A seismic data processing method, device and electronic equipment for a deep reflection Moho surface

Abstract

The application relates to the technical field of seismic exploration, and discloses a deep reflection Moho surface seismic data processing method, a deep reflection Moho surface seismic data processing device and electronic equipment, which comprises the following steps: carrying out grid processing on deep reflection Moho surface seismic data to obtain a grid processing result; carrying out tomographic static correction processing on the grid processing result to obtain a tomographic static correction processing result; carrying out amplitude compensation processing on the tomographic static correction processing result to obtain an amplitude compensation processing result; carrying out targeted denoising processing on the amplitude compensation processing result to obtain a targeted denoising processing result; carrying out velocity analysis processing on the targeted denoising processing result to obtain a velocity analysis processing result; and carrying out super-gather stack processing on the targeted denoising processing result according to the velocity analysis processing result to obtain a seismic profile. The application can obviously improve the signal-to-noise ratio of deep reflection Moho surface seismic data, improve imaging quality, and meet the requirements of deep reflection seismic continental dynamics research and deep crust structure research.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic exploration, and in particular to a deep reflection Moho surface seismic data processing method, device and electronic equipment. BACKGROUND

[0002] The existing conventional seismic data processing flow is mostly for processing the seismic reflection signals within the exploration depth of 4km to 5km, and the exploration field with the target layer buried depth greater than 6km is called the ultra-deep layer, and the exploration depth of the Moho surface is about 15km.

[0003] Due to the great depth of the Moho surface, the high-frequency signal is severely attenuated after the seismic wave propagates for a long distance, and the imaging is highly dependent on the low-frequency signal. In the existing conventional seismic data processing technology, the pursuit of shallow and middle layer resolution will lead to a sharp reduction of the signal-to-noise ratio of the ultra-deep layer, which affects the imaging effect of the ultra-deep Moho surface. Therefore, the existing conventional seismic data processing flow is not applicable to the imaging of the deep reflection Moho surface seismic data.

[0004] In addition, the existing deep exploration geophysical technology is mainly the method of seismology, and the deep reflection seismic technology is one of the effective means for studying the fine structure of the crust and upper mantle. This method uses the elastic wave reflection events generated by different physical interfaces to describe the interface, fracture and other geological structure characteristics. It provides the fine structure of the crust and upper mantle that cannot be obtained by other geophysical methods. The deep reflection seismic profile can obtain the reflection image from the Moho surface and the upper mantle, reveal the lithospheric structure, and solve the deep geological structure problem. Due to the characteristics of large detection depth, high resolution and accuracy and reliability of the deep reflection seismic method, in the future period, the deep reflection seismic technology will still be the main means for dynamic research and deep crust fine structure research, and therefore, it is necessary to use the single shot of normal energy excitation to explore the deep reflection.

[0005] However, the existing Moho surface exploration technology research is basically based on the 16s single shot of large cannon (excitation explosive of several hundred kilograms) and strong energy, and the research based on the 16s receiving single shot of normal energy excitation (excitation explosive of several dozen kilograms) is still less.

[0006] Therefore, there is an urgent need for a seismic data processing method specially for deep reflection Moho surface to solve the above technical problems. SUMMARY

[0007] In view of the above problems, the present application provides a deep reflection Moho surface seismic data processing method, device and electronic equipment, which can significantly improve the signal-to-noise ratio of the deep reflection Moho surface seismic data and improve the imaging quality.

[0008] The present application provides a deep reflection Moho surface seismic data processing method, which comprises:

[0009] grid the seismic data of the deep reflection Moho surface to obtain a grid processing result;

[0010] perform tomographic static correction processing on the grid processing result to obtain a tomographic static correction processing result;

[0011] perform amplitude compensation processing on the tomographic static correction processing result to obtain an amplitude compensation processing result;

[0012] perform targeted denoising processing on the amplitude compensation processing result to obtain a targeted denoising processing result;

[0013] perform velocity analysis processing on the targeted denoising processing result to obtain a velocity analysis processing result;

[0014] perform super-gather stacking processing on the targeted denoising processing result according to the velocity analysis processing result to obtain a seismic profile.

[0015] Further, before the grid processing on the seismic data of the deep reflection Moho surface, the method further comprises:

[0016] controlling the seismic wave detection device to collect the seismic data of the deep reflection Moho surface according to a preset collection strategy;

[0017] The preset collection strategy comprises:

[0018] exciting the seismic wave detection device by a seismic source under normal energy to collect the seismic data of the deep reflection Moho surface.

[0019] Further, the grid processing on the seismic data of the deep reflection Moho surface comprises:

[0020] performing grid processing on the seismic data of the deep reflection Moho surface according to a large bin processing method;

[0021] The large bin processing method comprises:

[0022] When performing the grid processing on the seismic data of the deep reflection Moho surface, the grid bin of the seismic data of the deep reflection Moho surface is set as a preset size large bin according to the interval between two adjacent seismic wave detection devices.

[0023] Further, the amplitude compensation processing on the tomographic static correction processing result comprises:

[0024] performing time function amplitude compensation processing on the vertical amplitude of the tomographic static correction processing result;

[0025] performing surface consistency amplitude compensation processing on the horizontal amplitude of the tomographic static correction processing result.

[0026] Further, the amplitude compensation processing result is subjected to targeted denoising processing, including:

[0027] The amplitude compensation processing result is subjected to targeted denoising processing through a linear noise attenuation method and an abnormal amplitude suppression method in sequence.

