CN113970789B - Full waveform inversion method and device, storage medium and electronic equipment - Google Patents
Full waveform inversion method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN113970789B CN113970789B CN202010722466.8A CN202010722466A CN113970789B CN 113970789 B CN113970789 B CN 113970789B CN 202010722466 A CN202010722466 A CN 202010722466A CN 113970789 B CN113970789 B CN 113970789B
- Authority
- CN
- China
- Prior art keywords
- target functional
- full waveform
- inversion
- waveform inversion
- solving
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 103
- 238000012545 processing Methods 0.000 claims abstract description 26
- 230000015572 biosynthetic process Effects 0.000 claims description 31
- 238000004088 simulation Methods 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 9
- 230000001131 transforming effect Effects 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 2
- 238000003384 imaging method Methods 0.000 abstract description 2
- 238000005755 formation reaction Methods 0.000 description 28
- 230000008569 process Effects 0.000 description 13
- 238000001514 detection method Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000009918 complex formation Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/303—Analysis for determining velocity profiles or travel times
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/282—Application of seismic models, synthetic seismograms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/622—Velocity, density or impedance
- G01V2210/6222—Velocity; travel time
Abstract
The application relates to the technical field of speed modeling and seismic imaging for oil and gas exploration and development, in particular to a full waveform inversion method, a full waveform inversion device, a storage medium and electronic equipment, and solves the problems of low resolution and insufficient precision of the full waveform inversion method when complex stratum is processed. The method comprises the following steps: acquiring actual seismic data and an initial velocity model; obtaining simulated seismic data according to an initial velocity model, and introducing a high-order total variation regularization term according to actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional; processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solution target functional; solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula; inputting the actual seismic data and the initial velocity model into a full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model.
Description
Technical Field
The application relates to the technical field of speed modeling and seismic imaging in oil and gas exploration and development, in particular to a full waveform inversion method, a full waveform inversion device, a storage medium and electronic equipment.
Background
The conventional full waveform inversion method takes an L2 norm (Euclidean norm) of errors of actual seismic data and simulated seismic data as a target functional, and solves an inversion result by minimizing the target functional, but the conventional target functional only contains a data fitting term and does not contain regularization constraint, the inversion is easy to fall into a local extremum problem, and an ideal high-resolution high-precision inversion result is not obtained, so that regularization constraint is needed to perform full waveform inversion.
In the related art, a relatively wide regularization method comprises a Tikhonov regularization method and a total variation regularization method, but an inversion result obtained by one method is still a smooth boundary, so that the requirement of high resolution is difficult to reach; the second method can improve inversion accuracy to some extent, but it is still difficult to obtain high-accuracy inversion results of boundary focusing for other complex formations.
Disclosure of Invention
Aiming at the technical problems that the resolution is not high and the precision is not enough when the full waveform inversion method is used for processing complex stratum, the application provides the full waveform inversion method, the full waveform inversion device, the storage medium and the electronic equipment.
In a first aspect, the present application provides a full waveform inversion method, the method comprising:
acquiring actual seismic data and an initial velocity model;
obtaining simulated seismic data according to the initial velocity model, and introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional;
processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solving target functional;
solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model.
According to an embodiment of the present application, optionally, in the full waveform inversion method, introducing a high-order full-variance regularization term according to the actual seismic data and the simulated seismic data, and constructing a high-order full-variance regularized target functional includes:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
Introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
wherein the full waveform inversion target functional comprises:
m represents a formation medium model parameter;
g (m) represents a forward modeling wavefield of seismic waves based on the formation medium model parameter m;
d represents observed seismic data.
According to an embodiment of the present application, optionally, in the full waveform inversion method, introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, including:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor;
T (l) representing a high-order total variation regularization operator;
l represents the total variation regularized differential order;
λ||T (l) m|| 1 representing a high order total variation regularization term.
According to an embodiment of the present application, optionally, in the full waveform inversion method, the higher-order total variation regularization term λ||t (l) m|| 1 A norm penalty term for the sparse regularization operator is included.
According to an embodiment of the present application, optionally, in the full waveform inversion method, the processing the Gao Jiequan variational regularized target functional through seismic wave forward modeling to obtain an inversion solution target functional includes:
Acquiring actual data and simulation data of a pre-stack seismic wave field;
matching the actual data with the simulation data through seismic wave forward modeling to obtain an inversion solving target functional;
the inversion solving the target functional includes:
wherein v represents the formation medium longitudinal wave velocity;
t represents the seismic wave propagation time;
x s representing shot coordinates, x r Representing the coordinates of the wave detection points;
u(t,x s ,x r the method comprises the steps of carrying out a first treatment on the surface of the v) represents a forward modeling wavefield of the seismic wave;
d obs (t,x s ,x r ) Representing an observed seismic wavefield.
According to an embodiment of the present application, optionally, in the full waveform inversion method, the solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula includes:
transforming the inversion solution target functional into the form of an equivalent constraint:
where η=t (l) v；
Converting the target functional transformed into an equivalent constrained form into an unconstrained optimized form:
replacing the first two items on the right of the equation in the inversion solving target functional in the optimized form with preset variables, and updating the inversion solving target functional:wherein J (v, eta) is a preset variable;
and processing the updated inversion solving target functional through an iterative algorithm to obtain a full waveform inversion iterative solving formula.
