External interference suppression method based on background noise recognition and related device

Abstract

The invention belongs to the technical field of petroleum seismic exploration, and provides an exogenous interference suppression method based on background noise recognition and a related device, and the method comprises the steps: carrying out the first arrival time pickup of original single-shot data obtained through seismic collection, and dividing the single-shot data into a background noise region and a signal + noise mixed region according to the picked first arrival time; identifying seismic traces containing external interference in the background noise area according to the energy difference; in a signal + noise mixing area, for the identified seismic trace containing the external interference, sparse representation is carried out on seismic data through a morphological component analysis method (Q wavelet transform and cosine transform); the sparse distribution coefficient is screened by using the characteristic difference between the effective signal and the external interference, and gradual decomposition of the effective signal and the external interference is realized through an iteration mode; and reconstructing the screened Q wavelet coefficient to obtain a shot gather record after exogenous interference suppression. Compared with a conventional denoising method which only utilizes apparent velocity, frequency and energy differences, the method can more accurately realize separation of external interference and effective reflected waves, and the external interference suppression effect is better.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil seismic exploration, and particularly relates to an external interference suppression method based on background noise recognition and a related device. BACKGROUND

[0002] In seismic exploration, common shot gather (shot gather) is the most original seismic data. The signal-to-noise ratio of the shot gather restricts the quality of subsequent velocity analysis, profile imaging, structure implementation and other seismic data processing and interpretation links, so improving the signal-to-noise ratio of the shot gather is a key factor affecting the quality of seismic exploration results. In seismic acquisition, external interference is very common, which seriously affects the signal-to-noise ratio of seismic data and has characteristics such as complex source, strong amplitude, wide frequency band and random distribution. In recent years, due to the increasing busy of traffic production activities such as highways, railways and factories, and the timeliness requirements of high-density seismic acquisition, the influence of external interference is becoming increasingly serious. Therefore, it is urgent to carry out external interference noise suppression of the shot gather.

[0003] At present, the external interference suppression method of the shot gather mainly focuses on f-k filtering, abnormal amplitude suppression and the like, and mainly uses the frequency, apparent velocity, energy and time-distance characteristics of the external interference and the effective reflection wave to denoise. However, due to the complex causes of the external interference, it is difficult to effectively separate the external interference and the effective signal by the above characteristics, and the denoising effect is not good. At the same time, in the external interference suppression, the noise characteristic analysis is also limited to the region below the first arrival, and the external interference and the effective reflection wave are mixed in this region, which is difficult to accurately distinguish, further affecting the external interference suppression effect. Therefore, it is extremely difficult to use conventional methods to suppress external interference on the shot gather, and the effect is not good, and the data quality is difficult to meet the current demand of oil and gas exploration.

[0004] The related technology to solve this problem is fk noise suppression. This method converts the shot gather data to the frequency-wave number-wave number domain through three-dimensional Fourier transform, separates the effective reflection wave and the external interference according to the frequency and apparent velocity difference, then realizes external interference suppression through filtering, and finally obtains the shot gather data after noise suppression through three-dimensional Fourier inverse transform. This method mainly uses the difference between the external interference and the effective reflection wave in frequency and apparent velocity to suppress the external interference on the shot gather, which is difficult to effectively separate the external interference and the effective reflection wave. At the same time, only the region below the first arrival is analyzed for characteristics, which cannot accurately measure the attribute characteristics of the external interference and the effective signal. In addition, affected by the time and space sampling interval, false frequencies are easily generated, and this method has high requirements for data regularity, and abnormalities are easily generated in the variable observation area. SUMMARY

[0005] The present application aims to provide an external interference suppression method based on background noise recognition and a related device to solve the problems of easy generation of false frequencies and easy generation of abnormalities in the variable observation area in the prior art.

