A multi-channel inversion method and system based on an improved genetic algorithm

Abstract

The application discloses a multi-channel inversion method and system based on an improved genetic algorithm, and relates to the technical field of geophysical inversion.The application adopts the improved genetic algorithm to realize inversion of seismic records when inverting the seismic records, adjusts fitness values of the inversion process, arranges fitness function values of each individual in descending order, increases the fitness values of the individuals corresponding to the first quarter of the fitness function values by a first preset value, reduces the fitness function values of the individuals corresponding to the last quarter of the fitness function values by a second preset value, and the fitness values of the individuals corresponding to the middle quarter of the fitness function values remain unchanged; then, selection and heredity are carried out by using the new fitness function values; thus, the excellent individuals cannot be eliminated due to the small number, and the poor individuals can be more easily eliminated due to the reduced fitness values, so that the genetic algorithm can better play the optimization effect, and the genetic algorithm can converge more quickly.

Description

Technical Field

[0001] The present invention relates to the field of geophysical inversion technology, and in particular to a multi-channel inversion method and system based on an improved genetic algorithm. Background Art

[0002] Seismic exploration has long been the most important and effective method for detecting oil and gas reservoirs in geophysical exploration. Seismic exploration technology uses the response of seismic waves to infer the properties of subsurface media and stratigraphic structure. The main process is as follows: Seismic waves are generated by artificial means such as industrial blasting or pile driving. These waves propagate downward through the strata and are reflected when they encounter elastic interfaces. Precision instruments on the surface then record the vibrations at various sampling points. Analysis of the characteristics of these recordings can help locate oil and gas reservoirs, predict their types, and determine lithologic characteristics. Seismic inversion is one of the key methods for reservoir characterization using seismic technology. This technique uses surface seismic data, constructs an objective function based on previous experience, and uses drilling and logging data as constraints to invert and image the subsurface structure and physical properties of the subsurface media. Seismic inversion has promoted the development of seismic lithology and fluid analysis techniques and provided a crucial basis for oil and gas reservoir exploration and development.

[0003] In the field of petroleum physics exploration, inversion techniques can be used to construct elastic parameter models of subsurface media from observed geophysical data using mathematical and physical relationships. Prestack seismic data, compared to post-stack seismic data, offers higher fidelity and more information on elastic parameters, and has garnered widespread attention and sustained development.

[0004] The classification of seismic inversion methods is numerous and complex. People have proposed many classification methods based on different input data, different problems to be solved and different implementation methods.

[0005] The traditional pre-stack inversion method is mainly AVO (Amplitude Varies with Offset) inversion, which uses Zoeppritz equation and various linear approximation formulas to describe the relationship between reflection coefficient or seismic wave amplitude and offset and incidence angle to invert the elastic parameters on both sides of the interface. Forward modeling is the basis of inversion, however, Zoeppritz equation is based on the assumption of single interface, which means that multiple waves, transmission loss, thin layer interference effect and other various propagation effects cannot be considered, on the one hand, causing the difference between the forward wave field and the actual wave field, on the other hand, causing the lack of inversion information and even the instability of inversion. The reflectivity method is a method that can efficiently and accurately forward model the seismic wave field of layered medium, which considers the full wave field seismic record containing various propagation effects in the forward modeling process, and can simulate part of the wave field separately according to the needs. Compared with the simulation of pre-stack seismic record by using Zoeppritz equation, the reflectivity method can establish a more reasonable relationship between model parameters and pre-stack seismic record, so it is suitable for accurate pre-stack seismic record synthesis.

[0006] The prior art generally uses the method of constructing an elastic parameter model to realize multi-channel pre-stack wave inversion, for example, Chinese invention patent CN115480311A discloses "a multi-channel pre-stack waveform inversion method and device", which comprises the following steps: step 1, obtaining an angle gather based on an elastic wave equation; step 2, obtaining a multi-channel inversion objective function based on the angle gather; step 3, removing the correlation between the elastic parameters; step 4, selecting an initial model of the elastic parameters, obtaining the perturbation of the elastic parameters relative to the initial model based on the multi-channel inversion objective function after removing the correlation between the elastic parameters and the initial model of the elastic parameters; step 5, repeatedly iterating step 4 to obtain the multi-channel pre-stack waveform inversion result. Although the patent solves the problems of large inversion error and insufficient inversion resolution to some extent by constructing an elastic model and iterating, the reduction of inversion effect is not obvious when multi-channel inversion is performed, therefore, the prior art urgently needs a multi-channel inversion method and system that can reduce inversion error. SUMMARY