According to an embodiment of the present application, optionally, in the full waveform inversion method, inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula to obtain a full waveform inversion velocity model of the seismic wave includes:
inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula;
solving the full waveform inversion iteration solving formula to obtain a full waveform inversion speed model of each iteration;
comparing the target functional value calculated by each iteration with a preset threshold value, and obtaining a seismic wave full waveform inversion speed model when the target functional value is smaller than the preset threshold value;
wherein the iterative solution formula comprises:
k represents the number of iterations;
b represents the iteration solution lead-in.
In a second aspect, the present application provides a full waveform inversion apparatus, the apparatus comprising:
the acquisition module is used for acquiring actual seismic data and an initial velocity model;
the building module is used for obtaining simulated seismic data according to the initial velocity model, introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data, and building a high-order total variation regularization target functional;
The conversion module is used for processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solution target functional;
the solving module is used for solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
and the execution module is used for inputting the actual seismic data and the initial velocity model into the full waveform inversion solving formula to obtain a full waveform inversion velocity model of the seismic wave, and obtaining a full waveform inversion image of the formation velocity according to the full waveform inversion velocity model of the seismic wave.
According to an embodiment of the present application, optionally, in the full waveform inversion apparatus, the step of introducing a high-order full-variance regularization term according to the actual seismic data and the simulated seismic data to construct a high-order full-variance regularization target functional includes:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
wherein the full waveform inversion target functional comprises:
m represents a formation medium model parameter;
G (m) represents a forward modeling wavefield of seismic waves based on the formation medium model parameter m;
d represents observed seismic data.
According to an embodiment of the present application, optionally, in the full waveform inversion apparatus, introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, including:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor;
T (l) representing a high-order total variation regularization operator;
l represents the total variation regularized differential order;
λ||T (l) m|| 1 representing a high order total variation regularization term.
According to an embodiment of the present application, optionally, in the full waveform inversion apparatus, the high-order total variation regularization term λ||t (l) m|| 1 A norm penalty term for the sparse regularization operator is included.
According to an embodiment of the present application, optionally, in the full waveform inversion apparatus, the processing the Gao Jiequan variance regularized target functional through forward modeling to obtain an inversion solution target functional includes:
acquiring actual data and simulation data of a pre-stack seismic wave field;
Matching the actual data with the simulation data through seismic wave forward modeling to obtain an inversion solving target functional;
the inversion solving the target functional includes:
wherein v represents the formation medium longitudinal wave velocity;
t represents the seismic wave propagation time;
x s representing shot coordinates, x r Representing the coordinates of the wave detection points;
u(t,x s ,x r the method comprises the steps of carrying out a first treatment on the surface of the v) represents a forward modeling wavefield of the seismic wave;
d obs (t,x s ,x r ) Representing an observed seismic wavefield.
According to an embodiment of the present application, optionally, in the full waveform inversion apparatus, the solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula includes:
transforming the inversion solution target functional into the form of an equivalent constraint:
where η=t (l) v；
Converting the target functional transformed into an equivalent constrained form into an unconstrained optimized form:
replacing the first two items on the right of the equation in the inversion solving target functional in the optimized form with preset variables, and updating the inversion solving target functional:wherein J (v, eta) is a preset variable;
and processing the updated inversion solving target functional through an iterative algorithm to obtain a full waveform inversion iterative solving formula.
According to an embodiment of the present application, optionally, in the full waveform inversion apparatus, inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula to obtain a full waveform inversion velocity model of the seismic wave includes:
Inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula;
solving the full waveform inversion iteration solving formula to obtain a full waveform inversion speed model of each iteration;
comparing the target functional value calculated by each iteration with a preset threshold value, and obtaining a seismic wave full waveform inversion speed model when the target functional value is smaller than the preset threshold value;
wherein the iterative solution formula comprises:
k represents the number of iterations and b represents the iteration solution introducing variable.
In a third aspect, the present application provides a storage medium storing a computer program executable by one or more processors for implementing a full waveform inversion method as described above.
In a fourth aspect, the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the memory and the processor are communicatively connected to each other, and the computer program, when executed by the processor, performs the full waveform inversion method described above.
Compared with the prior art, the full waveform inversion method, the full waveform inversion device, the storage medium and the electronic equipment have the beneficial effects that:
1. Introducing a high-order total variation regularization term in the full waveform inversion process, and improving the sparsity of the model by using a high-order total variation regularization operator, so that the sparsity of an inversion result is increased, the full waveform inversion convergence speed for regularization constraint is higher, and the inversion efficiency is improved;
2. solving a target functional optimization solving problem through a high-efficiency target functional solving algorithm, and improving the depicting capability of inversion result stratum boundaries;
3. the full waveform inversion method can be used for establishing a high-resolution and high-precision stratum velocity model of boundary focusing.
Drawings
The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings:
FIG. 1 is an inverse image of formation velocity full waveform without regularization;
FIG. 2 is a schematic flow chart of a full waveform inversion method according to an embodiment of the present disclosure;
FIG. 3 is a formation velocity image obtained from an initial velocity model provided in an embodiment of the present application;
FIG. 4 is a full waveform inversion image of formation velocity obtained by a full waveform inversion method according to an embodiment of the present application;
fig. 5 is a connection block diagram of a full waveform inversion apparatus according to an embodiment of the present application.
In the drawings, like parts are given like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The following will describe embodiments of the present application in detail with reference to the drawings and examples, thereby how to apply technical means to the present application to solve technical problems, and realizing processes achieving corresponding technical effects can be fully understood and implemented accordingly. The embodiments and the features in the embodiments can be combined with each other under the condition of no conflict, and the formed technical schemes are all within the protection scope of the application.