[0006] To achieve the above object, the application adopts the following technical solutions:

[0007] In the first aspect, the application provides an external interference suppression method based on background noise recognition, comprising:

[0008] Performing first arrival time picking on the original single-shot data obtained by seismic acquisition, and dividing the single-shot data into a background noise area and a signal+noise mixed area according to the picked first arrival time;

[0009] In the background noise area, identifying seismic traces containing external interference according to energy difference, and for the identified seismic traces containing external interference, performing sparse representation on the effective signal and external interference in the signal+noise mixed area through morphological component analysis (MCA) to obtain sparse distribution coefficients;

[0010] Screening the sparse distribution coefficients using the feature difference between the effective signal and the external interference, and realizing step-by-step decomposition of the effective signal and the external interference through an iterative manner;

[0011] Reconstructing the decomposed sparse distribution coefficients to obtain the shot gather record after external interference suppression.

[0012] Further, performing first arrival time picking on the original single-shot data obtained by seismic acquisition, and dividing the single-shot data into a background noise area and a signal+noise mixed area according to the picked first arrival time, comprising:

[0013] Performing first arrival picking on the three-dimensional original shot gather data collected in the work area, and dividing the three-dimensional common shot gather data based on the first arrival information, wherein the area above the first arrival is the background noise area, and the area below the first arrival is the signal+noise mixed area; in the background noise area, the seismic data contains external interference and random noise; in the signal+noise mixed area, the seismic data contains effective signal, external interference and random noise.

[0014] Further, in the background noise area, identifying seismic traces containing external interference according to energy difference, comprising:

[0015] In the background noise area, the seismic record only contains external interference and random noise; dividing the data in the background noise area into multiple areas according to a set number of channels, and sequentially comparing the average energy value of a single seismic trace in the area with the average energy value of all seismic traces in the area, and if the value is greater than a given threshold, it is considered that the seismic trace contains external interference.

[0016] Further, for the identified seismic traces containing external interference, performing sparse representation on the effective signal and external interference in the signal+noise mixed area through morphological component analysis (MCA) to obtain sparse distribution coefficients, comprising:

[0017] In the mixed zone, external interference and effective signal are decomposed using morphological component analysis theory. Morphological component analysis theory assumes that complex signals are composed of linear combinations of various morphological components and random noise. By constructing an overcomplete dictionary and determining sparse coefficients, each morphological component is reconstructed. Through spectral analysis of the original single-shot external interference and effective signal, it is found that external interference mostly has narrow-band characteristics, while the effective signal has wide-band characteristics. Based on the similarity principle, Q wavelet transform and cosine transform are selected as dictionaries for the effective signal and external interference, respectively. An overcomplete dictionary of the mixed zone seismic data is constructed using these two methods, and sparse distribution coefficients are obtained based on the overcomplete dictionary of the mixed zone seismic data.

[0018] Furthermore, the sparse distribution coefficients are screened using the characteristic differences between the effective signal and external interference, and the effective signal and external interference are gradually separated through an iterative process, including:

[0019] Based on the construction of an overcomplete dictionary, the sparse coefficients of Q wavelet transform and cosine transform are updated by optimizing the function. At the same time, in the process of calculating Q wavelet coefficients and cosine transform coefficients, the sparse coefficients are updated step by step by using the block coordinate relaxation method. The Q wavelet coefficients and cosine transform coefficients are continuously updated by step-by-step iteration until the set target is met, and the sparse coefficients at this time are taken as the optimal sparse coefficients.

[0020] Furthermore, the optimization function is specifically as follows:

[0021]

[0022] Where, x opt For the optimized Q-wavelet transform sparse coefficients, y opt The coefficients are the sparse coefficients of the optimized cosine transform, and λ is the Lagrange factor.

[0023] During the sparse coefficient optimization process, x and y are updated progressively, while y remains fixed. The formula for updating x is:

[0024]

[0025] Where x k+1 For the updated Q-wavelet transform sparse coefficients, T λ φ is the threshold factor. * For the inverse operator of φ;

[0026] Keeping x constant, the formula for updating y is:

[0027]

[0028] Where y k+1 For the updated Q-wavelet transform sparse coefficients, for The inverse operator of .