[0007] The present application aims to provide a multi-channel inversion method and system based on improved genetic algorithm, which significantly improves the inversion effect when multi-channel inversion is performed, and further improves the inversion accuracy. In order to achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0008] The present application provides a multi-channel inversion method based on improved genetic algorithm, which comprises the following steps:

[0009] Step S1, synthesizing multi-channel seismic records by using the reflectivity method;

[0010] Step S2, obtaining a final velocity model based on the multi-channel seismic records by pre-stack waveform inversion using an improved genetic algorithm; comprising:

[0011] Step S2.1, determining initial values ​​of inversion parameters using well logging data;

[0012] Step S2.2, encoding the inversion parameters to generate an initial model population;

[0013] Step S2.3: Given measured data, the measured data is compared with the multi-channel seismic record to form individuals, and the fitness function value of the objective function of each individual is calculated;

[0014] Step S2.4: Arrange the fitness function values ​​of each individual in the initial model population in descending order, and adjust the fitness function values;

[0015] Step S2.5: Based on the adjusted fitness function value, perform a genetic operation on each individual in the initial model population to obtain a new population;

[0016] Step S2.6: Determine whether the new population meets the accuracy requirement. If so, output the final velocity model. If not, continue iterating until the accuracy requirement is met.

[0017] Furthermore, the step S2.4 includes:

[0018] The fitness function value of each individual in the initial model population is arranged in descending order, and then the fitness value of the individuals corresponding to the fitness function values ​​in the first quarter is increased by a first preset value, and the fitness function value of the individuals corresponding to the fitness function values ​​in the second quarter is reduced by a second preset value. The fitness value of the individuals corresponding to the fitness function values ​​in the middle remains unchanged, and finally a new fitness function value is obtained; wherein the first preset value and the second preset value are values ​​set in advance.

[0019] Furthermore, the step S1 includes:

[0020] Step S1.1, calculating the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient, and the shear wave transmission coefficient in the frequency domain;

[0021] Step S1.2, multiplying the longitudinal wave reflection coefficient by the source wavelet to obtain a reflection spectrum;

[0022] Step S1.3, performing an inverse Fourier transform on the reflection wave spectrum to obtain a reflection wave record in the time-slowness domain;

[0023] Step S1.4, changing the incident angle of the reflected wave to a ray parameter to obtain a seismic record in the τ-p domain;

[0024] Step S1.5, inverse transforming the seismic record of the tau-p domain to obtain a reflection wave record in the x-t domain, which is a multi-channel seismic record synthesized by using the reflectivity method.

[0025] Further, the step S1.1 comprises:

[0026] Firstly, a layered stratum model for studying the propagation of P and S waves after the plane wave is incident on the stratum is designed; then the displacement and stress relationship on the contact surface of each layer in the layered stratum model is obtained through equations; finally, the reflection coefficient is obtained according to the displacement and stress relationship on the contact surface of each layer; the reflection coefficient comprises a P wave reflection coefficient, an S wave reflection coefficient, a P wave transmission coefficient and an S wave transmission coefficient.

[0027] Further, in the step S1.2, the reflection spectrum is a reflection spectrum in the frequency-slowness domain.

[0028] Further, the step S2.3 comprises:

[0029] Step S2.3.1, setting a target function of each individual in the initial model population, the target function being used for representing the error between the measured data and the synthesized seismic record; the expression of the target function being:

[0030] Objvalue = norm (c-data) ;

[0031] In the formula, Objvalue represents the target function, norm represents the norm value of the error used by the target function, c represents the synthesized seismic record obtained in the step S1, and data represents the measured data.

[0032] Step S2.3.2, calculating the fitness function value of the target function of each individual in the initial model population; the expression of the fitness function being:

[0033] Fitt = 1 / (Objvalue*Objvalue+0.001) ;

[0034] In the formula, Fitt represents the fitness function.

[0035] Further, in the step S2.5, the genetic operation is cross and mutation by using a crossover operator and a mutation operator.

[0036] Further, in the step S2.6, the accuracy requirement is to meet the signal-to-noise ratio and error convergence requirement of the seismic data.