The full waveform inversion method (Full Waveform Inversion) uses the amplitude and phase information of the seismic waves to obtain reliable model parameters by iteratively updating the model parameters to minimize the difference between the actual seismic records and the composite records. The full waveform inversion method has the characteristics of high resolution and high precision, can provide a high-precision speed field for prestack time and depth migration, and provides reliable speed data for lithology judgment and oil and gas reservoir identification, but the realization of the full waveform inversion method has a plurality of difficulties at present, such as instability of inversion problem solution; strong nonlinearity of inversion problem; the massive nature of the data processed and the high complexity of the time space, etc. Therefore, improving the efficiency and accuracy of full waveform inversion of seismic waves is a work of great theoretical significance and practical value. Forward numerical modeling is an important theoretical basis for full waveform inversion, and accuracy of the results affects later operations of seismic data processing.
Fig. 1 is an unreinforced full waveform inversion image of formation velocity, and as shown in fig. 1, in a conventional full waveform inversion method, an L2 norm of an error between actual seismic data and simulated seismic data is used as a target functional, and an inversion result is solved by minimizing the target functional, but the conventional target functional only contains a data fitting term, does not contain regularization constraint, and inversion is easy to fall into a local extremum problem, so that an ideal high-resolution high-precision inversion result is not obtained, and therefore regularization constraint is required to be carried out on full waveform inversion.
And the more widely used regularization methods in the current-stage seismic inversion comprise a Tikhonov regularization method (Ji Hong Rockwell regularization method) and a total variation regularization method.
The Tikhonov regularization method cannot obtain a good inversion result due to smooth transition in constraint, because for smooth parameters, the regularization term is small, but when the parameters are discontinuous or change is large, the regularization term becomes large, and at the moment, the Tikhonov regularization method cannot obtain a good inversion result and is even unstable. Therefore, the inversion result obtained by the Tikhonov regularization method is still a smooth boundary, and the requirement of high resolution is difficult to achieve.
And, during seismic inversion, the earth medium model parameters are often discontinuous, such as stratum velocity field, high-velocity difference stratum is possible to exist, the problem of velocity mutation can be solved by the full-variance regularization method, however, the full-variance regularization still has some limitations affecting the performance, the regularization penalty term utilizes the sparsity of the model, the sparsity is inapplicable to non-massive models, and the first-order difference of the complex-structure model is not sparse. Although the full variation regularization method can improve inversion accuracy to a certain extent, the full variation regularization method has a good effect on solving the high-speed difference geologic body, but high-accuracy inversion results of boundary focusing are still difficult to obtain for other complex stratum.
The application provides a full waveform inversion method, a full waveform inversion device, a storage medium and electronic equipment, and solves the technical problems that the full waveform inversion method in the related art is low in resolution and insufficient in precision when processing complex stratum.
Example 1
Fig. 2 is a flow chart of a full waveform inversion method provided in an embodiment of the present application, and as shown in fig. 2, the method includes:
step S110: acquiring observation seismic data and an initial velocity model;
Step S120: obtaining simulated seismic data according to the initial velocity model, and introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional;
step S130: processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solving target functional;
step S140: solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
step S150: inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model.
Further, the step of introducing a high-order total variation regularization term to construct a high-order total variation regularization target functional according to the actual seismic data and the simulated seismic data includes:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
Wherein the full waveform inversion target functional comprises:
m represents a formation medium model parameter;
g (m) represents a forward modeling wavefield of seismic waves based on the formation medium model parameter m;
d represents observed seismic data.
Specifically, m represents a stratum medium model parameter, which can be stratum medium speed or other elastic parameters;
g (m) is a seismic wave forward modeling wavefield based on the formation medium model parameter m, describing the seismic wave field propagation process.
The formation medium model is an existing model, and a modeling mode and related parameters thereof are not specifically described in this embodiment.
Further, introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, including:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor;
T (l) representing a high-order total variation regularization operator;
l represents the total variation regularized differential order;
λ||T (l) m|| 1 representing a high order total variation regularization term.
In particular, inWhen (I) >Representing hamiltonian, for m, hamiltonian is not requiredContinuous, only the hamiltonian is required to meet the bounded variation;
λ represents a regularization factor for controlling the balance between the data residual term and the high-order full-variational term, which may be set to spatially varying such that the high-order full-variational regularization term has different weights at different locations in the full waveform inversion model space.
Further, the high-order total variation regularization term lambda T (l) m|| 1 The norm penalty term of the sparse regularization operator is included, and the sparse regularization operator is L1 regularization (L1 regularization or lasso).
Specifically, the high-order total variation regularization term can better utilize the sparsity of the full waveform inversion model, thereby restricting the inversion process.
According to the full waveform inversion method, the high-order full-variation regularization term is introduced in the full waveform inversion process, the sparsity of the full waveform inversion model is increased by utilizing the high-order full-variation regularization term, the sparsity of the inversion result is increased, and the convergence speed and the inversion efficiency of full waveform inversion are improved.
Further, the processing the Gao Jiequan variational regularized target functional through forward seismic wave simulation to obtain an inversion solution target functional includes:
Acquiring actual data and simulation data of a pre-stack seismic wave field;
matching the actual data with the simulation data through seismic wave forward modeling to obtain an inversion solving target functional;
the inversion solving the target functional includes:
wherein v represents the formation medium longitudinal wave velocity;
t represents the seismic wave propagation time;
x s representing shot coordinates, x r Representing the coordinates of the wave detection points;
u(t,x s ,x r the method comprises the steps of carrying out a first treatment on the surface of the v) represents the forward simulated wavefield of the seismic wave；
d obs (t,x s ,x r ) Representing an observed seismic wavefield.