[0029] Furthermore, the sparse distribution coefficients after separation are reconstructed to obtain the shot gather record after suppression of external interference, including:

[0030] After obtaining the optimal sparse coefficients, the removed external interference is represented as the vector product of the cosine transform sparse coefficients and the cosine transform, and the seismic data after suppressing external interference is represented as the vector product of the Q wavelet transform coefficients and the Q wavelet transform.

[0031] Secondly, the present invention provides an external interference suppression system based on background noise identification, comprising:

[0032] The region division module is used to pick the first arrival time of the raw single-shot data obtained from seismic acquisition, and divide the single-shot data into background noise area and signal + noise mixed area based on the picked first arrival time.

[0033] The anomaly identification module is used to identify seismic traces containing external interference in the background noise area based on energy differences. For the identified seismic traces containing external interference, morphological component analysis (MCA) is used to sparsely represent the effective signal and external interference in the signal + noise mixed area to obtain the sparse distribution coefficient.

[0034] The sparse coefficient update module is used to filter the sparse distribution coefficients by utilizing the characteristic differences between the effective signal and external interference, and to gradually separate the effective signal and external interference through an iterative method.

[0035] The data reconstruction module is used to reconstruct the sparse distribution coefficients after separation to obtain the shot collection records after suppressing external interference.

[0036] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an external interference suppression method based on background noise identification.

[0037] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of an external interference suppression method based on background noise identification.

[0038] Compared with the prior art, the present invention has the following technical effects:

[0039] The application divides common shot gather data into background noise area and signal+noise mixed area by first arrival information, and automatically identifies seismic traces containing external interference according to energy difference in the background noise area; in the signal+noise mixed area, Q wavelet transform is selected as the base function of effective signal and cosine transform is selected as the base function of external interference by combining the spectral characteristics of external interference and effective signal, and the super-complete dictionary is constructed by Q wavelet transform and cosine transform, and the sparse distribution coefficients of Q wavelet transform and cosine transform are gradually determined by the block relaxation iteration method; the effective signal and external interference can be reconstructed by the sparse coefficients and Q wavelet transform and cosine transform, and the effective suppression of external interference is finally realized.

[0040] The method first divides shot gather into background noise area and signal+noise mixed area by first arrival, which improves the utilization rate of data in the background noise area and more accurately identifies seismic traces containing external interference in the background noise area; secondly, for seismic data in the mixed area, data reconstruction is realized by morphological component analysis method, wherein based on spectral characteristic analysis of external interference and effective signal, cosine transform and Q wavelet transform are selected as the base functions of external interference and effective signal respectively to construct a super-complete dictionary; compared with the conventional denoising method which only uses apparent velocity, frequency and energy difference, this method can more accurately separate external interference and effective reflection and has better external interference suppression effect. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A flowchart of an external interference suppression method based on background noise recognition according to an embodiment of the application is shown;

[0042] Figure 2 A raw shot gather record according to an embodiment of the application is shown;

[0043] Figure 3 A first arrival picking schematic diagram of a raw shot gather record according to an embodiment of the application is shown;

[0044] Figure 4 A region division schematic diagram of a raw shot gather record according to an embodiment of the application is shown.

[0045] Figure 5 An external interference seismic trace identification schematic diagram of a raw shot gather record according to an embodiment of the application is shown;

[0046] Figure 6 A shot gather record after external interference suppression according to an embodiment of the application is shown;

[0047] Figure 7 A first arrival picking schematic diagram of a shot gather record after external interference suppression according to an embodiment of the application is shown;

[0048] Figure 8 A structure schematic diagram of an external interference suppression processing system based on background noise recognition according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0050] Related term explanation:

[0051] Common shot gather: In seismic exploration, it is simply called shot gather, and is the most original data record in seismic exploration. It is usually the information received by a receiver associated with the seismic wave field generated by a seismic source at a shot point from the start of the shot time, arranged in a predetermined order to form a data set.

