Method, system, device and storage medium for weak energy noise suppression of seismic data

Abstract

Embodiments of the present application relate to a weak energy noise suppression method, system, device and storage medium for seismic data, wherein the method comprises: obtaining shot gather data to be suppressed for weak energy noise, performing frequency eigenvalue statistics on each seismic trace signal in the shot gather data, and screening out seismic traces whose frequency eigenvalues do not meet a first preset condition; calculating the root mean square amplitude of each seismic trace as a first eigenvalue, screening out seismic traces whose root mean square amplitudes are lower than an energy threshold as weak energy traces, and increasing the amplitude of the weak energy traces to meet a second preset condition; again calculating the root mean square amplitude of each seismic trace as a second eigenvalue, removing the seismic traces whose relationship between the first eigenvalue and the second eigenvalue meets a third preset condition, and performing amplitude compensation on the current remaining seismic trace data after removal. The present scheme can automatically identify and effectively suppress weak energy noise.

Description

Technical Field

[0001] This invention relates to the field of earthquake data processing technology, and in particular to a method, system, device, and storage medium for suppressing weak energy noise in earthquake data. Background Technology

[0002] In seismic exploration, the quality of seismic data directly determines the accuracy of geological interpretation and reservoir prediction. Low-energy noise, such as environmental microseismic activity and detector thermal noise, is a key factor affecting data quality because of its low energy and tendency to overlap with the effective signal frequency.

[0003] Currently, the identification and suppression of weak energy noise mainly focuses on the acquisition stage, achieving on-site quality control through hardware means such as optimizing detector deployment and adjusting acquisition parameters. However, these methods are insufficient to cover the hidden weak energy noise in the processing stage. Weak energy noise in the seismic data processing stage is highly coupled with the effective signal in the time and frequency domains and is dispersed, leading to a lack of relevant research. This results in insufficient robustness, stability, and comprehensiveness of nodal shot gather energy anomaly detection, hindering the effectiveness of subsequent processing steps.

[0004] Therefore, there is an urgent need for a method to effectively suppress weak-energy noise in seismic data. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and storage medium for suppressing weak energy noise in seismic data, which can automatically identify and suppress weak energy noise, thereby ensuring improved data energy consistency after subsequent surface uniformity amplitude compensation.

[0006] To address the aforementioned problems, in a first aspect, embodiments of the present invention provide a method for acquiring shot gather data for suppressing weak energy noise. This involves statistically analyzing the frequency characteristic values ​​of each seismic trace signal in the shot gather data, filtering out seismic traces whose frequency characteristic values ​​do not meet a first preset condition; calculating the root mean square amplitude of each seismic trace as a first characteristic value, filtering out seismic traces whose root mean square amplitude is below an energy threshold as weak energy traces, increasing the amplitude of the weak energy traces to meet a second preset condition; recalculating the root mean square amplitude of each seismic trace as a second characteristic value; removing seismic traces whose relationship between the first and second characteristic values ​​meets a third preset condition; and then performing amplitude compensation on the remaining seismic trace data after removal.

[0007] In addition, in some embodiments, before acquiring the shot gather data for suppressing weak energy noise as described above, the method includes: acquiring seismic single-shot data; performing initial denoising on the seismic single-shot data to obtain amplitude-preserving shot gather data; performing surface consistency amplitude compensation on the amplitude-preserving shot gather data; performing data sorting and common reflection point stacking to obtain a seismic stack profile; if abnormal amplitudes appear in the seismic stack profile, then using the amplitude-preserving shot gather data as the shot gather data for suppressing weak energy noise.

[0008] In addition, in some embodiments, the initial denoising of the seismic single-shot data as described above includes: performing preset denoising processing on the seismic single-shot data to suppress noise; the preset denoising processing includes at least: abnormal noise attenuation and Fx domain coherent noise suppression.