The method for obtaining inversion solving target functional by matching the actual data with the simulation data through seismic wave forward modeling comprises the following steps: and comparing the actual data with the simulation data through seismic wave forward modeling, and continuously correcting the Gao Jiequan variation regularization target functional to minimize errors of the actual data and the simulation data so as to enable a simulation result to be as close as possible to the actual data.
Specifically, take the time domain acoustic wave equation as an example. The time domain acoustic wave equation includes:
wherein v represents the formation medium longitudinal wave velocity;
u represents a seismic wavefield;
t represents the seismic wave propagation time;
x and z represent sampling point spatial locations;
s denotes a seismic source.
Acquiring actual data and simulation data of a pre-stack seismic wave field;
comparing the actual data with the simulation data through a forward modeling method to obtain an inversion solving target functional:
wherein x is s Representing shot coordinates, x r Representing the coordinates of the wave detection points;
u(t,x s ,x r the method comprises the steps of carrying out a first treatment on the surface of the v) represents a forward modeling wavefield of the seismic wave;
d obs representing an observed seismic wavefield.
Further, solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula, including:
transforming the inversion solution target functional into the form of an equivalent constraint:
where η=t (l) v；
Converting the target functional transformed into an equivalent constrained form into an unconstrained optimized form:
replacing the first two items on the right of the equation in the inversion solving target functional in the optimized form with preset variables, and updating the inversion solving target functional:wherein J (v, eta) is a preset variable;
and processing the updated inversion solving target functional through an iterative algorithm to obtain a full waveform inversion iterative solving formula.
Specifically, η has no practical meaning, is an intermediate parameter, and facilitates expression of a formula.
Further, the inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula to obtain a full waveform inversion velocity model of the seismic wave includes:
Inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula;
solving the full waveform inversion iteration solving formula to obtain a full waveform inversion speed model of each iteration;
comparing the target functional value calculated by each iteration with a preset threshold value, and obtaining a seismic wave full waveform inversion speed model when the target functional value is smaller than the preset threshold value;
wherein the iterative solution formula comprises:
k represents the number of iterations and b represents the iteration solution introducing variable.
Specifically, k is selected according to the simulation result of the full waveform inversion model;
the preset threshold value can be preset according to stratum actual data, and represents an inversion termination threshold value.
And comparing the target functional value calculated by each iteration with a preset threshold value, and when the iteration times reach a termination condition (namely the target functional value is smaller than the preset threshold value), obtaining an inversion result which is closest to an actual full waveform inversion speed model, wherein the full waveform inversion speed model of the iteration inversion result is the required full waveform inversion speed model of the seismic waves.
According to the method and the device, the target functional solving algorithm is introduced in the full waveform inversion process to solve the target functional, so that the depicting capacity of the inversion result stratum boundary is improved.
For example, acquiring actual seismic data and an initial velocity model, obtaining simulated seismic data according to the initial velocity model, and constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data:
introducing a high-order total variation regularization term lambda I T into the full waveform inversion target functional (l) m|| 1 Obtaining Gao Jiequan variational regularization target functional:
acquiring actual data and simulated data of a pre-stack seismic wave field, comparing the actual data with the simulated data through seismic wave forward modeling, and continuously correcting the Gao Jiequan variation regularized target functional to minimize errors of the actual data and the simulated data, thereby obtaining an inversion solving target functional:
transforming the inversion solution target functional into the form of an equivalent constraint:
then converting the inversion solving target functional converted into the equivalent constraint form into an unconstrained final form:
replacing the first two items on the right of the equation in the inversion solving target functional in the optimized form with preset variables, and updating the inversion solving target functional to obtain the following steps:
then, an iteration solution formula is obtained through an iteration algorithm:
inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula, and solving the full waveform inversion iteration solving formula to obtain a full waveform inversion velocity model of each iteration;
And comparing the target functional value calculated in each iteration with a preset threshold value, determining that the full waveform inversion speed model with the target functional value smaller than the preset threshold value is the required full waveform inversion speed model of the seismic waves, and obtaining a full waveform inversion image of the formation speed according to the full waveform inversion speed model of the seismic waves.
The full waveform inversion method provided by the application comprises the following steps: acquiring actual seismic data and an initial velocity model; obtaining simulated seismic data according to the initial velocity model, and introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional; processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solving target functional; solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula; inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model, so as to provide a reliable basis for lithology judgment and oil and gas reservoir identification of the stratum. According to the full waveform inversion method, a high-order full-variation regularization term is introduced in the full waveform inversion process, the sparsity of the model is improved by using the high-order full-variation regularization operator, the sparsity of an inversion result is increased, the full waveform inversion convergence speed for regularization constraint is higher, and the inversion efficiency is improved; and then solving the objective functional optimization solving problem through a high-efficiency objective functional solving algorithm, improving the capability of describing the inversion result stratum boundary, and obtaining a seismic wave full waveform inversion image through a full waveform inversion model, thereby providing a reliable basis for lithology judgment and reservoir identification.
Example two
The present embodiment uses an initial velocity model to test the full waveform inversion method of the present application.
The initial velocity model size includes: the transverse grid number and the longitudinal grid number are respectively as follows: transverse nx=201, longitudinal nz=116, and transverse and longitudinal grid spacing includes: transverse dx=20m, longitudinal dz=20m, and the speed range is 3364 m/s-7000 m/s.
