Feature reflection wave travel time inversion method, device, equipment and storage medium

Abstract

The application provides a feature reflection wave travel time inversion method, device, equipment and storage medium. The method comprises the following steps: performing extended domain migration imaging on observed seismic shot gather data to obtain an extended domain common imaging point gather; selecting a target stratum from a zero offset profile of the extended domain common imaging point gather based on the extended domain common imaging point gather; selecting a common imaging point gather of the target stratum from the extended domain common imaging point gather based on the target stratum; performing de-migration forward simulation on the common imaging point gather to obtain feature reflection wave data of the target stratum; establishing a travel time inversion framework based on the feature reflection wave data to obtain simulated reflection wave data; calculating reflection wave travel time difference based on the feature reflection wave data and the simulated reflection wave data; calculating a gradient of travel time inversion based on the travel time difference; and updating a velocity model based on the gradient of travel time inversion. The method can restore various wave number components of the velocity model, thereby improving inversion efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reflected wave velocity modeling, in particular to a characteristic reflected wave travel time inversion method, device, equipment and storage medium. BACKGROUND

[0002] At present, FWI obtains good results in wide azimuth and large aperture observation seismic data, mainly relying on the information carried by transmitted wave and over critical angle reflected wave to update the medium and low wave number components of the stratum. However, the acquisition of such high quality seismic data is expensive. When the seismic data lacks such large offset information, in the medium and deep part of the stratum, the FWI can only update the high wave number components corresponding to the reflected wave, which is equivalent to the least square reverse time migration (LSRTM); and the inaccuracy of the medium and low wave number components makes the spatial position of the updated high wave number components incorrect, and the inversion is easily trapped in local extreme value. When the transmitted or refracted data of large aperture is lacking, the medium and deep part of the stratum is often only illuminated by the reflected wave. Therefore, how to use the reflected wave information to update the medium and low wave number components in the medium and deep part of the model has become the focus of the research on seismic inversion.

[0003] According to the judgment criterion of the target functional, the reflected wave inversion can be divided into two types of imaging domain and data domain. The imaging domain reflected wave inversion method usually uses the flattening or focusing of the common image gather (CIG) to judge the accuracy of the velocity, and uses the residual moveout / depth difference (RMO) on the CIG or the difference of adjacent trace energy to update the background velocity model. The imaging domain reflected wave inversion can be divided into the travel time tomography or migration velocity analysis (Ray-based tomography or MVA) based on the ray theory and the wave equation migration velocity analysis (WEMVA) based on the wave equation. The migration velocity analysis based on the ray theory has become a mature technology for updating the medium and deep part of the seismic model in actual production, and compared with the wave equation method, the ray method is more efficient and flexible, but needs manual intervention (picking residual curvature) in the inversion process, and is restricted by the ray theory, and has problems such as focal point scattering, shadow zone and the like in complex regions. The WEMVA method based on the wave theory can well avoid these problems, and it mainly realizes the update of the velocity model through the energy maximization of the imaging gather, the difference similarity optimization (penalizing the difference of the image in the imaging gather, DSO) and the like. The WEMVA method uses the wave equation to describe the forward problem of the wave field, overcomes the high frequency approximation of the ray theory, realizes the automatic inversion, and thus can be applied to the velocity estimation of complex medium, and its disadvantage is that the calculation amount is huge, and there is a gradient artifact problem. SUMMARY

[0004] In view of the problem that seismic data usually lacks long-offset signals and effective low-frequency components, which makes it difficult to recover low-wave-number components of a medium-deep parameter model, the application provides a feature reflection traveltime inversion method, device, equipment and storage medium, which can recover various wave-number components of a velocity model, thereby improving the inversion efficiency.

[0005] The application provides a feature reflection traveltime inversion method, which comprises the following steps:

[0006] Perform extended-domain migration imaging on observed seismic shot gather data to obtain extended-domain common imaging point gathers;

[0007] Based on the extended-domain common imaging point gathers, a target stratum is selected from a zero-offset profile of the extended-domain common imaging point gathers;

[0008] Based on the target stratum, a common imaging point gather of the target stratum is selected from the extended-domain common imaging point gathers;

[0009] Perform de-migration forward modeling on the common imaging point gather to obtain feature reflection data of the target stratum;

[0010] Based on the feature reflection data, a traveltime inversion framework is established to obtain simulated reflection data;

[0011] Based on the feature reflection data and the simulated reflection data, reflection traveltime differences are calculated;

[0012] Based on the traveltime differences, gradients of traveltime inversion are calculated;

[0013] Based on the gradients of traveltime inversion, a velocity model is updated.

[0014] In some embodiments, the extended-domain migration imaging on the observed seismic shot gather data to obtain the extended-domain common imaging point gathers can be expressed as:

[0015] I(x, θ) = -∫dxdθdωω 2 u0(x, x s , θ, ω)G * (x, x r , θ, ω)d obs (x r , x s , ω);

[0016] In the formula, I(x, θ) is the extended-domain common imaging point gather, θ is an angle, x is a spatial position, x r is a spatial position of a receiver, x s is a source position, ω is an angular frequency, u0 is a background wave field, G * is an adjoint operator of G0, G0 is a background field Green function, and dobs observed seismic shot gather data.