[0028] The amplitude compensation processing result includes a low-frequency signal with a frequency less than a preset frequency threshold.

[0029] Further, the targeted denoising processing result is subjected to velocity analysis processing, including:

[0030] The deep reflection velocity of the deep reflection Moho seismic data is obtained by referring to the velocity of the preset deep reflection seismic data and combining the velocity scanning analysis stacking velocity.

[0031] Further, the targeted denoising processing result is subjected to super gather stacking processing according to the velocity analysis processing result, including:

[0032] The targeted denoising processing result is subjected to super gather stacking processing according to a multiple coverage observation method or a horizontal stacking method.

[0033] The application further provides a deep reflection Moho seismic data processing device, and the device comprises:

[0034] A gridding processing module is configured to perform gridding processing on the deep reflection Moho seismic data to obtain a gridding processing result.

[0035] A static correction processing module is configured to perform tomographic static correction processing on the gridding processing result to obtain a tomographic static correction processing result.

[0036] An amplitude compensation processing module is configured to perform amplitude compensation processing on the tomographic static correction processing result to obtain an amplitude compensation processing result.

[0037] A denoising processing module is configured to perform targeted denoising processing on the amplitude compensation processing result to obtain a targeted denoising processing result.

[0038] A velocity analysis processing module is configured to perform velocity analysis processing on the targeted denoising processing result to obtain a velocity analysis processing result.

[0039] A stacking processing module is configured to perform super gather stacking processing on the targeted denoising processing result according to the velocity analysis processing result to obtain a seismic profile.

[0040] The application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program which, when executed by one or more processors, implements the steps of the above method.

[0041] The application further provides an electronic device comprising a memory and one or more processors, wherein the memory stores a computer program, and when the computer program is executed by the one or more processors, the steps of the method described above are performed.

[0042] The application provides a deep reflection Moho surface seismic data processing method and device and electronic equipment, through sequentially performing grid processing, tomographic static correction processing, amplitude compensation processing, targeted denoising processing, velocity analysis processing and super gather stack processing on seismic data, the signal-to-noise ratio of the deep reflection Moho surface seismic data is obviously improved, the imaging quality is improved, and the needs of deep reflection seismic dynamics research and deep crust structure research are met. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0044] In addition, it should be further pointed out that, for the convenience of description, only the parts related to the present application are shown in the drawings. The drawings of the specification which form a part of the present application are used to provide further understanding of the present application, the schematic embodiments and their descriptions in the present application are used to explain the present application, and do not constitute improper limitation on the present application. In the drawings:

[0045] Figure 1 A deep reflection Moho surface seismic data processing method step flow chart provided in the embodiment one of the present application;

[0046] Figure 2 An effect diagram after processing and stacking of seismic data using different bins (left: 10m bin, right: 20m bin);

[0047] Figure 3 A stacking profile schematic diagram before targeted denoising processing using the embodiment one of the present application;

[0048] Figure 4 A stacking profile schematic diagram after targeted denoising processing using the embodiment one of the present application;

[0049] Figure 5 A single shot effect diagram before and after targeted denoising processing (left: before targeted denoising processing, right: after targeted denoising processing) using the embodiment one of the present application;

[0050] Figure 6Fig. 1 is a schematic diagram of the effect comparison of the normal stack and the super-gather stack on seismic data (left: normal stack, right: super-gather stack) ;

[0051] Figure 7 Fig. 2 is a schematic diagram of the stack effect of the conventional processing profile on seismic data;

[0052] Figure 8 Fig. 3 is a schematic diagram of the stack effect of the seismic data processing method for deep reflection Moho provided by the embodiment one of the present application;

[0053] Figure 9 Fig. 4 is a schematic diagram of the structure of the seismic data processing device for deep reflection Moho provided by the embodiment two of the present application;

[0054] Figure 10 Fig. 5 is a schematic diagram of the structure of the electronic device provided by the embodiment five of the present application;

[0055] Reference signs:

[0056] Figure 9 In the figure: 901-grid processing module, 902-static correction processing module, 903-amplitude compensation processing module, 904-noise removal processing module, 905-velocity analysis processing module, 906-stack processing module;

[0057] Figure 10 In the figure: 1000-electronic device, 1001-processor, 1002-communication bus, 1003-user interface, 1004-communication interface, 1005-memory. DETAILED DESCRIPTION

[0058] The present application will be further described below with reference to the embodiments shown in the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0059] It should be noted that: in these embodiments, the relative arrangement, numerical expression and numerical value of the components and steps described are not limited to the scope of the present application, unless otherwise specifically stated.

[0060] It should also be understood that, for any component, data or structure mentioned in the embodiments of the present application, one or more can be generally understood, without explicit limitation or in the context of the opposite indication given by the preceding or subsequent text.

[0061] It should also be understood that the description of the various embodiments of the present application emphasizes the differences between the various embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, they will not be repeated.

[0062] The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application or its application or uses.

[0063] The techniques, methods, and devices known to those of ordinary skill in the relevant art(s) for which embodiments of the application can not be discussed in detail herein but should be considered as part of the specification.

[0064] Embodiments of the application can be applied to terminal devices, computer systems, servers, and the like electronic devices, which can operate with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations that can be suitable for use with terminal devices, computer systems, servers, and the like electronic devices include, but are not limited to, personal computers, server computers, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputers, mainframe computers, and distributed cloud computing technology environments that include any of the above systems, and the like.