[0037] The application further provides a multi-channel inversion system based on the improved genetic algorithm, the system comprising a synthesis unit and an inversion unit; wherein,

[0038] A synthesis unit for synthesizing multi-channel seismic records using a reflectivity method;

[0039] An inversion unit is used to obtain a final velocity model based on the seismic record and prestack waveform inversion using an improved genetic algorithm; and comprises:

[0040] An acquisition module, which uses the well logging data to establish an initial velocity model, and performs a forward modeling operation based on the initial velocity model to obtain synthetic seismic records;

[0041] A generation module, based on the initial velocity model, determines parameters of the improved genetic algorithm, encodes the parameters, and generates an initial model population;

[0042] A first calculation module is configured to compare the measured data with the synthetic seismic record given the measured data, and calculate the fitness function value of the objective function of each individual in the initial model population;

[0043] An adjustment module arranges the fitness function value of each individual in the initial model population in descending order and adjusts the fitness function value at the same time;

[0044] an operation module, performing a genetic operation on each individual in the initial model population based on the adjusted fitness function value to obtain a new population;

[0045] The judgment module judges whether the new population meets the accuracy requirement. If so, the final velocity model is output; if not, the iteration is continued until the accuracy requirement is met.

[0046] Furthermore, the synthesis unit includes: a second calculation module, a first acquisition module, a second acquisition module, a third acquisition module and a fourth acquisition module; wherein,

[0047] The second calculation module is used to calculate the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient and the shear wave transmission coefficient in the frequency domain;

[0048] A first obtaining module is used to multiply the longitudinal wave reflection coefficient by the source wavelet to obtain a reflection wave spectrum;

[0049] A second acquisition module is used to perform inverse Fourier transform on the reflection wave spectrum to obtain a reflection wave record in the time-slowness domain;

[0050] a third acquisition module, configured to change the incident angle of the reflected wave into a ray parameter to obtain a seismic record in the τ-p domain;

[0051] The fourth acquisition module is used to inversely transform the seismic record in the τ-p domain to obtain the reflection wave record in the xt domain, where the reflection wave record in the xt domain is the multi-channel seismic record synthesized by the reflectivity method.

[0052] The technical effects and advantages of the present invention are as follows:

[0053] When inverting earthquake records, the present invention adopts an improved genetic algorithm to realize the inversion of earthquake records, that is, the fitness value is adjusted, the fitness function value of each individual is arranged in descending order, and then the fitness value of the individuals corresponding to the fitness function values ​​of the first quarter is increased by a first preset value, and the fitness function value of the individuals corresponding to the fitness function values ​​of the second quarter is reduced by a second preset value, and the fitness value of the individuals corresponding to the middle fitness function values ​​remains unchanged; then the new fitness function value is adopted for selection and inheritance; thereby, excellent individuals will not be eliminated due to too small a number, and poor individuals will be more easily eliminated due to the reduction of fitness values, so that the genetic algorithm can better play the optimization effect and also make the genetic algorithm converge faster.

[0054] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flow chart of a multi-channel inversion method based on an improved genetic algorithm provided by the present invention;

[0057] Figure 2 A flow chart of obtaining a final velocity model by prestack waveform inversion based on an improved genetic algorithm provided by the present invention;

[0058] Figure 3 A comparison chart of the original genetic algorithm inversion and the improved genetic algorithm inversion provided by the present invention;

[0059] Figure 4 This is a schematic diagram of a multi-channel inversion system based on an improved genetic algorithm provided by the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] In order to solve the shortcomings of the prior art, the present invention provides a multi-channel inversion method based on an improved genetic algorithm. Figure 1 The present invention provides a multi-channel inversion method based on an improved genetic algorithm, such as Figure 1 As shown, the method includes the following steps:

[0062] Step S1, synthesizing multi-channel seismic records using a reflectivity method;

[0063] Specifically, step S1 includes steps S1.1-S1.5;

[0064] Step S1.1, calculating the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient, and the shear wave transmission coefficient in the frequency domain;

[0065] Specifically, step S1.1 includes: first designing a layered formation model to study the propagation of longitudinal and shear waves after a plane wave is incident on the formation; then obtaining the displacement-stress relationship on the contact surface of each layer in the layered formation model using a wave equation; and finally calculating the reflection coefficient based on the displacement-stress relationship on the contact surface of each layer;

[0066] Furthermore, the reflection coefficient includes: longitudinal wave reflection coefficient, shear wave reflection coefficient, longitudinal wave transmission coefficient and shear wave transmission coefficient;

[0067] Step S1.2, multiplying the longitudinal wave reflection coefficient by the source wavelet to obtain a reflection spectrum;