The seismic source and the wave detectors are positioned on the ground surface, 40 cannons of data are shared, the cannon spacing is 100m, and the wave detector spacing is not 20m. Scalar wave forward modeling is carried out by adopting a high-order finite difference method, the theoretical wavelet is a Rake wavelet with a main frequency of 20Hz, the seismic recording time is 1.5s, and the time sampling interval is 0.001s.
Fig. 3 is a stratum velocity image obtained by an initial velocity model according to an embodiment of the present application, as shown in fig. 3, when the full waveform inversion method of the present application is not used, the boundaries of the full waveform inversion image of the seismic wave obtained by the initial velocity model are smooth and fuzzy, and the resolution is low.
Fig. 4 is a full waveform inversion image of formation velocity obtained by the full waveform inversion method according to the embodiment of the present application, and as shown in fig. 4, after the full waveform inversion method of the present application is adopted in the initial velocity model, the formation boundary of the obtained full waveform inversion image of formation velocity is characterized clearly, and the boundary is focused, with high resolution and high precision.
In this embodiment, the specific embodiment process of the method steps of the full waveform inversion method for testing the initial velocity model can be referred to as embodiment one, and this embodiment is not repeated here.
Example III
Fig. 5 is a connection block diagram of a full waveform inversion apparatus 200 according to an embodiment of the present application, and as shown in fig. 5, the full waveform inversion apparatus 200 includes:
an acquisition module 201, configured to acquire actual seismic data and an initial velocity model;
the construction module 202 is configured to obtain simulated seismic data according to the initial velocity model, introduce a high-order total variation regularization term according to the actual seismic data and the simulated seismic data, and construct a high-order total variation regularization target functional;
the conversion module 203 is configured to process the Gao Jiequan variance regularized target functional through seismic wave forward modeling to obtain an inversion solution target functional;
the solving module 204 is configured to solve the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
and the execution module 205 is configured to input the actual seismic data and the initial velocity model into the full waveform inversion solution formula to obtain a full waveform inversion velocity model of the seismic wave, and obtain a full waveform inversion image of the formation velocity according to the full waveform inversion velocity model of the seismic wave.
Further, the step of introducing a high-order total variation regularization term to construct a high-order total variation regularization target functional according to the actual seismic data and the simulated seismic data includes:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
wherein the full waveform inversion target functional comprises:
m represents a formation medium model parameter;
g (m) represents a forward modeling wavefield of seismic waves based on the formation medium model parameter m;
d represents observed seismic data.
Further, introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, including:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor;
T (l) representing a high-order total variation regularization operator;
l represents the total variation regularized differential order;
λ||T (l) m|| 1 representing a high order total variation regularization term.
Further, the high-order total variation regularization term lambda T (l) m|| 1 A norm penalty term for the sparse regularization operator is included.
Further, the processing the Gao Jiequan variance regularized target functional through seismic wave forward modeling to obtain an inversion solution target functional includes:
acquiring actual data and simulation data of a pre-stack seismic wave field;
matching the actual data with the simulation data through seismic wave forward modeling to obtain an inversion solving target functional;
the inversion solving the target functional includes:
wherein v represents the formation medium longitudinal wave velocity;
t represents the seismic wave propagation time;
x s representing shot coordinates, x r Representing the coordinates of the wave detection points;
u(t,x s ,x r the method comprises the steps of carrying out a first treatment on the surface of the v) represents a forward modeling wavefield of the seismic wave;
d obs (t,x s ,x r ) Representing an observed seismic wavefield.
Further, the solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula includes:
transforming the inversion solution target functional into the form of an equivalent constraint:
where η=t (l) v；
Converting the target functional transformed into an equivalent constrained form into an unconstrained optimized form:
replacing the first two items on the right of the equation in the inversion solving target functional in the optimized form with preset variables, and updating the inversion solving target functional: Wherein J (v, eta) is a preset variable;
and processing the updated inversion solving target functional through an iterative algorithm to obtain a full waveform inversion iterative solving formula.
Further, the inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula to obtain a full waveform inversion velocity model of the seismic wave includes:
inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula;
solving the full waveform inversion iteration solving formula to obtain a full waveform inversion speed model of each iteration;
comparing the target functional value calculated by each iteration with a preset threshold value, and obtaining a seismic wave full waveform inversion speed model when the target functional value is smaller than the preset threshold value;
wherein the iterative solution formula comprises:
k represents the number of iterations;
b represents the iteration solution introduced variable.
In this embodiment, the specific embodiment of the method steps can be referred to as embodiment one, and this embodiment is not repeated here.
Example IV
The present embodiment also provides a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which computer program, when executed by a processor, can implement the method steps as in the first embodiment.
Wherein the method steps executed by the processor include:
acquiring actual seismic data and an initial velocity model;
obtaining simulated seismic data according to the initial velocity model, and introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional;
processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solving target functional;
solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model.
Further, the step of introducing a high-order total variation regularization term to construct a high-order total variation regularization target functional according to the actual seismic data and the simulated seismic data includes:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
Wherein the full waveform inversion target functional comprises:
m represents a formation medium model parameter;
g (m) represents a forward modeling wavefield of seismic waves based on the formation medium model parameter m;
d represents observed seismic data.
Further, introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, including:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor;
T (l) representing a high-order total variation regularization operator;
l represents the total variation regularized differential order;
λ||T (l) m|| 1 representing a high order total variation regularization term.