[0017] In some embodiments, the reverse migration forward modeling of the common imaging point gather to obtain the characteristic reflection data of the target formation can be expressed as:

[0018] d obs =∫dxdθdωω 2 u0(x,x s ,θ,ω)I(x,θ)G0(x,x r ,θ,ω);

[0019] In the formula, d obs is the observed seismic shot gather data, I(x, θ) is the extended domain common imaging point gather, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, G0 is the background field Green function, and u0 is the background wave field.

[0020] From the observed seismic shot gather data, according to the requirements of inversion imaging, it can be abstracted as the following transformation:

[0021]

[0022] In the formula, d is the characteristic reflection data, and Q is the variation operator.

[0023] In some embodiments, the objective function of the travel time inversion framework can be expressed as:

[0024]

[0025] In the formula, τ(t) represents the travel time residual, τ is the travel time difference, E is the objective function, t is the time, characteristic reflection data, characteristic reflection data reflection data after travel time inversion.

[0026] In some embodiments, the gradient of the travel time inversion based on the travel time difference can be expressed as:

[0027]

[0028] In the formula, is the gradient, is the Frechet differential of the time domain reflection wave field to the background model, is the adjoint source of the wave equation reflection travel time inversion, and a time derivative of d, is a second order time derivative of d.

[0029] Embodiments of the present application provide a feature reflection wave travel time inversion device, comprising:

[0030] a migration imaging module, configured to perform extended domain migration imaging on the seismic shot gather data to obtain an extended domain common imaging point gather;

[0031] a first selection module, configured to select a target formation from a zero offset profile of the extended domain common imaging point gather based on the extended domain common imaging point gather;

[0032] a second selection module, configured to select a common imaging point gather of the target formation from the extended domain common imaging point gather based on the target formation;

[0033] a de-migration module, configured to perform de-migration forward modeling on the common imaging point gather to obtain feature reflection wave data of the target formation;

[0034] a travel time inversion module, configured to establish a travel time inversion framework based on the feature reflection wave data to obtain simulated reflection wave data;

[0035] a first calculation module, configured to calculate reflection wave travel time difference based on the feature reflection wave data and the simulated reflection wave data;

[0036] a second calculation module, configured to calculate a gradient of travel time inversion based on the travel time difference;

[0037] an updating module, configured to update a velocity model based on the gradient of travel time inversion.

[0038] In some embodiments, the extended domain migration imaging is performed on the observed seismic shot gather data to obtain the extended domain common imaging point gather, which can be expressed as:

[0039] I(x,θ)=-∫dxdθdωω 2 u0(x,x s ,θ,ω)G * (x,x r ,θ,ω)d obs (x r ,x s ,ω);

[0040] In the formula, I(x,θ) is the extended domain common imaging point gather, θ is an angle, x is a spatial position, x r is a spatial position of a receiver, x s is a source position, ω is an angular frequency, u0 is a background wave field, G * is an adjoint operator of G0, G0 is a background field Green function, and dobs observed seismic shot gather data.

[0041] In some embodiments, the reverse migration forward modeling of the common imaging point gather to obtain the characteristic reflection wave data of the target formation can be expressed as:

[0042] d obs =∫dxdθdωω 2 u0(x,x s ,θ,ω)I(x,θ)G0(x,x r ,θ,ω);

[0043] In the formula, d obs is observed seismic shot gather data, I(x,θ) is an extended domain common imaging point gather, x is a spatial position, x r is a spatial position of a receiver, x s is a source position, ω is an angular frequency, G0 is a background field Green function, and u0 is a background wave field.

[0044] From the observed seismic shot gather data, according to the requirements of inversion imaging, the following transformation can be abstracted:

[0045]

[0046] In the formula, d is characteristic reflection wave data, and Q is a variation operator.

[0047] Embodiments of the present application provide an electronic device, comprising:

[0048] A memory and a processor, the memory has a computer program stored thereon, and the computer program is executed by the processor to perform the method of any one of the above.

[0049] Embodiments of the present application provide a heat preservation device, comprising: the electronic device described above.

[0050] Embodiments of the present application provide a storage medium, the computer program stored in the storage medium can be executed by one or more processors, and can be used to implement the characteristic reflection wave travel time inversion method of any one of the above.

[0051] The application provides a feature reflection wave travel time inversion method, device, equipment and storage medium. The method comprises the following steps: performing extended domain migration imaging on observed seismic shot gather data to obtain extended domain common imaging point gathers; selecting a target stratum from a zero migration profile of the extended domain common imaging point gathers based on the extended domain common imaging point gathers; selecting common imaging point gathers of the target stratum from the extended domain common imaging point gathers based on the target stratum; performing inverse migration forward simulation on the common imaging point gathers to obtain feature reflection wave data of the target stratum; establishing a travel time inversion framework based on the feature reflection wave data to obtain simulated reflection wave data; calculating reflection wave travel time differences based on the feature reflection wave data and the simulated reflection wave data; calculating a gradient of travel time inversion based on the travel time differences; and updating a velocity model based on the gradient of travel time inversion. The method can restore various wave number components of the velocity model, thereby improving inversion efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0052] In the following, the application will be described in more detail based on embodiments and with reference to the drawings.

[0053] Figure 1 An implementation flowchart of a feature reflection wave travel time inversion method provided by an embodiment of the application;

[0054] Figure 2 An extended domain common imaging point gather diagram provided by an embodiment of the application;

[0055] Figure 3 A zero migration profile diagram of an extended domain common imaging point gather provided by an embodiment of the application;

[0056] Figure 4 (a) A first layer feature reflection wave data diagram provided by an embodiment of the application;

[0057] Figure 4 (b) A second layer feature reflection wave data diagram provided by an embodiment of the application;

[0058] Figure 5 (a) An update amount diagram corresponding to two layer feature reflection wave data provided by an embodiment of the application;

[0059] Figure 5 (b) An update amount diagram corresponding to second layer feature reflection wave data provided by an embodiment of the application;

[0060] Figure 6 A velocity model diagram provided by an embodiment of the application;

[0061] Figure 7 Observed seismic shot gather data of a seismic single shot provided by an embodiment of the application;

[0062] Figure 8 (a) a schematic diagram of observed seismic shot gather data collected in the field in a certain exploration area provided by an embodiment of the present application;

[0063] Figure 8 (b) a schematic diagram of characteristic reflection seismic data provided by an embodiment of the present application;

[0064] Figure 9 a schematic diagram of another extended spread reflection imaging point gather provided by an embodiment of the present application;

[0065] Figure 10 a schematic diagram of another zero-offset profile provided by an embodiment of the present application;

[0066] Figure 11 a schematic diagram of reflection traveltime inversion corresponding to observed seismic shot gather data provided by an embodiment of the present application;

[0067] Figure 12 a schematic diagram of reflection traveltime inversion corresponding to characteristic reflection seismic data provided by an embodiment of the present application;

[0068] Figure 13 a schematic diagram of a structure of a characteristic reflection traveltime inversion device provided by an embodiment of the present application;

[0069] Figure 14 a schematic diagram of a component structure of an electronic device provided by an embodiment of the present application.

[0070] In the drawings, the same components are designated by the same reference numerals, and the drawings are not drawn according to actual proportions. DETAILED DESCRIPTION

[0071] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0072] In the following description, “some embodiments” are described, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0073] If the application file appears "first\second\third" similar description, add the following description, in the following description, the term "first\second\third" is only to distinguish similar objects, not represent the specific order of the object, can be understood, "first\second\third" can be exchanged in the specific order or sequence, as far as possible, so that the application described herein can be implemented in the order described or illustrated.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification herein is for describing the embodiments of the application only and is not intended to limit the application.

[0075] Embodiment one

[0076] Based on the problems in the related art, the application embodiment provides a characteristic reflected wave travel time inversion method. The execution subject of the characteristic reflected wave travel time inversion method can be an electronic device. The electronic device can be a heat preservation device, a notebook computer, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated message device, and a portable game device), and various types of terminals. The electronic device can also be implemented as a server. The server can be a stand-alone physical server, a server cluster composed of multiple physical servers, or a distributed system. The server can also be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0077] The characteristic reflected wave travel time inversion method provided in the application embodiment can realize the functions by calling program codes through the processor of the electronic device. The program codes can be saved in a computer storage medium.

[0078] The application embodiment provides a characteristic reflected wave travel time inversion method, Figure 1 The implementation flowchart of the characteristic reflected wave travel time inversion method provided in the application embodiment is shown in Figure 1 as follows, including:

[0079] Step S1: extended domain migration imaging is performed on observed seismic shot gather data to obtain an extended domain common imaging point gather;

[0080] Step S2: based on the extended domain common imaging point gather, a target stratum is selected from a zero-offset profile of the extended domain common imaging point gather;

[0081] Step S3: selecting a common imaging point gather of the target formation from the extended domain common imaging point gathers;

[0082] Step S4: performing reverse migration forward modeling on the common imaging point gather to obtain characteristic reflection wave data of the target formation;

[0083] Step S5: establishing a travel time inversion framework based on the characteristic reflection wave data to obtain simulated reflection wave data;

[0084] Step S6: calculating reflection wave travel time difference based on the characteristic reflection wave data and the simulated reflection wave data;

[0085] Step S7: calculating a gradient of travel time inversion based on the travel time difference;

[0086] Step S8: updating the velocity model based on the gradient of travel time inversion.

[0087] In the embodiments of the present application, the extended domain common imaging point gather can be obtained by performing extended domain migration imaging on the observed seismic shot gather data, and the observed seismic shot gather data is as shown in Figure 7 , the extended domain common imaging point gather is as shown in Figure 2 , the target formation is selected on the zero migration profile of the extended domain common imaging point gather, and the zero migration profile is as shown in Figure 3 , and after the target formation is selected, the common imaging point gather corresponding to the target formation can be determined based on the extended domain common imaging point gather, the characteristic reflection wave data of the target formation can be obtained by performing reverse migration forward modeling on the common imaging point gather corresponding to the target formation, and the characteristic reflection wave data is as shown in Figure 4 (a) and Figure 4 (b), Figure 4 (a) is the first layer characteristic reflection wave data, Figure 4(b) is the second layer characteristic reflection wave data. By regarding the extended domain migration and the de-migration as a forward transformation from the imaging domain to the data domain, the continuity and consistency of the extended domain gather are improved, thereby improving the quality of the pre-stack data. Compared with the method of removing the surface wave by low-pass filtering, the application can remove most of the surface wave while retaining the low-frequency effective signal. In addition, it can also recover missing seismic traces, normalize the data, and improve the resolution of the data, thereby effectively suppressing random noise and ensuring the accuracy of the signal. By expanding the time series of the related phase axis signal, effective characteristic reflection wave information can be extracted from low signal-to-noise ratio seismic data. After obtaining the characteristic reflection wave data of the target layer, a travel time inversion framework can be established based on the characteristic reflection wave data of the target layer, thereby obtaining simulated reflection wave data. Based on the characteristic reflection wave data and the simulated reflection wave data of the target layer, the travel time difference is calculated, and then the gradient of the travel time inversion is calculated based on the travel time difference. Then, the gradient of the travel time inversion is used as the update amount of the target layer to update the velocity model. The update amount of the target layer is as follows Figure 5 (a) and Figure 5 (b) shown in Figure 5 (a) is the update amount corresponding to the two-layer characteristic reflection wave data, Figure 5 (b) is the update amount corresponding to the second layer characteristic reflection wave data, Figure 6 is the velocity model. By repeating the above steps, various wave number components of the velocity model can be gradually recovered, thereby improving the inversion efficiency.

[0088] In summary, by performing extended domain migration imaging on the observed seismic shot gather data, an extended domain common imaging point gather is obtained. Based on the extended domain common imaging point gather, a target layer is selected from the zero migration profile of the extended domain common imaging point gather. Based on the target layer, a common imaging point gather of the target layer is selected from the extended domain common imaging point gather. The common imaging point gather is de-migrated and forward simulated to obtain characteristic reflection wave data of the target layer. Based on the characteristic reflection wave data, a travel time inversion framework is established to obtain simulated reflection wave data. Based on the characteristic reflection wave data and the simulated reflection wave data, the reflection wave travel time difference is calculated. Based on the travel time difference, the gradient of the travel time inversion is calculated. Based on the gradient of the travel time inversion, the velocity model is updated. Various wave number components of the velocity model can be recovered, thereby improving the inversion efficiency.

[0089] Embodiment Two

[0090] Based on the problems in the related art, the feature reflected wave travel time inversion method provided in the embodiments of the present application has an execution subject which can be an electronic device. The electronic device can be a heat preservation device, a notebook computer, a tablet computer, a desktop computer, a set top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated message device, a portable game device) and various types of terminals, and can also be implemented as a server. The server can be a stand-alone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0091] The feature reflected wave travel time inversion method provided in the embodiments of the present application can realize the functions by calling program codes of a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0092] It should be noted that in conventional reflected seismic exploration, the propagation process of seismic waves is as follows: a seismic wave field is excited by a seismic source on the ground and propagates downward, a reflected wave is generated at the underground wave impedance interface, and then propagates upward to the ground to be recorded by a geophone. The seismic wave field can be described by a conventional density scalar acoustic wave equation, which can be expressed as a linear system:

[0093]

[0094] In the formula, is a Laplace operator, x is a spatial position, v(x) is a velocity at the spatial position x, f(t) is a source time function, x s is a source point position, u is an underground seismic wave, u(x, t; x s ) represents an underground seismic wave field with a source at x s , a time of t, and a spatial position of x, and δ is an impulse;

[0095] For simplicity, the above wave equation can be written in a matrix form:

[0096] L(m(x), ω)u(x, ω) = F(x, ω);

[0097] In the formula, represents a wave field propagation operator, m(x) indicates a model parameter at the spatial position x, ω is an angular frequency, and F is a source term;

[0098] To describe the propagation and scattering of reflected waves more directly, the velocity model can be decomposed into a smooth background model v0 and a high-frequency reflectivity model I, and the velocity model is expressed as follows:

[0099]

[0100] where v is the velocity model, represents a concept of addition, v0 and I contain different physical dimensions, and I can be understood as the square of the slowness field disturbance;

[0101] Neglecting the effect of multiple scattering, based on the first-order Born approximation, the matrix form of the wave equation can be decomposed as follows:

[0102] Background wave field propagation equation:

[0103] L0u0=F;

[0104] Scattered wave field propagation equation:

[0105] L0δu=Ku0;

[0106] where u0 and δu represent the background wave field and the scattered wave field respectively, and u≈u0+δu, is the propagation operator, and K=ω 2 I is the perturbation operator, and Ku0 is a "quadratic virtual source";

[0107] When the underground disturbance has a certain extension, the superposition of the scattered wave field is the reflected wave;

[0108] In seismic exploration, the seismic wave is received by a limited geophone on the ground surface:

[0109] d(m(x))=Ru(m(x))≈Ru0(v0)+Rδu(v0,I);

[0110] where d represents the full wave field data of the seismic record, R represents the sampling operator of the geophone, and m is the model parameter. Ru0(v0) and Rδu(v0,I) correspond to the direct wave and the first reflected wave in the seismic data, respectively;

[0111] Take the partial derivative of the background wave field propagation equation and the scattered wave field propagation equation with respect to the reflectivity model I:

[0112]

[0113]

[0114] Because the propagation operator L0 has no response on the smooth background model, we have Simplify as follows:

[0115]

[0116]

[0117] where, denotes the inverse operator of the propagator L0, and the above equation is the response of the scattered wave field to the reflectivity model, defined as the back-propagated background field Green function from the receiver location x r satisfies:

[0118] G0= L -1 -1 r ;

[0119] where, L -1 -1 r denotes the inverse operator of the propagator L, x e is the spatial location of the receiver, and δ(x-x obs ,ω) is the frequency domain impulse function. The response of the scattered wave field δu to the reflectivity model at the spatial location x in the single shot-receiver case can be obtained, which is expressed as:

[0120] d 2 =Rδu(v0,I)=∫dxdωω s u0(x,x r ,ω)IG0(x,x obs ,ω);

[0121] where, d r is the observed seismic shot gather data, Rδu(v0,I) is the first-order reflected wave in the seismic data, x is the spatial location, x s is the spatial location of the receiver, x 2 is the location of the source, ω is the angular frequency, G0 is the background field Green function, u0 is the background wave field, δu is the scattered wave field, and I is the high-frequency reflectivity model.

[0122] Based on the above principle, the feature reflected wave travel time inversion method provided by the embodiments of the present application comprises the following steps:

[0123] Step S1: performing extended domain migration imaging on the observed seismic shot gather data to obtain an extended domain common imaging point gather;

[0124] In step S1, the observed seismic shot gather data is subjected to extended domain migration imaging to obtain an extended domain common imaging point gather, which can be expressed as:

[0125] I(x,θ)=-∫dxdθdωω s u0(x,x * ,θ,ω)G r (x,x obs ,θ,ω)d (xr ,x s ,ω);

[0126] where I(x, θ) is the extended domain common imaging gathers, θ is the angle, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, u0 is the background wavefield, G * is the adjoint operator of G0, G0 is the background field Green function, d obs is the observed seismic shot gather data.

[0127] Step S2: selecting a target formation from a zero-offset profile of the extended domain common imaging gathers based on the extended domain common imaging gathers;

[0128] Step S3: selecting a common imaging gather of the target formation from the extended domain common imaging gathers based on the target formation;

[0129] Step S4: performing reverse migration forward modeling on the common imaging gather to obtain characteristic reflection wave data of the target formation;

[0130] In step S4, the reverse migration forward modeling on the common imaging gather to obtain characteristic reflection wave data of the target formation can be expressed as:

[0131] d obs =∫dxdθdωω 2 u0(x,x s ,θ,ω)I(x,θ)G0(x,x r ,θ,ω);

[0132] where d obs is the observed seismic shot gather data, I(x, θ) is the extended domain common imaging gathers, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, G0 is the background field Green function, and u0 is the background wavefield.

[0133] From the observed seismic shot gather data, according to the requirements of inversion imaging, it can be abstracted as the following transformation:

[0134]

[0135] where d is the characteristic reflection wave data, and Q is a variation operator.

[0136] Step S5: establishing a travel time inversion framework based on the characteristic reflection wave data to obtain simulated reflection wave data;

[0137] In step S5, the objective function of the traveltime inversion framework can be expressed as:

[0138]

[0139] where τ(t) represents the traveltime residual, τ is the traveltime difference, E is the objective function, t is time, characteristic reflection wave data, characteristic reflection wave data reflection wave data after traveltime inversion.

[0140] Step S6: calculating the reflection wave traveltime difference based on the characteristic reflection wave data and the simulated reflection wave data;

[0141] Step S7: calculating the gradient of the traveltime inversion based on the traveltime difference;

[0142] In step S7, the calculation of the gradient of the traveltime inversion based on the traveltime difference can be expressed as:

[0143]

[0144] where, is the gradient, is the Frechet derivative of the time-domain reflection wave field to the background model, is the adjoint source of the wave equation reflection wave traveltime inversion, where denotes the time derivative of d, is the time second derivative of d.

[0145] Step S8: updating the velocity model based on the gradient of the traveltime inversion.

[0146] In the embodiments of the present application, the extended domain common imaging point gathers can be obtained by performing extended domain migration imaging on the observed seismic shot gather data, such as Figure 7 , and the extended domain common imaging point gathers are as shown in Figure 2 , the target formation is selected on the zero-offset profile of the extended domain common imaging point gathers, and the zero-offset profile is as shown in Figure 3 , after the target formation is selected, the common imaging point gathers corresponding to the target formation can be determined based on the extended domain common imaging point gathers, the characteristic reflection wave data of the target formation can be obtained by performing reverse migration forward simulation on the common imaging point gathers corresponding to the target formation, and the characteristic reflection wave data is as shown in Figure 4 (a) and Figure 4 (b), Figure 4 (a) is the first layer characteristic reflection wave data, Figure 4(b) is the second layer characteristic reflection wave data. By regarding the extended domain migration and the inverse migration as a forward transformation from the imaging domain to the data domain, the continuity and consistency of the extended domain gather are improved, thereby improving the quality of the pre-stack data. Compared with the method of removing the surface wave by low-pass filtering, the application can remove most of the surface wave while retaining the low-frequency effective signal. In addition, the missing seismic trace can be recovered, the data can be normalized, and the resolution of the data can be improved, thereby effectively suppressing random noise and ensuring the accuracy of the signal. By expanding the time series of the correlation event signals, effective characteristic reflection wave information can be extracted from low signal-to-noise ratio seismic data. After obtaining the characteristic reflection wave data of the target layer, a travel time inversion framework can be established based on the characteristic reflection wave data of the target layer, thereby obtaining simulated reflection wave data. Based on the characteristic reflection wave data and the simulated reflection wave data of the target layer, the travel time difference is calculated, and then the gradient of the travel time inversion is calculated based on the travel time difference. Then, the gradient of the travel time inversion is used as the update amount of the target layer to update the velocity model. The update amount of the target layer is as shown in Figure 5 (a) and Figure 5 (b) shown in Figure 5 (a) is the update amount corresponding to the two-layer characteristic reflection wave data, Figure 5 (b) is the update amount corresponding to the second layer characteristic reflection wave data, Figure 6 is the velocity model. By repeating the above steps, various wave number components of the velocity model can be gradually recovered, thereby improving the inversion efficiency.

[0147] In summary, by performing extended domain migration imaging on the observed seismic shot gather data, an extended domain common imaging point gather is obtained. Based on the extended domain common imaging point gather, a target layer is selected from the zero migration profile of the extended domain common imaging point gather. Based on the target layer, a common imaging point gather of the target layer is selected from the extended domain common imaging point gather. The common imaging point gather is inverse migrated and forward simulated to obtain characteristic reflection wave data of the target layer. A travel time inversion framework is established based on the characteristic reflection wave data to obtain simulated reflection wave data. Based on the characteristic reflection wave data and the simulated reflection wave data, the reflection wave travel time difference is calculated. Based on the travel time difference, the gradient of the travel time inversion is calculated. Based on the gradient of the travel time inversion, the velocity model is updated. Various wave number components of the velocity model can be recovered, thereby improving the inversion efficiency.

[0148] Embodiment Three

[0149] In the embodiments of the application, a certain exploration area seismic record is used as an example to prove that Figure 8 (a) is the observed seismic data collected in the field, i.e. the observed seismic shot gather data. The observed seismic shot gather data is subjected to extended domain migration imaging to obtain an extended domain common imaging point gather. The extended domain common imaging point gather is as shown in Figure 9 .Figure 10 For Figure 9 The corresponding zero-offset profile, the target formation is selected from the zero-offset profile, the common imaging point gather of the target formation is extracted, the processed characteristic reflection wave data is obtained by reverse migration of the common imaging point gather of the target formation, and the characteristic reflection wave data is as shown in Figure 8 (b) shown, by comparison, it can be seen that Figure 8 (b) The resolution and event continuity of the extracted characteristic reflection wave data are obviously better than those of the observed seismic shot gather data. Figure 11 And Figure 12 The inversion velocity results corresponding to the observed seismic shot gather data and the characteristic reflection wave data extracted by the application respectively, and by comparison, it can be seen that Figure 12 The inversion result has higher resolution and is more consistent with the formation structure of the migration profile ( Figure 10 ).

[0150] Example four

[0151] Based on the foregoing examples, the embodiments of the application provide a characteristic reflection wave travel time inversion device, each module included in the device and each unit included in each module can be realized by a processor in a computer device; of course, it can also be realized by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), microprocessor (MPU, Microprocessor Unit), digital signal processor (DSP, Digital Signal Processing) or field programmable gate array (FPGA, Field Programmable Gate Array) and the like.

[0152] The embodiments of the application provide a characteristic reflection wave travel time inversion device, Figure 13 A structural schematic diagram of a characteristic reflection wave travel time inversion device provided by the embodiments of the application is as shown in Figure 13 , which comprises:

[0153] The migration imaging module is configured to perform extended domain migration imaging on the seismic shot gather data to obtain an extended domain common imaging point gather.

[0154] In some embodiments, the extended domain migration imaging is performed on the observed seismic shot gather data to obtain the extended domain common imaging point gather, and the process can be expressed as:

[0155] I(x,θ)=-∫dxdθdωω 2 u0(x,x s ,θ,ω)G * (x,x r ,θ,ω)d obs (x r ,xs , ω);

[0156] where I (x, θ) is the extended domain common imaging gathers, θ is the angle, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, u0is the background wavefield, G * is the adjoint operator of G0, G0is the background field Green function, d obs is the observed seismic shot gather data.

[0157] The first selecting module is configured to select a target formation from a zero-offset profile of the extended domain common imaging gathers based on the extended domain common imaging gathers.

[0158] The second selecting module is configured to select a common imaging gather of the target formation from the extended domain common imaging gathers based on the target formation.

[0159] The reverse migration module is configured to perform reverse migration forward modeling on the common imaging gather to obtain characteristic reflection wave data of the target formation.

[0160] In some embodiments, the reverse migration forward modeling on the common imaging gather to obtain the characteristic reflection wave data of the target formation can be expressed as:

[0161] d obs = ∫dxdθdωω 2 u0(x, x s , θ, ω)I (x, θ)G0(x, x r , θ, ω);

[0162] where d obs is the observed seismic shot gather data, I (x, θ) is the extended domain common imaging gathers, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, G0is the background field Green function, and u0is the background wavefield.

[0163] From the observed seismic shot gather data, according to the requirements of inversion imaging, the following transformation can be abstracted:

[0164]

[0165] where d is the characteristic reflection wave data, and Q is a variation operator.

[0166] The traveltime inversion module is configured to establish a traveltime inversion framework based on the characteristic reflection wave data to obtain simulated reflection wave data.

[0167] In some embodiments, the objective function of the traveltime inversion framework can be expressed as:

[0168]

[0169] where τ(t) represents the traveltime residual, τ is the traveltime difference, E is the objective function, t is time, characteristic reflection data, is the characteristic reflection data reflection data after traveltime inversion.

[0170] A first calculation module is configured to calculate a traveltime difference of reflection based on the characteristic reflection data and the simulated reflection data.

[0171] A second calculation module is configured to calculate a gradient of traveltime inversion based on the traveltime difference.

[0172] In some embodiments, the calculation of the gradient of traveltime inversion based on the traveltime difference can be expressed as:

[0173]

[0174] where, is the gradient, is the Frechet derivative of the time-domain reflection field to the background model, is the adjoint source of the wave equation reflection traveltime inversion, where represents the time derivative of d, is the time second derivative of d.

[0175] An updating module is configured to update the velocity model based on the gradient of traveltime inversion.

[0176] It should be noted that, in the embodiments of the present application, if the characteristic reflection traveltime inversion method described above is realized in the form of a software function module and is sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk, and various storage media that can store program codes. Thus, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0177] Correspondingly, the embodiment of the present application provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps in the characteristic reflection wave travel time inversion method provided in the above embodiment.

[0178] Embodiment five

[0179] The embodiment of the present application provides an electronic device; Figure 14 The constituent structure schematic diagram of the electronic device provided by the embodiment of the present application is shown in Figure 14 The electronic device 400 includes a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between the components. The user interface 403 can include a display screen, and the external communication interface 404 can include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the characteristic reflection wave travel time inversion method stored in the memory to implement the steps in the characteristic reflection wave travel time inversion method provided in the above embodiment,

[0180] The embodiment of the present application provides a characteristic reflection wave travel time inversion method, which includes:

[0181] Extended domain migration imaging is performed on the observed seismic shot gather data to obtain an extended domain common imaging point gather;

[0182] Based on the extended domain common imaging point gather, a target formation is selected from a zero-offset profile of the extended domain common imaging point gather;

[0183] Based on the target formation, a common imaging point gather of the target formation is selected from the extended domain common imaging point gather;

[0184] Reverse migration forward modeling is performed on the common imaging point gather to obtain characteristic reflection wave data of the target formation;

[0185] Based on the characteristic reflection wave data, a travel time inversion framework is established to obtain simulated reflection wave data;

[0186] Based on the characteristic reflection wave data and the simulated reflection wave data, reflection wave travel time difference is calculated;

[0187] Based on the travel time difference, the gradient of travel time inversion is calculated;

[0188] Based on the gradient of travel time inversion, the velocity model is updated.

[0189] In some embodiments, extended domain migration imaging is performed on the observed seismic shot gather data to obtain an extended domain common imaging point gather, which can be expressed as:

[0190] I(x, θ) = -∫dx dx dω ω 2 u0(x, x s , θ, ω) G * (x, x r , θ, ω) d obs (x r , x s , ω);

[0191] where I(x, θ) is the extended domain common imaging point gather, θ is the angle, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, u0is the background wavefield, G * is the adjoint operator of G0, G0is the background field Green function, d obs is the observed seismic shot gather data.

[0192] In some embodiments, the de-migrating the common imaging point gather to obtain the characteristic reflection data of the target formation can be expressed as:

[0193] d obs = ∫dx dx dω ω 2 u0(x, x s , θ, ω) I(x, θ) G0(x, x r , θ, ω);

[0194] where d obs is the observed seismic shot gather data, I(x, θ) is the extended domain common imaging point gather, x is the spatial position, x r is the spatial position of the receiver, x s is the source position, ω is the angular frequency, G0is the background field Green function, u0is the background wavefield.

[0195] From the observed seismic shot gather data, according to the requirements of inversion imaging, it can be abstracted as the following transformation:

[0196]

[0197] where d is the characteristic reflection data, Q is the variation operator.

[0198] In some embodiments, the objective function of the traveltime inversion framework can be expressed as:

[0199]

[0200] where τ(t) represents the traveltime residual, τ is the traveltime difference, E is the objective function, t is the time, characteristic reflection data, characteristic reflection wave data reflection wave data after traveltime inversion is performed.

[0201] In some embodiments, the calculating the gradient of the traveltime inversion based on the traveltime difference can be expressed as:

[0202]

[0203] wherein, is the gradient, is the Frechet derivative of the time-domain reflection wavefield to the background model, is the adjoint source of the wave equation reflection traveltime inversion, wherein denotes the time derivative of d, is the time second derivative of d.

[0204] It should be noted that the description of the storage medium and the electronic device embodiments above is similar to the description of the method embodiments above, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application.

[0205] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily mean the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that the size of the sequence number of each process in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The sequence number of the above embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments.

[0206] It should be noted that in this document, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, object or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, object or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, object or device including the element.

[0207] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling, or direct coupling or communication connection between the components can be indirect coupling or communication connection through some interfaces, devices, or units, and can be electrical, mechanical, or in other forms.

[0208] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place, or distributed on multiple network units; and some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0209] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional units.

[0210] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by a program instructing related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the above-mentioned method embodiments when executed; and the foregoing storage medium includes mobile storage devices, read-only memories (ROMs), magnetic discs or optical discs, and various storage media that can store program codes.

[0211] Alternatively, the integrated units of the present application, if implemented in the form of software functional modules and sold or used as independent products, can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes several instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes mobile storage devices, ROMs, magnetic discs or optical discs, and various storage media that can store program codes.

[0212] The above merely provides the implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the change or replacement within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for travel time inversion of characteristic reflected waves, characterized in that, include: Extended domain migration imaging was performed on the observed seismic shot gather data to obtain extended domain common imaging point gathers; Based on the extended domain common imaging point gather, the target formation is selected from the zero-offset profile of the extended domain common imaging point gather; Based on the target formation, a common imaging point gather of the target formation is selected from the extended domain common imaging point gather; Inverse migration forward modeling was performed on the common imaging point gather to obtain the characteristic reflection wave data of the target stratum; A travel-time inversion framework is established based on the characteristic reflected wave data to obtain simulated reflected wave data; Based on the characteristic reflected wave data and simulated reflected wave data, the travel time difference of the reflected waves is calculated; Based on the travel time difference, the gradient of travel time inversion is calculated; The velocity model is updated based on the gradient obtained from the travel time inversion.

2. The method according to claim 1, characterized in that, The process of performing extended domain migration imaging on the observed seismic shot gather data to obtain extended domain common imaging point gathers can be described as follows: In the formula, For angle, The spatial location of the detector point. Location of the epicenter. Angular frequency, For the background wave field, The adjoint operator, for .

3. The method according to claim 1, characterized in that, The inverse migration forward modeling of the common imaging point gather, to obtain the characteristic reflection wave data of the target stratum, can be described as follows: In the formula, for , for , for , The spatial location of the detector point. Location of the epicenter. Angular frequency, For the background field Green function, For the background wave field, For angle; From observation In this process, based on the requirements of inversion imaging, it can be abstracted into the following transformation: ； In the formula, For characteristic reflected wave data, For change operators.

4. The method according to claim 1, characterized in that, The objective functional of the time-travel inversion framework can be expressed as: In the formula, Indicates the travel time residual. To get around the time difference, For the target functional, For time, for , The reflected wave data after travel-time inversion.

5. The method according to claim 4, characterized in that, The calculation of the gradient for travel time inversion based on the travel time difference can be expressed as follows: In the formula, The Frechet derivative of the time-domain reflected wavefield with respect to the background model. This is the accompanying source for the travel time inversion of the reflected wave in the wave equation, where, , express Time derivative, for The second derivative in time, .

6. A characteristic reflected wave travel time inversion device, characterized in that, include: The migration imaging module is used to perform extended domain migration imaging on seismic shot gather data to obtain extended domain common imaging point gathers; The first selection module is used to select a target formation from the zero-offset profile of the extended domain common imaging point gather based on the extended domain common imaging point gather. The second selection module is used to select a common imaging point set of the target formation from the extended domain common imaging point set based on the target formation; The inverse migration module is used to perform inverse migration forward modeling on the common imaging point gather to obtain the characteristic reflection wave data of the target stratum. The travel time inversion module is used to establish a travel time inversion framework based on the characteristic reflected wave data to obtain simulated reflected wave data. The first calculation module is used to calculate the travel time difference of reflected waves based on the characteristic reflected wave data and the simulated reflected wave data. The second calculation module is used to calculate the gradient of the time travel inversion based on the time travel difference; The update module is used to update the velocity model based on the gradient obtained from the travel time inversion.

7. The apparatus according to claim 6, characterized in that, Extended-domain migration imaging is performed on the observed seismic shot gather data to obtain extended-domain common imaging point gathers. This process can be described as follows: In the formula, For angle, The spatial location of the detector point. Location of the epicenter. Angular frequency, For the background wave field, The adjoint operator, for .

8. The apparatus according to claim 6, characterized in that, The inverse migration forward modeling of the common imaging point gather, to obtain the characteristic reflection wave data of the target stratum, can be described as follows: ； In the formula, for , for , for , The spatial location of the detector point. Location of the epicenter. Angular frequency, For the background field Green function, For the background wave field, For angle; From observation In this process, based on the requirements of inversion imaging, it can be abstracted into the following transformation: ； In the formula, For characteristic reflected wave data, For change operators.

9. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the method as described in any one of claims 1 to 5.

10. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method as described in any one of claims 1 to 5.

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