[0068] Preferably, the reflection spectrum is a reflection spectrum in the frequency-slowness domain

[0069] Step S1.3, performing an inverse Fourier transform on the reflection wave spectrum to obtain a reflection wave record in the time-slowness domain;

[0070] Preferably, the step S1.2 obtains the reflection spectrum in the frequency-slowness domain, which needs to be converted into the reflection spectrum in the time-slowness domain for calculating the subsequent reflection wave record. Therefore, the present invention adopts the inverse Fourier transform method to obtain the reflection wave record in the time-slowness domain;

[0071] Step S1.4, changing the incident angle of the reflected wave to a ray parameter to obtain a seismic record in the τ-p domain;

[0072] Step S1.5, inversely transforming the seismic record in the τ-p domain to obtain a reflection wave record in the xt domain;

[0073] The reflection wave record in the xt domain is a seismic record synthesized using the reflectivity method;

[0074] It is worth emphasizing that the seismic record is a multi-channel seismic record.

[0075] Step S2: obtaining a final velocity model through pre-stack waveform inversion based on an improved genetic algorithm.

[0076] Specifically, Figure 2 The flowchart of the present invention for obtaining the final velocity model through prestack waveform inversion based on the improved genetic algorithm is as follows: Figure 2 As shown, step S2 includes steps S2.1-S2.6;

[0077] Step S2.1: establishing an initial velocity model using well logging data, and performing forward modeling based on the initial velocity model to obtain synthetic seismic records;

[0078] Preferably, the initial velocity model includes: an initial longitudinal wave velocity model and an initial shear wave velocity model;

[0079] Step S2.2, determining the parameters of the improved genetic algorithm, encoding the parameters, and generating an initial model population;

[0080] Preferably, all parameters of the improved genetic algorithm are encoded according to actual conditions, then connected to form a chromosome, and an initial population is randomly generated.

[0081] Preferably, real numbers are used for encoding, and the real number encoding specifically includes: inverted parameters are represented by real numbers;

[0082] Step S2.3: Given measured data, the measured data is compared with the synthetic seismic record, and the fitness function value of the objective function of each individual is calculated;

[0083] Preferably, the measured data is a measured seismic record, which is used for comparison with the synthetic seismic record;

[0084] Specifically, the step S2.3 includes steps S2.3.1-S2.3.2;

[0085] Step S2.3.1, setting an individual objective function, wherein the objective function is used to characterize the error between the measured data and the synthetic seismic record;

[0086] Furthermore, the objective function is expressed as:

[0087] Objvalue = norm(c-data) ;

[0088] In the formula, Objvalue represents the objective function, norm represents a norm value of the objective function using an error, c represents the synthetic seismic record obtained in step S1, and data represents the measured data.

[0089] Step S2.3.2, calculating the fitness function value of the objective function of each individual;

[0090] Preferably, the expression of the fitness function for fitness function value calculation is:

[0091] Fitt = 1 / (Objvalue*Objvalue+0.001) ;

[0092] In the formula, Fitt represents the fitness function, and Objvalue represents the objective function.

[0093] Step S2.4, arranging the fitness function values of each individual in descending order, and adjusting the fitness function values;

[0094] The application adopts an adjustment strategy for fitness values to ensure that the algorithm functions better.

[0095] Preferably, the adjustment strategy for fitness values is as follows: arranging the fitness function values of each individual in descending order, then increasing the fitness values of the individuals corresponding to the first quarter of the fitness function values by a first preset value, decreasing the fitness function values of the individuals corresponding to the last quarter of the fitness function values by a second preset value, and keeping the fitness values of the individuals corresponding to the middle quarter of the fitness function values unchanged; and then selecting and breeding using the new fitness function values.

[0096] Preferably, the first preset value and the second preset value are values set in advance.

[0097] Step S2.5, performing genetic operations to obtain a new population;

[0098] Specifically, the genetic operations are performed using a crossover operator and a mutation operator for crossover and mutation.

[0099] The selected individuals in the population need to undergo a crossover operation to generate new individuals. Crossover essentially randomly selects two individuals, exchanges part of the genes of the selected two individuals according to a crossover probability and a certain exchange method, thereby generating two individuals with different external characteristics from the original individuals. In the early iteration stage, a large crossover probability can quickly increase the richness of the population and speed up the evolution of the population. In the later iteration stage, only a small crossover probability can avoid destroying good individuals.