Further, the high-order total variation regularization term lambda T (l) m|| 1 A norm penalty term for the sparse regularization operator is included.
Further, the processing the Gao Jiequan variance regularized target functional through seismic wave forward modeling to obtain an inversion solution target functional includes:
acquiring actual data and simulation data of a pre-stack seismic wave field;
matching the actual data with the simulation data through seismic wave forward modeling to obtain an inversion solving target functional;
The inversion solving the target functional includes:
wherein v represents the formation medium longitudinal wave velocity;
t represents the seismic wave propagation time;
x s representing shot coordinates, x r Representing the coordinates of the wave detection points;
u(t,x s ,x r the method comprises the steps of carrying out a first treatment on the surface of the v) represents a forward modeling wavefield of the seismic wave;
d obs (t,x s ,x r ) Representing an observed seismic wavefield.
Further, the solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula includes:
transforming the inversion solution target functional into the form of an equivalent constraint:
where η=t (l) v；
Converting the target functional transformed into an equivalent constrained form into an unconstrained optimized form:
replacing the first two items on the right of the equation in the inversion solving target functional in the optimized form with preset variables, and updating the inversion solving target functional:wherein J (v, eta) is a preset variable;
and processing the updated inversion solving target functional through an iterative algorithm to obtain a full waveform inversion iterative solving formula.
Further, the inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula to obtain a full waveform inversion velocity model of the seismic wave includes:
inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula;
Solving the full waveform inversion iteration solving formula to obtain a full waveform inversion speed model of each iteration;
comparing the target functional value calculated by each iteration with a preset threshold value, and obtaining a seismic wave full waveform inversion speed model when the target functional value is smaller than the preset threshold value;
wherein the iterative solution formula comprises:
k represents the number of iterations;
b represents the iteration solution introduced variable.
In this embodiment, the specific embodiment of the method steps can be referred to as embodiment one, and this embodiment is not repeated here.
Example five
An electronic device provided in an embodiment of the present application may include: a processor and a memory, said memory having stored thereon a computer program, said memory and said processor being communicatively coupled to each other, which when executed by said processor performs the full waveform inversion method as described in embodiment one.
The processor may be an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a digital signal processor (Digital Signal Processor, abbreviated as DSP), a digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), a programmable logic device (Programmable Logic Device, abbreviated as PLD), a field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), a controller, a microcontroller, a microprocessor, or other electronic component implementation for performing the full waveform inversion method in the above embodiment.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In summary, the full waveform inversion method, device, storage medium and electronic equipment provided by the application include: acquiring actual seismic data and an initial velocity model; obtaining simulated seismic data according to the initial velocity model, and introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional; processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solving target functional; solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula; inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model.
According to the full waveform inversion method, a high-order full-variation regularization term is introduced in the full waveform inversion process, the sparsity of the model is improved by using the high-order full-variation regularization operator, the sparsity of an inversion result is increased, the full waveform inversion convergence speed for regularization constraint is higher, and the inversion efficiency is improved; then solving a target functional optimization solving problem through a high-efficiency target functional solving algorithm, and improving the depicting capability of inversion result stratum boundaries; the full waveform inversion method can establish a high-resolution and high-precision stratum velocity model with boundary focusing, and obtain full waveform inversion images of the earthquake waves through the full waveform inversion model, so that reliable basis is provided for lithology judgment and oil and gas reservoir identification of stratum.
In the embodiments provided in the present application, it should be understood that the disclosed method may be implemented in other manners. The method embodiments described above are merely illustrative.
It should be noted that, in this document, 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, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Although the embodiments disclosed in the present application are described above, the above description is only for the convenience of understanding the present application, and is not intended to limit the present application. Any person skilled in the art to which this application pertains will be able to make any modifications and variations in form and detail of implementation without departing from the spirit and scope of the disclosure, but the scope of the patent claims of this application shall be subject to the scope of the claims that follow.
Claims (8)
1. A full waveform inversion method, the method comprising:
acquiring actual seismic data and an initial velocity model;
obtaining simulated seismic data according to the initial velocity model, and introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data to construct a high-order total variation regularization target functional;
processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solving target functional;
solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
inputting the actual seismic data and the initial velocity model into the full waveform inversion iteration solving formula to obtain a seismic wave full waveform inversion velocity model, and obtaining a stratum velocity full waveform inversion image according to the seismic wave full waveform inversion velocity model;
The step of introducing a high-order total variation regularization term to construct a high-order total variation regularization target functional according to the actual seismic data and the simulated seismic data, comprising:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
wherein the full waveform inversion target functional comprises:
m represents a stratum medium model parameter, G (m) represents a seismic wave forward modeling wave field based on the stratum medium model parameter m, and d represents observed seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, wherein the method comprises the following steps of:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor, T (l) Represents a high-order full-variance regularization operator, l represents a full-variance regularization difference order, and lambda T (l) m|| 1 Representing a high order total variation regularization term.
2. The method of claim 1, wherein the higher order total variation regularization term λ||t (l) m|| 1 A norm penalty term for the sparse regularization operator is included.
3. The method of claim 2, wherein the processing the Gao Jiequan variational regularized target functional by seismic wave forward modeling to obtain an inversion solution target functional comprises:
acquiring actual data and simulation data of a pre-stack seismic wave field;
matching the actual data with the simulation data through seismic wave forward modeling to obtain an inversion solving target functional;
the inversion solving the target functional includes:
wherein v represents the longitudinal wave velocity of stratum medium, t represents the propagation time of seismic wave, and x s Representing shot coordinates, x r Representing the coordinates of the detector, u (t, x s ,x r The method comprises the steps of carrying out a first treatment on the surface of the v) represents the forward simulated wave field of the seismic wave, d obs (t,x s ,x r ) Representing an observed seismic wavefield.