[0065] Terminal devices, computer systems, servers, and the like electronic devices can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like, which perform particular tasks or implement particular abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in local or remote computer system storage media including memory storage devices.

[0066] The main results of the deep reflection seismic profile study of the Moho surface are as follows: ① It is not a surface, but a velocity gradient or interchanging layer with a thickness of about 3-4 km or even thicker, and double Moho layers appear in some areas; ② In different tectonic backgrounds, the development degree is different, such as the lower Moho surface in stable craton has poor reflectivity, while in orogenic belts, especially in extensional tectonic zones, the Moho surface is continuous and has good reflectivity, forming the boundary between the reflective lower crust and the transparent upper mantle; ③ Most of the Moho surfaces are newly formed, which are not constant in the history of the earth, but have the characteristics of spatial and temporal dynamic evolution, and also have strong lateral heterogeneity, forming the bottom boundary of the reflective lower crust; ④ Deep seismic reflection profiles reveal that the faulting of the Moho surface is caused by the multi-periodicity of the Moho surface formation, and the flat and continuous Moho surface reflection reflects the newly formed crustal structure.

[0067] As can be known from the background art, the existing conventional seismic data processing flow is mostly for processing seismic reflection signals within an exploration depth of 4km to 5km, and the exploration field with a target layer buried deeper than 6km is referred to as an ultra-deep layer, and the exploration depth of the Moho surface is about 15km.

[0068] Due to the great depth of the Moho surface, after the seismic wave propagates for a long distance, the high-frequency signal is greatly attenuated, and the imaging is highly dependent on the low-frequency signal. In the existing conventional seismic data processing technology, while pursuing the resolution of the shallow and middle layers, the signal-to-noise ratio of the ultra-deep layer is sharply reduced, which affects the imaging effect of the ultra-deep Moho surface. Therefore, the existing conventional seismic data processing flow is not applicable to the imaging of the deep reflection Moho surface seismic data.

[0069] In addition, the existing deep exploration geophysical technology is mainly a seismic method, and the deep reflection seismic technology is one of the effective means for studying the fine structure of the crust and upper mantle. This method uses the elastic wave reflection events generated by different physical interfaces to describe the interface, fracture and other geological structure characteristics. It provides the fine structure of the crust and upper mantle that cannot be obtained by other geophysical methods. The deep reflection seismic profile can obtain reflection images from the Moho surface and the upper mantle, reveal the lithospheric structure, and solve the deep geological structure problem. Due to the characteristics of large detection depth, high resolution and accuracy and reliability of the deep reflection seismic method, in the future period, the deep reflection seismic technology will still be the main means for studying the dynamics and the fine structure of the deep crust. Therefore, it is necessary to use the single shot of normal energy excitation to explore the deep reflection.

[0070] However, the existing Moho surface exploration technology research is basically based on the 16s single shot of large cannon (exciting explosive of several hundred kilograms) and strong energy, and the research based on the 16s receiving single shot of normal energy excitation (exciting explosive of several dozen kilograms) is still less.

[0071] Therefore, the present application provides a seismic data processing method suitable for deep reflection Moho surface to solve the above technical problems.

[0072] Example One

[0073] In the embodiment one of the present application, as shown in Figure 1 a deep reflection Moho surface seismic data processing method is provided, and the method specifically includes the following steps:

[0074] Step S101: performing grid processing on the deep reflection Moho surface seismic data to obtain a grid processing result.

[0075] Step S102: performing tomographic static correction processing on the grid processing result to obtain a tomographic static correction processing result.

[0076] Step S103: performing amplitude compensation processing on the tomographic static correction processing result to obtain an amplitude compensation processing result.

[0077] Step S104: performing targeted denoising on the amplitude compensation processing result to obtain a targeted denoising processing result.

[0078] Step S105: performing speed analysis on the targeted denoising processing result to obtain a speed analysis processing result.

[0079] Step S106: Perform super gather stacking processing on the targeted denoising processing results according to the velocity analysis processing results to obtain a seismic profile.

[0080] Optionally, in step S101, gridding the seismic data of the deep reflection Moho surface includes:

[0081] The seismic data of deep reflection Moho surface are gridded according to the large-surface processing method.

[0082] Among them, large surface processing methods include:

[0083] When gridding the seismic data of the deep reflection Moho surface, the grid bins of the seismic data of the deep reflection Moho surface are set to large bins of a preset size according to the distance between two adjacent seismic wave detection devices.

[0084] It should be understood that, in step S101 , the gridding processing result refers to the seismic data obtained after the gridding processing.

[0085] In seismic data processing, grid setting is crucial, as it affects the start and end point numbers of the data, as well as the number of times the data is covered.

[0086] In conventional data processing, selecting a smaller grid spacing improves the data resolution while meeting the coverage number. However, deep reflection data has a higher requirement for the signal-to-noise ratio. Using a larger grid spacing can increase the coverage number. The increase in the coverage number can suppress random interference and improve the data signal-to-noise ratio. Therefore, the present invention adopts a larger bin to improve the data signal-to-noise ratio as much as possible.