[0052] First arrival time: In seismic exploration, the time when the seismic wave front excited by the seismic source reaches the receiver.

[0053] Morphological component analysis method: Also known as MCA (Morphological Component Analysis), it is a signal processing method. The method regards a complex signal as a linear combination of several components with different characteristics (i.e. different morphological components), assumes that a sub-dictionary can be found for each type of component, which can be sparsely represented, and the sub-dictionary cannot be sparsely represented for other types of morphological components. After meeting the above assumptions, the complex signal (containing multiple morphological components) can be sparsely represented by a super-complete dictionary composed of these sub-dictionaries.

[0054] Cosine transform: It is a real-valued transform related to Fourier transform, which has very good energy concentration for signals with high correlation. In this paper, the local discrete cosine transform is used as a sparse representation dictionary for external interference, which is beneficial to the sparse representation of external interference waveforms with local correlation characteristics.

[0055] Q wavelet transform: it is a kind of wavelet transform with adjustable Q factor, also known as TQWT (Tunable Q-Factor Wavelet Transform). For oscillation signals, the Q factor is the ratio of the center frequency to the bandwidth, and the specific Q value should be reasonably selected according to the oscillation behavior of the signal.

[0056] The application is further described below in combination with the drawings:

[0057] Example 1, please refer to Figure 1 The application provides an external interference suppression method based on background noise recognition, comprising:

[0058] The first arrival time of the original single-shot data obtained by seismic acquisition is picked up, and the single-shot data is divided into a background noise area and a signal+noise mixed area according to the picked-up first arrival time.

[0059] In the background noise area, the seismic trace containing external interference is recognized according to the energy difference, and for the recognized seismic trace containing external interference, the effective signal and external interference in the signal+noise mixed area are sparsely represented by morphological component analysis (MCA), to obtain sparse distribution coefficients.

[0060] The sparse distribution coefficients are screened by using the characteristic difference between the effective signal and the external interference, and the step-by-step separation of the effective signal and the external interference is realized by an iterative method.

[0061] The separated sparse distribution coefficients are reconstructed to obtain the shot gather record after external interference suppression.

[0062] The application first divides the shot gather into a background noise area and a signal+noise mixed area by the first arrival time, which on the one hand improves the utilization rate of the data in the background noise area, and on the other hand more accurately recognizes the seismic trace containing external interference in the background noise area. Compared with the conventional denoising method which only uses apparent velocity, frequency and energy difference, this method can more accurately separate the external interference and the effective reflection wave, and the external interference suppression effect is better.

[0063] Example 2, the application provides an external interference suppression method based on background noise recognition, comprising:

[0064] First, the three-dimensional common shot gather data is divided into intervals based on the first arrival information, and the region above the first arrival is the background noise area, and the region below the first arrival is the signal+noise mixed area. In the background noise area, the seismic data contains external interference and random noise; in the mixed area, the seismic data contains effective signal, external interference and random noise.

[0065] Secondly, according to the energy difference of different seismic trace data in the background noise area, the seismic trace containing external source interference is automatically identified. In the background noise area, only external source interference and random noise are contained in the seismic record. From the energy angle, the random noise energy of each seismic trace is basically the same, and the energy of the seismic trace containing external source interference is obviously stronger than that of other seismic traces. Therefore, the data in the background noise area is divided into multiple regions according to the set number of channels, and the average energy value of a single seismic trace in the region is compared with the average energy value of all seismic traces in the region in turn. If it is greater than a given threshold, it is considered that the seismic trace contains external source interference.

[0066] Then in the mixed area, the external source interference and the effective signal are decomposed by the morphological component analysis theory. The morphological component analysis theory assumes that a complex signal is linearly combined by multiple different morphological component components and random noise, and the sparse coefficient is determined by constructing a super-complete dictionary, so that each morphological component component can be reconstructed. The present application finds that the external source interference has a narrow frequency band characteristic and the effective signal has a wide frequency band characteristic through spectral analysis of the original single shot external source interference and effective signal. Based on the similarity principle, the super-complete dictionary is constructed by selecting Q wavelet transform and cosine transform.

[0067] At the same time, in the process of calculating the Q wavelet coefficient and the cosine transform coefficient, the block coordinate relaxation method is used to update the sparse coefficient step by step. The expected sparse coefficient needs to meet two requirements, one is that the sum of the sparse coefficient one norm is minimum, and the other is that the error between the original data and the reconstructed data is less than a given threshold. Based on these two targets, the optimization function of this problem is constructed. And through the way of step-by-step iteration, the Q wavelet coefficient and the cosine transform coefficient are constantly updated until the requirements are met, and the sparse coefficient at this time is taken as the optimal sparse coefficient.

[0068] Finally, after obtaining the optimal sparse coefficient, the removed external source interference can be represented as the vector product of the cosine transform sparse coefficient and the cosine transform, and the seismic data after suppressing the external source interference can be represented as the vector product of the Q wavelet transform coefficient and the Q wavelet transform. Through this method, the effective suppression of external source interference can be realized.

[0069] Embodiment 3,

[0070] The present application provides a kind of external source interference suppression method based on background noise identification, comprising the following steps S101 to step S501.

[0071] Step S101, combined with first information, background noise area and signal+noise mixed area are divided.

[0072] Specifically, the first arrival of the three-dimensional original shot gather data collected in the working area is picked up, and each original shot gather data is divided into background noise area and signal+noise mixed area combined with the first information.

[0073] In the background noise area, the energy generated by the artificial seismic source has not yet arrived, so it only contains external interference and random noise. The seismic data in this area can be expressed as:

[0074] S1 = s w + s r

[0075] where S1 represents the seismic data in the background noise area, s w represents external interference, s r represents random noise.

[0076] In the signal+noise mixed area, the energy generated by the artificial seismic source has arrived, so it contains external interference, random noise, and effective signal (here, the energy wave related to the artificial seismic source is regarded as the effective signal). The seismic signal in this area can be expressed as:

[0077] S2 = s s + s w + s r

[0078] where S2 represents the seismic data in the mixed area, s s represents the effective signal, s w represents external interference, s r represents random noise.

[0079] For example, as shown in FIG. 1, it is a raw two-dimensional shot gather record in a certain work area. Figure 2 In FIG. 1, the vertical axis is the time axis, and the horizontal axis is the offset. As shown in FIG. 2, it is the first arrival picking of a raw two-dimensional shot gather record in a certain work area. Figure 2 In FIG. 2, the vertical axis is the time axis, and the horizontal axis is the offset. In the figure, the thick black line is the first arrival picking range. As shown in FIG. 3, it is the regional division of a raw two-dimensional shot gather record according to the first arrival in a certain work area. Figure 3 In FIG. 3, the vertical axis is the time axis, and the horizontal axis is the offset. The upper area is the background noise area, and the lower area is the signal+noise mixed area. Figure 3 Figure 4 In FIG. 3, the vertical axis is the time axis, and the horizontal axis is the offset. The upper area is the background noise area, and the lower area is the signal+noise mixed area. Figure 4

[0080] Step S201, in the background noise area, automatically identify the seismic trace containing external interference according to the energy difference.

[0081] First, the background noise area is divided into multiple regions according to the set number of channels, and the average energy value of all seismic traces in the region is calculated. The specific formula is as follows:

[0082]

[0083] where A zone is the average energy value of the region, nx is the number of seismic traces in the region, and nt is the number of sampling points of the seismic trace in the region.​​ij is the amplitude value of a single sampling point in the region.

[0084] Secondly, the average energy value of each seismic trace in the region is calculated, and the calculation formula of the average energy value of a single seismic trace is as follows:

[0085]

[0086] where A trc is the average energy value of a single seismic trace, A i is the amplitude value of a single sampling point in a single seismic trace.

[0087] Finally, the ratio of the single trace average energy value to the regional average energy value is calculated, and if it is greater than a given threshold, it is considered that the seismic trace has external source interference. The specific formula is as follows:

[0088]

[0089] where co e is the ratio of the single trace average energy value to the regional average energy value.

[0090] As Figure 5 shown in the figure is the identification of a seismic trace with external source interference in a certain work area. Figure 5 In the figure, the vertical axis is the time axis and the horizontal axis is the offset.

[0091] Step S301, the sparse distribution coefficients of the effective signal and the external source interference in the mixed area are calculated respectively by the morphological component analysis method.

[0092] In the morphological component analysis theory, for a complex signal to be analyzed, it is assumed that it is linearly combined by multiple different morphological component components and random noise, and any signal component can be expressed as the product of sparse coefficients and a dictionary. The key of this theory is to find appropriate basis functions to construct an overcomplete dictionary.

[0093] Through the analysis of the spectral characteristics of the signal and the external source interference, it is found that the external source interference presents narrow-band characteristics, and the effective signal presents wide-band characteristics. Based on the similarity of the spectral characteristics, Q wavelet transform and cosine transform are selected as the basis functions of the effective signal and the external source interference respectively, and an overcomplete dictionary of the mixed area seismic data is constructed by the two.

[0094] For the seismic data in the mixed area, it can be expressed as:

[0095] S2=s s +s w +s r

[0096] where S2 represents the seismic data in the mixed area, s s represents the effective signal, sw denotes the extraneous interference, s r denotes the random noise.

[0097] In actual operation, the random noise is generally ignored, and the mixed zone seismic data can be represented as:

[0098] S2 = s s + s w

[0099] The seismic data is represented by using a super-complete dictionary constructed by Q wavelet transform and cosine transform, and the specific form is as follows:

[0100]

[0101] wherein S2 represents the seismic data of the mixed zone, φ represents the dictionary constructed by Q wavelet transform, x represents the Q wavelet transform sparse coefficient corresponding to the effective signal, denotes the dictionary constructed by cosine transform, and y represents the cosine transform sparse coefficient corresponding to the extraneous interference.

[0102] Step S401, using the characteristic difference between the extraneous interference and the effective reflection wave for iterative separation.

[0103] On the basis of constructing the super-complete dictionary, the sparse coefficients of the Q wavelet transform and the cosine transform are updated by the following optimization function, and the specific form is as follows:

[0104]

[0105] wherein x op t is the optimized Q wavelet transform sparse coefficient, y op t is the optimized cosine transform sparse coefficient, and λ is the Lagrange factor.

[0106] In the sparse coefficient optimization process, x and y are updated gradually, for example, in the kth iteration, y is first kept fixed, and the formula for updating x is:

[0107]

[0108] wherein x k+1 is the updated Q wavelet transform sparse coefficient, T λ is a threshold factor, and φ * is the inverse operator of φ.

[0109] Then x is kept fixed, and the formula for updating y is:

[0110]

[0111] wherein y k+1 is the updated Q wavelet transform sparse coefficient, is The inverse operator of .

[0112] Step S501: Reconstruct the earthquake data by using the optimized Q wavelet coefficients to obtain the noise-suppressed seismic data.

[0113] By progressively updating the sparsity coefficients, the final sparsity coefficients, x, can be obtained after meeting a threshold condition. opt With y opt .

[0114] The removed external interference can be represented as:

[0115] s w =φx opt

[0116] Where s w φ represents external interference, φ represents the dictionary constructed by Q wavelet transform, and x represents external interference. opt This represents the sparse coefficients of the Q wavelet transform corresponding to the optimized effective signal.

[0117] Seismic data after removing external interference can be represented as:

[0118]

[0119] Where s s Indicates a valid signal. The dictionary y represents the construct constructed by the cosine transform. opt This represents the cosine transform sparse coefficients corresponding to the external interference after optimization.

[0120] like Figure 6 The image shows the common shot gather after suppressing external interference using this method. (And...) Figure 2 Compared to previous methods, the signal-to-noise ratio of seismic data is significantly improved, and the phase axes of the first arrival and effective reflection waves are clearer. For example... Figure 7 The image shows the initial arrival pickup of the common shot gather after suppressing external interference, compared with... Figure 3 In comparison, the maximum offset distance of the initial arrival picking in the right half arrangement has been extended from 1252 meters to 3363 meters, which is beneficial to improving the accuracy of near-surface modeling and static correction.

[0121] Please see Figure 8 In another embodiment of the present invention, an external interference suppression system based on background noise identification is provided, which can be used to implement the above-mentioned external interference suppression method based on background noise identification. Specifically, the system includes:

[0122] The region division module is used to pick the first arrival time of the raw single-shot data obtained from seismic acquisition, and divide the single-shot data into background noise area and signal + noise mixed area based on the picked first arrival time.

[0123] An abnormal trace identification module is configured to identify a seismic trace containing external source interference according to energy difference in a background noise area, and a morphological component analysis (MCA) is performed on the effective signal and the external source interference in a signal+noise mixed area to obtain sparse distribution coefficients.

[0124] A sparse coefficient updating module is configured to filter the sparse distribution coefficients according to feature difference between the effective signal and the external source interference, and to separate the effective signal and the external source interference gradually through iteration.

[0125] A data reconstruction module is configured to reconstruct the separated sparse distribution coefficients to obtain shot record after external source interference suppression.

[0126] The division of the modules in the embodiments of the present application is illustrative, and is merely a logical function division, and another division mode can be used in actual implementation, and each function module in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module.

[0127] In another embodiment of the present application, a computer device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the external source interference suppression method based on background noise identification.

[0128] In still another embodiment, the present application provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer system, for storing programs and data. It should be understood that the computer readable storage medium here can include both built-in storage medium in the computer system, and also can include the extended storage medium supported by the computer system. The computer readable storage medium provides a storage space, which stores an operating system of the terminal. In addition, one or more instructions adapted to be loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium here can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the above-mentioned embodiments of the method for suppressing exogenous interference based on background noise identification.

[0129] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1 The means for implementing the function specified in one or more flows and / or blocks.

[0131] These computer program instructions can also be stored in a computer readable storage medium capable of directing the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction means, which implements the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocksFigure 1 the function specified in the one or more blocks.

[0132] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flow Figure 1 the flow or flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0133] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the above embodiments of the present application have been described in detail, those skilled in the art should understand: the specific embodiments of the present application can be modified or replaced by the same, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered within the scope of protection of the claims of the present application.

Claims

1. A method of exogenous interference suppression based on background noise recognition, characterized in that, The method comprises the following steps: First arrival time picking is performed on original single shot data obtained by seismic acquisition, and the single shot data is divided into background noise area and signal+noise mixed area according to the picked first arrival time. In the background noise area, the seismic data contains external source interference and random noise; in the signal+noise mixed area, the seismic data contains effective signal, external source interference and random noise. In the background noise area, the seismic data contains external source interference and random noise; the data in the background noise area is divided into multiple regions according to a set number of channels, and the average energy value of a single seismic channel in the region is compared with the average energy value of all seismic channels in the region in turn. If it is greater than a given threshold value, it is considered that the seismic channel contains external source interference. In the mixed area, external source interference and effective signal are decomposed by morphological component analysis theory. The morphological component analysis theory assumes that a complex signal is composed of multiple different morphological component components and random noise. The sparse coefficients are obtained by constructing an overcomplete dictionary and determining the sparse coefficients.

2. The method of background noise identification based exogenous interference suppression as claimed in claim 1, wherein, The sparse distribution coefficients are screened by using the characteristic difference between the effective signal and the external source interference, and the step-by-step separation of the effective signal and the external source interference is realized through an iterative method. The separated sparse distribution coefficients are reconstructed to obtain the shot record after the suppression of the external source interference.

3. The method of background noise identification based exogenous interference squelching of claim 1, wherein, The method comprises the following steps: First arrival time picking is performed on original single shot data obtained by seismic acquisition, and the single shot data is divided into background noise area and signal+noise mixed area according to the picked first arrival time.

4. The method of background noise identification based exogenous interference suppression as claimed in claim 3, wherein, In the background noise area, the seismic data contains external source interference and random noise; in the signal+noise mixed area, the seismic data contains effective signal, external source interference and random noise. In the background noise area, the seismic data contains external source interference and random noise; the data in the background noise area is divided into multiple regions according to a set number of channels, and the average energy value of a single seismic channel in the region is compared with the average energy value of all seismic channels in the region in turn. If it is greater than a given threshold value, it is considered that the seismic channel contains external source interference.

5. The method of background noise identification based exogenous interference squelching of claim 4, wherein, In the mixed area, external source interference and effective signal are decomposed by morphological component analysis theory. The morphological component analysis theory assumes that a complex signal is composed of multiple different morphological component components and random noise. The sparse coefficients are obtained by constructing an overcomplete dictionary and determining the sparse coefficients. In the mixed area, external source interference and effective signal are decomposed by morphological component analysis theory. The morphological component analysis theory assumes that a complex signal is composed of multiple different morphological component components and random noise. The sparse coefficients are obtained by constructing an overcomplete dictionary and determining the sparse coefficients. The sparse distribution coefficients are screened by using the characteristic difference between the effective signal and the external source interference, and the step-by-step separation of the effective signal and the external source interference is realized through an iterative method. On the basis of constructing the over-complete dictionary, the sparse coefficients of Q wavelet transform and cosine transform are updated by optimizing function, and the sparse coefficients are gradually updated by using block coordinate relaxation method in the process of calculating Q wavelet coefficients and cosine transform coefficients, the Q wavelet coefficients and cosine transform coefficients are continuously updated by iterative method until the set target is met, and the sparse coefficients at this time are taken as the optimal sparse coefficients.

6. The method of background noise identification based exogenous interference squelching of claim 5, wherein, The optimization function is specifically: Wherein, x opt is the optimized Q wavelet transform sparse coefficient, y opt is the optimized cosine transform sparse coefficient, and λ is a Lagrange factor. In the process of optimizing the sparse coefficients, x and y are gradually updated, y is kept fixed, and the formula for updating x is: where x k+1 is the updated Q wavelet transform sparse coefficient, T λ is a threshold factor, φ * is the inverse operator of φ; The formula for updating y is: where y k+1 is the updated Q wavelet transform sparse coefficient, is the inverse operator of 7. The method of background noise identification based exogenous interference squelching of claim 1, wherein, The separated sparse distribution coefficients are reconstructed to obtain the shot record after suppressing external interference, including: After obtaining the optimal sparse coefficients, the removed external interference is expressed as the vector product of the cosine transform sparse coefficients and the cosine transform, and the seismic data after suppressing external interference is expressed as the vector product of the Q wavelet transform coefficients and the Q wavelet transform.

8. An exogenous interference suppression system based on background noise identification, characterized in that, Including: The region division module is used for first break time picking of the original single shot data obtained by seismic acquisition, and the single shot data is divided into background noise area and signal+noise mixed area according to the picked first break time; The abnormal trace identification module is used for identifying seismic traces containing external interference according to energy difference in the background noise area, and for the identified seismic traces containing external interference, the effective signal and external interference in the signal+noise mixed area are sparsely represented by morphological component analysis (MCA) to obtain sparse distribution coefficients; The sparse coefficient updating module is used for screening the sparse distribution coefficients by using the feature difference between the effective signal and the external interference, and gradually separating the effective signal and the external interference by iterative method; The data reconstruction module is used for reconstructing the separated sparse distribution coefficients to obtain the shot record after suppressing external interference.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the external interference suppression method based on background noise identification according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the external interference suppression method based on background noise identification according to any one of claims 1 to 7.

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