[0009] In addition, in some embodiments, the frequency characteristic value statistics of each seismic trace signal in the shot gather data as described above, and the screening of seismic traces whose frequency characteristic values ​​do not meet the first preset condition, include: performing spectrum analysis on each seismic trace signal, and extracting the dominant frequency, the number of zero-value sampling points and the number of non-zero-value sampling points of each seismic trace signal as the frequency characteristic value based on the spectrum analysis results; screening the seismic traces whose dominant frequency, the number of zero-value sampling points and the number of non-zero-value sampling points do not meet the first preset condition; the first preset condition is: the dominant frequency is zero, or the number of non-zero-value sampling points is less than half of the total number of sampling points in the current seismic trace signal.

[0010] In addition, in some embodiments, the energy threshold determination step described above includes: pre-setting the width of the median filter based on the spatial distribution width of the upper wave recorded by a single shot; determining a neighborhood range of half the width of the window corresponding to each seismic trace based on the width window, and using the median of the root mean square amplitude of the seismic trace within the neighborhood range as the normal amplitude energy reference; and setting the energy threshold based on the amplitude energy reference.

[0011] In addition, in some embodiments, the increase of the amplitude of the weak energy channel to meet the second preset condition as described above includes: multiplying the amplitude value of each sampling point in the weak energy channel by a dynamic gain coefficient so that the increase of the root mean square amplitude of the weak energy channel meets the second preset condition; the second preset condition is that the root mean square amplitude of the increased weak energy channel is the normal amplitude energy reference.

[0012] In addition, in some embodiments, removing seismic traces whose relationship between the first feature value and the second feature value satisfies a third preset condition, as described above, includes: calculating the ratio of the first feature value to the second feature value for each seismic trace; removing the seismic trace from the shot gather data when the ratio satisfies the third preset condition, wherein the third preset condition is that the ratio is less than a preset threshold.

[0013] This invention also provides a low-energy noise suppression system for seismic data, comprising: The data filtering module is used to acquire shot gather data containing weak energy noise to be suppressed, perform frequency characteristic value statistics on each seismic trace signal in the shot gather data, and filter out seismic traces whose frequency characteristic values ​​do not meet the first preset condition; the energy enhancement module is used to calculate the root mean square amplitude of each seismic trace as the first characteristic value, filter out seismic traces whose root mean square amplitude is lower than the energy threshold as weak energy traces, and enhance the amplitude of the weak energy traces to meet the second preset condition; the amplitude compensation module is used to recalculate the root mean square amplitude of each seismic trace as the second characteristic value, remove seismic traces whose relationship between the first characteristic value and the second characteristic value meets the third preset condition, and perform amplitude compensation on the remaining seismic trace data after removal.

[0014] This invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the weak energy noise suppression method for seismic data as described above.

[0015] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the low-energy noise suppression method for seismic data as described above.

[0016] The scheme in this embodiment achieves initial screening of frequency characteristics based on the inherent frequency attributes of the signal. Subsequently, it enhances weak energy noise while ensuring signal integrity through energy threshold screening and weak energy channel amplitude adjustment. Invalid channels are eliminated by comparing amplitude characteristics twice, further improving the effectiveness and consistency of shot gathering. Finally, multi-dimensional amplitude compensation is combined to eliminate energy unevenness caused by propagation and acquisition differences, resulting in a significant improvement in the signal-to-noise ratio of the processed seismic data. This provides a high-quality data foundation for subsequent processes such as velocity analysis, horizontal stacking, and reservoir prediction. Attached Figure Description

[0017] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 This is a flowchart illustrating the low-energy noise suppression method for seismic data according to the first embodiment of the present invention. Figure 2 This is a flowchart illustrating the low-energy noise suppression method for seismic data according to the second embodiment of the present invention. Figure 3 This is a flowchart illustrating the determination of the energy threshold in step 102 of the fourth embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of the low-energy noise suppression system for seismic data according to the fifth embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device according to the sixth embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure 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 this disclosure 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 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.

[0021] Example 1 The first embodiment of the present invention relates to a method for suppressing weak energy noise in seismic data, such as... Figure 1 As shown, the method in this embodiment specifically includes the following steps 101-103: Step 101: Obtain shot gather data for the weak energy noise to be suppressed, perform frequency characteristic value statistics on each seismic trace signal in the shot gather data, and filter out seismic traces whose frequency characteristic values ​​do not meet the first preset condition.

[0022] Specifically, shot gather data of weak-energy noise to be suppressed is acquired. This shot gather data contains multiple seismic traces reflecting subsurface geological information, i.e., the converted electrical signals of seismic waves received by a single-channel geophone. Each signal is a mixture of effective signal and various types of noise. Since the frequency distribution of effective seismic signals has a clear geological correlation, while weak-energy noise often exhibits frequency shifts, such as low-frequency drift or high-frequency clutter, this embodiment uses frequency feature screening to initially eliminate abnormal traces. In practice, frequency domain singular spectrum analysis or fast Fourier transform can be used to convert each seismic trace signal from the time domain to the frequency domain, extracting frequency feature values. In this embodiment, the frequency feature values ​​are characteristics that reflect the signal condition and can be parameters describing the signal frequency distribution, such as peak frequency, center frequency, and bandwidth.

[0023] Furthermore, the first preset condition in this embodiment can be set based on the geological conditions of the work area and the sampling theorem. For example, the highest cutoff frequency can be determined according to the sampling frequency, and the effective signal frequency range can be statistically analyzed in combination with historical effective data. Seismic traces that exceed the range, such as those with peak frequencies below 5Hz or above 100Hz, can be screened out to reduce subsequent processing interference. This embodiment does not limit the specific first preset condition.

[0024] Step 102: Calculate the root mean square amplitude of each seismic trace as the first characteristic value, screen seismic traces with root mean square amplitude below the energy threshold as weak energy traces, and increase the amplitude of the weak energy traces to meet the second preset condition.

[0025] Among them, the root mean square amplitude is a key indicator for measuring the energy intensity of a signal. It is calculated by taking the square root of the sum of the squares of all sampled values ​​of the signal divided by the number of sample points, and can objectively reflect the average energy level.

[0026] Specifically, this step considers the positive correlation between root-mean-square amplitude and signal energy. Since weak energy channels have amplitudes significantly lower than the effective signal level, an energy threshold is set to filter weak energy channels. In this embodiment, the energy threshold can be determined using statistical methods, such as calculating the mean μ and standard deviation σ of the root-mean-square amplitude of all seismic channels and setting the threshold to μ-1.5σ, or using a filter to filter the seismic channel signals by giving a filter width.

[0027] To enhance the identifiability of the effective signal while avoiding excessive noise amplification, this embodiment increases the amplitude of the signal identified as a weak energy channel to meet a second preset condition. In this embodiment, the second preset condition can dynamically allocate gain based on the frequency characteristics of the weak energy channel. For example, a high gain can be applied to frequency bands overlapping with the effective signal frequency, while a low gain can be applied to frequency bands deviating from the effective signal frequency, so that the increased amplitude approaches the average energy level of the effective signal; or an amplitude increase baseline can be set, and the weak energy channel can be increased to the baseline, etc. This embodiment does not impose specific limitations on this.

[0028] Step 103: Calculate the root mean square amplitude of each seismic trace again as the second characteristic value. Remove seismic traces whose relationship between the first characteristic value and the second characteristic value meets the third preset condition. After removal, perform amplitude compensation on the remaining seismic trace data.

[0029] Specifically, after adjusting the amplitude of the weak energy traces, the root mean square amplitude of each seismic trace is recalculated and used as the second characteristic value of each trace. By calculating the ratio of the two root mean square amplitudes, the seismic traces whose ratio meets the third preset condition are identified as weak energy noise and are then removed. The third preset condition can be set to a certain threshold requirement for the ratio of the second characteristic value to the first characteristic value; if the ratio is lower than the threshold, it indicates a weak energy trace and is removed.

[0030] After removal, amplitude compensation is performed on the remaining seismic traces. Amplitude compensation can adopt a multi-dimensional optimization strategy, combining spherical diffusion compensation and regional amplitude compensation. For example, for energy attenuation caused by geometric diffusion, time velocity compensation formula or exponential compensation formula is used for compensation; for energy unevenness caused by regional lithological differences, the regional compensation factor of stable marker layers is extracted for lateral correction, so that the effective signal energy distribution of the remaining shot gathers tends to be balanced, and data quality control is completed after suppressing weak energy noise.

[0031] Compared with related technologies, this embodiment achieves initial screening of frequency characteristics based on the inherent frequency attributes of the signal. Subsequently, it enhances weak energy noise while ensuring signal integrity through energy threshold screening and weak energy channel amplitude adjustment. Invalid channels are eliminated by comparing amplitude characteristics twice, further improving the effectiveness and consistency of shot gathering. Finally, multi-dimensional amplitude compensation is combined to eliminate energy unevenness caused by propagation and acquisition differences, resulting in a significant improvement in the signal-to-noise ratio of the processed seismic data. This provides a high-quality data foundation for subsequent processes such as velocity analysis, horizontal stacking, and reservoir prediction.

[0032] Example 2 The second embodiment of the present invention provides a method for suppressing weak energy noise in seismic data. This embodiment is a supplement to the first embodiment described above. The supplement is that it explains the additional steps before obtaining the shot gather data to be suppressed for weak energy noise in step 101, such as... Figure 2 As shown, this embodiment includes the following steps 201-203.

[0033] Step 201: Acquire seismic single-shot data, perform initial denoising on the seismic single-shot data, and obtain amplitude-preserving shot gather data.

[0034] Among them, single-shot earthquake data refers to the set of raw earthquake data synchronously collected by all ground receiving detectors after a single source excitation. It includes seismic wave signals that propagate from the source to various underground layers and reflect back to the ground, while also being mixed with various non-target signals such as strong energy interference (such as direct waves and surface waves) and environmental noise.

[0035] This embodiment takes into account that since there are a lot of strong energy interferences in the original seismic single-shot data, directly entering the weak energy noise processing process will mask the characteristics of the weak energy noise, resulting in poor subsequent identification and suppression effects. Therefore, it is necessary to perform initial denoising first. Initial denoising is used to remove large-value interferences such as surface wave interference, mechanical interference, environmental noise, and random noise in the original seismic single-shot data.

[0036] In one example, pre-defined denoising processing is performed on seismic single-shot data to suppress noise; the pre-defined denoising processing includes at least: anomalous noise attenuation and Fx domain coherent noise suppression.

[0037] This implementation does not limit the specific preset denoising algorithm. For example, it can use anomalous noise attenuation, Fx-domain coherent noise suppression, or wavelet thresholding denoising methods based on the time-frequency domain. The entire denoising process strictly follows the amplitude preservation principle to avoid energy loss of the effective signal, and finally obtains amplitude-preserved shot gather data.

[0038] Step 202: After performing surface consistency amplitude compensation on the amplitude-preserving shot gather data, data sorting and common reflection point stacking are performed to obtain the seismic stacking profile.

[0039] Specifically, during the propagation of seismic waves, differences in surface excitation conditions, reception conditions, and near-surface medium inhomogeneity can lead to systematic differences in the effective signal amplitude of different seismic traces within the same shot gather. These differences are unrelated to the actual reflected energy of the underground geological body, and without compensation, the accuracy of subsequent data sorting and stacking will be affected. Therefore, this embodiment employs a surface consistency amplitude correction algorithm. By statistically analyzing the amplitude attenuation patterns of each seismic trace in the shot gather data, a surface consistency compensation factor model is established. This model uses the shot point, receiver point, and offset as independent variables, and obtains the corresponding compensation coefficients for each factor through least squares fitting. The amplitude of each seismic trace is then multiplied by the corresponding compensation coefficient to correct for amplitude inhomogeneity caused by differences in surface conditions. After compensation, a common reflection point-based sorting algorithm is used. The common reflection point positions of each seismic trace are calculated based on the spatial coordinates of the source and receiver. Seismic traces corresponding to the same common reflection point are classified as common reflection point shot gathers, and then each common reflection point shot gather is horizontally stacked. By aligning the signals from each seismic trace in the shot concentration in the time domain and summing them, the coherence of the effective signals is utilized to enhance their energy while suppressing random noise, ultimately obtaining a seismic stacking profile.

[0040] Step 203: If abnormal amplitudes appear in the seismic stack profile, the amplitude-preserving shot gather data will be used as the shot gather data for suppressing weak energy noise.

[0041] Specifically, the amplitude distribution of the seismic stack profile should reflect the true condition of the subsurface geological body. If there are anomalous areas in the stack profile where the amplitude suddenly increases, decreases, or becomes disordered, it indicates that the corresponding amplitude-preserving shot gather data may contain weak-energy noise that has not been initially denoised. This noise is not effectively suppressed during the stacking process and instead affects the stacking effect. Therefore, this amplitude-preserving shot gather data needs to be identified as shot gather data with weak-energy noise to be suppressed and enter the subsequent weak-energy noise identification and suppression process. If the amplitude distribution of the stack profile is uniform and there are no obvious anomalies, it indicates that the amplitude-preserving shot gather data is of good quality and can be directly used for subsequent geological interpretation or other processing procedures. In practical applications, an amplitude statistics method based on a sliding window can be used. The sliding window size is set, and each sampling point of the seismic stack profile is traversed. The mean and standard deviation of the amplitude within the window are calculated. If the amplitude value of a sampling point exceeds the range of mean ± 2 times the standard deviation, it is determined to be an abnormal amplitude point. When the number of abnormal amplitude points in a certain area reaches a preset proportion, such as 30%, the area is determined to be an abnormal amplitude area, and the amplitude-preserving shot gather data corresponding to that area is marked as shot gather data to be processed.

[0042] Compared with related technologies, this embodiment achieves the screening of target shot set data by performing a preprocessing process on single-shot data, ensuring the targeting and effectiveness of subsequent weak-energy noise suppression.

[0043] Example 3 The third embodiment of the present invention provides a method for suppressing weak energy noise in seismic data. This embodiment is a supplement to the first embodiment described above. The supplement is that step 101 is explained in detail. This embodiment specifically includes the following contents.

[0044] Spectral analysis was performed on each seismic trace signal, and the dominant frequency, number of zero-value sampling points and number of non-zero-value sampling points of each seismic trace signal were extracted as frequency characteristic values ​​based on the spectral analysis results. Seismic traces whose dominant frequency, number of zero-value sampling points, and number of non-zero-value sampling points do not meet the first preset condition are selected; the first preset condition is: the dominant frequency is zero, or the number of non-zero-value sampling points is less than half of the total number of sampling points in the current seismic trace signal.

[0045] The dominant frequency refers to the frequency point with the most concentrated energy in the seismic trace signal spectrum. It is a key parameter reflecting the frequency attributes of the effective signal. Effective seismic signals, constrained by the reflection characteristics of underground geological bodies, inevitably possess a definite dominant frequency (e.g., the dominant frequency of shallow reflection signals is typically 30-80Hz, while the dominant frequency of deep reflection signals is 10-40Hz). Ineffective traces, on the other hand, often lack a stable dominant frequency or have a dominant frequency of zero. The number of zero-value sampling points refers to the number of sampling points in the seismic trace signal with a value of zero or close to zero. The number of non-zero-value sampling points is the total number of sampling points in the seismic trace minus the number of zero-value sampling points. Both together reflect the proportion of effective data in the signal. The number of non-zero-value sampling points in an effective seismic signal should cover most of the recording duration, while ineffective traces often contain a large number of continuous zero-value sampling points, resulting in an extremely low proportion of non-zero values.

[0046] Specifically, the spectrum analysis employs either Fast Fourier Transform (FFT) or Discrete Fourier Transform (DFT) algorithms to convert the seismic trace's time-domain signal into a frequency-domain spectrum curve. For a seismic trace signal of length N, the amplitude spectrum corresponding to N / 2 frequency points is calculated using FFT, and the frequency corresponding to the peak value in the amplitude spectrum is the dominant frequency of the seismic trace. The number of zero-value sampling points is determined by traversing all sampled values ​​of the seismic trace and statistically analyzing |x|. i |≤ε (ε is the threshold for determining zero, such as 0.001×max (|x i |), x i The number of sampling points is obtained by subtracting the number of zero-value sampling points from the total number of sampling points.

[0047] Based on the three frequency feature values ​​extracted above, seismic traces that do not meet the first preset condition are filtered out. The first preset condition is explicitly set as follows: the dominant frequency is zero, or the number of non-zero value sampling points is less than half of the total number of sampling points in the current seismic trace signal. The principle behind this preset condition is as follows: a seismic trace with a dominant frequency of zero indicates that its signal has no frequency range with concentrated energy, and essentially there is no effective reflected wave signal. It is mostly a dead channel caused by uncoupled detectors or line faults, or a full-band noise channel. On the other hand, a seismic trace with less than half of the total number of non-zero value sampling points means that more than 50% of the sampling values ​​in the signal are invalid zero values, and the proportion of effective information is low. Even if it contains a small amount of weak energy signal, it will cause subsequent processing failure due to insufficient data integrity. If such seismic traces are retained, they will greatly interfere with the identification accuracy of weak energy noise.

[0048] This embodiment filters out seismic traces that meet either the condition of having a zero dominant frequency or having a non-zero sampling point count that accounts for half of the total number of sampling points. The filtered seismic traces will not participate in the subsequent weak energy noise suppression, thereby reducing the impact of invalid data on processing efficiency and effectiveness.

[0049] Example 4 The fourth embodiment of the present invention relates to a method for suppressing weak energy noise in seismic data. This embodiment is a supplement to the foregoing embodiments, and the supplement lies in the description of the step of determining the energy threshold in step 102, as follows: Figure 3 As shown, this embodiment specifically includes the following steps: Step 301: Pre-set the width window of the median filter based on the spatial distribution width of the upper wave recorded by a single shot.

[0050] In this embodiment, the surface wave is a low-frequency, high-energy wave propagating along the Earth's surface within a seismic wave pattern. It exhibits a significant spatial distribution pattern in single-shot records, covering a certain number of continuous seismic traces laterally (in the direction of the seismic trace arrangement), forming a fixed spatial distribution width. This width remains stable within the same work area and under similar surface conditions. The median filter is a nonlinear filtering tool that effectively suppresses interference from outliers, preserves the intermediate trend of the data sequence, and is suitable for extracting normal baseline values ​​for seismic trace amplitudes. The neighborhood refers to a local area composed of a specific number of seismic traces extending to both sides of the target seismic trace, ensuring that the seismic traces within the area are affected by the same surface conditions and excitation / reception conditions, resulting in consistent amplitude characteristics.

[0051] Specifically, since the spatial distribution width of surface waves directly reflects the lateral coverage of normal amplitude signals in a single-shot record, setting the filter width based on this width ensures that the filter window can completely encompass the normal seismic traces within the influence range of the surface waves, avoiding distortion of the reference value due to an excessively narrow window or the introduction of abnormal amplitudes from irrelevant regions due to an excessively wide window. Specifically, the spatial distribution of surface waves can be visualized using the amplitude profile of a single-shot record. Surface waves appear as continuous stripes of low frequency and strong amplitude in the amplitude profile. The number of seismic traces laterally covered by these stripes can be counted. For example, if a single-shot record shows surface waves covering 30 seismic traces, the width of the median filter can be set to a value consistent with the spatial distribution width, such as 30 traces. If there are slight fluctuations in the surface wave distribution in a single-shot record, the average value of multiple counts can be used as the filter width. For example, if the surface wave widths of three single-shot records are 28, 30, and 32 traces respectively, the average value of 30 traces can be used as the filter width.