[0100] Individuals in a population that have undergone crossover need to undergo mutation operations to produce new individuals with mutant characteristics. Mutation is essentially the process of randomly selecting a gene on an individual, and mutating some of the genes of the selected individual based on the mutation probability and a certain mutation method, thereby producing individuals with different external characteristics from the original individual.

[0101] Step S2.6: Determine whether the new population meets the accuracy requirements. If so, output the final velocity model. If not, continue iterating until the accuracy requirements are met.

[0102] The accuracy requirement is to meet the signal-to-noise ratio and error convergence requirements of seismic data.

[0103] Example

[0104] An embodiment of the present invention provides a multi-channel inversion method based on an improved genetic algorithm, the method comprising the following steps:

[0105] Step S1, synthesizing multi-channel seismic records using a reflectivity method;

[0106] Forward modeling is the foundation for achieving high accuracy and efficiency in inversion. Forward modeling involves obtaining data through a model. In other words, the medium parameters of the specific structure of the stratum are known to solve the seismic shot records. In fact, in the seismic inversion process, the forward modeling of seismic waves accounts for a high proportion of the computational workload and computing time, and the accuracy and efficiency of the inversion mainly depend on the accuracy and efficiency of the forward simulation. In fact, there are many forward modeling methods. As a commonly used forward modeling method, the reflectivity method has received continuous attention from researchers. The reflectivity method utilizes various information of the full wave field, such as multiple reflection waves and conversion waves in each stratum, and can effectively simulate the stratum.

[0107] In this embodiment, taking the example of obtaining reflected waves after a plane wave is incident on a layered stratum, how to synthesize multi-channel seismic records using the reflectivity method is described;

[0108] Specifically, step S1 includes steps S1.1-S1.5;

[0109] Step S1.1, calculating the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient, and the shear wave transmission coefficient in the frequency domain; specifically, first designing a layered stratum model to study the propagation of longitudinal waves and shear waves after a plane wave is incident on the stratum; then obtaining the displacement-stress relationship on the contact surface of each layer in the layered stratum model through an equation; and obtaining the reflection coefficient based on the displacement-stress relationship on the contact surface of each layer, wherein the reflection coefficient includes: the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient, and the shear wave transmission coefficient;

[0110] Step S1.2, correlating the longitudinal wave reflection coefficient with the source wavelet to obtain a reflection spectrum, wherein the reflection spectrum is a reflection spectrum in the frequency-slowness domain;

[0111] Step S1.3: Perform an inverse Fourier transform on the reflection spectrum to obtain a reflection wave record in the time-slowness domain. Specifically, step S1.2 obtains the reflection wave spectrum in the frequency-slowness domain. It is also necessary to use an inverse Fourier transform method to convert the reflection wave spectrum in the frequency-slowness domain into a reflection spectrum in the time-slowness domain for calculating subsequent reflection wave records.

[0112] Step S1.4, changing the incident angle of the reflected wave to a ray parameter to obtain a seismic record in the τ-p domain;

[0113] Step S1.5: Inversely transform the seismic record in the τ-p domain to obtain a reflection wave record in the xt domain. The reflection wave record in the xt domain is a seismic record synthesized using the reflectivity method, and the seismic record is a multi-channel seismic record.

[0114] Step S2: obtaining the final velocity model based on pre-stack waveform inversion using an improved genetic algorithm;

[0115] Specifically, step S2 includes steps S2.1-S2.6;

[0116] Step S2.1, determining initial values ​​of inversion parameters using well logging data;

[0117] The inversion parameters include longitudinal wave velocity and shear wave velocity;

[0118] Step S2.2: Encode the inversion parameters to generate an initial model population. Specifically, the inversion parameters are encoded and then connected to form a chromosome, and the initial model population is randomly generated. To better achieve local adjustment of individuals in the population, this embodiment uses real numbers for encoding. The real number encoding specifically includes: the inversion parameters are represented by real numbers;

[0119] Step S2.3: Given measured data, the measured data is compared with the multi-channel seismic record to form individuals, and the fitness function value of the objective function of each individual is calculated. The measured data is the measured seismic record used for comparison with the multi-channel seismic record in step 1;

[0120] Specifically, step S2.3 includes:

[0121] Step S2.3.1: Set an individual objective function, which is used to characterize the error between the measured data and the synthetic seismic record; the expression of the objective function is:

[0122] Objvalue = norm(c-data);

[0123] In the formula, Objvalue represents the objective function, norm represents the norm value of the error adopted by the objective function, c represents the synthetic seismic record obtained in step 1, and data represents the measured data.