[0087] Specifically, Figure 2 The effect diagram after processing and superimposing seismic data with different bins (left: 10m bin, right: 20m bin) is shown. Figure 2 It can be seen that the signal-to-noise ratio of the seismic data using 20m bins is improved compared with that using 10m bins. Therefore, it can be seen that the large bin processing idea proposed in the present invention has an obvious effect on improving the signal-to-noise ratio of seismic data of deep reflection Moho surface.

[0088] It should be noted that in step S101, the seismic wave detection device includes a geophone, and the preset size can be determined by a technician according to the interval (i.e., the trace interval) between the two adjacent seismic wave detection devices, and the present application does not make special limitations on this.

[0089] For example, assuming that the interval between the two adjacent seismic wave detection devices is 40 m, the existing seismic data processing generally sets the bin to 20 m, and the present application sets the grid bin to a multiple of 20 m, for example, 40 m, that is, the preset size is determined to be 40 m, so that the grid bin is set to a large bin of 40 m.

[0090] In step S102, the tomographic static correction processing result can be processed by using the existing tomographic static correction processing method, and the processing idea is the same as that in the conventional tomographic static correction processing, and the present application does not make special limitations on this.

[0091] It should be understood that in step S102, the tomographic static correction processing result refers to the seismic data obtained after the tomographic static correction processing.

[0092] Static correction is a correction and elimination of the influence of the surface elevation and the low subsurface and the change of the low-speed zone on the travel time of the reflected wave. Static correction is a basic work for realizing common reflection point stacking, which not only affects the signal-to-noise ratio and vertical resolution of the stacked profile, but also affects the quality of the stacking velocity analysis.

[0093] In step S103, the amplitude compensation processing of the tomographic static correction processing result includes:

[0094] The time function amplitude compensation processing is performed on the vertical amplitude of the tomographic static correction processing result.

[0095] The surface consistency amplitude compensation processing is performed on the horizontal amplitude of the tomographic static correction processing result.

[0096] It should be understood that in step S103, the amplitude compensation processing result refers to the seismic data obtained after the amplitude compensation processing.

[0097] When the seismic wave propagates in the stratum, the wave front face continuously expands with the increase of the propagation distance. Since the total energy generated by the seismic wave excitation is constant, the energy density per unit area on the wave front face continuously decreases, and the amplitude of the seismic wave continuously decreases with the increase of the propagation distance, which is called wave front diffusion.

[0098] When seismic waves propagate in underground media, the actual rock layers are not completely elastic. The incomplete elasticity of the rock layers causes the elastic energy of the seismic waves to be irreversibly converted into heat energy and consumed, thereby causing the amplitude of the seismic waves to attenuate. This phenomenon of seismic wave amplitude attenuation caused by the incomplete elasticity of the medium is called absorption.

[0099] The purpose of true amplitude recovery is to compensate and correct the attenuation and distortion of seismic wave energy as much as possible, mainly including wavefront diffusion energy compensation, formation absorption energy compensation and surface consistency energy adjustment.

[0100] In step S103 of the present invention, longitudinal amplitude compensation processing is performed on the tomographic static correction processing result according to the time function, thereby eliminating the influence of wavefront diffusion.

[0101] In step S103 of the present invention, surface consistency amplitude compensation is performed on the lateral amplitude in the tomographic static correction processing result to obtain a compensation result, thereby reducing the energy difference between shots and traces.

[0102] In step S103 of the present invention, energy loss in the longitudinal time direction is compensated using a time function amplitude compensation process to compensate for the energy attenuated by seismic waves in medium and deep layers. This maintains the relative relationship between the longitudinal amplitudes of the seismic data and makes the energy relationship between shallow, medium, and deep layers more reasonable. In the transverse spatial direction, surface consistency amplitude compensation is performed to eliminate energy imbalances between shots and traces caused by excitation and reception factors.

[0103] In step S104, performing targeted denoising on the amplitude compensation processing result includes:

[0104] The amplitude compensation processing results are targetedly denoised using the linear noise attenuation method and the abnormal amplitude suppression method.

[0105] The amplitude compensation processing result includes a low-frequency signal having a frequency less than a preset frequency threshold.

[0106] It should be understood that the targeted denoising result refers to the seismic data after targeted denoising.

[0107] Noise is equivalent to the concept of "interference waves." In certain exploration methods, all waves other than the effective wave (signal) are called noise. In reflection wave exploration, all waves other than the primary effective wave are interference waves (noise). Interference waves can be divided into two categories based on the patterns of their occurrence: random interference and regular interference.

[0108] Random noise refers to interference waves that do not have a specific main frequency or a specific propagation direction, such as the movement of the wind and grass.

[0109] Regular noise refers to interference waves with a certain main frequency and apparent velocity, such as surface waves, sound waves, shallow refraction waves, side waves, etc.

[0110] Considering that imaging deep reflection seismic data requires more low-frequency information, and to protect low-frequency signals, the present invention uses linear noise attenuation and abnormal amplitude suppression in step S104 to denoise the amplitude compensation results. These amplitude compensation results include low-frequency signals with frequencies below a preset frequency threshold. In other words, compared to existing seismic data processing methods that also use surface roll attenuation techniques for low-frequency signals, the present invention uses linear noise attenuation and abnormal amplitude suppression in step S104 to denoise only low-frequency signals with frequencies below the preset frequency threshold, rather than surface roll attenuation, thereby protecting low-frequency signals.

[0111] It should be noted that the present invention does not impose any special limitation on the value of the preset frequency threshold, which can be determined by technicians according to actual needs.

