Method and system for optimizing signal-to-noise ratio of post-stack two-dimensional seismic data

Abstract

The invention relates to the technical field of seismic data processing, and discloses a signal-to-noise ratio optimization method and system for post-stack two-dimensional seismic data. The method comprises the following steps: performing well-to-seismic joint calibration on original two-dimensional seismic data to determine main target stratum distribution, and performing spectral analysis to identify a noise frequency band; based on the main target layer distribution, performing segmented Q compensation processing on the original data to obtain a seismic data volume after Q compensation processing; on the basis, spectrum shaping processing is carried out according to the identified noise frequency band so as to suppress the noise and preliminarily improve the signal-to-noise ratio; and finally, diffusion filtering processing is carried out on the data volume after spectrum shaping processing, and a final two-dimensional seismic data volume with an improved signal-to-noise ratio is obtained. According to the method, through series application of Q compensation, spectrum shaping and diffusion filtering, the continuity of the event is effectively improved, the definition of fracture imaging is improved, and a more reliable data basis is provided for structural interpretation and trap evaluation.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration seismic data processing, specifically to a method and system for optimizing the signal-to-noise ratio of post-stack two-dimensional seismic data. Background Technology

[0002] Two-dimensional seismic exploration, as a method of geophysical exploration, plays a crucial role in the field of oil and gas exploration. It typically uses a seismic source to generate seismic waves and then places a receiver underground to record the seismic wave signals. By analyzing the received seismic wave signals, the physical properties and geological structure information of the subsurface medium can be determined.

[0003] With the continuous advancement of exploration technology, 3D seismic exploration technology is now fully mature and is widely used in various fields due to its higher signal-to-noise ratio compared to 2D seismic exploration. However, the cost of 3D seismic exploration is much higher than that of 2D seismic exploration, so some areas with low exploration levels are still in the 2D seismic exploration stage.

[0004] The signal-to-noise ratio of 2D seismic data is affected by many factors, which can be summarized into three aspects: acquisition, geology, and processing. These factors include complex surface conditions (deserts, mountains, etc.), poor detector coupling, and environmental noise interference; geological conditions such as drastic changes in low-velocity zones and the shielding effect of special lithologies (such as volcanic rocks); and processing aspects such as inadequate static correction and incomplete noise suppression.

[0005] Based on its influencing factors, improving the signal-to-noise ratio (SNR) of 2D seismic data currently focuses on three main aspects: data acquisition, processing, and the application of new technologies. In the acquisition phase, high-density, wide-line acquisition is typically employed to increase coverage density and adjust the coupling between the geophone and the surface, thereby improving the imaging accuracy and SNR of complex structures. In the processing phase, noise suppression is primarily used to enhance the effective signal, such as intelligent denoising based on the transform domain and unsupervised denoising based on deep learning.

[0006] The methods described above all address the signal-to-noise ratio (SNR) issue at its source. However, in actual exploration work, both acquisition and reprocessing incur very high financial and time costs. Furthermore, during the interpretation phase, current methods for improving SNR, such as filtering, time-varying frequency-division deconvolution, and frequency-division reconstruction, are primarily designed for 3D seismic data; these methods are rarely used to improve the SNR of 2D seismic data.

[0007] Practice in interpreting 3D seismic data has shown that targeted processing of seismic data during the interpretation phase can significantly improve work efficiency and accelerate exploration progress. The same method can be experimentally applied to 2D seismic data. Currently, in some less explored areas, both domestically and internationally, especially in the Middle East, data from the 1980s and 1990s are predominantly available. The biggest problems with these data are low coverage and low signal-to-noise ratio. Furthermore, due to time, cost, and operational constraints, it is difficult to start from scratch with new acquisitions. Summary of the Invention

[0008] This invention provides a signal-to-noise ratio optimization method for post-stack 2D seismic data, solving the problems of long processing time and low efficiency in existing 2D seismic data processing. To a certain extent, it compensates for the technical problems of poor phase axis continuity, weak deep-layer signals, and unclear fracture imaging caused by the difficulty in optimizing the combination of technical methods and their lack of specificity in the processing stage.

[0009] This invention is achieved through the following technical solution:

[0010] Firstly, this application provides a method for optimizing the signal-to-noise ratio of post-stack two-dimensional seismic data, comprising the following steps:

[0011] The original 2D seismic data was subjected to well-seismic joint calibration to determine the distribution of the main target layers, and spectral analysis was performed to identify noise sources by combining seismic reflection characteristics.

[0012] Based on the main target layer distribution, the original two-dimensional seismic data is segmented Q-compensation processing to obtain the seismic data volume after Q-compensation processing.

[0013] Based on the Q-compensated seismic data volume, spectral shaping is performed according to the identified noise frequency bands to obtain a seismic data volume with an initial improved signal-to-noise ratio.

[0014] The data volume after spectral shaping is subjected to diffusion filtering to obtain the final two-dimensional seismic data volume with improved signal-to-noise ratio.

[0015] A further optimized scheme is that the well-seismic joint calibration is completed by synthesizing seismic records from the sonic transit time curve and density curve measured in the well, and then matching and comparing them with the actual seismic traces near the well.

[0016] The spectral analysis analyzes the spectral characteristics above and below the target layer through spectral scanning, and identifies noise sources by combining seismic reflection characteristics.

[0017] A further optimization scheme is that the piecewise Q-compensation processing includes:

[0018] The seismic profile is divided into the target layer segment, the segment above the target layer, and the segment below the target layer, with the target layer as the boundary.

[0019] Assign different Q values ​​to each segment;

[0020] The original two-dimensional seismic data is segmented and compensated based on the assigned Q value.

[0021] A further optimized solution is that the spectral shaping process includes:

[0022] Select the wavelet type and adjust the frequency according to the identified main noise source frequency band.

[0023] A further optimization scheme involves selecting the wavelet type and adjusting the frequency based on the identified main noise source frequency band, specifically including the following steps:

[0024] Choose from Yu's wavelet, Lake wavelet, Gauss wavelet, or Butterworth wavelet, and adjust the frequency accordingly based on the noise frequency band characteristics and processing requirements.

[0025] The next optimization step is that the diffusion filtering process includes:

[0026] A diffusion filtering algorithm based on partial differential equations is adopted;

[0027] Noise is filtered out from the seismic data volume through iterative calculations.

[0028] A further optimization scheme is proposed, wherein the specific rules for Q-value allocation are as follows:

[0029] Assign a relatively high Q value to the target layer;

[0030] Based on the actual signal-to-noise ratio, relatively low Q values ​​are assigned to the upper and lower segments of the target layer.

[0031] A further optimization scheme is that, in the segmented Q-compensation process, the Q value is given by the direct assignment method.

[0032] Secondly, this application provides a signal-to-noise ratio (SNR) optimization system for post-stack two-dimensional seismic data, used to implement the SNR optimization method for post-stack two-dimensional seismic data as described above, including:

[0033] The target layer distribution acquisition module is used to perform well-seismic joint calibration on the raw 2D seismic data to determine the distribution of the main target layers, and to perform spectral analysis, combined with seismic reflection characteristics to identify noise sources;

[0034] The compensation processing module is communicatively connected to the target layer distribution acquisition module and is used to perform segmented Q-compensation processing on the original two-dimensional seismic data based on the main target layer distribution to obtain the seismic data volume after Q-compensation processing.

[0035] The spectral shaping module is communicatively connected to the compensation processing module and is used to perform spectral shaping processing on the seismic data volume after Q compensation processing according to the identified noise frequency bands to obtain a seismic data volume with an initial improved signal-to-noise ratio.

[0036] The diffusion filtering processing module is communicatively connected to the spectral shaping processing module and is used to perform diffusion filtering processing on the spectral-shaped data volume to obtain a final two-dimensional seismic data volume with improved signal-to-noise ratio.

[0037] A further optimization is that the processing control module further includes:

[0038] The quality monitoring unit is used to monitor the data quality of each processing step in real time.

[0039] The parameter optimization unit establishes bidirectional communication with the quality monitoring unit and the data processing unit to dynamically adjust the Q value, wavelet type, and iteration number parameters based on the processing effect.

[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0041] This application addresses two-dimensional seismic data. Based on well-seismic calibration and spectral analysis, it employs Q-compensation to compensate for seismic energy and then uses spectral shaping to suppress noise and improve the signal-to-noise ratio. This, to some extent, solves the problems of poor phase axis continuity and unclear fracture imaging, providing technical support for structural interpretation and trap evaluation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0043] Figure 1 A flowchart illustrating the signal-to-noise ratio optimization method for post-stack 2D seismic data provided in this application embodiment;

[0044] Figure 2 A comparison of the effectiveness of the signal-to-noise ratio optimization method for post-stack 2D seismic data provided in this application example in a low signal-to-noise ratio 2D seismic data application case;

[0045] Figure 3 A comparison of the effectiveness of the signal-to-noise ratio optimization method for post-stack 2D seismic data provided in this application embodiment in another application case with low signal-to-noise ratio 2D seismic data. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0047] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.

[0048] Q: Quality Factor;

[0049] Ricker Wavelet: Ricker Wavelet;

[0050] Gaussian Wavelet: Gaussian wavelet;

[0051] Butterworth Wavelet: Butterworth wavelet;

[0052] Diffusion Filter: A filter that filters through diffusion.

[0053] SNR: Signal-to-Noise Ratio.

[0054] Firstly, such as Figure 1 As shown, this application provides a method for optimizing the signal-to-noise ratio of post-stack two-dimensional seismic data, including the following steps:

[0055] Step S1: Perform well-seismic co-calibration on the original 2D seismic data to determine the distribution of the main target layers, and perform spectral analysis to identify noise sources by combining seismic reflection characteristics;

[0056] Step S2: Based on the main target layer distribution, perform segmented Q-compensation processing on the original two-dimensional seismic data to obtain the seismic data volume after Q-compensation processing;

[0057] Step S3: Based on the seismic data volume after Q compensation processing, spectral shaping processing is performed according to the identified noise frequency bands to obtain a seismic data volume with an initial improved signal-to-noise ratio;

[0058] Step S4: Perform diffusion filtering on the spectral-shaping data volume to obtain the final two-dimensional seismic data volume with improved signal-to-noise ratio.

[0059] This embodiment, based on the target layer distribution determined by well-seismic joint calibration and the noise frequency bands identified by spectral analysis, effectively compensates for the energy attenuation and phase distortion of seismic waves in the target layer through segmented Q-compensation processing, restoring the continuity of the phase axis; then, spectral shaping processing is performed according to the noise characteristics to suppress specific noise frequency bands in the frequency domain, initially improving the signal-to-noise ratio; finally, diffusion filtering smooths the background noise while protecting geological structural information such as faults and river boundaries, thus obtaining high-quality two-dimensional seismic data with clear phase axes, prominent structural features, and direct applicability for detailed interpretation.

[0060] In one embodiment, step S1: performing well-seismic co-calibration on the original two-dimensional seismic data to determine the distribution of the main target layers, and performing spectral analysis, combined with seismic reflection characteristics to identify noise sources, specifically includes the following steps:

[0061] Step S11: Synthesize seismic records using the acoustic transit-time curves and density curves measured in the well, and compare them with actual seismic traces near the well to determine the distribution range of the main target layer in the seismic data, i.e., the distribution of the main target layer; specifically, synthesize artificial seismic records using the acoustic transit-time curves and density curves measured in the well; compare and calibrate the waveform characteristics of the synthesized record with the corresponding actual seismic traces near the well; based on the waveform matching relationship, accurately define the reflection position and lateral distribution range of the main target layer in the seismic data;

[0062] Step S12: Analyze the spectral characteristics of the target layer above and below using spectral scanning and wavelet transform, and combine this with seismic reflection characteristics to determine the noise source. Frequency components refer to the simple harmonic wave components of each individual frequency that constitute a complex seismic signal. They collectively determine the signal's spectral characteristics and waveform morphology. Analyzing frequency components can identify the noise source. Specifically, first observe the actual seismic data. High-frequency noise appears as dense spikes on the seismic profile, roughening the phase axis; it is random and discontinuous. Low-frequency noise appears as broad, gentle fluctuations, causing the phase axis to bend overall, affecting the profile by distorting the structural morphology and obscuring deep signals. Furthermore, frequency-based comparative analysis of the seismic data using wavelet transform further determines whether the noise originates from high or low frequencies.

[0063] In one embodiment, based on the determination of the main target layer distribution by well seismic calibration and the identification of noise frequency bands by spectral analysis, the Q-compensation process performs targeted energy compensation for the target layer segment to solve the problem of discontinuity of the in-phase axis caused by noise; step S2: based on the main target layer distribution, the original two-dimensional seismic data is subjected to segmented Q-compensation processing to obtain the Q-compensated seismic data volume, specifically including the following steps:

[0064] Step S21: Seismic profile segmentation guided by geological strata; specifically, using the main target layer determined by well-seismic joint calibration as the boundary, the entire seismic profile is divided into three data segments with clear geological significance: the segment above the target layer, the segment below the target layer, and the segment below the target layer. This segmentation ensures that subsequent signal processing can focus on key geological targets and achieve differentiated processing;

[0065] Step S22: Perform differentiated Q-value compensation processing on each segment of the data to obtain the Q-compensated seismic data volume; assign different Q values ​​to segments with different signal-to-noise ratio characteristics. The Q value characterizes the formation's ability to absorb seismic wave energy, and its relationship with amplitude attenuation is defined by the following formula:

[0066]

[0067] In the formula, The frequency at time t The amplitude; It is the initial amplitude; It is frequency; It refers to the time of transmission; It is a quality factor;

[0068] The Q-value allocation strategy directly depends on the signal-to-noise ratio (SNR) characteristics of each segment. Specifically, segments with poor SNR and significant energy attenuation require higher Q-values ​​(e.g., above 100) to implement stronger energy compensation; conversely, segments with high SNR are assigned lower Q-values ​​(e.g., 20-50) for moderate compensation, thus avoiding overcompensation that introduces noise or spurious in-phase axes. The Q-value setting range is usually wide (e.g., 10-1000), with larger values ​​indicating stronger compensation. This application prefers the direct assignment method, i.e., empirically assigning a specific Q-value based on the SNR. This method is more direct and controllable than obtaining a continuous Q-field through inversion, and the final parameters can be optimized by comparing processing effects.

[0069] Finally, by assigning differentiated Q values ​​to segments and implementing compensation, the compensation process involves constructing filters that reverse the attenuation process to recover high-frequency energy and correct phase distortion. This yields Q-compensated seismic data with optimized overall signal-to-noise ratio and significantly enhanced continuity of the target layer's phase axis, laying the foundation for subsequent structural and reservoir interpretation. For example, in practice, Q values ​​of 20, 100, and 30 can be assigned to the three segments respectively to achieve targeted compensation effects.

[0070] In one embodiment, step S3: Based on the Q-compensated seismic data volume, spectral shaping is performed according to the identified noise frequency bands to obtain a seismic data volume with an initially improved signal-to-noise ratio, specifically including the following steps:

[0071] Step S31: Select the appropriate wavelet type based on the geological purpose and noise characteristics. Specifically, when the noise mainly comes from high frequencies, it is advisable to use the Yu wavelet with a narrow main lobe and small side lobe amplitude for frequency reduction processing, which can effectively protect structural details such as fault surfaces while suppressing high-frequency noise. When high-resolution processing is required, the Lake wavelet can be used to enhance the sharpness of the main lobe of the effective wave and improve resolution. For processing scenarios that require smooth frequency transitions or precise frequency cutoff, Gaussian wavelets and Butterworth wavelets are applicable respectively. This application, considering the fact that high-frequency noise is usually prominent in 2D seismic data, preferably uses the Yu wavelet for frequency reduction processing.

[0072] Step S32: Based on the noise source, design and apply a specific filter in the frequency domain to specifically adjust the amplitude spectrum of the seismic record, and obtain the data volume after spectrum shaping. The process can be represented as follows:

[0073]

[0074] in, The frequency spectrum of the original seismic signal. Frequency spectrum after selective adjustment by the filter These are the frequency values ​​of each frequency component in the seismic wave signal;

[0075] By multiplying the seismic data spectrum with the filter frequency response, selective adjustments are made to different frequency components. Through these targeted frequency adjustments, noise in specific frequency bands is ultimately suppressed, making the in-phase axis characteristics of the target reflector layer clearer and obtaining a data volume with an initial improved signal-to-noise ratio, laying the foundation for subsequent processing.

[0076] Unlike traditional seismic data processing methods, spectral shaping is used as an interpretative processing technique in this invention. It can specifically suppress noise and improve the signal-to-noise ratio without the need for re-acquisition or reprocessing, making it particularly suitable for optimizing older data.

[0077] The combination of Q-compensation and spectral shaping methods for 2D seismic data improves the signal-to-noise ratio through interpretive processing, addressing issues of continuity and fracture imaging of the same phase axis.

[0078] In one embodiment, diffusion filtering is an image denoising technique based on partial differential equations. It uses an intelligent, controlled "diffusion" process to smooth internal image noise while selectively preserving or even enhancing important edge and boundary information. Through multiple iterative algorithms, it continuously optimizes the balance between "recognition" and "smoothing," ultimately suppressing background noise while highlighting discontinuities such as faults and river boundaries, making the phase axis more continuous and smooth, and the geological features clearer. Step S4: Performing diffusion filtering on the spectral-shaped data volume to obtain the final two-dimensional seismic data volume with improved signal-to-noise ratio is specifically implemented as follows:

[0079] The diffusion filtering algorithm based on partial differential equations, through its intelligent and controllable diffusion process, smooths noise within the data volume while selectively protecting and enhancing important geological edge information such as faults and river boundaries. By setting the number of iterations (e.g., 5), this diffusion filtering process optimizes noise removal based on partial differential equations. After continuously optimizing the balance between edge recognition and noise smoothing, it can effectively filter out background noise, making the seismic phase axis more continuous and smooth, and highlighting key geological features clearly. Ultimately, it yields two-dimensional seismic data that can be directly used for fine structural interpretation.

[0080] The signal-to-noise ratio (SNR) optimization method for post-stack 2D seismic data provided in this application significantly improves the SNR when applied to low SNR 2D seismic data. For example... Figure 2 and Figure 3 As shown, the processed seismic data exhibits enhanced continuity of phase axes and clearer fracture imaging, verifying the practicality of the method.

[0081] Figure 2 (a) is a schematic diagram of the original two-dimensional seismic data provided in the embodiments of this application;

[0082] Figure 2 (b) is a schematic diagram of a seismic profile after Q-compensation processing provided in an embodiment of this application;

[0083] Figure 2 (c) is a schematic cross-sectional view of the final result after spectrum shaping and diffusion filtering provided in the embodiments of this application;

[0084] Figure 3 (a) is a schematic diagram of another original two-dimensional seismic data provided in an embodiment of this application;

[0085] Figure 3 (b) is a schematic diagram of a seismic profile after technical processing according to the present application, provided in an embodiment of this application.

[0086] Secondly, this application provides a signal-to-noise ratio (SNR) optimization system for post-stack two-dimensional seismic data, used to implement the SNR optimization method for post-stack two-dimensional seismic data as described above, including:

[0087] The target layer distribution acquisition module is used to perform well-seismic joint calibration on the raw 2D seismic data to determine the distribution of the main target layers, and to perform spectral analysis, combined with seismic reflection characteristics to identify noise sources;

[0088] The compensation processing module is communicatively connected to the target layer distribution acquisition module and is used to perform segmented Q-compensation processing on the original two-dimensional seismic data based on the main target layer distribution to obtain the seismic data volume after Q-compensation processing.

[0089] The spectral shaping module is communicatively connected to the compensation processing module and is used to perform spectral shaping processing on the seismic data volume after Q compensation processing according to the identified noise frequency bands to obtain a seismic data volume with an initial improved signal-to-noise ratio.

[0090] The diffusion filtering processing module is communicatively connected to the spectral shaping processing module and is used to perform diffusion filtering processing on the spectral-shaped data volume to obtain a final two-dimensional seismic data volume with improved signal-to-noise ratio.

[0091] In one embodiment, the processing control module further includes:

[0092] The quality monitoring unit is used to monitor the data quality of each processing step in real time.

[0093] The parameter optimization unit establishes bidirectional communication with the quality monitoring unit and the data processing unit to dynamically adjust the Q value, wavelet type, and iteration number parameters based on the processing effect.

[0094] The functions of each module in the above-mentioned signal-to-noise ratio optimization system for post-stack 2D seismic data correspond to the steps in the above-mentioned method embodiment for optimizing the signal-to-noise ratio of post-stack 2D seismic data. Their functions and implementation processes will not be described in detail here.

[0095] Thirdly, embodiments of this application provide a signal-to-noise ratio optimization device for post-stack two-dimensional seismic data. The signal-to-noise ratio optimization device for post-stack two-dimensional seismic data can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0096] In this embodiment of the application, the signal-to-noise ratio optimization device for post-stack 2D seismic data may include a processor, a memory, a communication interface, and a communication bus.

[0097] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0098] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal devices within the signal-to-noise ratio (SNR) optimization equipment for post-stack 2D seismic data, as well as interfaces for interconnecting the SNR optimization equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0099] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0100] The processor can be a general-purpose processor, which can call the signal-to-noise ratio (SNR) optimization program for post-stack 2D seismic data stored in memory and execute the SNR optimization method for post-stack 2D seismic data provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the SNR optimization program for post-stack 2D seismic data is called can refer to the various embodiments of the SNR optimization method for post-stack 2D seismic data in this application, and will not be repeated here.

[0101] Fourthly, embodiments of this application also provide a readable storage medium.

[0102] This application has a storage medium that stores a signal-to-noise ratio (SNR) optimization program for post-stack 2D seismic data. When the SNR optimization program for post-stack 2D seismic data is executed by a processor, it implements the steps of the SNR optimization method for post-stack 2D seismic data as described above.

[0103] The method implemented when the signal-to-noise ratio optimization procedure for post-stack 2D seismic data is executed can be referred to in various embodiments of the signal-to-noise ratio optimization method for post-stack 2D seismic data in this application, and will not be repeated here.

[0104] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for signal-to-noise ratio optimization of poststack 2D seismic data, characterized in that, Includes the following steps: The original 2D seismic data was subjected to well-seismic joint calibration to determine the distribution of the main target layers, and spectral analysis was performed to identify noise sources by combining seismic reflection characteristics. Based on the main target layer distribution, the original two-dimensional seismic data is segmented Q-compensation processing to obtain the seismic data volume after Q-compensation processing. Based on the Q-compensated seismic data volume, spectral shaping is performed according to the identified noise frequency bands to obtain a seismic data volume with an initial improved signal-to-noise ratio. The data volume after spectral shaping is subjected to diffusion filtering to obtain the final two-dimensional seismic data volume with improved signal-to-noise ratio.

2. The method for signal-to-noise ratio optimization of deghosted 2D seismic data according to claim 1, characterized in that, The well-seismic joint calibration is accomplished by synthesizing seismic records from the acoustic transit time curves and density curves measured during drilling, and then matching and comparing them with actual seismic traces near the well. The spectral analysis analyzes the spectral characteristics above and below the target layer through spectral scanning, and identifies noise sources by combining seismic reflection characteristics.

3. The method for signal-to-noise ratio optimization of deghosted 2D seismic data according to claim 1, wherein, The segmented Q-compensation process includes: The seismic profile is divided into the target layer segment, the segment above the target layer, and the segment below the target layer, with the target layer as the boundary. Assign different Q values ​​to each segment; The original two-dimensional seismic data is segmented and compensated based on the assigned Q value.

4. The signal-to-noise ratio optimization method for post-stack two-dimensional seismic data according to claim 1, characterized in that, The spectral shaping process includes: Select the wavelet type and adjust the frequency according to the identified main noise source frequency band.

5. The signal-to-noise ratio optimization method for post-stack two-dimensional seismic data according to claim 4, characterized in that, The selection of wavelet type, based on the identified main noise source frequency band, involves frequency adjustment, specifically including the following steps: Choose from Yu's wavelet, Lake wavelet, Gauss wavelet, or Butterworth wavelet, and adjust the frequency accordingly based on the noise frequency band characteristics and processing requirements.

6. The signal-to-noise ratio optimization method for post-stack two-dimensional seismic data according to claim 1, characterized in that, The diffusion filtering process includes: A diffusion filtering algorithm based on partial differential equations is adopted; Noise is filtered out from the seismic data volume through iterative calculations.

7. The signal-to-noise ratio optimization method for post-stack two-dimensional seismic data according to claim 3, characterized in that, The specific rules for Q-value allocation are as follows: Assign a relatively high Q value to the target layer; Based on the actual signal-to-noise ratio, relatively low Q values ​​are assigned to the upper and lower segments of the target layer.

8. The signal-to-noise ratio optimization method for post-stack two-dimensional seismic data according to claim 5, characterized in that, In the segmented Q-compensation process, the Q value is given by direct assignment.

9. A signal-to-noise ratio (SNR) optimization system for post-stack two-dimensional seismic data, used to implement the SNR optimization method for post-stack two-dimensional seismic data as described in any one of claims 1-8, characterized in that, include: The target layer distribution acquisition module is used to perform well-seismic joint calibration on the raw 2D seismic data to determine the distribution of the main target layers, and to perform spectral analysis, combined with seismic reflection characteristics to identify noise sources; The compensation processing module is communicatively connected to the target layer distribution acquisition module and is used to perform segmented Q-compensation processing on the original two-dimensional seismic data based on the main target layer distribution to obtain the seismic data volume after Q-compensation processing. The spectral shaping module is communicatively connected to the compensation processing module and is used to perform spectral shaping processing on the seismic data volume after Q compensation processing according to the identified noise frequency bands to obtain a seismic data volume with an initial improved signal-to-noise ratio. The diffusion filtering module is communicatively connected to the spectral shaping module and is used to perform diffusion filtering on the spectral-shaped data volume to obtain a final two-dimensional seismic data volume with improved signal-to-noise ratio.

10. The signal-to-noise ratio optimization system for post-stack two-dimensional seismic data according to claim 9, characterized in that, The processing control module further includes: The quality monitoring unit is used to monitor the data quality of each processing step in real time. The parameter optimization unit establishes bidirectional communication with the quality monitoring unit and the data processing unit to dynamically adjust the Q value, wavelet type, and iteration number parameters based on the processing effect.

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