an optimal inversion frequency, forward the initial formation velocity model by using one frequency or a group of frequencies in the optimal inversion frequency, generate a surface receiving wavefield by combining one single shot data in the current frequency and the forward result, generate a basis function of the single shot data by using a spectrum continuation method, where the basis function and the surface receiving wavefield may recover low frequency data in the single shot data, the low frequency data is critical to full-wavelength inversion, generate a frequency domain gradient field of the single shot data based on the low frequency data, and so on, generate a gradient field of all single shot data at the frequency or in the frequency group, forward the formation velocity model by using the gradient field and an optimization step length, and then use the forward result as the initial formation velocity model of the next optimal inversion frequency group of the optimal inversion frequency, iteration is carried out until an optimal inversion result is obtained, or all the optimal inversion frequencies or the optimal inversion frequency groups are completely iterated, the method starts from actual data, frequency spectrum reconstruction is carried out on the actual data by using different basis functions through a frequency spectrum continuation principle (modulation) and by using the frequency shifting characteristics of signals, the low-frequency part in the seismic data is recovered, the low-frequency data can reduce the nonlinear problem of inversion, the target function of the method is relatively smooth, the target function can be easily converged to a global minimum point, and the dependence degree of the method on an initial model is relatively low. And in the initial stage of inversion, relatively accurate smooth background speed and large-scale structure are obtained by using relatively low frequency data, and on the basis, fine structure is carved by using high frequency data, so that the inversion stability can be improved, the target function is gradually converged near the global minimum value, and a relatively good inversion result can be obtained.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (16)
1. A full waveform inversion method is characterized in that an optimal inversion frequency group is used for forward modeling of a formation velocity model; the method for generating the earth surface receiving wave field by utilizing the original single shot data and the forward result of the stratum velocity model is characterized by comprising the following steps of:
generating a basis function of the original single shot data by using a spectrum continuation method;
recovering low-frequency data of the original single shot data by using the basis functions and the earth surface receiving wave field;
generating a frequency domain gradient field of a preset number of single shot data sets according to the low-frequency data;
inverting the stratum velocity model by using the frequency domain gradient field and the optimized step length;
and performing iterative operation, and forward modeling the inverted formation velocity model by using another optimal inversion frequency group to obtain a final inversion result of the formation velocity model.
2. The full waveform inversion method of claim 1, prior to forward modeling the formation velocity model using an optimal set of inversion frequencies, further comprising:
and integrally transmitting the speed model data to a GPU video memory.
3. The full waveform inversion method of claim 2, further comprising, prior to the transferring the velocity model data in its entirety to a GPU memory:
selecting an optimal inversion frequency by using a Sirgue frequency optimization strategy;
and grouping the optimal inversion frequencies according to a preset frequency interval.
4. The full waveform inversion method of claim 1, wherein forward modeling the velocity model using an optimal set of inversion frequencies comprises:
and performing seismic wave numerical simulation according to the optimal inversion frequency group and the stratum velocity model by using a 12-order space finite difference time domain propagation operator to generate a forward result of the stratum velocity model.
5. The full waveform inversion method of claim 1 wherein generating a surface receive wavefield using raw single shot data and forward modeling of a formation velocity model comprises:
and generating a frequency domain earth surface receiving wave field of an optimal inversion frequency group by using a discrete Fourier transform method according to the original single shot data and the forward modeling result of the stratum velocity model.
6. The full waveform inversion method of claim 1, wherein generating a frequency domain gradient field of a preset number of single shot data sets from the low frequency data comprises:
generating a residual wave field by using the low-frequency data and the original single-shot data;
a frequency domain gradient field is generated using the residual wave field.
7. The full waveform inversion method of claim 1, wherein said inverting the formation velocity model using the frequency domain gradient field and an optimization step size comprises:
calculating the sum of gradient fields of each frequency domain;
and inverting the stratum velocity model by using the sum of the gradient fields of the frequency domains and the optimized step length.