[0124] Step S2.3.2, calculating the fitness function value of the objective function of each individual; the expression of the fitness function is:

[0125] Fitt = 1 / (Objvalue*Objvalue+0.001);

[0126] In the formula, Fitt represents the fitness function, and Objvalue represents the objective function

[0127] Step S2.4, arranging the fitness function values of each individual in descending order, and adjusting the fitness function values; specifically, in the original genetic algorithm, individuals with higher fitness values are generally selected to be assigned to the next generation with a higher probability, thereby providing a good foundation for subsequent crossover and mutation, enabling the population to develop in a better direction, and embodying the principle of survival of the fittest in the biological evolution process. Individuals with higher fitness values in the original genetic algorithm are copied with a higher probability, but this method has the following deficiencies: in the early stage of genetic, excellent individuals may be directly eliminated due to random selection or too few excellent individuals, thereby causing the original genetic algorithm to fail to effectively play the selection effect. The embodiment aims at the above-mentioned defects, and adopts the strategy of adjusting the fitness values to ensure that the algorithm better plays a role. Specifically, the strategy of adjusting the fitness values is as follows: arranging the fitness function values of each individual in descending order, then increasing the fitness values of the individuals corresponding to the first quarter of the fitness function values by a first preset value, decreasing the fitness function values of the individuals corresponding to the last quarter of the fitness function values by a second preset value, and keeping the fitness values of the individuals corresponding to the middle quarter of the fitness function values unchanged; finally, the new fitness function values are obtained for the next step of selection and genetic. The first preset value and the second preset value are both values set in advance, and the values of the first preset value and the second preset value can be set according to the selection effect and the convergence speed of the genetic algorithm in the calculation process by those skilled in the art.

[0128] In fact, since the fitness function values are adjusted in the embodiment, excellent individuals will not be eliminated due to too few numbers, and poor individuals will be more easily eliminated due to the decreased fitness values, thereby enabling the genetic algorithm to better play the selection effect, and also enabling the genetic algorithm to converge faster.

[0129] Step S2.5: Perform genetic operations to obtain a new population. Specifically, the genetic operations are crossover and mutation operations using a crossover operator and a mutation operator. Selected individuals in the population must undergo a crossover operation to produce new individuals. Crossover essentially involves randomly selecting two individuals and exchanging some of their genes according to the crossover probability and a certain exchange method, thereby producing two individuals with different external characteristics from the original individuals. In the early stages of iteration, the crossover probability is high, which can quickly increase the richness of the population and accelerate the rate of population evolution. In the later stages of iteration, only a smaller crossover probability can be used to avoid destroying excellent individuals. Individuals in the population that have undergone crossover must undergo a mutation operation to produce new individuals with mutant characteristics. Mutation essentially involves randomly selecting a gene on an individual and mutating some of its genes according to the mutation probability and a certain mutation method, thereby producing individuals with different external characteristics from the original individuals.

[0130] Step S2.6: Determine whether the new population meets the accuracy requirements. If so, output the final velocity model. If not, continue iterating until the accuracy requirements are met; the accuracy requirements are the signal-to-noise ratio and error convergence requirements of the seismic data.

[0131] For example, Figure 3 The comparison diagram of the original genetic algorithm inversion and the improved genetic algorithm inversion provided by the present invention is as follows: Figure 3 As shown in the figure, the inversion results obtained by using the original genetic algorithm and the improved genetic algorithm of this embodiment for a certain block are as follows: Figure 3 It can be seen from the figure that the improved genetic algorithm proposed in this embodiment is used for inversion, and the inversion result is obviously better than the original genetic algorithm.

[0132] The present invention also discloses a multi-channel inversion system based on an improved genetic algorithm. Figure 4 This is a schematic diagram of a multi-channel inversion system based on an improved genetic algorithm of the present invention. Figure 4As shown, the system comprises: a synthetic unit and an inversion unit; wherein the synthetic unit is configured to synthesize multi-channel seismic records by using the reflectivity method; the inversion unit is configured to obtain a final velocity model based on pre-stack waveform inversion of the multi-channel seismic records by using an improved genetic algorithm; the system comprises: an acquisition module configured to establish an initial velocity model by using well logging data, and perform forward operation according to the initial velocity model to acquire synthetic seismic records; a generation module configured to determine parameters of the improved genetic algorithm based on the initial velocity model, encode the parameters, and generate an initial model population; a first calculation module configured to give measured data, compare the measured data with the synthetic seismic records, and calculate fitness function values of an objective function of each individual in the initial model population; an adjustment module configured to arrange the fitness function values of each individual in the initial model population in descending order, and adjust the fitness function values; an operation module configured to perform genetic operation on each individual in the initial model population based on the adjusted fitness function values to obtain a new population; and a judgment module configured to judge whether the new population meets accuracy requirements, if yes, output the final velocity model, and if not, continue iteration until the accuracy requirements are met.