[0112] Preferably, the preset frequency threshold is 10 Hz, that is, for low-frequency signals below 10 Hz, a linear noise attenuation method and an abnormal amplitude suppression method are sequentially used for denoising.

[0113] Figure 3 Schematic diagram of superimposed cross sections before the targeted denoising process in an embodiment of the present invention is adopted. Figure 4 It is a schematic diagram of superimposed cross sections after the targeted denoising process in an embodiment of the present invention.

[0114] from Figure 3 、 Figure 4 It can be seen that after adopting the targeted denoising processing in the embodiment of the present invention, the effective signal on the superimposed section is significantly protected and the signal-to-noise ratio of the data is significantly improved.

[0115] Figure 5 The single shot effect diagram before and after the targeted denoising process in the embodiment of the present invention (left: before targeted denoising process, right: after targeted denoising process) is shown. Figure 5 It can be seen that after denoising, the weak effective signal hidden in the strong linearity is well presented, so the targeted denoising idea proposed in the present invention is feasible.

[0116] comprehensive Figure 3 、 Figure 4 as well as Figure 5 The present invention uses targeted denoising processing, combined with a linear noise attenuation method and an abnormal amplitude suppression method, to significantly suppress various noises. The effective waveform is natural and faithful, and there is no obvious effective signal in the noise, resulting in a good denoising effect.

[0117] In step S105, the velocity analysis processing result of the targeted denoising processing result comprises:

[0118] Referring to the velocity of the preset deep reflection seismic data and combining the velocity scanning analysis stack velocity, the deep reflection velocity of the deep reflection Moho seismic data is obtained.

[0119] Specifically, the velocity of the preset deep reflection seismic data is determined by a technician according to actual needs, and the present application does not make special limitations thereon.

[0120] Preferably, the velocity of the deep reflection seismic data of the Xiongan region can be used as the velocity of the preset deep reflection seismic data.

[0121] The propagation velocity of seismic waves in the underground medium is a very important parameter in seismic data processing and interpretation. The velocity parameter is not only related to the quality of many aspects of seismic data processing, but also provides important information about the underground structure and lithology.

[0122] Velocity analysis is a crucial link in the process of seismic data processing. In the case of accurate velocity field, seismic data can better reflect the characteristics of underground structure through stacking and migration processing. Otherwise, false images may be produced, and even incorrect interpretation results may be produced. Accurate and reliable velocity analysis is the basis of seismic data processing.

[0123] With the increase of exploration depth, the imaging effect of seismic data is less sensitive to velocity. In one implementation, the preset velocity analysis processing in step S105 of the present application can specifically comprise: on the basis of analyzing the velocity spectrum, referring to the velocity of the deep reflection data of the Xiongan region, and combining the velocity scanning analysis stack velocity, the relatively accurate deep reflection velocity is finally obtained.

[0124] Specifically, the velocity analysis processing in step S105 of the present application can be realized by using existing analysis software, such as Omega seismic processing software.

[0125] In step S106, the super gather stacking processing is performed on the targeted denoising processing result according to the velocity analysis processing result, comprising:

[0126] According to the multiple coverage observation method or the horizontal stacking method, the super gather stacking processing is performed on the targeted denoising processing result.

[0127] Specifically, the super gather stacking processing can comprise:

[0128] Defining the gather ready to participate in stacking as CMP, the adjacent gathers are defined as CMP-2, CMP-1, CMP, CMP+1, CMP+2, and the super gather stacking is that the adjacent 5 gathers are stacked as CMP, and so on.

[0129] In the field, the method of multiple coverage is used, and in the indoor processing, the horizontal stacking technique is used, and finally the horizontal stacking section is obtained, and this whole set of work is called common reflection point stacking method.

[0130] Horizontal stacking is to stack the signals from different excitation points of the same reflection point in the underground received by different receiving points after the moveout correction, and this method can improve the quality of the seismic record, especially the effect of suppressing a regular interference wave is the best, and compared with the combination method, the effect of suppressing random interference is better.

[0131] In terms of signal-to-noise ratio, stacking is the most effective technology to improve signal-to-noise ratio. Generally, when generating a stacked section, single common point data, i.e. CMP data, is directly used for stacking, and in step S106 of the present application, the super-gather stacking processing is to make the adjacent common point gather of the stacked data participate in the stacking of the present gather at the same time, so that the signal-to-noise ratio of the stacked data is improved and the effective reflection is strengthened.

[0132] Figure 6 For the effect comparison diagram of the seismic data after normal stacking and super-gather stacking (left: normal stacking, right: super-gather stacking), from Figure 6 It can be seen that after super-gather stacking, the signal-to-noise ratio of the stacked section is greatly improved, and the imaging effect of deep reflection is greatly improved.

[0133] Figure 7 For the effect diagram of the profile stacking of the seismic data after the conventional processing, Figure 8 For the effect diagram of the profile stacking of the seismic data after the deep reflection Moho surface seismic data processing method provided by the present application, compared with Figure 7 and Figure 8 It can be found that by using the deep reflection Moho surface seismic data processing method provided by the present application, through the grid processing, the targeted low-frequency protection denoising processing and the super-gather stacking processing, the signal-to-noise ratio and the imaging effect of the deep reflection data are greatly improved.

[0134] Optionally, before step S101, the method further comprises:

[0135] Step S107: controlling the seismic wave detection equipment to collect the deep reflection Moho surface seismic data according to the preset acquisition strategy.