8. A full waveform inversion apparatus comprising: the forward modeling unit is used for forward modeling the formation velocity model by utilizing an optimal inversion frequency group; the earth surface receiving wave field generating unit is used for generating an earth surface receiving wave field by utilizing the original single shot data and the forward modeling result of the stratum velocity model; the method is characterized in that:
the base function generating unit is used for generating the base function of the original single shot data by using a spectrum continuation method;
the low-frequency data recovery unit is used for recovering the low-frequency data of the original single shot data by using the basis functions and the earth surface receiving wave field;
the frequency domain gradient field generating unit is used for generating frequency domain gradient fields of a preset number of single shot data sets according to the low-frequency data;
the inversion unit is used for inverting the stratum velocity model by utilizing the frequency domain gradient field and the optimized step length;
and the iteration unit is used for executing iteration operation, and performing forward modeling on the inverted stratum velocity model by using another optimal inversion frequency group to obtain a final inversion result of the stratum velocity model.
9. The full waveform inversion apparatus of claim 8, further comprising:
and the GPU transmission unit is used for integrally transmitting the speed model data to the GPU video memory.
10. The full waveform inversion apparatus of claim 8, further comprising:
the optimal inversion frequency selecting unit is used for selecting the optimal inversion frequency by utilizing a Sirgue frequency optimal selection strategy;
and the optimal inversion frequency grouping unit is used for grouping the optimal inversion frequencies according to a preset frequency interval.
11. The full waveform inversion apparatus of claim 8, wherein the forward unit is specifically configured to: and performing seismic wave numerical simulation according to the optimal inversion frequency group and the stratum velocity model by using a 12-order space finite difference time domain propagation operator to generate a forward result of the stratum velocity model.
12. The full waveform inversion apparatus of claim 8, wherein the surface receive wavefield generation unit is specifically configured to: and generating a frequency domain earth surface receiving wave field of an optimal inversion frequency group by using a discrete Fourier transform method according to the original single shot data and the forward modeling result of the stratum velocity model.
13. The full waveform inversion apparatus of claim 8, wherein the frequency domain gradient field generating unit comprises:
the residual wave field generating module is used for generating a residual wave field by utilizing the low-frequency data and the original single-shot data;
a frequency domain gradient field generation module for generating a frequency domain gradient field using the residual wave field.
14. The full waveform inversion apparatus of claim 8, wherein the inversion unit comprises:
the frequency domain gradient field summation module is used for calculating the sum of the frequency domain gradient fields;
and the stratum velocity model inversion module is used for inverting the stratum velocity model by using the sum of the gradient fields of all frequency domains and the optimized step length.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the full waveform inversion method of any one of claims 1 to 7 are implemented when the program is executed by the processor.
16. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, is adapted to carry out the steps of the full waveform inversion method as claimed in any one of the claims 1 to 7.
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| CN110595962B (en) * | 2019-09-29 | 2021-11-30 | 山东理工大学 | Non-negative TSVD dynamic light scattering inversion method for self-adaptive sampling of particle size distribution |
| CN114114392B (en) * | 2020-09-01 | 2023-11-28 | 中国石油天然气股份有限公司 | Layer speed model building method and device |
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| CN103135132B (en) * | 2013-01-15 | 2015-07-01 | 中国科学院地质与地球物理研究所 | Hybrid-domain full wave form inversion method of central processing unit (CPU)/graphics processing unit (GPU) synergetic parallel computing |
| MX356326B (en) * | 2013-10-28 | 2018-05-23 | Bp Corp North America Inc | Two stage seismic velocity model generation. |
| CN105445798B (en) * | 2014-08-21 | 2018-08-07 | 中国石油化工股份有限公司 | A kind of full waveform inversion method and system based on gradient processing |
| CN105353405B (en) * | 2014-08-21 | 2018-03-09 | 中国石油化工股份有限公司 | A kind of full waveform inversion method and system |
| CN106291680A (en) * | 2015-05-29 | 2017-01-04 | 中国石油化工股份有限公司 | A kind of data low frequency continuation method |
| CN106501852B (en) * | 2016-10-21 | 2018-06-08 | 中国科学院地质与地球物理研究所 | A kind of multiple dimensioned full waveform inversion method of three-dimensional acoustic wave equation arbitrarily-shaped domain and device |
| CN107589451B (en) * | 2017-09-05 | 2018-08-31 | 中国科学院地质与地球物理研究所 | Seismic full-field shape inversion method and device |
| CN109633742B (en) * | 2019-01-08 | 2020-03-27 | 中国科学院地质与地球物理研究所 | Full waveform inversion method and device |
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