Noise channel identification and reconstruction method and device based on multi-attribute fusion

Abstract

The invention provides a noise channel identification and reconstruction method and device based on multi-attribute fusion. The method comprises the following steps: configuring a seed gun according to a geological task, dividing a plurality of region sets by taking an excitation point as a center, determining a time window range of a seismic channel in a region through spatial interpolation of a seed gun first arrival wave time window, and generating seismic channel data. And analyzing the signal energy of the first arrival wave time window and the noise time window, carrying out anomaly identification in combination with statistical characteristics, and if abnormal, dividing the abnormal channels into abnormal channels and carrying out zero filling processing. Constructing a three-dimensional data volume, and performing multi-scale curvelet transformation on the three-dimensional data volume to obtain a curvelet coefficient; and carrying out iteration on the curvelet coefficient to realize reconstruction of the seismic channel, and correcting the amplitude of the reconstructed seismic channel through spatial interpolation to obtain reconstructed shot domain data so as to complete identification and reconstruction of the noise channel. According to the method, the abnormal channels are effectively eliminated, the quality of the seismic data with the low signal-to-noise ratio is improved, and a reliable data basis is provided for high-precision imaging under complex geological conditions.

Description

Technical Field

[0001] This invention belongs to the field of petroleum geophysical exploration data processing technology, and particularly relates to a method and device for noise channel identification and reconstruction based on multi-attribute fusion. Background Technology

[0002] Seismic exploration, as a core tool for oil and gas resource exploration, provides crucial data for subsurface structural analysis and reservoir prediction by artificially generating seismic waves and acquiring formation reflection signals. However, due to complex near-surface conditions (such as undulating terrain, low-velocity zones, and weathered layers) and environmental noise (mechanical vibrations and human activity interference), raw seismic data often contains numerous anomalous noise channels (such as dead channels, distorted channels, and random high-noise channels). This leads to the masking of effective signals, deviations in first-arrival travel time calculations, and a decrease in imaging resolution, directly affecting the reliability of subsequent structural interpretation and reservoir inversion. Therefore, how to efficiently identify and repair anomalous channels during the preprocessing stage has become a key technical challenge for improving seismic data quality.

[0003] Traditional noise processing methods suffer from three main limitations: methods based on first-arrival characteristics (such as fixed energy thresholds or travel time fitting) are prone to misjudgment under complex surface conditions; statistical attribute screening often uses global thresholds, which are difficult to adapt to non-uniform noise distributions; transform domain denoising techniques (such as two-dimensional curve transform) lack the utilization of three-dimensional spatial correlation, and fixed threshold strategies are prone to damaging effective signals in low signal-to-noise ratio regions. Furthermore, existing technologies exhibit poor adaptability to complex near-surface structures, high anomaly miss rates, and low first-arrival pickup rates; noise suppression and signal fidelity are difficult to balance; and multi-parameter methods rely on manual tuning, resulting in low computational efficiency and difficulty in meeting the needs of large-scale exploration.

[0004] The above technical issues urgently need to be resolved. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a method, system, electronic device, and storage medium for noise channel identification and reconstruction based on multi-attribute fusion.

[0006] The first aspect of this invention discloses a method for noise channel identification and reconstruction based on multi-attribute fusion, the method comprising: Step S1: Configure seed guns according to the geological task, and divide several regional sets with the excitation point as the center. Determine the time window range of seismic traces in several regional sets by spatial interpolation of the first arrival time window of the seed gun, and generate seismic trace data containing noise time window data and first arrival time window data. Step S2: Mark noise windows of equal length above the first arrival time window of the seismic trace, analyze the signal energy of the first arrival time window and the noise time window, and identify anomalies by combining the statistical characteristics of the seismic trace data, and classify the seismic traces with identified anomalies as anomalous traces. Step S3: After zeroing the anomalous traces, construct a three-dimensional data volume of the seismic trace data, and obtain the curve coefficients by performing multi-scale curve transformation on the three-dimensional data volume; Step S4: Based on the iterative threshold function, the dynamic screening and reconstruction of the seismic traces are achieved by iterating the curve coefficients, and the amplitude of the reconstructed seismic traces is corrected by spatial interpolation to obtain the reconstructed shot domain data, thereby completing the identification and reconstruction of noise traces.