4. A method according to claim 3, wherein said solving the inversion solution target functional to obtain a full waveform inversion iterative solution formula comprises:
transforming the inversion solution target functional into the form of an equivalent constraint:
where η=t (l) v；
Converting the target functional transformed into an equivalent constrained form into an unconstrained optimized form:
solving the inversion of the optimized form for the equal in the target functional The first two items on the right are replaced by preset variables, and the inversion solving target functional is updated:wherein J (v, eta) is a preset variable;
and processing the updated inversion solving target functional through an iterative algorithm to obtain a full waveform inversion iterative solving formula.
5. The method of claim 4, wherein said inputting said actual seismic data and said initial velocity model into said full waveform inversion iterative solution formula yields a full waveform seismic inversion velocity model, comprising:
inputting the actual seismic data and the initial velocity model into the full waveform inversion iterative solution formula;
solving the full waveform inversion iteration solving formula to obtain a full waveform inversion speed model of each iteration;
comparing the target functional value calculated by each iteration with a preset threshold value, and obtaining a seismic wave full waveform inversion speed model when the target functional value is smaller than the preset threshold value;
wherein the iterative solution formula comprises:
k represents the number of iterations and b represents the iteration solution introducing variable.
6. A full waveform inversion apparatus, the apparatus comprising:
the acquisition module is used for acquiring actual seismic data and an initial velocity model;
The building module is used for obtaining simulated seismic data according to the initial velocity model, introducing a high-order total variation regularization term according to the actual seismic data and the simulated seismic data, and building a high-order total variation regularization target functional;
the conversion module is used for processing the Gao Jiequan variation regularization target functional through seismic wave forward modeling to obtain an inversion solution target functional;
the solving module is used for solving the inversion solving target functional to obtain a full waveform inversion iteration solving formula;
the execution module is used for inputting the actual seismic data and the initial velocity model into the full waveform inversion solving formula to obtain a full waveform inversion velocity model of the seismic wave, and obtaining a full waveform inversion image of the formation velocity according to the full waveform inversion velocity model of the seismic wave;
the solving module specifically comprises:
constructing a full-waveform inversion target functional according to the actual seismic data and the simulated seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional;
wherein the full waveform inversion target functional comprises:
m represents a formation medium model parameter;
G (m) represents a forward modeling wavefield of seismic waves based on the formation medium model parameter m;
d represents observed seismic data;
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularized target functional, wherein the method comprises the following steps of:
introducing a high-order total variation regularization term into the full waveform inversion target functional to obtain a Gao Jiequan variation regularization target functional, wherein the Gao Jiequan variation regularization target functional comprises:
wherein λ represents a regularization factor;
T (l) representing a high-order total variation regularization operator;
l represents the total variation regularized differential order;
λ||T (l) m|| 1 representing a high order total variation regularization.
7. A storage medium storing a computer program executable by one or more processors for implementing the full waveform inversion method of any one of claims 1 to 5.
8. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, said memory and said processor being communicatively coupled to each other, which computer program, when executed by said processor, performs the full waveform inversion method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010722466.8A CN113970789B (en) | 2020-07-24 | 2020-07-24 | Full waveform inversion method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010722466.8A CN113970789B (en) | 2020-07-24 | 2020-07-24 | Full waveform inversion method and device, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113970789A CN113970789A (en) | 2022-01-25 |
| CN113970789B true CN113970789B (en) | 2024-04-09 |
Family
ID=79585708
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010722466.8A Active CN113970789B (en) | 2020-07-24 | 2020-07-24 | Full waveform inversion method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113970789B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117687083A (en) * | 2022-09-09 | 2024-03-12 | 中国石油化工股份有限公司 | Full waveform inversion method, full waveform inversion equipment and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102928870A (en) * | 2012-09-21 | 2013-02-13 | 中国石油天然气股份有限公司勘探开发研究院廊坊分院 | Nonlinear earthquake pre-stack elastic parameter inverting method based on regularization |
| JP2014174128A (en) * | 2013-03-13 | 2014-09-22 | Toyama Prefecture | Predictive information transmitting system and predictive information transmitting method for subduction-zone earthquakes |
| CN105467451A (en) * | 2016-01-13 | 2016-04-06 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Seismic reflection coefficient inversion method based on total variation minimized constraint |
| CN106842295A (en) * | 2015-12-04 | 2017-06-13 | 中国石油化工股份有限公司 | The waveform inversion method of logging information constrained |
| CN107422379A (en) * | 2017-07-27 | 2017-12-01 | 中国海洋石油总公司 | Multiple dimensioned seismic full-field shape inversion method based on local auto-adaptive convexification method |