[0136] The preset acquisition strategy comprises: exciting the seismic wave detection equipment by the seismic source under normal energy to collect the deep reflection Moho surface seismic data.

[0137] Specifically, the seismic source comprises a dynamite seismic source, the seismic wave detection equipment comprises a geophone, and the normal energy refers to the energy provided when ten kilograms of dynamite explodes.

[0138] The deep reflection Moho seismic data is collected by the explosive source exciting the receiver, and the exploration depth of the Moho is about 15km, so the deep reflection Moho seismic data collected in the step S107 is the 16s receiving single shot of normal energy excitation.

[0139] The deep reflection Moho seismic data processing method provided in the embodiment can obviously improve the signal-to-noise ratio of the deep reflection Moho seismic data, improve the imaging quality, and meet the needs of the deep reflection seismic dynamics research and deep crust structure research.

[0140] Example Two

[0141] In the embodiment two of the present application, as shown in Figure 9 The device specifically comprises:

[0142] The grid processing module 901 is configured to perform grid processing on the deep reflection Moho seismic data, and obtain a grid processing result.

[0143] The static correction processing module 902 is configured to perform tomographic static correction processing on the grid processing result, and obtain a tomographic static correction processing result.

[0144] The amplitude compensation processing module 903 is configured to perform amplitude compensation processing on the tomographic static correction processing result, and obtain an amplitude compensation processing result.

[0145] The denoising processing module 904 is configured to perform targeted denoising processing on the amplitude compensation processing result, and obtain a targeted denoising processing result.

[0146] The velocity analysis processing module 905 is configured to perform velocity analysis processing on the targeted denoising processing result, and obtain a velocity analysis processing result.

[0147] The stacking processing module 906 is configured to perform super-trace gather stacking processing on the targeted denoising processing result according to the velocity analysis processing result, and obtain a seismic profile.

[0148] Optionally, the device further comprises:

[0149] The control acquisition module is configured to control the seismic wave detection equipment to collect the deep reflection Moho seismic data according to the preset acquisition strategy.

[0150] The preset acquisition strategy comprises:

[0151] The seismic wave detecting device is excited by a seismic source under normal energy to collect the seismic data of the deep reflection Moho surface.

[0152] Specifically, the seismic source comprises an explosive source, the seismic wave detecting device comprises a geophone, and the normal energy refers to the energy provided by the explosion of ten kilograms of explosives.

[0153] Optionally, the seismic data of the deep reflection Moho surface is subjected to gridding processing, comprising:

[0154] The seismic data of the deep reflection Moho surface is subjected to gridding processing according to a large bin processing method.

[0155] The large bin processing method comprises:

[0156] In the gridding processing of the seismic data of the deep reflection Moho surface, the grid bin of the seismic data of the deep reflection Moho surface is set as a large bin of a preset size according to the interval between two adjacent seismic wave detecting devices.

[0157] In seismic data processing, the setting of a grid is very important, which relates to the start and end line point numbers of data and also relates to the fold of data.

[0158] In conventional data processing, a smaller grid interval is selected to improve the resolution of data while meeting the fold, and the deep reflection data has a higher requirement on signal-to-noise ratio, so a larger grid interval can improve the fold, and the improvement of the fold can suppress random interference and improve the signal-to-noise ratio of data, so the present application adopts a larger bin to improve the signal-to-noise ratio of data as much as possible.

[0159] Optionally, the gridding processing result is subjected to tomographic static correction processing, comprising:

[0160] The gridding processing result is subjected to tomographic static correction processing by using an existing tomographic static correction processing method, and the processing thought is the same as that in conventional tomographic static correction of seismic data processing, and the present application does not specially limit this.

[0161] Optionally, the tomographic static correction processing result is subjected to amplitude compensation processing, comprising:

[0162] The vertical amplitude of the tomographic static correction processing result is subjected to time function amplitude compensation processing.

[0163] The horizontal amplitude of the tomographic static correction processing result is subjected to surface consistency amplitude compensation processing.

[0164] The energy loss in the longitudinal time direction in the application adopts time function amplitude compensation processing, compensates the energy of the middle and deep layers of the seismic wave, can keep the relative relationship of the longitudinal amplitude of the seismic data, and makes the energy relationship of the shallow, middle and deep layers of the seismic data more reasonable. In the horizontal spatial direction, the surface consistency amplitude compensation processing is carried out, so as to eliminate the energy imbalance phenomenon between the shots and the traces caused by the excitation factors and the receiving factors.

[0165] Optionally, the amplitude compensation processing result is subjected to targeted denoising processing, including:

[0166] The amplitude compensation processing result is subjected to targeted denoising processing by a linear noise attenuation method and an abnormal amplitude suppression method in sequence.

[0167] The amplitude compensation processing result includes a low-frequency signal with a frequency less than a preset frequency threshold.

[0168] It should be noted that the application does not specially limit the value of the preset frequency threshold, which can be determined by the technical personnel according to the actual needs. Preferably, the preset frequency threshold is 10 Hz.

[0169] Optionally, the targeted denoising processing result is subjected to velocity analysis processing, including:

[0170] The deep reflection velocity of the deep reflection Moho seismic data is obtained by referring to the velocity of the preset deep reflection seismic data and combining the velocity scanning analysis stacking velocity.

[0171] Preferably, the velocity of the preset deep reflection seismic data is the velocity of the Xiongan region deep reflection seismic data.

[0172] Optionally, the targeted denoising processing result is subjected to super-trace stack processing according to the velocity analysis processing result, including:

[0173] The targeted denoising processing result is subjected to super-trace stack processing according to the multiple coverage observation method or the horizontal stack method.

[0174] The deep reflection Moho seismic data processing device provided by the embodiment can obviously improve the signal-to-noise ratio of the deep reflection Moho seismic data, improve the imaging quality, and meet the needs of the deep reflection seismic dynamics research and deep crust structure research.

[0175] Example Three

[0176] In the third embodiment of the present application, a computer program product is also provided, which comprises a computer program or instructions, and when the computer program or instructions are executed by a processor, all or part of the steps of the deep reflection Moho seismic data processing method described in the above embodiments are implemented.

[0177] The deep reflection Moho seismic data processing method comprises:

[0178] The deep reflection Moho seismic data is subjected to gridding processing to obtain a gridding processing result.

[0179] The gridding processing result is subjected to tomographic static correction processing to obtain a tomographic static correction processing result.

[0180] The tomographic static correction processing result is subjected to amplitude compensation processing to obtain an amplitude compensation processing result.

[0181] The amplitude compensation processing result is subjected to targeted denoising processing to obtain a targeted denoising processing result.

[0182] The targeted denoising processing result is subjected to velocity analysis processing to obtain a velocity analysis processing result.

[0183] The targeted denoising processing result is subjected to super-gather stacking processing according to the velocity analysis processing result to obtain a seismic profile.

[0184] Optionally, before the deep reflection Moho seismic data is subjected to gridding processing, the method further comprises:

[0185] The seismic wave detection equipment is controlled to collect the deep reflection Moho seismic data according to a preset acquisition strategy.

[0186] The preset acquisition strategy comprises:

[0187] The seismic wave detection equipment is excited by a seismic source under normal energy to collect the deep reflection Moho seismic data.

[0188] Optionally, the deep reflection Moho seismic data is subjected to gridding processing, which comprises:

[0189] The deep reflection Moho seismic data is subjected to gridding processing according to a large bin processing method.

[0190] The large bin processing method comprises:

[0191] When the deep reflection Moho seismic data is subjected to gridding processing, the grid bin of the deep reflection Moho seismic data is set to a preset size of a large bin according to the spacing between two adjacent seismic wave detection equipment.

[0192] Optionally, the tomographic static correction processing result is subjected to amplitude compensation processing, which comprises:

[0193] The vertical amplitude of the tomographic static correction processing result is subjected to time function amplitude compensation processing;

[0194] The horizontal amplitude of the tomographic static correction processing result is subjected to surface consistency amplitude compensation processing.

[0195] Optionally, the amplitude compensation processing result is subjected to targeted denoising processing, including:

[0196] The amplitude compensation processing result is subjected to targeted denoising processing by a linear noise attenuation method and an abnormal amplitude suppression method in sequence.

[0197] The amplitude compensation processing result includes a low frequency signal with a frequency less than a preset frequency threshold.

[0198] Optionally, the targeted denoising processing result is subjected to velocity analysis processing, including:

[0199] The deep reflection velocity of the deep reflection Moho seismic data is obtained by referring to the velocity of the preset deep reflection seismic data and combining with the velocity scanning analysis stacking velocity.

[0200] Optionally, the targeted denoising processing result is subjected to super gather stack processing according to the velocity analysis processing result, including:

[0201] The targeted denoising processing result is subjected to super gather stack processing according to a multiple coverage observation method or a horizontal stack method.

[0202] Further, the computer program product can include one or more computer executable components configured to perform the embodiments when the program is run; the computer program product can also include a computer program tangibly embodied on a computer readable medium, the computer program containing program code for performing any of the methods of the present embodiments. In such embodiments, the computer program can be downloaded and installed from a network via the communication part, and / or installed from a removable medium.

[0203] Example Four

[0204] In the fourth embodiment of the present application, a computer readable storage medium is also provided, which stores a computer program. When executed by one or more processors, the computer program implements all or part of the steps of the deep reflection Moho seismic data processing method described in the above embodiments, which will not be repeated here.

[0205] The various function units in the embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. When the functions are realized in the form of software function units and sold or used as independent products, the functions can be stored in a computer readable storage medium.

[0206] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the readable storage medium include an electric connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read only memory (EEPROM), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0207] Example Five

[0208] In the fifth embodiment of the present application, an electronic device 1000 is also provided, which can be a mobile phone, a computer, a tablet computer or the like. Figure 10 The electronic device provided in the embodiment of the present application has a component structure diagram as shown in the figure. Figure 10 As shown in the figure, the electronic device 1000 includes at least one processor 1001, at least one communication bus 1002, a user interface 1003, at least one external communication interface 1004, and a memory 1005. The communication bus 1002 is configured to realize the connection and communication between the components. The user interface 1003 can include a display screen, and the external communication interface 1004 can include a standard wired interface and a wireless interface. The memory 1005 stores a computer program. The memory 1005 and the one or more processors 1001 are in communication connection with each other. When the computer program is executed by the one or more processors, the processor 1001 is configured to execute the computer program stored in the memory to realize all or part of the steps of the deep reflection Moho seismic data processing method in the above embodiments. The embodiment will not be repeated here.

[0209] The processor can be an Application Specific Integrated Cricuit (ASIC), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic elements, which are used to execute all or part of the steps of the method for processing seismic data of deep reflection Moho surface described in the above embodiments, and the embodiments will not be repeated here.

[0210] The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0211] The method and device for processing seismic data of deep reflection Moho surface and the electronic device provided by the application improve the signal-to-noise ratio of the seismic data of deep reflection Moho surface, improve the imaging quality, and meet the requirements of deep reflection seismic continental dynamics research and deep crust structure research.

[0212] The basic principles of the application are described above in combination with specific embodiments, but it should be pointed out that the advantages, advantages, effects and the like mentioned in the application are only examples and are not limiting, and these advantages, advantages, effects and the like cannot be considered as the must-have of each embodiment of the present disclosure.

[0213] In addition, the specific details disclosed above are only for the purpose of example and for the purpose of understanding, and are not limiting, and the above details do not limit the application to the above specific details.

[0214] The block diagrams of the devices, apparatuses, equipment, systems involved in the present application are only illustrative examples and are not intended to require or imply that the connection, arrangement, configuration must be as shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems can be connected, arranged, configured in any manner. Words such as "include", "contain", "have", and the like are open-ended words, mean "including but not limited to", and can be used interchangeably.

[0215] It is also necessary to point out that in the devices, apparatuses and methods of the present disclosure, the various components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalents of the present application.

[0216] The above description of disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0217] The above description has been given for the purpose of illustration and description. Furthermore, this description does not intend to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. A method for processing seismic data of a deep reflection Moho surface, characterized in that: The method comprises: Grid processing is performed on the seismic data of the deep reflection Moho surface to obtain grid processing results; Performing tomographic static correction processing on the gridding processing result to obtain a tomographic static correction processing result; performing amplitude compensation processing on the tomographic static correction processing result to obtain an amplitude compensation processing result; Performing targeted denoising on the amplitude compensation processing result to obtain a targeted denoising processing result; Performing speed analysis on the targeted denoising processing result to obtain a speed analysis processing result; The targeted denoising processing result is subjected to super gather stacking processing according to the velocity analysis processing result to obtain a seismic profile.

2. The method for processing seismic data of deep reflection Moho surface according to claim 1, characterized in that: Before gridding the seismic data of the deep reflection Moho surface, the method further includes: Controlling the seismic wave detection equipment to collect seismic data of the deep reflection Moho surface according to a preset acquisition strategy; The preset acquisition strategy includes: The seismic wave detection equipment is excited by a seismic source at normal energy to collect seismic data of the deep reflection Moho surface.

3. The method for processing seismic data of deep reflection Moho surface according to claim 1, characterized in that: The gridding process of the seismic data of the deep reflection Moho surface includes: Gridding the seismic data of the deep reflection Moho surface according to a large-surface processing method; The large-surface processing method includes: When gridding the seismic data of the deep reflection Moho surface, the grid bins of the seismic data of the deep reflection Moho surface are set to large bins of a preset size according to the distance between two adjacent seismic wave detection devices.

4. The method for processing seismic data of deep reflection Moho surface according to claim 1, characterized in that: The performing amplitude compensation processing on the tomographic static correction processing result includes: performing time function amplitude compensation processing on the longitudinal amplitude of the tomographic static correction processing result; The lateral amplitude of the tomographic static correction result is subjected to surface consistency amplitude compensation processing.

5. The method for processing seismic data of deep reflection Moho surface according to claim 1, characterized in that: The performing targeted denoising on the amplitude compensation processing result includes: Performing targeted denoising on the amplitude compensation processing result by sequentially using a linear noise attenuation method and an abnormal amplitude suppression method; The amplitude compensation processing result includes a low-frequency signal having a frequency less than a preset frequency threshold.

6. The method for processing seismic data of deep reflection Moho surface according to claim 1, characterized in that: The performing speed analysis on the targeted denoising processing result includes: The deep reflection velocity of the seismic data of the deep reflection Moho surface is obtained by referring to the velocity of the preset deep reflection seismic data and combining the stacking velocity with the velocity scanning analysis.

7. The method for processing seismic data of deep reflection Moho surface according to claim 1, characterized in that: The performing super gather stacking processing on the targeted denoising processing result according to the velocity analysis processing result comprises: According to a multiple coverage observation method or a horizontal stacking method, super gather stacking processing is performed on the targeted denoising processing result.

8. A seismic data processing device for deep reflection Moho surface, characterized in that: The device comprises: Grid processing module, used to perform grid processing on seismic data of deep reflection Moho surface to obtain grid processing results; a static correction processing module, configured to perform tomographic static correction processing on the gridding processing result to obtain a tomographic static correction processing result; an amplitude compensation processing module, configured to perform amplitude compensation processing on the tomographic static correction processing result to obtain an amplitude compensation processing result; a denoising processing module, configured to perform targeted denoising on the amplitude compensation processing result to obtain a targeted denoising processing result; A speed analysis processing module, configured to perform speed analysis processing on the targeted denoising processing result to obtain a speed analysis processing result; The stacking processing module is used to perform super gather stacking processing on the targeted denoising processing result according to the velocity analysis processing result to obtain a seismic profile.

9. A computer-readable storage medium, characterized in that The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the steps of the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and one or more processors, wherein a computer program is stored in the memory, and when the computer program is executed by the one or more processors, the steps of the method according to any one of claims 1 to 7 are performed.

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