[0007] According to the method of the first aspect of the present invention, in step S1, multiple seismic traces are divided with the shot point as the center, and the time window range of the multiple seismic traces is determined by spatial interpolation of the seed shot first arrival time window, including: Based on the address task, with the firing point as the center point, the shot line as the vertical axis, and the receiver line as the horizontal axis, several regions are divided in a rectangular coordinate system. Based on the different locations of the work area, seed shots are selected, and the seismic traces of the seed shots are sorted in ascending order of shot-receiver distance and then divided into several sets of regions. Based on the three-dimensional spatial interpolation method, by designing the first arrival time window for each of the aforementioned regional sets and calculating the spatial interpolation of the first arrival time window, the time window range of seismic traces in several of the aforementioned regional sets is determined, and seismic trace data containing noise time window data and first arrival time window data is generated.

[0008] According to the method of the first aspect of the present invention, in step S2, the signal energy of the first arrival time window and the noise time window is analyzed, including: Starting from the top of the first arrival time window of the seismic trace, noise data of the same length as the first arrival time window data is selected upwards to generate noise time window data; Through formula Calculate the original signal envelope within the first arrival time window data and the noise time window data, where, The original signal, The virtual seismic traces are obtained by performing Hilbert transform on the original signal, where t is time and j is the j-th seismic trace. Through formula The original signal envelope is compensated to generate a compensated envelope.

[0009] According to the method of the first aspect of the present invention, in step S2, the signal energy of the first arrival time window and the noise time window is analyzed, including: Using the sliding operator, through the formula The mean value of each point in the first arrival time window data is obtained to generate the mean value point of the first arrival time window, where h is the length of the sliding time window and t is the sampling point number in the time window of the j-th seismic trace. Using the sliding operator, through the formula Obtain the mean value of each point within the noise time window data to generate the noise time window mean value point, where h is the length of the sliding time window and t is the sampling point number within the time window of the j-th seismic trace; The absolute values ​​of the mean points of the first arrival time window are taken and summed to generate the first arrival sampling set. Take the absolute values ​​of the noise time window mean points and sum them to generate a noise sampling set. ; Through formula The ratio of the first arrival wave sampling set to the noise sampling set is calculated. If the calculated energy ratio is... If the calculated energy ratio is not less than the preset energy ratio, the seismic trace is classified as an abnormal trace; if the calculated energy ratio is less than the preset energy ratio, the seismic trace is classified as a valid seismic trace.

[0010] According to the method of the first aspect of the present invention, in step S2, the statistical characteristics of the seismic trace data include: Using the skewness formula Calculate the skewness of the seismic trace data ; Through the kurtosis formula Calculate the kurtosis of the seismic trace data ; in, It is the i-th data point of the seismic trace. is the mean of the seismic trace data, and n is the number of seismic trace sampling points; When the calculated skewness is greater than the preset mean skewness, or the absolute value of the kurtosis is greater than the preset standard deviation of kurtosis, the seismic trace is classified as an anomalous trace. When the calculated skewness is not greater than the preset mean skewness and the absolute value of the kurtosis is not greater than the preset standard deviation of kurtosis, the seismic trace is classified as a valid seismic trace.

[0011] According to the method of the first aspect of the present invention, in step S4, the dynamic screening and reconstruction of the seismic trace based on the curve wave coefficients using an iterative threshold function includes: Sort the curve coefficients in ascending order and calculate the average value of the curve coefficients; Based on the average value of the curve coefficients, by iterating the maximum threshold... Iteration minimum threshold Design and construct the function formula for the iterative threshold.

[0012] Where d is the d-th iteration, D is the total number of iterations, and d is greater than 1 and less than or equal to D; When the curve coefficient is less than the iteration threshold during the iteration process, the curve coefficient is set to 0. After completing the iteration of the curved wave coefficients, the iterated curved wave coefficients are inversely transformed to complete the dynamic screening and reconstruction of the seismic traces and generate shot domain data.

[0013] According to the method of the first aspect of the present invention, in step S4, the amplitude of the reconstructed seismic trace is corrected by spatial interpolation, including: Set the root mean square amplitude value of the reconstructed seismic trace in the shot domain data to... , Based on the three-dimensional spatial interpolation method, the amplitude values ​​of adjacent channels are obtained by performing spatial interpolation on the reconstructed seismic traces. , If the formula is satisfied When the reconstructed seismic trace is normalized, the root mean square amplitude value of the reconstructed seismic trace is replaced with the amplitude value of the adjacent trace to obtain the reconstructed shot domain data, thereby completing the identification and reconstruction of the noise trace. in, The preset amplitude value; If the formula is not satisfied Then the gun domain data is used as the reconstructed gun domain data to complete the identification and reconstruction of the noise channel.

[0014] A second aspect of this invention discloses a noise channel identification and reconstruction device based on multi-attribute fusion, the device comprising: The first processing module is configured to configure seed guns according to the geological task, divide several regional sets with the excitation point as the center, determine the time window range of seismic traces in several regional sets through spatial interpolation of the first arrival time window of the seed gun, and generate seismic trace data containing noise time window data and first arrival time window data. The second processing module is configured to mark noise windows of equal length above the first arrival time window of the seismic trace, analyze the signal energy of the first arrival time window and the noise time window, and identify anomalies by combining the statistical characteristics of the seismic trace data, and classify the seismic traces with identified anomalies as anomalous traces. The third processing module is configured to perform zero-filling processing on the abnormal traces, construct a three-dimensional data volume of the seismic trace data, and obtain the curve coefficients by performing multi-scale curve transformation on the three-dimensional data volume (step S3 is copied here). The fourth processing module is configured to dynamically screen and reconstruct the seismic traces based on the curve coefficients using an iterative threshold function, correct the amplitude of the reconstructed seismic traces using spatial interpolation, obtain reconstructed shot domain data, and complete the identification and reconstruction of noise traces.

[0015] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a noise channel identification and reconstruction method based on multi-attribute fusion according to any one of the first aspects of this disclosure.

[0016] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a noise channel identification and reconstruction method based on multi-attribute fusion according to any one of the first aspects of this disclosure.

[0017] As can be seen, this invention provides an efficient and robust scheme for noise trace detection and data reconstruction. The scheme configures seed shots according to the geological task and divides several regions centered on the excitation point. The time window range of seismic traces within a region is determined by spatial interpolation of the first-arrival time window of the seed shot, generating seismic trace data. The signal energy of the first-arrival time window and the noise time window is analyzed, and anomalies are identified using statistical characteristics. Anomalies are classified as anomalous traces and zeroed out. A three-dimensional data volume is then constructed, and multi-scale curvelet transform is applied to obtain curvelet coefficients. The curvelet coefficients are iteratively processed to reconstruct the seismic traces. Spatial interpolation is used to correct the amplitude of the reconstructed seismic traces, obtaining reconstructed shot domain data, thus completing the identification and reconstruction of noise traces.

[0018] In summary, the proposed solution can effectively eliminate outliers, improve the reconstruction quality of low signal-to-noise ratio seismic data, and provide a reliable data foundation for high-precision imaging under complex geological conditions. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a noise channel identification and reconstruction method based on multi-attribute fusion according to an embodiment of the present invention; Figure 2 This is an image of a single-shot seismic record acquired according to a noise channel identification and reconstruction method based on multi-attribute fusion according to an embodiment of the present invention. Figure 3 An anomaly trace identification map of a single-shot seismic record according to an embodiment of the present invention, which is based on a multi-attribute fusion-based method for noise trace identification and reconstruction. Figure 4 An anomaly reconstruction map of a single-shot seismic record according to an embodiment of the present invention, which is a noise trace identification and reconstruction method based on multi-attribute fusion. Figure 5 This is a structural diagram of a noise channel identification and reconstruction system based on multi-attribute fusion according to an embodiment of the present invention; Figure 6 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] This invention provides a method and apparatus for noise trace identification and reconstruction based on multi-attribute fusion. The method includes: configuring seed shots according to the geological task, dividing the area into several regions centered on the excitation point, determining the time window range of seismic traces within each region through spatial interpolation of the seed shot's first-arrival time window, and generating seismic trace data. The signal energy of the first-arrival time window and the noise time window is analyzed, and anomalies are identified using statistical characteristics; anomalies are classified as anomalous traces and zeroed out. A three-dimensional data volume is then constructed, and curvelet coefficients are obtained by performing multi-scale curvelet transform. The curvelet coefficients are iteratively processed to reconstruct the seismic traces, and the amplitude of the reconstructed seismic traces is corrected through spatial interpolation to obtain reconstructed shot domain data, thus completing the identification and reconstruction of noise traces. This invention effectively eliminates anomalous traces, improves the identification accuracy of low signal-to-noise ratio seismic data, and provides a reliable data foundation for high-precision imaging under complex geological conditions.

[0023] The first aspect of this invention discloses a method for noise channel identification and reconstruction based on multi-attribute fusion. Figure 1 This is a flowchart of a noise channel identification and reconstruction method based on multi-attribute fusion according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes: Step S1: Configure seed guns according to the geological task, and divide several regional sets with the excitation point as the center. Determine the time window range of seismic traces in several regional sets by spatial interpolation of the first arrival time window of the seed gun, and generate seismic trace data containing noise time window data and first arrival time window data. Step S2: Mark noise windows of equal length above the first arrival time window of the seismic trace, analyze the signal energy of the first arrival time window and the noise time window, and identify anomalies by combining the statistical characteristics of the seismic trace data, and classify the seismic traces with identified anomalies as anomalous traces. Step S3: After zeroing the anomalous traces, construct a three-dimensional data volume of the seismic trace data, and obtain the curve coefficients by performing multi-scale curve transformation on the three-dimensional data volume; Step S4: Based on the iterative threshold function, the dynamic screening and reconstruction of the seismic traces are achieved by iterating the curve coefficients, and the amplitude of the reconstructed seismic traces is corrected by spatial interpolation to obtain the reconstructed shot domain data, thereby completing the identification and reconstruction of noise traces.

[0024] In step S1, based on the address task, with the position of the seed gun firing point as the center point, the gun line as the vertical axis, and the receiver line as the horizontal axis, several regions are divided in the rectangular coordinate system. The region set takes the integer power of 2 greater than or equal to 0. The larger the value of N, the smaller the range of the initial arrival time window.

[0025] At different locations in the work area, select the same or different seed shots. If the near-surface structure is relatively horizontal, select a seed shot that can represent the structural characteristics of the exploration area and divide it into one region. If the near-surface structure is complex, select multiple seed shots and divide it into as many regions as possible. Use three-dimensional spatial interpolation to accurately determine the range of each first arrival wave.

[0026] Then, the seismic traces from the seed shots are sorted in ascending order of shot-receiver distance and divided into several regional sets. Based on three-dimensional spatial interpolation, the time window range of the seismic traces in the several regional sets is determined by designing the first arrival time window for each regional set and calculating the spatial interpolation of the first arrival time window, thereby generating seismic trace data that includes noise time window data and first arrival time window data.

[0027] In some embodiments, the three-dimensional interpolation method may be Thiessen polygon, inverse distance weighting, Kriging interpolation, or other methods.

[0028] Specifically, such as Figure 2As shown, this method is applied to a complex onshore seismic field with dramatic surface undulations and large lateral variations in near-surface velocity. Based on the geological task, this method is implemented using single-shot data (m=500 traces, n=3000 sampling points per trace) collected by a pre-designed observation system (receiver spacing L=50 meters, number of receivers S=10). Five shots with different surface characteristics were selected as seed shots within the field. A coordinate system was established centered on the excitation point of one of these shots, dividing its 500 seismic traces into N=8 region sets. Within each region, after sorting by shot-receiver distance, the first-arrival time window range of the seed shot in that region was determined manually or using an automatic picking tool; for example, the time window for region 1 was from sampling point 100 to 180. Using three-dimensional spatial interpolation (Kriging interpolation in this example), the time window information of these five seed shots in the eight regions was extended to all other shots in the field, thus determining a personalized first-arrival time window for each trace of each shot to be processed.

[0029] In step S2, starting from the top of the first arrival time window of the seismic trace, noise data of the same length as the first arrival time window data is selected upwards to generate noise time window data.

[0030] Through formula Calculate the original signal envelope within the first arrival time window data and the noise time window data, where, The original signal, The virtual seismic trace is obtained by performing Hilbert transform on the original signal, where t is time and j is the j-th seismic trace.

[0031] Through formula The original signal envelope is compensated to generate a compensated envelope.

[0032] Then, using the sliding operator, through the formula Obtain the mean value of each point within the first arrival time window data to generate the first arrival time window mean point. And use the formula... Obtain the mean value of each point within the noise time window data to generate the noise time window mean point, where h is the length of the sliding time window and t is the sampling point number within the time window of the j-th seismic trace.

[0033] The absolute values ​​of the mean points of the first arrival time window are taken and summed to generate the first arrival sampling set. Take the absolute values ​​of the noise time window mean points and sum them to generate a noise sampling set. .

[0034] Through formula The ratio of the first arrival wave sampling set to the noise sampling set is calculated. If the calculated energy ratio is... If the calculated energy ratio is not less than a preset value, the seismic trace is classified as an anomalous trace; if the calculated energy ratio is less than the preset value, the seismic trace is classified as a valid seismic trace. The preset value is 0.9 by default. If it is well shot data, a larger value is used; otherwise, if it is source data, a smaller value is used.

[0035] And calculate the skewness and kurtosis of all seismic traces for a single shot. Specifically, the skewness formula... Calculate the skewness of the seismic trace data Through the kurtosis formula Calculate the kurtosis of the seismic trace data .in, It is the i-th data point of the seismic trace. is the mean of the seismic trace data, and n is the number of seismic trace sampling points.

[0036] Further calculations were performed to obtain the mean skewness of all seismic traces from a single shot. and kurtosis standard deviation .

[0037] When the calculated skewness is greater than the product of the preset coefficient and the absolute value of the mean skewness, that is... Or, the absolute value of the kurtosis is greater than the product of a preset coefficient and the standard deviation of the kurtosis, i.e. The seismic trace is then classified as an anomaly trace. When the calculated skewness is not greater than a preset mean skewness value, and the absolute value of the kurtosis is not greater than a preset standard deviation of kurtosis, the seismic trace is classified as a valid seismic trace. The preset coefficients here refer to... The default value is 1.5. If the signal-to-noise ratio of the data is high, the value should be smaller, and vice versa.

[0038] Specifically, such as Figure 3 As shown, for channel j=150, the determined first arrival time window is... Sampling points 105 to 185, noise time window The sampling points are from 25 to 105. Hilbert transforms are performed on the data from both time windows to calculate the envelope energy. The energy ratio is then calculated. If the value is less than the preset value of 0.9, an alarm is triggered, indicating an anomaly.

[0039] Simultaneously, the skewness of the complete seismic trace (3000 sampling points) was calculated to be 0.15, and the kurtosis to be 4.2. The mean skewness of the entire 500 traces was calculated to be 0.5; the standard deviation of the kurtosis was 0.8. (The following is a partial translation of the original text: "Take...") The skewness anomaly threshold is 0.5 * 1.5 = 0.75, and the kurtosis anomaly threshold is 1.5 * 0.8 = 1.2. Since the skewness of this channel is 0.15 < 0.75 and the absolute kurtosis value of 4.2 is less than the threshold of 1.2, the alarm for mechanism two is not triggered.

[0040] However, since Mechanism 1 had already determined it to be abnormal, channel 150 was ultimately marked as an abnormal channel, and all 3,000 of its sampling points were filled to zero.

[0041] In step S3, after zeroing the abnormal traces, the seismic trace data of all seismic traces in one shot are assembled into a three-dimensional data volume according to the receiver line, receiver point, and sampling point. Then, a three-dimensional curvilinear transformation is performed to obtain the curvilinear coefficients.

[0042] In addition, mathematical transformation methods such as Fourier transform and Radon transform can be used to obtain coefficients and reconstruct them.

[0043] Here, s is the s-th receiving line, where s is greater than or equal to 1 and less than or equal to S (maximum receiving line), m is the m-th receiving point on the s-th receiving line, where m is greater than or equal to 1 and less than or equal to M (maximum receiving point), and n is the number of samples collected by the m-th receiving point on the s-th receiving line.

[0044] Specifically, all 500 data points (including abnormal channels that have been zeroed) from this gun are organized into a three-dimensional data volume A(10, 50, 3000) according to the receiving lines (s=1 to 10), receiving points (m=1 to 50), and time sampling points (n=1 to 3000). A three-dimensional curvelet transform is then performed on this data volume to obtain a series of curvelet coefficients representing signal components of different scales and directions.

[0045] In step S4, the curvature coefficients for each scale and each angle are calculated. The curvature coefficients for each scale are sorted from smallest to largest, and the average value of the curvature coefficients for each scale is calculated. Then, design the maximum threshold for each scale iteration. Iteration minimum threshold ,in , ,here , All are values ​​greater than or equal to 1.

[0046] Reconstruct the function formula for the iterative threshold. , where d is the d-th iteration, and D is the total number of iterations. d is greater than 1 and less than or equal to D. The number of iterations D is set according to the signal-to-noise ratio of the data. For data with a high signal-to-noise ratio, the number of iterations is less, and vice versa. The default is 20 iterations.

[0047] During the iteration process, if the curve wave coefficient of a certain seismic trace in a certain iteration is less than the iteration threshold, this curve wave coefficient is set to 0. After the iteration is completed, the newly set curve wave coefficient is inversely transformed to obtain the shot domain data.

[0048] For reconstructed anomalies in the gun domain data, a preset amplitude value is set. If the root mean square amplitude value of the reconstructed anomaly and the amplitude values ​​of its adjacent traces satisfy the formula... If the reconstructed seismic trace is normalized, the root mean square amplitude value of the reconstructed seismic trace is replaced by the amplitude value of the adjacent trace.

[0049] Specifically, such as Figure 4 As shown, the total number of iterations is set to D=20. For a specific scale, the average value of the curve coefficient is 0.05. Design the maximum iteration threshold for this scale. minimum The iterative threshold function is: In the first iteration (d=1), the threshold... All curve coefficients with absolute values ​​less than 0.1 are set to zero, and the first reconstructed data is obtained after inverse transformation. This process is repeated 20 times, gradually reducing the threshold to 0.01, thus achieving progressively refined signal reconstruction.

[0050] For the reconstructed track 150 (the original anomalous track), its root mean square amplitude A = 1.2e5 is calculated. The expected amplitude value at this location is obtained by interpolation using the inverse distance weighting method with neighboring tracks (such as tracks 149 and 151, which are valid tracks). =1.0e5. Setting =0.5, the calculated relative deviation is |(1.2e5 - 1.0e5) / 1.0e5| = 0.2. Since 0.2 < 0.5, the correction condition is not met, therefore the amplitude of the reconstructed trace is retained. If the deviation is greater than 0.5, then it is necessary to use... =1.0e5 is used to normalize and correct the data in this channel. Finally, high-quality reconstructed gun domain data after identification, reconstruction and correction are output, completing the identification and reconstruction of the noise channel.

[0051] In summary, the proposed solution can effectively eliminate outliers and suppress noise, significantly improving the identification accuracy of low signal-to-noise ratio seismic data. At the same time, it can reconstruct noisy data through mathematical transformation, providing a reliable data foundation for high-precision imaging under complex geological conditions.

[0052] The second aspect of the present invention discloses a noise channel identification and reconstruction device based on multi-attribute fusion. Figure 5 This is a structural diagram of a noise channel identification and reconstruction device based on multi-attribute fusion according to an embodiment of the present invention; as shown. Figure 5 As shown, the device 100 includes: The first processing module 101 is configured to configure seed guns according to the geological task, divide several regional sets with the excitation point as the center, determine the time window range of seismic traces in several regional sets through spatial interpolation of the first arrival time window of the seed gun, and generate seismic trace data containing noise time window data and first arrival time window data. The second processing module 102 is configured to mark noise windows of equal length above the first arrival time window of the seismic trace, analyze the signal energy of the first arrival time window and the noise time window, and identify anomalies by combining the statistical characteristics of the seismic trace data, and classify the seismic traces that are identified as anomalies into anomalous traces. The third processing module 103 is configured to perform zero-filling processing on the abnormal traces, construct a three-dimensional data volume of the seismic trace data, and obtain the curve coefficients by performing multi-scale curve transformation on the three-dimensional data volume (step S3 is copied over). The fourth processing module 104 is configured to dynamically screen and reconstruct the seismic traces based on the curve coefficients using an iterative threshold function, correct the amplitude of the reconstructed seismic traces using spatial interpolation, obtain reconstructed shot domain data, and complete the identification and reconstruction of noise traces.

[0053] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the noise channel identification and reconstruction method based on multi-attribute fusion according to any one of the first aspects of this invention.

[0054] Figure 6 This is a structural diagram of an electronic device according to an embodiment of the present invention, such as... Figure 6 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0055] Those skilled in the art will understand that Figure 6The structure shown is merely a structural diagram of the part related to the technical solution of this disclosure and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0056] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a noise channel identification and reconstruction method based on multi-attribute fusion according to any one of the first aspects of this invention.

[0057] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0058] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for noise channel identification and reconstruction based on multi-attribute fusion, characterized in that, The method includes: Step S1: Configure seed guns according to the geological task, and divide several regional sets with the excitation point as the center. Determine the time window range of seismic traces in several regional sets by spatial interpolation of the first arrival time window of the seed gun, and generate seismic trace data containing noise time window data and first arrival time window data. Step S2: Mark noise windows of equal length above the first arrival time window of the seismic trace, analyze the signal energy of the first arrival time window and the noise time window, and identify anomalies by combining the statistical characteristics of the seismic trace data, and classify the seismic traces with identified anomalies as anomalous traces. Step S3: After zeroing the abnormal traces, construct a three-dimensional data volume of the seismic trace data, and obtain the curve coefficients by performing multi-scale curve transformation on the three-dimensional data volume; Step S4: Based on the iterative threshold function, the dynamic screening and reconstruction of the seismic traces are achieved by iterating the curve coefficients, and the amplitude of the reconstructed seismic traces is corrected by spatial interpolation to obtain the reconstructed shot domain data, thereby completing the identification and reconstruction of noise traces.

2. The noise channel identification and reconstruction method based on multi-attribute fusion according to claim 1, characterized in that, In step S1, multiple seismic traces are divided centered on the shot point. The time window range of the multiple seismic traces is determined by spatial interpolation of the seed shot's first arrival time window, including: Based on the address task, with the firing point as the center point, the shot line as the vertical axis, and the receiver line as the horizontal axis, several regions are divided in a rectangular coordinate system. Based on the different locations of the work area, seed shots are selected, and the seismic traces of the seed shots are sorted in ascending order of shot-receiver distance and then divided into several sets of regions. Based on the three-dimensional spatial interpolation method, by designing the first arrival time window for each of the aforementioned regional sets and calculating the spatial interpolation of the first arrival time window, the time window range of seismic traces in several of the aforementioned regional sets is determined, and seismic trace data containing noise time window data and first arrival time window data is generated.

3. The noise channel identification and reconstruction method based on multi-attribute fusion according to claim 1, characterized in that, In step S2, the signal energy of the first arrival time window and the noise time window is analyzed, including: Starting from the top of the first arrival time window of the seismic trace, noise data of the same length as the first arrival time window data is selected upwards to generate noise time window data; Through formula Calculate the original signal envelope within the first arrival time window data and the noise time window data, where, The original signal, The virtual seismic traces are obtained by performing Hilbert transform on the original signal, where t is time and j is the j-th seismic trace. Through formula The original signal envelope is compensated to generate a compensated envelope.

4. The noise channel identification and reconstruction method based on multi-attribute fusion according to claim 3, characterized in that, In step S2, the signal energy of the first arrival time window and the noise time window is analyzed, including: Using the sliding operator, through the formula The mean value of each point in the first arrival time window data is obtained to generate the mean value point of the first arrival time window, where h is the length of the sliding time window and t is the sampling point number in the time window of the j-th seismic trace. Using the sliding operator, through the formula Obtain the mean value of each point within the noise time window data to generate the noise time window mean value point, where h is the length of the sliding time window and t is the sampling point number within the time window of the j-th seismic trace; The absolute values ​​of the mean points of the first arrival time window are taken and summed to generate the first arrival sampling set. Take the absolute values ​​of the noise time window mean points and sum them to generate a noise sampling set. ; Through formula The ratio of the first arrival wave sampling set to the noise sampling set is calculated. If the calculated energy ratio is... If the calculated energy ratio is not less than the preset energy ratio, the seismic trace is classified as an abnormal trace; if the calculated energy ratio is less than the preset energy ratio, the seismic trace is classified as a valid seismic trace.

5. The noise channel identification and reconstruction method based on multi-attribute fusion according to claim 1, characterized in that, In step S2, the statistical characteristics of the seismic trace data include: Using the skewness formula Calculate the skewness of the seismic trace data ; Through the kurtosis formula Calculate the kurtosis of the seismic trace data ; in, It is the i-th data point of the seismic trace. is the mean of the seismic trace data, and n is the number of seismic trace sampling points; When the calculated skewness is greater than the preset mean skewness, or the absolute value of the kurtosis is greater than the preset standard deviation of kurtosis, the seismic trace is classified as an anomalous trace. When the calculated skewness is not greater than the preset mean skewness and the absolute value of the kurtosis is not greater than the preset standard deviation of kurtosis, the seismic trace is classified as a valid seismic trace.

6. The noise channel identification and reconstruction method based on multi-attribute fusion according to claim 1, characterized in that, In step S4, the seismic trace is dynamically screened and reconstructed based on the curve wave coefficients using an iterative threshold function, including: Sort the curve coefficients in ascending order and calculate the average value of the curve coefficients; Based on the average value of the curve coefficients, by iterating the maximum threshold... Iteration minimum threshold Design and construct the function formula for the iterative threshold. Where d is the d-th iteration, D is the total number of iterations, and d is greater than 1 and less than or equal to D; When the curve coefficient is less than the iteration threshold during the iteration process, the curve coefficient is set to 0. After completing the iteration of the curved wave coefficients, the iterated curved wave coefficients are inversely transformed to complete the dynamic screening and reconstruction of the seismic traces and generate shot domain data.

7. The noise channel identification and reconstruction method based on multi-attribute fusion according to claim 1, characterized in that, In step S4, the amplitude of the reconstructed seismic trace is corrected by spatial interpolation, including: Set the root mean square amplitude value of the reconstructed seismic trace in the shot domain data to... , Based on the three-dimensional spatial interpolation method, the amplitude values ​​of adjacent channels are obtained by performing spatial interpolation on the reconstructed seismic traces. , If the formula is satisfied When the reconstructed seismic trace is normalized, the root mean square amplitude value of the reconstructed seismic trace is replaced with the amplitude value of the adjacent trace to obtain the reconstructed shot domain data, thereby completing the identification and reconstruction of the noise trace. in, The preset amplitude value; If the formula is not satisfied Then the gun domain data is used as the reconstructed gun domain data to complete the identification and reconstruction of the noise channel.

8. A noise channel identification and reconstruction system based on multi-attribute fusion, characterized in that, The system includes: The first processing module is configured to configure seed guns according to the geological task, divide several regional sets with the excitation point as the center, determine the time window range of seismic traces in several regional sets through spatial interpolation of the first arrival time window of the seed gun, and generate seismic trace data containing noise time window data and first arrival time window data. The second processing module is configured to mark noise windows of equal length above the first arrival time window of the seismic trace, analyze the signal energy of the first arrival time window and the noise time window, and identify anomalies by combining the statistical characteristics of the seismic trace data, and classify the seismic traces with identified anomalies as anomalous traces. The third processing module is configured to perform zero-filling processing on the abnormal traces, construct a three-dimensional data volume of the seismic trace data, and obtain the curve coefficients by performing multi-scale curve transformation on the three-dimensional data volume (step S3 is copied here). The fourth processing module is configured to dynamically screen and reconstruct the seismic traces based on the curve coefficients using an iterative threshold function, correct the amplitude of the reconstructed seismic traces using spatial interpolation, obtain reconstructed shot domain data, and complete the identification and reconstruction of noise traces.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the noise channel identification and reconstruction method based on multi-attribute fusion as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the noise channel identification and reconstruction method based on multi-attribute fusion as described in any one of claims 1 to 7.

Images