| CN108415072A (en) * | 2018-01-25 | 2018-08-17 | 北京奥能恒业能源技术有限公司 | The Two-parameter Regularization Method of seismic data inverting |
| CN109557540A (en) * | 2018-10-29 | 2019-04-02 | 西安电子科技大学 | Total variation regularization relevance imaging method based on target scattering coefficient nonnegativity restrictions |
| CN110244351A (en) * | 2019-04-22 | 2019-09-17 | 西安石油大学 | A kind of Uniform Construction inversion method of different constraint Geophysical Inverse Problems |
| CN110857999A (en) * | 2018-08-24 | 2020-03-03 | 中国石油化工股份有限公司 | High-precision wave impedance inversion method and system based on full waveform inversion |
| CN111290016A (en) * | 2020-03-04 | 2020-06-16 | 中国石油大学(华东) | Full waveform speed modeling inversion method based on geological model constraint |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8515721B2 (en) * | 2009-10-01 | 2013-08-20 | Schlumberger Technology Corporation | Method for integrated inversion determination of rock and fluid properties of earth formations |
| CA2810960A1 (en) * | 2010-09-28 | 2012-04-05 | Rene-Edouard Andre Michel Plessix | Earth model estimation through an acoustic full waveform inversion of seismic data |
| US9075159B2 (en) * | 2011-06-08 | 2015-07-07 | Chevron U.S.A., Inc. | System and method for seismic data inversion |
| WO2019071504A1 (en) * | 2017-10-12 | 2019-04-18 | 南方科技大学 | Two-point ray tracing based seismic travel time tomography inversion method |
| US11041971B2 (en) * | 2018-07-02 | 2021-06-22 | Exxonmobil Upstream Research Company | Full wavefield inversion with an image-gather-flatness constraint |
-
2020
- 2020-07-24 CN CN202010722466.8A patent/CN113970789B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102928870A (en) * | 2012-09-21 | 2013-02-13 | 中国石油天然气股份有限公司勘探开发研究院廊坊分院 | Nonlinear earthquake pre-stack elastic parameter inverting method based on regularization |
| JP2014174128A (en) * | 2013-03-13 | 2014-09-22 | Toyama Prefecture | Predictive information transmitting system and predictive information transmitting method for subduction-zone earthquakes |
| CN106842295A (en) * | 2015-12-04 | 2017-06-13 | 中国石油化工股份有限公司 | The waveform inversion method of logging information constrained |
| CN105467451A (en) * | 2016-01-13 | 2016-04-06 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Seismic reflection coefficient inversion method based on total variation minimized constraint |
| CN107422379A (en) * | 2017-07-27 | 2017-12-01 | 中国海洋石油总公司 | Multiple dimensioned seismic full-field shape inversion method based on local auto-adaptive convexification method |
| CN108415072A (en) * | 2018-01-25 | 2018-08-17 | 北京奥能恒业能源技术有限公司 | The Two-parameter Regularization Method of seismic data inverting |
| CN110857999A (en) * | 2018-08-24 | 2020-03-03 | 中国石油化工股份有限公司 | High-precision wave impedance inversion method and system based on full waveform inversion |
| CN109557540A (en) * | 2018-10-29 | 2019-04-02 | 西安电子科技大学 | Total variation regularization relevance imaging method based on target scattering coefficient nonnegativity restrictions |
| CN110244351A (en) * | 2019-04-22 | 2019-09-17 | 西安石油大学 | A kind of Uniform Construction inversion method of different constraint Geophysical Inverse Problems |
| CN111290016A (en) * | 2020-03-04 | 2020-06-16 | 中国石油大学(华东) | Full waveform speed modeling inversion method based on geological model constraint |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113970789A (en) | 2022-01-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3171203B1 (en) | Adaptive ensemble-based method and device for highly-nonlinear problems | |
| RU2693495C1 (en) | Complete wave field inversion with quality factor compensation | |
| Shin et al. | A comparison between the behavior of objective functions for waveform inversion in the frequency and Laplace domains | |
| WO2018129844A1 (en) | Seismic diffracted wave separation method and device | |
| CN112925012B (en) | Seismic full-waveform inversion method and device | |
| WO2011139413A1 (en) | Artifact reduction in method of iterative inversion of geophysical data | |
| US10310117B2 (en) | Efficient seismic attribute gather generation with data synthesis and expectation method | |
| WO2020009752A1 (en) | Full wavefield inversion with an image-gather-flatness constraint | |
| CN110927791B (en) | Method and device for predicting fluid by utilizing seismic data based on deep learning | |
| CN113970789B (en) | Full waveform inversion method and device, storage medium and electronic equipment | |
| CN107942374A (en) | Diffracted wave field extracting method and device | |
| Waheed et al. | A holistic approach to computing first-arrival traveltimes using neural networks | |
| CN111273346B (en) | Method, device, computer equipment and readable storage medium for removing deposition background | |
| Abbasi et al. | Automatic nonhyperbolic velocity analysis by polynomial chaos expansion | |
| Zhang* et al. | Genetic algorithm full waveform inversion for microseismic location | |
| US20230140656A1 (en) | Method and system for determining seismic processing parameters using machine learning | |
| Alali et al. | Integrating u-nets into a multi-scale waveform inversion for salt body building | |
| Yang et al. | Wasserstein distance-based full-waveform inversion with a regularizer powered by learned gradient | |
| CN110794469B (en) | Gravity inversion method based on minimum geological feature unit constraint | |
| Li et al. | A comprehensive review of seismic inversion based on neural networks | |
| Zhang | Ensemble methods of data assimilation in porous media flow for non-Gaussian prior probability density | |
| CN114185090B (en) | Lithology and elastic parameter synchronous inversion method and device, electronic equipment and medium | |
| Fang | Source estimation and uncertainty quantification for wave-equation based seismic imaging and inversion | |
| CN113219534B (en) | Pre-stack depth migration speed quality control method and device, medium and electronic equipment | |
| Decker et al. | A variational approach for picking optimal surfaces from semblance-like panels |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |