First arrival picking method, device and equipment for complex seismic data, medium and product

Abstract

The present application relates to the technical field of petroleum geophysical exploration, in particular to a first arrival picking method, device, equipment, medium and product of complex seismic data, wherein the method comprises: performing parallel processing on each single-shot seismic data to obtain the first arrival of the single-shot seismic data; judging whether a preset picking rate is reached; if the preset picking rate is reached, merging the first arrivals of multiple single-shot seismic data to obtain the final first arrival; the efficiency of first arrival picking of complex low signal-to-noise ratio mass seismic data is improved.

Description

Technical Field

[0001] This invention relates to the field of petroleum geophysical exploration technology, and particularly to a method, apparatus, equipment, medium, and product for acquiring first arrivals of complex seismic data. Background Technology

[0002] With the continuous development of onshore oil and gas exploration in my country and the deepening application of high-density point and wide-azimuth acquisition, seismic data is becoming increasingly complex, and the volume of seismic data is also growing rapidly at tens or hundreds of TB levels. The increasingly complex data quality and the massive data processing requirements have brought severe challenges to initial arrival picking operations.

[0003] For a long time, obtaining high-quality first arrivals from complex, low signal-to-noise ratio, massive seismic data has required a lot of manpower and resources. It usually takes several people one or two months or even longer to complete the task, resulting in extremely low productivity. This is a major pain point in seismic data processing projects and seriously restricts the entire seismic data processing cycle.

[0004] There is a technical problem in this field of inefficient first arrival picking for complex, low signal-to-noise ratio, massive seismic data. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, medium, and product for first arrival picking of complex seismic data, solving the technical problem of low efficiency in first arrival picking of complex, low signal-to-noise ratio, massive seismic data.

[0006] In a first aspect, the present invention provides a method for first arrival picking of complex seismic data, the method comprising: performing parallel processing on each single-shot seismic data to obtain the first arrival of the single-shot seismic data; determining whether a preset picking rate has been reached; and if the preset picking rate has been reached, merging the first arrivals of multiple single-shot seismic data to obtain the final first arrival.

[0007] In some embodiments, after determining whether a preset pickup rate has been reached, the method further includes: if the preset pickup rate has not been reached, fitting the first arrival data of multiple single-shot seismic data to obtain a reference first arrival; and re-executing the step of parallel processing of each single-shot seismic data based on the reference first arrival.

[0008] In some embodiments, the step of parallel processing of individual seismic data includes: preprocessing the individual seismic data in parallel on a per-shot basis; calculating the reference region of the preprocessed individual seismic data in parallel on a per-shot basis; calculating the first arrival of the individual shot based on the reference region in parallel on a per-shot basis; and / or removing anomalous first arrivals of the individual shot in parallel on a per-shot basis.

[0009] In some embodiments, the step of re-performing parallel processing of each shot seismic data based on the reference first arrival includes: calculating the reference region of a shot in parallel based on the reference first arrival of the preprocessed shot seismic data on a shot-by-shot basis; calculating the first arrival of a shot in parallel based on the reference region on a shot-by-shot basis; and / or removing anomalous first arrivals of a shot in parallel on a shot-by-shot basis.

[0010] In some embodiments, the preprocessing steps for single-shot seismic data include: static elevation correction of the single-shot seismic data, detection and marking of anomalous seismic traces, wavelet application, and / or time delay gain.

[0011] In some embodiments, the step of calculating the first arrival of a single shot includes: picking the first arrival based on the energy method and the information entropy method, and iterating the first arrival based on the direct wave refracted wave model to calculate the first arrival on a single track.

[0012] Secondly, the present invention provides a first arrival acquisition device for complex seismic data. The device includes: a single-shot processing module for parallel processing of each single-shot seismic data to obtain the first arrival of the single-shot seismic data; an iterative judgment module for determining whether a preset acquisition rate has been reached; and a first arrival output module for merging the first arrivals of multiple single-shot seismic data to obtain the final first arrival if the preset acquisition rate has been reached.

[0013] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above-described methods for acquiring first arrivals of complex seismic data.

[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the first arrival picking method for complex seismic data according to any of the above aspects.

[0015] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the first arrival picking method for complex seismic data according to any of the above aspects.

[0016] This invention provides a method, apparatus, device, medium, and product for first arrival picking of complex seismic data. The method includes: parallel processing of individual seismic data to obtain the first arrival of the individual seismic data; determining whether a preset picking rate has been reached; if the preset picking rate has been reached, merging the first arrivals of multiple individual seismic data to obtain the final first arrival; thereby improving the efficiency of first arrival picking for complex, low signal-to-noise ratio, massive seismic data. Attached Figure Description

[0017] The invention will now be described in more detail with reference to embodiments and the accompanying drawings:

[0018] Figure 1 This is a flowchart illustrating a first arrival picking method for complex seismic data provided in an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of the structure of a first arrival picking device for complex seismic data provided in an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of automatic first arrival picking for complex low signal-to-noise ratio seismic data provided in an embodiment of this application;

[0021] Figure 4 This is a schematic diagram illustrating the automatic data pickup effect after applying the application instance process of a certain data according to an embodiment of this application.

[0022] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention and to fully understand and implement the process of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The embodiments of the present invention and the various features therein can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0026] With the continuous development of onshore oil and gas exploration in my country and the deepening application of high-density point and wide-azimuth acquisition, seismic data is becoming increasingly complex, and the volume of seismic data is also growing rapidly, reaching tens or even hundreds of TB. The increasingly complex data quality and the massive data processing demands have brought severe challenges to first-arrival acquisition. For a long time, obtaining high-quality first-arrivals from complex, low-signal-noise-ratio, massive seismic data has required a significant investment of manpower and resources, typically involving several people and taking one or two months or even longer to complete. This extremely low productivity is a major pain point in seismic data processing projects, severely restricting the entire seismic data processing cycle. The technical problem of low efficiency in first-arrival acquisition of complex, low-signal-noise-ratio, massive seismic data exists in this field.

[0027] The technical solution of this application will be described below with reference to specific embodiments.

[0028] Example 1

[0029] Figure 1 This is a flowchart illustrating a first-arrival picking method for complex seismic data provided in an embodiment of this application, as shown below. Figure 1 As shown in the technical solution of this embodiment, a method for first arrival picking of complex seismic data is provided. The method includes: performing parallel processing on each single-shot seismic data to obtain the first arrival of the single-shot seismic data; determining whether a preset picking rate has been reached; if the preset picking rate has been reached, merging the first arrivals of multiple single-shot seismic data to obtain the final first arrival.

[0030] The acquisition of first arrivals from complex, low-signal-to-noise-ratio (SNR) massive seismic data has long been characterized by inefficiency. For example, when dealing with seismic data exhibiting poor SNR and complex terrain (such as loess plateaus and mountains), obtaining high-quality first arrivals previously often required several people and took one or two months or even longer, resulting in enormous human and material costs and extremely low production efficiency, severely restricting the overall seismic data processing cycle. This field faces the technical problem of inefficiency in acquiring first arrivals from complex, low-signal-to-noise-ratio massive seismic data.

[0031] In this embodiment, the technical solution employs parallel processing of individual seismic data to obtain the initial arrival (IHA) of each individual seismic source, and then determines whether a preset pick rate has been reached. Parallel processing here refers to performing related operations on multiple individual seismic sources simultaneously, unlike the previous method of processing individual source data one by one, which greatly saves time. Through this parallel processing, the IHA results for each individual source can be obtained quickly. Then, it is determined whether the preset pick rate has been reached; if so, the IHAs of multiple individual source seismic sources are merged to obtain the final IHA. This process of first processing individual source data in parallel to obtain IHAs, and then performing subsequent operations based on the pick rate, effectively avoids the time-consuming drawbacks of the traditional method of processing one source at a time, greatly improving the overall efficiency of IHA acquisition.

[0032] The technical solution in this embodiment, by processing individual seismic data in parallel, eliminates the need for sequential processing of individual data points when dealing with massive amounts of complex, low signal-to-noise ratio seismic data, thus saving significant time. For example, when processing large seismic datasets containing numerous individual data points, processing them sequentially could previously take a long time to complete the initial arrival picking. Now, through parallel processing, the arrivals of each individual shot can be obtained in a shorter time. Then, after determining that a preset picking rate has been reached, a merging operation is performed to obtain the final arrivals. This significantly improves the efficiency of the entire arrival picking process. Work that previously required substantial manpower and time can now be completed in a relatively short time, effectively shortening the overall seismic data processing cycle, reducing labor costs, and improving work efficiency. This allows for more efficient arrival picking of complex, low signal-to-noise ratio seismic data across massive datasets.

[0033] Example 2

[0034] Based on the above embodiments, after determining whether the preset pickup rate has been reached, the method further includes: if the preset pickup rate has not been reached, fitting the first arrival data of multiple single-shot seismic data to obtain a reference first arrival; and re-executing the step of parallel processing of each single-shot seismic data based on the reference first arrival.

[0035] In the process of first-arrival acquisition for complex, low-signal-to-noise-ratio (SNR) massive seismic data, how to further improve the first-arrival acquisition rate when the preset acquisition rate is not reached is an urgent problem to be solved. As often happens in actual processing of this type of seismic data, after processing using conventional methods, the first-arrival acquisition rate does not reach the ideal level, making subsequent work difficult to carry out effectively and affecting the progress and quality of the entire seismic data processing. There is a technical problem in this field of effectively improving the first-arrival acquisition rate of complex, low-signal-to-noise-ratio massive seismic data when the preset acquisition rate is not reached.

[0036] In this embodiment, after determining whether a preset pickup rate has been reached, if the preset pickup rate has not been reached, the first-arrival data of multiple single-shot seismic data will be fitted to obtain a reference first-arrival. Here, fitting refers to using a preset mathematical algorithm to calculate a more reasonable and representative reference first-arrival based on existing first-arrival data. Based on this reference first-arrival, the parallel processing steps for each single-shot seismic data are re-executed. That is, using the fitted reference first-arrival as a new reference, a series of parallel processing operations, such as preprocessing, calculating the reference area, calculating the first-arrival, and removing anomalous first-arrivals, are performed on the single-shot seismic data again. Through continuous adjustment and optimization, the first-arrival pickup rate is expected to be improved.

[0037] In this embodiment, when the preset pickup rate is not met, a reference first arrival is obtained by fitting the first arrival data of multiple single-shot seismic datasets. For example, when processing a complex, low signal-to-noise ratio seismic dataset, the initial first arrival pickup rate may be unsatisfactory. In this case, by fitting the existing first arrival data, a reference first arrival that better reflects the overall situation is obtained. Then, the parallel processing steps are re-executed based on this reference first arrival, which is equivalent to providing a more accurate starting point for subsequent processing. During the parallel processing again, operations on each single-shot seismic dataset can be performed more specifically. For example, parameters can be better adjusted based on the reference first arrival in the preprocessing stage, and iterative calculations using direct wave and refracted wave models can be performed more accurately in the first arrival calculation stage. This continuous adjustment and optimization gradually improves the first arrival pickup rate, ultimately enabling more effective acquisition of first arrivals that meet the requirements. This ensures the smooth progress of the entire seismic data processing work, improves the first arrival pickup rate of complex, low signal-to-noise ratio, massive seismic datasets, and avoids problems such as subsequent work stagnation or quality degradation caused by substandard first arrival pickup rates.

[0038] Example 3

[0039] Based on the above embodiments, the steps of parallel processing of each single-shot seismic data include: preprocessing the single-shot seismic data in parallel on a single-shot basis; calculating the reference area of ​​the single-shot seismic data in parallel on a single-shot basis; calculating the first arrival of the single-shot based on the reference area in parallel on a single-shot basis; and / or removing anomalous first arrivals of the single-shot in parallel on a single-shot basis.

[0040] In the initial arrival acquisition of massive amounts of complex, low signal-to-noise ratio seismic data, how to efficiently and reasonably process individual seismic data in parallel is a pressing technical problem. Traditional processing methods often operate on individual seismic data one by one, which is inefficient and fails to fully utilize the correlations and characteristics between the data. When faced with massive amounts of complex, low signal-to-noise ratio seismic data, this step-by-step processing method leads to slow progress in the entire initial arrival acquisition process, failing to meet the needs of actual production. There is a lack of effective methods in this field for parallel processing of individual seismic data.

[0041] In this embodiment, the parallel processing of individual seismic data includes several aspects. First, preprocessing is performed on a per-shot basis, including static elevation correction, anomaly trace detection and marking, wavelet application, and / or delay gain. Static elevation correction reduces the impact of the surface on the data; anomaly trace detection and marking mitigates the adverse effects of anomalous data; and wavelet application and delay gain improve the first-arrival signal-to-noise ratio and identification accuracy. Next, a reference region is calculated for each preprocessed seismic data shot in parallel. Then, the first arrival of each shot is calculated in parallel based on the reference region. Furthermore, anomalous first arrivals can be removed from each shot in parallel. This series of parallel operations on a per-shot basis fully utilizes the independence of individual data and the commonalities between data points, improving processing efficiency while ensuring the accuracy of the results.

[0042] The technical solution in this embodiment demonstrates significant advantages when dealing with complex, low signal-to-noise ratio (SNR) seismic data through parallel processing on a per-shot basis. For example, when processing large-scale seismic datasets, preprocessing operations are performed simultaneously on each single-shot data point, such as synchronous static elevation correction. This quickly adjusts for deviations caused by surface factors, making subsequent processing more accurate. During anomaly trace detection and marking, all single-shot data can be checked simultaneously, promptly identifying and marking anomalous data that may affect the results, thus improving data quality. Then, the reference area and first arrival of each shot are calculated in parallel, and anomalous first arrivals are removed, significantly shortening processing time. Compared to the traditional method of processing single-shot data one by one, this parallel processing method can complete the processing of all single-shot data in a shorter time, improving the efficiency of the entire first arrival picking process. Furthermore, because each step is optimized for single-shot data, the accuracy of the processing results is guaranteed, making the final first arrival data more reliable and effectively promoting the smooth progress of first arrival picking for complex, low SNR seismic data.

[0043] Example 4

[0044] Based on the above embodiments, the steps of parallel processing of each single-shot seismic data are re-executed based on the reference first arrival, including: calculating the reference area of ​​a single shot in parallel based on the reference first arrival for the preprocessed single-shot seismic data on a single-shot basis; calculating the first arrival of a single shot in parallel based on the reference area on a single-shot basis; and / or removing anomalous first arrivals of a single shot in parallel on a single-shot basis.

[0045] In the process of first-arrival picking for complex, low-signal-to-noise-ratio, massive seismic data, a problem that needs to be solved is how to re-execute parallel processing of individual seismic data based on reference first-arrivals if the preset picking rate is not achieved. When the first-arrival picking rate is found to be insufficient after processing according to the conventional workflow, there has been a lack of clear and effective methods to reasonably and efficiently perform parallel processing of individual seismic data based on newly obtained reference first-arrivals. This makes it difficult to further improve the first-arrival picking rate, affecting the overall seismic data processing effect. There is a technical problem in this field where it is difficult to re-execute parallel processing of individual seismic data based on reference first-arrivals when the preset picking rate is not achieved.

[0046] In this embodiment, the steps of re-executing parallel processing of individual seismic data based on reference first arrivals are as follows: First, on a per-shot basis, the reference region of each individual shot is calculated in parallel based on the reference first arrival. Here, the reference first arrivals obtained from the fitting in the above embodiment are used as an important basis to recalculate the reference region of each individual shot, making the calculation of the reference region more accurate. Next, on a per-shot basis, the first arrival of each individual shot is calculated in parallel based on the reference region. Guided by the reference first arrival, the calculation of the first arrival can be more accurately located and calculated. Furthermore, abnormal first arrivals of individual shots can be removed in parallel on a per-shot basis, timely eliminating abnormal factors that may affect the results. Through this series of parallel operations on a per-shot basis based on reference first arrivals, targeted processing can be performed on cases where the preset pickup rate is not achieved, further optimizing the processing process and improving the first arrival pickup rate.

[0047] The technical solution of this embodiment, when the preset pickup rate is not reached, re-executes the parallel processing steps based on the reference first arrival. For example, when processing a complex low signal-to-noise ratio seismic dataset, if the first arrival pickup rate is not up to standard after the initial processing, the parallel processing is re-executed using the previously fitted reference first arrival. On a per-shot basis, the reference area for each shot is recalculated based on the reference first arrival, making the new reference area more consistent with the actual situation and providing a more accurate basis for subsequent first arrival calculations. During the parallel calculation of the first arrival of a single shot, the more accurate reference first arrival serves as a guide, enabling more precise capture of the first arrival signal and improving the accuracy of the first arrival calculation. Simultaneously, abnormal first arrivals of individual shots are removed in parallel, ensuring the purity of the processing results. Through this series of operations, the situation where the preset pickup rate is not reached is continuously optimized, gradually increasing the first arrival pickup rate until it meets the requirements. This effectively solves the problems of subsequent work stagnation or quality degradation caused by the substandard first arrival pickup rate, ensuring the smooth progress of the entire seismic data processing work and improving the first arrival pickup efficiency of complex, low signal-to-noise ratio, massive seismic data.

[0048] Example 5

[0049] Based on the above embodiments, the steps for preprocessing single-shot seismic data include: static elevation correction of single-shot seismic data, detection and marking of anomalous seismic traces, wavelet application and / or delay gain.

[0050] In the first-arrival acquisition of massive amounts of complex, low signal-to-noise ratio (SNR) seismic data, effective preprocessing of single-shot seismic data is a crucial issue. Because complex, low SNR seismic data typically contains significant noise, the first-arrival signal is often masked by this noise. Inadequate preprocessing can severely impact the accuracy and efficiency of subsequent first-arrival acquisition. Previous preprocessing methods have limitations in their specificity and effectiveness, leading to considerable difficulties in processing such seismic data. There is a current technical challenge in the field of effective preprocessing of single-shot seismic data, lacking a suitable method.

[0051] In this embodiment, the preprocessing steps for single-shot seismic data include: static elevation correction, anomaly trace detection and marking, wavelet application, and / or time delay gain. Static elevation correction aims to reduce the influence of the ground surface on the data. For example, when the ground surface is undulating, static elevation correction can adjust the differences in seismic wave propagation time caused by differences in ground height, making the subsequently processed data more accurate. Anomaly trace detection and marking aims to reduce the adverse effects of anomalous data on the data, promptly identifying and marking anomalous traces that may be caused by instrument malfunction, external interference, etc., to avoid these anomalous data interfering with subsequent processing. Wavelet application and time delay gain can improve the signal-to-noise ratio and identification accuracy of the first arrival. By reasonably applying the wavelet and setting the time delay gain parameters, the first arrival signal can be made more prominent against a noisy background, facilitating accurate identification and pickup.

[0052] The technical solution in this embodiment, after performing the aforementioned preprocessing operations on single-shot seismic data, has achieved excellent results in processing complex, low-signal-to-noise ratio (SNR) massive seismic data. For example, when processing a seismic dataset containing a large amount of complex noise, static elevation correction is first performed to adjust for differences in seismic wave propagation time caused by surface factors, resulting in more accurate data. Next, abnormal seismic trace detection and marking are performed, promptly identifying and marking abnormal seismic traces caused by instrument malfunctions, ensuring the data quality of subsequent processing. Then, through wavelet application and delay gain, the SNR of the first arrival is significantly improved, making the first arrival signal more prominent against the noise background, facilitating accurate identification and pickup. This allows subsequent first arrival pickup to proceed more smoothly, improving the accuracy and efficiency of first arrival pickup, effectively solving problems such as stagnation or quality degradation in subsequent work caused by inadequate preprocessing, and laying a solid foundation for the entire first arrival pickup process for complex, low-signal-to-noise ratio massive seismic data.

[0053] Example 6

[0054] Based on the above embodiments, the steps for calculating the first arrival of a single shot include: picking the first arrival based on the energy method and the information entropy method, and iterating the first arrival based on the direct wave refracted wave model to calculate the first arrival on a single track.

[0055] In the first-arrival acquisition of complex, low-signal-to-noise-ratio (SNR) seismic data, accurately calculating the first arrival of a single shot is a crucial issue. Due to the characteristics of complex, low-SNR seismic data, the first-arrival signal is often weak and easily affected by noise. Traditional calculation methods may struggle to accurately capture the first-arrival signal, leading to inaccurate first-arrival acquisition and impacting the overall seismic data processing effectiveness. Therefore, there is a technical problem in this field regarding how to accurately calculate the first arrival of a single shot.

[0056] In this embodiment, the steps for calculating the first arrival of a single shot include: picking the first arrival based on the energy method and the information entropy method, and iterating the first arrival based on the direct wave refraction model to calculate the first arrival on a single trace. The energy method uses the energy characteristics of seismic waves to determine the location of the first arrival. By analyzing the changes in seismic wave energy at different locations, the point of energy abrupt change is likely the location of the first arrival. The information entropy method, from an information theory perspective, determines the location of the first arrival based on the changes in the information entropy carried by the seismic wave. Then, iterating the first arrival based on the direct wave refraction model involves continuously adjusting the calculation results of the first arrival using the principles of the direct wave refraction model to make it more accurate. By combining these methods, the first arrival signal can be captured more accurately in complex low signal-to-noise ratio seismic data.

[0057] The technical solution in this embodiment employs an energy-based and information entropy-based method to pick first arrivals, and iteratively calculates the first arrival of a single shot based on a direct-wave refraction model. This approach has achieved significant results in processing complex, low-signal-noise-ratio (SNR) seismic data. For example, when processing a complex, low-SNR seismic dataset, the first arrival signal is very weak and masked by a large amount of noise. The energy method analyzes the changes in seismic wave energy, identifying potential first arrival locations. The information entropy method is then used to further filter and confirm these locations, making the first arrival locations more accurate. Next, the first arrival is iteratively calculated based on the direct-wave refraction model, continuously adjusting the calculation results to better reflect the actual situation. This results in more accurate first arrivals calculated for each trace, improving the accuracy of first arrival picking and effectively solving problems such as work stoppage or quality degradation caused by inaccurate first arrival calculations. This ensures the smooth progress of the entire seismic data processing workflow and enables more accurate first arrival picking for complex, low-SNR seismic data.

[0058] Example 7

[0059] Figure 2 This is a schematic diagram of the structure of a first-arrival picking device for complex seismic data provided in an embodiment of this application, as shown below. Figure 2 As shown in the technical solution of this embodiment, a first arrival picking device for complex seismic data is provided. The device includes: a single-shot processing module for parallel processing of each single-shot seismic data to obtain the first arrival of the single-shot seismic data; an iterative judgment module for judging whether a preset picking rate has been reached; and a first arrival output module for merging the first arrivals of multiple single-shot seismic data if the preset picking rate has been reached to obtain the final first arrival.

[0060] The acquisition of first arrivals from complex, low-signal-to-noise-ratio (SNR) massive seismic data has long been characterized by inefficiency. For example, when dealing with seismic data exhibiting poor SNR and complex terrain (such as loess plateaus and mountains), obtaining high-quality first arrivals previously often required several people and took one or two months or even longer, resulting in enormous human and material costs and extremely low production efficiency, severely restricting the overall seismic data processing cycle. This field faces the technical problem of inefficiency in acquiring first arrivals from complex, low-signal-to-noise-ratio massive seismic data.

[0061] In this embodiment, the technical solution employs parallel processing of individual seismic data to obtain the initial arrival (IHA) of each individual seismic source, and then determines whether a preset pick rate has been reached. Parallel processing here refers to performing related operations on multiple individual seismic sources simultaneously, unlike the previous method of processing individual source data one by one, which greatly saves time. Through this parallel processing, the IHA results for each individual source can be obtained quickly. Then, it is determined whether the preset pick rate has been reached; if so, the IHAs of multiple individual source seismic sources are merged to obtain the final IHA. This process of first processing individual source data in parallel to obtain IHAs, and then performing subsequent operations based on the pick rate, effectively avoids the time-consuming drawbacks of the traditional method of processing one source at a time, greatly improving the overall efficiency of IHA acquisition.

[0062] The technical solution in this embodiment, by processing individual seismic data in parallel, eliminates the need for sequential processing of individual data points when dealing with massive amounts of complex, low signal-to-noise ratio seismic data, thus saving significant time. For example, when processing large seismic datasets containing numerous individual data points, processing them sequentially could previously take a long time to complete the initial arrival picking. Now, through parallel processing, the arrivals of each individual shot can be obtained in a shorter time. Then, after determining that a preset picking rate has been reached, a merging operation is performed to obtain the final arrivals. This significantly improves the efficiency of the entire arrival picking process. Work that previously required substantial manpower and time can now be completed in a relatively short time, effectively shortening the overall seismic data processing cycle, reducing labor costs, and improving work efficiency. This allows for more efficient arrival picking of complex, low signal-to-noise ratio seismic data across massive datasets.

[0063] Based on the above embodiments, after determining whether the preset pickup rate has been reached, the device further includes: if the preset pickup rate has not been reached, fitting the first arrival data of multiple single-shot seismic data to obtain a reference first arrival; and re-executing the step of parallel processing of each single-shot seismic data based on the reference first arrival.

[0064] In the process of first-arrival acquisition for complex, low-signal-to-noise-ratio (SNR) massive seismic data, how to further improve the first-arrival acquisition rate when the preset acquisition rate is not reached is an urgent problem to be solved. As often happens in actual processing of this type of seismic data, after processing using conventional methods, the first-arrival acquisition rate does not reach the ideal level, making subsequent work difficult to carry out effectively and affecting the progress and quality of the entire seismic data processing. There is a technical problem in this field of effectively improving the first-arrival acquisition rate of complex, low-signal-to-noise-ratio massive seismic data when the preset acquisition rate is not reached.

[0065] In this embodiment, after determining whether a preset pickup rate has been reached, if the preset pickup rate has not been reached, the first-arrival data of multiple single-shot seismic data will be fitted to obtain a reference first-arrival. Here, fitting refers to using a preset mathematical algorithm to calculate a more reasonable and representative reference first-arrival based on existing first-arrival data. Based on this reference first-arrival, the parallel processing steps for each single-shot seismic data are re-executed. That is, using the fitted reference first-arrival as a new reference, a series of parallel processing operations, such as preprocessing, calculating the reference area, calculating the first-arrival, and removing anomalous first-arrivals, are performed on the single-shot seismic data again. Through continuous adjustment and optimization, the first-arrival pickup rate is expected to be improved.

[0066] In this embodiment, when the preset pickup rate is not met, a reference first arrival is obtained by fitting the first arrival data of multiple single-shot seismic datasets. For example, when processing a complex, low signal-to-noise ratio seismic dataset, the initial first arrival pickup rate may be unsatisfactory. In this case, by fitting the existing first arrival data, a reference first arrival that better reflects the overall situation is obtained. Then, the parallel processing steps are re-executed based on this reference first arrival, which is equivalent to providing a more accurate starting point for subsequent processing. During the parallel processing again, operations on each single-shot seismic dataset can be performed more specifically. For example, parameters can be better adjusted based on the reference first arrival in the preprocessing stage, and iterative calculations using direct wave and refracted wave models can be performed more accurately in the first arrival calculation stage. This continuous adjustment and optimization gradually improves the first arrival pickup rate, ultimately enabling more effective acquisition of first arrivals that meet the requirements. This ensures the smooth progress of the entire seismic data processing work, improves the first arrival pickup rate of complex, low signal-to-noise ratio, massive seismic datasets, and avoids problems such as subsequent work stagnation or quality degradation caused by substandard first arrival pickup rates.

[0067] Based on the above embodiments, the steps of parallel processing of each single-shot seismic data include: preprocessing the single-shot seismic data in parallel on a single-shot basis; calculating the reference area of ​​the single-shot seismic data in parallel on a single-shot basis; calculating the first arrival of the single-shot based on the reference area in parallel on a single-shot basis; and / or removing anomalous first arrivals of the single-shot in parallel on a single-shot basis.

[0068] In the initial arrival acquisition of massive amounts of complex, low signal-to-noise ratio seismic data, how to efficiently and reasonably process individual seismic data in parallel is a pressing technical problem. Traditional processing methods often operate on individual seismic data one by one, which is inefficient and fails to fully utilize the correlations and characteristics between the data. When faced with massive amounts of complex, low signal-to-noise ratio seismic data, this step-by-step processing method leads to slow progress in the entire initial arrival acquisition process, failing to meet the needs of actual production. There is a lack of effective devices for parallel processing of individual seismic data in this field.

[0069] In this embodiment, the parallel processing of individual seismic data includes several aspects. First, preprocessing is performed on a per-shot basis, including static elevation correction, anomaly trace detection and marking, wavelet application, and / or delay gain. Static elevation correction reduces the impact of the surface on the data; anomaly trace detection and marking mitigates the adverse effects of anomalous data; and wavelet application and delay gain improve the first-arrival signal-to-noise ratio and identification accuracy. Next, a reference region is calculated for each preprocessed seismic data shot in parallel. Then, the first arrival of each shot is calculated in parallel based on the reference region. Furthermore, anomalous first arrivals can be removed from each shot in parallel. This series of parallel operations on a per-shot basis fully utilizes the independence of individual data and the commonalities between data points, improving processing efficiency while ensuring the accuracy of the results.

[0070] The technical solution in this embodiment demonstrates significant advantages when dealing with complex, low signal-to-noise ratio (SNR) seismic data through parallel processing on a per-shot basis. For example, when processing large-scale seismic datasets, preprocessing operations are performed simultaneously on each single-shot data point, such as synchronous static elevation correction. This quickly adjusts for deviations caused by surface factors, making subsequent processing more accurate. During anomaly trace detection and marking, all single-shot data can be checked simultaneously, promptly identifying and marking anomalous data that may affect the results, thus improving data quality. Then, the reference area and first arrival of each shot are calculated in parallel, and anomalous first arrivals are removed, significantly shortening processing time. Compared to the traditional method of processing single-shot data one by one, this parallel processing method can complete the processing of all single-shot data in a shorter time, improving the efficiency of the entire first arrival picking process. Furthermore, because each step is optimized for single-shot data, the accuracy of the processing results is guaranteed, making the final first arrival data more reliable and effectively promoting the smooth progress of first arrival picking for complex, low SNR seismic data.

[0071] Based on the above embodiments, the steps of parallel processing of each single-shot seismic data are re-executed based on the reference first arrival, including: calculating the reference area of ​​a single shot in parallel based on the reference first arrival for the preprocessed single-shot seismic data on a single-shot basis; calculating the first arrival of a single shot in parallel based on the reference area on a single-shot basis; and / or removing anomalous first arrivals of a single shot in parallel on a single-shot basis.

[0072] In the process of first-arrival picking for complex, low-signal-to-noise-ratio, massive seismic data, a problem needs to be solved: how to re-execute parallel processing of individual seismic data based on reference first-arrivals when the preset picking rate is not achieved. Previously, when the first-arrival picking rate was found to be insufficient after processing according to the conventional procedure, there was a lack of a clear and effective device to perform reasonable and efficient parallel processing of individual seismic data based on newly obtained reference first-arrivals. This made it difficult to further improve the first-arrival picking rate, affecting the overall seismic data processing effect. The art faces the technical problem of finding it difficult to re-execute parallel processing of individual seismic data based on reference first-arrivals when the preset picking rate is not achieved.

[0073] In this embodiment, the steps of re-executing parallel processing of individual seismic data based on reference first arrivals are as follows: First, on a per-shot basis, the reference region of each individual shot is calculated in parallel based on the reference first arrival. Here, the reference first arrivals obtained from the fitting in the above embodiment are used as an important basis to recalculate the reference region of each individual shot, making the calculation of the reference region more accurate. Next, on a per-shot basis, the first arrival of each individual shot is calculated in parallel based on the reference region. Guided by the reference first arrival, the calculation of the first arrival can be more accurately located and calculated. Furthermore, abnormal first arrivals of individual shots can be removed in parallel on a per-shot basis, timely eliminating abnormal factors that may affect the results. Through this series of parallel operations on a per-shot basis based on reference first arrivals, targeted processing can be performed on cases where the preset pickup rate is not achieved, further optimizing the processing process and improving the first arrival pickup rate.

[0074] The technical solution of this embodiment, when the preset pickup rate is not reached, re-executes the parallel processing steps based on the reference first arrival. For example, when processing a complex low signal-to-noise ratio seismic dataset, if the first arrival pickup rate is not up to standard after the initial processing, the parallel processing is re-executed using the previously fitted reference first arrival. On a per-shot basis, the reference area for each shot is recalculated based on the reference first arrival, making the new reference area more consistent with the actual situation and providing a more accurate basis for subsequent first arrival calculations. During the parallel calculation of the first arrival of a single shot, the more accurate reference first arrival serves as a guide, enabling more precise capture of the first arrival signal and improving the accuracy of the first arrival calculation. Simultaneously, abnormal first arrivals of individual shots are removed in parallel, ensuring the purity of the processing results. Through this series of operations, the situation where the preset pickup rate is not reached is continuously optimized, gradually increasing the first arrival pickup rate until it meets the requirements. This effectively solves the problems of subsequent work stagnation or quality degradation caused by the substandard first arrival pickup rate, ensuring the smooth progress of the entire seismic data processing work and improving the first arrival pickup efficiency of complex, low signal-to-noise ratio, massive seismic data.

[0075] Based on the above embodiments, the steps for preprocessing single-shot seismic data include: static elevation correction of single-shot seismic data, detection and marking of anomalous seismic traces, wavelet application and / or delay gain.

[0076] In the first-arrival acquisition of massive amounts of complex, low signal-to-noise ratio (SNR) seismic data, effective preprocessing of single-shot seismic data is a crucial issue. Because complex, low SNR seismic data typically contains significant noise, the first-arrival signal is often masked by this noise. Inadequate preprocessing can severely impact the accuracy and efficiency of subsequent first-arrival acquisition. Previous preprocessing devices have limitations in their specificity and effectiveness, leading to numerous difficulties in processing this type of seismic data. There is a current technical challenge in the field of effective preprocessing of single-shot seismic data, lacking suitable equipment.

[0077] In this embodiment, the preprocessing steps for single-shot seismic data include: static elevation correction, anomaly trace detection and marking, wavelet application, and / or time delay gain. Static elevation correction aims to reduce the influence of the ground surface on the data. For example, when the ground surface is undulating, static elevation correction can adjust the differences in seismic wave propagation time caused by differences in ground height, making the subsequently processed data more accurate. Anomaly trace detection and marking aims to reduce the adverse effects of anomalous data on the data, promptly identifying and marking anomalous traces that may be caused by instrument malfunction, external interference, etc., to avoid these anomalous data interfering with subsequent processing. Wavelet application and time delay gain can improve the signal-to-noise ratio and identification accuracy of the first arrival. By reasonably applying the wavelet and setting the time delay gain parameters, the first arrival signal can be made more prominent against a noisy background, facilitating accurate identification and pickup.

[0078] The technical solution in this embodiment, after performing the aforementioned preprocessing operations on single-shot seismic data, has achieved excellent results in processing complex, low-signal-to-noise ratio (SNR) massive seismic data. For example, when processing a seismic dataset containing a large amount of complex noise, static elevation correction is first performed to adjust for differences in seismic wave propagation time caused by surface factors, resulting in more accurate data. Next, abnormal seismic trace detection and marking are performed, promptly identifying and marking abnormal seismic traces caused by instrument malfunctions, ensuring the data quality of subsequent processing. Then, through wavelet application and delay gain, the SNR of the first arrival is significantly improved, making the first arrival signal more prominent against the noise background, facilitating accurate identification and pickup. This allows subsequent first arrival pickup to proceed more smoothly, improving the accuracy and efficiency of first arrival pickup, effectively solving problems such as stagnation or quality degradation in subsequent work caused by inadequate preprocessing, and laying a solid foundation for the entire first arrival pickup process for complex, low-signal-to-noise ratio massive seismic data.

[0079] Based on the above embodiments, the step of calculating the first arrival of a single shot includes: picking up the first arrival based on the energy device and the information entropy device, and iterating the first arrival based on the direct wave refracted wave model to calculate the first arrival on a single track.

[0080] In the acquisition of first arrivals from complex, low signal-to-noise ratio (SNR) seismic data, accurately calculating the first arrival of a single shot is a crucial issue. Due to the characteristics of complex, low SNR seismic data, the first arrival signal is often weak and easily affected by noise. Traditional computing devices may struggle to accurately capture the first arrival signal, leading to inaccurate first arrival acquisition and impacting the overall seismic data processing effectiveness. Therefore, there is a technical problem in this field regarding how to accurately calculate the first arrival of a single shot.

[0081] In this embodiment, the steps for calculating the first arrival of a single shot include: acquiring the first arrival based on an energy device and an information entropy device, and iterating on the first arrival based on a direct-wave refraction model to calculate the first arrival on a single trace. The energy device uses the energy characteristics of seismic waves to determine the location of the first arrival; by analyzing the changes in seismic wave energy at different locations, it identifies points of energy abrupt change, which are likely the locations of the first arrival. The information entropy device, from an information theory perspective, determines the location of the first arrival based on changes in the information entropy carried by the seismic wave. Then, iterating on the first arrival based on the direct-wave refraction model involves continuously adjusting the calculation results to make them more accurate. By combining these devices, the first arrival signal can be captured more accurately in complex, low signal-to-noise ratio seismic data.

[0082] The technical solution in this embodiment employs an energy-based and information entropy-based first arrival (FA) device to pick up the FAW (First Arrival) signal, and then iteratively calculates the FAW signal based on a direct-wave refraction model. This method has achieved significant results in processing complex, low-signal-noise-ratio (SNR) seismic data. For example, when processing a complex, low-SNR seismic dataset, the FAW signal is very weak and masked by a large amount of noise. By analyzing the changes in seismic wave energy using the energy device, potential FAW locations are identified. The information entropy device is then used to further filter and confirm these locations, making the FAW locations more accurate. Next, the FAW signal is iteratively calculated based on the direct-wave refraction model, continuously adjusting the calculation results to better reflect the actual situation. This results in more accurate FAW calculations for each trace, improving the accuracy of FAW pickup and effectively solving problems such as work stoppage or quality degradation caused by inaccurate FAW calculations. This ensures the smooth progress of the entire seismic data processing workflow and enables more accurate FAW pickup for complex, low-SNR seismic data.

[0083] Example 8

[0084] In the technical solution of this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of any of the above embodiments of the first arrival picking method for complex seismic data.

[0085] In the technical solution of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the first arrival picking method for complex seismic data in any of the above embodiments.

[0086] In the technical solution of this embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the above embodiments of the first arrival picking method for complex seismic data.

[0087] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for performing the methods in the above embodiments. The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0088] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0089] In addition, the computer device may also include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., a keyboard, mouse, speakers, etc.). The processor can communicate with external devices via the I / O bus through a wired or wireless network. In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions, when executed by the processor, perform the steps of the various functions and / or methods in the embodiments described herein.

[0090] Example 9

[0091] Based on the above embodiments, this embodiment provides an application example.

[0092] This application example provides an automatic first arrival acquisition method for complex, low signal-to-noise ratio seismic data. This application example falls under the research of petroleum geophysical exploration technology within the field of Earth sciences.

[0093] With the continuous development of onshore oil and gas exploration in my country and the deepening application of high-density point and wide-azimuth acquisition, seismic data is becoming increasingly complex, and the volume of seismic data is also growing rapidly at tens or hundreds of TB levels. The increasingly complex data quality and the massive data processing requirements have brought severe challenges to initial arrival picking operations.

[0094] For a long time, obtaining high-quality first arrivals from complex, low-signal-to-noise-ratio (LSNR) massive seismic data (poor signal-to-noise ratio, complex terrain, loess plateaus, mountains, etc.) has required a significant investment of manpower and resources. It typically involves several people and takes one or two months, or even longer, to complete, resulting in extremely low productivity. This is a major pain point in seismic data processing projects and severely restricts the overall processing cycle. This field addresses the technical problem of low efficiency in first arrival acquisition from complex, low-signal-to-noise-ratio massive seismic data.

[0095] To address the problem of low efficiency in first-arrival picking for complex, low signal-to-noise ratio seismic data, the academic and industrial communities have been making unremitting efforts and have published numerous articles on automatic first-arrival picking. Currently, various picking methods have been developed, including correlation methods, energy ratio methods, fractal methods, neural networks, and artificial intelligence. However, the effectiveness and applicability of each method for different data, especially data from complex areas, largely depend on the characteristics and quality of the seismic data. In other words, there are varying degrees of data adaptability issues, resulting in significant differences in effectiveness.

[0096] There are also many representative commercial first-arrival picking software or modules in the industry. The first-arrival picking methods used in industrial production mainly include single-track boundary detection, cross-correlation, peak-to-valley ratio, and artificial neural network methods. For complex area data, these automatic picking methods each have their own advantages and disadvantages. The final first-arrival picking effect is affected by a variety of factors, such as the picking time window, seed shot source, and number of hidden nodes. The accuracy of automatic first-arrival picking is relatively low, and a lot of manual inspection and correction for each shot is usually required. Overall, the technical problem of low productivity in the first-arrival stage has not been changed.

[0097] With the increasing complexity and density of seismic data acquisition, the scale of seismic data is growing exponentially. The manual labor intensity in the initial arrival stage is enormous, almost unbearable, and increasingly restricts the effectiveness and efficiency of seismic data processing. Therefore, for the initial arrival stage of complex, low signal-to-noise ratio seismic data, there is an urgent need for innovation in technology, processes, and workflows.

[0098] Therefore, this application example focuses on complex low signal-to-noise ratio seismic data. Based on Afapa, a software with independent intellectual property rights developed by Sinopec Geophysical Research Institute, a method to improve the first arrival picking rate was developed. The method is effective and the first arrival picking efficiency has been greatly improved.

[0099] This application example presents an efficient method for complex, low signal-to-noise ratio (SNR) seismic data, especially for mid-to-long-range first-arrivals with weak energy and low SNR. The method comprises the following steps in the longitudinal direction: (1) preprocessing of single-shot seismic data; (2) automatic calculation of the reference area for single-shot data; (3) iterative calculation of first-arrivals using multiple methods for single-shot data; (4) removal of anomalous first-arrivals for single-shot data; (5) removal of anomalous first-arrivals for multi-shot data; (6) checking if the picking rate is reached; if so, proceed to step (7); otherwise, proceed to step (8); (7) fitting first-arrivals for single-shot data, returning to step (2); (8) automatic merging of multiple sets of first-arrivals for single-shot data; (9) forming the final delivered first-arrivals. This method iterates multiple times between single-shot and multi-shot approaches, fully considering the impact of geographical location on the first-arrival results while also prioritizing efficiency, ultimately providing the required first-arrivals. This application example emphasizes the multi-step construction method.

[0100] Figure 3 This is an automatic first-arrival acquisition method for complex, low signal-to-noise ratio seismic data. The following section will detail an application example based on the above process:

[0101] (1) Preprocessing of single-shot seismic data.

[0102] Complex low signal-to-noise ratio seismic data generally have complex noise, with the first arrival signal being masked by severe noise. Therefore, preprocessing of seismic data is necessary. The preprocessing methods used in this method include: static elevation correction, anomaly trace detection and marking, wavelet application, and time delay gain. Among these, static elevation correction can reduce the influence of the ground surface on the data, anomaly trace detection and marking can reduce the adverse effects of anomalous data on the data, which is beneficial to improving the stability of the picking algorithm, and wavelet application and time delay gain can improve the signal-to-noise ratio of the first arrival and the accuracy of identification.

[0103] (2) Automatic calculation of reference area for a single gun.

[0104] The algorithm for automatically calculating the reference area on a single-shot basis is not described in detail in this application example. This application example emphasizes that the parallel calculation of shot set data can be achieved under this constraint, thereby improving efficiency.

[0105] (3) Iterative calculation of initial arrival using multiple methods for a single shot.

[0106] Determining the first arrival on a single trajectory requires a multi-step process. In this application example, it is important to emphasize that the method of automatically picking the first arrival using energy and information entropy methods on a single-shot basis, and iteratively calculating the first arrival on a single trajectory based on the direct wave refracted wave model, enables parallel computation of shot gather data and improves efficiency.

[0107] (4) Remove abnormal initial arrivals for single shot.

[0108] This application example emphasizes the use of a single shot as a unit, under which parallel computation of shot collection data can be achieved, thus improving efficiency.

[0109] (5) Remove abnormal initial arrivals for multiple shots.

[0110] As exploration work increasingly shifts towards complex seismic areas, the acquired seismic data is mostly low signal-to-noise ratio data, susceptible to noise interference. Existing automatic first-arrival (FA) acquisition methods generate numerous anomalous FAWs, requiring extensive manual inspection and removal. This process is tedious and time-consuming, and multiple people handling the acquisition and correction can easily lead to stratigraphic mismatch. In this scenario, batch quality control is necessary. A prerequisite for this process is that all data must be automatically acquired on a per-shot basis. The parameters entered in this step need to be determined based on the specific seismic data. Generally, when the single-shot acquisition rate is insufficient, more anomalous FAWs should be removed, and vice versa.

[0111] (6) Check if the pickup rate has been reached. If the pickup rate has been reached, proceed to step (7); otherwise, proceed to step (8).

[0112] For complex seismic data with low signal-to-noise ratio, the automatic first arrival picking rate of a single shot is generally not very high, usually not reaching 90%. Therefore, in order to improve the picking rate, it is necessary to remove a large number of anomalous first arrivals in this step and retain only accurate data.

[0113] (7) For multi-shot fitting reference initial arrival, to step (2).

[0114] The purpose of this step is to calculate the accurate reference first arrival, especially for single-shot mid-to-long-range offset data. The first arrival signal is weak, and the reference first arrival fitted based on the surrounding multi-shot data will be more accurate. This can avoid the problem of insufficient accurate first arrival data for a single shot. It also takes into account the similarity of the shot collection data positions, which can effectively improve the stability of the reference first arrival fitted by multi-shot data. Then proceed to step (2) to start the iteration process.

[0115] (8) Single gun automatically merges multiple initial arrivals.

[0116] (9) Forming the initial arrival of the final delivery.

[0117] This step is necessary because, in actual operation, the initial arrivals generated by different parameters are not completely consistent. If there are multiple results, the results will be automatically merged, which can effectively improve the pickup rate of the initial arrival of a single shot.

[0118] To verify the effectiveness of this application example, a complex low signal-to-noise ratio data set was selected. At mid-to-long offset positions, the first-arrival signal was extremely weak, almost invisible. After adopting the technical solution of this application example, most of the weak signal positions were able to be picked up, effectively improving the first-arrival pickup rate of low signal-to-noise ratio data.

[0119] Figure 4 This is the automatic first-arrival picking effect after applying this application example workflow to a specific dataset. This application example effectively improves the first-arrival picking rate for complex, low signal-to-noise ratio seismic data, and effectively addresses the problem of low efficiency in first-arrival picking for massive amounts of seismic data in complex areas (poor signal-to-noise ratio, complex terrain, loess plateaus, mountains, etc.). (Once high-quality first-arrivals are obtained, a significant amount of manpower and resources are required, often involving several people and taking one or two months or even longer to complete, resulting in extremely low productivity), thus improving the overall seismic data processing cycle. This application example has been integrated into the Sinopec Geophysical Research Institute's proprietary software Afapa. The specific usage steps are as follows:

[0120] 1. Use Afapa's batch job submission function to submit multiple sets of jobs with different parameters for complex low signal-to-noise ratio data;

[0121] 2. After the task is completed, use Afapa's batch quality control function to automatically control the quality of different initial arrival results and generate reference areas in batches;

[0122] 3. Based on the new reference area, use the Afapa batch job submission function to automatically re-pick the initial arrival;

[0123] 4. After the job is completed, use the automatic merging function of different initial arrival results in Afapa advanced quality control to generate the final initial arrival result.

[0124] For complex low signal-to-noise ratio seismic data, a method to improve the first arrival pick rate was developed based on Afapa, a software with independent intellectual property rights developed by Sinopec Geophysical Research Institute. This method is simple to operate, effective, and significantly improves efficiency. The method includes the following steps in the vertical direction: (1) preprocessing of single-shot seismic data; (2) automatic calculation of reference area for single-shot data; (3) iterative calculation of first arrival for single-shot data using multiple methods; (4) removal of anomalous first arrivals for single-shot data; (5) removal of anomalous first arrivals for multi-shot data; (6) checking whether the pick rate has been reached. If the pick rate has been reached, step (7) is executed; otherwise, step (8) is executed; (7) fitting first arrivals for single-shot data, back to step (2); (8) automatic merging of multiple sets of first arrivals for single-shot data; (9) forming the final delivered first arrival. The above method iterates multiple times between single-shot and multi-shot data, fully considering the influence of geographical location on the first arrival results, while also taking efficiency into account, and finally provides the first arrival that meets the requirements.

[0125] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0126] It should be noted that, in this invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0127] While the embodiments disclosed in this invention are as described above, the above content is merely for the purpose of facilitating understanding of this invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this invention; however, the scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for picking first arrivals in complex seismic data, characterized in that, The method includes: The first arrival of each single-shot seismic data is obtained by parallel processing of the single-shot seismic data. Determine whether the preset pickup rate has been achieved; If the preset pickup rate is reached, the first arrivals of multiple single-shot seismic data will be merged to obtain the final first arrival.

2. The first arrival picking method for complex seismic data according to claim 1, characterized in that, After the step of determining whether the preset pickup rate has been reached, the method further includes: If the preset picking rate is not reached, the first arrival data of multiple single-shot seismic data are fitted to obtain a reference first arrival; Based on the reference initial arrival, the step of parallel processing of each individual seismic source data is re-executed.

3. The first arrival picking method for complex seismic data according to claim 2, characterized in that, The steps for parallel processing of seismic data from each individual seismic source include: Preprocessing of individual seismic data in parallel is performed on a per-shot basis. The reference area for each shot is calculated in parallel on a per-shot basis using preprocessed single-shot seismic data. The initial arrival of a single shot is calculated in parallel based on the reference area, on a per-shot basis; and / or On a per-shot basis, abnormal first arrivals of individual shots are removed in parallel.

4. The first arrival picking method for complex seismic data according to claim 3, characterized in that, The step of re-executing the parallel processing of each single-shot seismic data based on the reference first arrival includes: On a per-shot basis, the reference area of ​​a single shot is calculated in parallel on the preprocessed single-shot seismic data based on the reference first arrival. The initial arrival of a single shot is calculated in parallel based on the reference area, on a per-shot basis; and / or On a per-shot basis, abnormal first arrivals of individual shots are removed in parallel.

5. The first arrival picking method for complex seismic data according to claim 3, characterized in that, The steps for preprocessing single-shot seismic data include: Single-shot seismic data is used for static elevation correction, anomaly trace detection and marking, wavelet application, and / or delay gain.

6. The first arrival picking method for complex seismic data according to claim 3, characterized in that, The steps for calculating the initial arrival of a single shot include: The first arrival is picked up based on the energy method and the information entropy method, and the first arrival is calculated on a single channel by iterating on the first arrival based on the direct wave refracted wave model.

7. A first arrival acquisition device for complex seismic data, characterized in that, The device includes: The single-shot processing module is used to process the seismic data of each single shot in parallel to obtain the first arrival of the single-shot seismic data; The iterative judgment module is used to determine whether the preset pickup rate has been reached; The initial arrival output module is used to merge the initial arrivals of multiple single-shot seismic data to obtain the final initial arrival if a preset pickup rate is reached.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the first arrival picking method for complex seismic data according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the first arrival picking method for complex seismic data as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the first arrival picking method for complex seismic data as described in any one of claims 1 to 6.

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