[0133] Further, the synthetic unit comprises: a second calculation module, a first obtaining module, a second obtaining module, a third obtaining module, and a fourth obtaining module; wherein the second calculation module is configured to calculate a P-wave reflection coefficient, a S-wave reflection coefficient, a P-wave transmission coefficient, and a S-wave transmission coefficient in a frequency domain; the first obtaining module is configured to multiply the P-wave reflection coefficient by a source wavelet to obtain a reflection spectrum; the second obtaining module is configured to perform inverse Fourier transform on the reflection spectrum to obtain a reflection record in a time-slowness domain; the third obtaining module is configured to change an incidence angle of the reflection to a ray parameter to obtain a seismic record in a tau-p domain; and the fourth obtaining module is configured to perform inverse transformation on the seismic record in the tau-p domain to obtain a reflection record in an x-t domain, which is the multi-channel seismic record synthesized by using the reflectivity method.

[0134] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some technical features, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-channel inversion method based on an improved genetic algorithm, characterized in that: The method comprises the following steps: Step S1, synthesizing multi-channel seismic records using a reflectivity method; Step S2, obtaining a final velocity model based on the multi-channel seismic records by pre-stack waveform inversion using an improved genetic algorithm; comprising: Step S2.1, determining initial values ​​of inversion parameters using well logging data; Step S2.2, encoding the inversion parameters to generate an initial model population; Step S2.3: Given measured data, the measured data is compared with the multi-channel seismic record to form individuals, and the fitness function value of the objective function of each individual is calculated; The step S2.3 includes: Step S2.3.1: Set the objective function of each individual in the initial model population. The objective function is used to characterize the error between the measured data and the synthetic seismic record. The expression of the objective function is: Objvalue = norm(c-data); Where, Objvalue represents the objective function, norm represents the norm value of the error adopted by the objective function, c represents the synthetic seismic record obtained in step S1, and data represents the measured data; Step S2.3.2: Calculate the fitness function value of the objective function for each individual in the initial model population; the fitness function is expressed as: Fitt=1 / (Objvalue * Objvalue+0.001); Where Fitt represents the fitness function; Step S2.4: Arrange the fitness function values ​​of each individual in the initial model population in descending order and adjust the fitness function values; The step S2.4 includes: The fitness function values ​​of each individual in the initial model population are arranged in descending order, and then for the individuals corresponding to the fitness function values ​​in the first quarter, their fitness values ​​are increased by a first preset value, and for the individuals corresponding to the fitness function values ​​in the second quarter, their fitness function values ​​are reduced by a second preset value, and the fitness values ​​of the individuals corresponding to the fitness function values ​​in the middle remain unchanged, and finally a new fitness function value is obtained; wherein the first preset value and the second preset value are values ​​set in advance; Step S2.5: Based on the adjusted fitness function value, perform genetic operations on each individual in the initial model population to obtain a new population; Step S2.6: Determine whether the new population meets the accuracy requirement. If so, output the final velocity model. If not, continue iterating until the accuracy requirement is met.

2. The multi-channel inversion method based on the improved genetic algorithm according to claim 1, characterized in that: The step S1 comprises: Step S1.1, calculating the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient, and the shear wave transmission coefficient in the frequency domain; Step S1.2, multiplying the longitudinal wave reflection coefficient by the source wavelet to obtain a reflection spectrum; Step S1.3, performing an inverse Fourier transform on the reflection wave spectrum to obtain a reflection wave record in the time-slowness domain; Step S1.4: Change the incident angle of the reflected wave to a ray parameter to obtain Earthquake records in the region; Step S1.5: The seismic record in the xt domain is inversely transformed to obtain the reflection wave record in the xt domain, and the reflection wave record in the xt domain is the multi-channel seismic record synthesized by the reflectivity method.

3. The multi-channel inversion method based on the improved genetic algorithm according to claim 2, characterized in that: The step S1.1 includes: First, a layered formation model is designed to study the propagation of longitudinal and shear waves after a plane wave is incident on the formation; then, the relationship between displacement and stress on the contact surface of each layer in the layered formation model is obtained through equations; finally, the reflection coefficient is calculated based on the displacement and stress relationship on the contact surface of each layer; the reflection coefficient includes: longitudinal wave reflection coefficient, shear wave reflection coefficient, longitudinal wave transmission coefficient and shear wave transmission coefficient.

4. The multi-channel inversion method based on an improved genetic algorithm according to claim 2, characterized in that: In step S1.2, the reflection spectrum is a reflection spectrum in the frequency-slowness domain.

5. The multi-channel inversion method based on an improved genetic algorithm according to claim 1, characterized in that: In step S2.5, the genetic operation is to perform crossover and mutation using a crossover operator and a mutation operator.

6. The multi-channel inversion method based on an improved genetic algorithm according to claim 1 or 5, characterized in that: In step S2.6, the accuracy requirement is to meet the signal-to-noise ratio and error convergence requirements of the seismic data.

7. A multi-channel inversion system based on an improved genetic algorithm, characterized in that: The system includes: a synthesis unit and an inversion unit; wherein, A synthesis unit for synthesizing multi-channel seismic records using a reflectivity method; An inversion unit is used to obtain a final velocity model based on the seismic record and prestack waveform inversion using an improved genetic algorithm; and comprises: An acquisition module, which uses the well logging data to establish an initial velocity model, and performs a forward modeling operation based on the initial velocity model to obtain synthetic seismic records; A generation module, based on the initial velocity model, determines parameters of the improved genetic algorithm, encodes the parameters, and generates an initial model population; A first calculation module is configured to compare the measured data with the synthetic seismic record given the measured data, and calculate the fitness function value of the objective function of each individual in the initial model population; The first calculation module is specifically configured to: The objective function of each individual in the initial model population is set. The objective function is used to characterize the error between the measured data and the synthetic seismic record. The expression of the objective function is: Objvalue = norm(c-data); Where, Objvalue represents the objective function, norm represents the norm value of the error adopted by the objective function, c represents the synthetic seismic record obtained in step S1, and data represents the measured data; Calculate the fitness function value of the objective function for each individual in the initial model population; the expression of the fitness function is: Fitt=1 / (Objvalue * Objvalue+0.001); Where Fitt represents the fitness function; The adjustment module arranges the fitness function values ​​of each individual in the initial model population in descending order and adjusts the fitness function values ​​at the same time; The adjustment module is specifically used for: The fitness function values ​​of each individual in the initial model population are arranged in descending order, and then for the individuals corresponding to the fitness function values ​​in the first quarter, their fitness values ​​are increased by a first preset value, and for the individuals corresponding to the fitness function values ​​in the second quarter, their fitness function values ​​are reduced by a second preset value, and the fitness values ​​of the individuals corresponding to the fitness function values ​​in the middle remain unchanged, and finally a new fitness function value is obtained; wherein the first preset value and the second preset value are values ​​set in advance; The operation module performs genetic operations on each individual in the initial model population to obtain a new population based on the adjusted fitness function value; The judgment module judges whether the new population meets the accuracy requirement. If so, the final velocity model is output; if not, the iteration is continued until the accuracy requirement is met.

8. The multi-channel inversion system based on the improved genetic algorithm according to claim 7, characterized in that: The synthesis unit includes: a second calculation module, a first acquisition module, a second acquisition module, a third acquisition module and a fourth acquisition module; wherein, The second calculation module is used to calculate the longitudinal wave reflection coefficient, the shear wave reflection coefficient, the longitudinal wave transmission coefficient and the shear wave transmission coefficient in the frequency domain; A first obtaining module is used to multiply the longitudinal wave reflection coefficient by the source wavelet to obtain a reflection spectrum; A second acquisition module is used to perform inverse Fourier transform on the reflection wave spectrum to obtain a reflection wave record in the time-slowness domain; The third obtaining module is used to change the incident angle of the reflected wave into a ray parameter to obtain Earthquake records in the region; The fourth acquisition module is used to obtain the The seismic record in the xt domain is inversely transformed to obtain the reflection wave record in the xt domain, and the reflection wave record in the xt domain is the multi-channel seismic record synthesized by the reflectivity method.

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