[0052] Step 302: Based on the width window, determine the neighborhood range of half the window width corresponding to each seismic trace, and use the median of the root mean square amplitude of the seismic traces within the neighborhood range as the normal amplitude energy reference.

[0053] Specifically, for each seismic trace in the shot gather data, a neighborhood is defined centered on that trace. The neighborhood width is equal to the median filter width / 2. For example, a 30-trace filter width corresponds to a 15-trace neighborhood. The root mean square amplitude of all seismic traces within this neighborhood is calculated. The root mean square amplitude is calculated by taking the square root of the sum of the squares of the signal sample values ​​divided by the number of sample points. These root mean square amplitudes are sorted by value, and the median value is taken. If the number of seismic traces in the neighborhood is even, the average of the two median values ​​is taken. This median value is the normal amplitude energy reference within the current width window. By traversing all seismic traces, the local normal amplitude energy reference corresponding to each trace can be obtained, ensuring the spatial adaptability of the reference value.

[0054] Specifically, defining the neighborhood range based on half the filter width ensures that the seismic traces within the neighborhood are in the same surface wave influence area as the target seismic trace, and that the amplitude is consistent with the influence of surface conditions and propagation paths. The calculated root mean square amplitude can reflect the normal energy level of the area. In contrast, the median statistical method can eliminate the interference of individual abnormally high or low amplitude traces within the neighborhood, and is more stable than the mean statistical method, thus obtaining a reliable normal amplitude energy benchmark.

[0055] Step 303: Set the energy threshold based on the amplitude energy reference.

[0056] Specifically, the energy threshold needs to be lower than the normal amplitude energy benchmark and be able to effectively distinguish between weak energy channels and normal channels. If the threshold is too high, some normal low-energy effective signal channels will be misclassified as weak energy channels; if the threshold is too low, target channels affected by weak energy noise cannot be effectively screened. In practice, the energy threshold can be set by multiplying the benchmark value by an adjustment coefficient. The adjustment coefficient is determined based on the statistical results of the intensity of weak energy noise in the work area, with a value range of 0.3-0.6: when the weak energy noise in the work area is strong, the adjustment coefficient is 0.5-0.6 to ensure that more weak energy channels affected by noise are covered; when the weak energy noise in the work area is weak, the adjustment coefficient is 0.3-0.4 to avoid normal low-energy effective signal channels being misscreened. For example, if the normal amplitude energy benchmark of a certain seismic channel is 1200, then when the adjustment coefficient is 0.5, the corresponding energy threshold is 600, that is, seismic channels with a root mean square amplitude lower than 600 will be judged as weak energy channels.

[0057] The energy threshold set by the method in this embodiment can adapt to the surface wave distribution and noise level of the work area, avoiding misjudgment or omission of weak energy channels caused by subjectively setting the threshold, and providing a reliable basis for subsequent screening of weak energy channels.

[0058] In one example, step 102, which involves increasing the amplitude of the weak energy channel to satisfy a second preset condition, includes the following steps: Multiply the amplitude value of each sampling point in the weak energy channel by the dynamic gain coefficient so that the root mean square amplitude of the weak energy channel is increased to meet the second preset condition. The second preset condition is that the root mean square amplitude of the enhanced weak energy channel is the normal amplitude energy reference.

[0059] In this embodiment, the value of the dynamic gain coefficient K must satisfy: original amplitude of the weak energy channel × K = normal amplitude energy reference. This coefficient K is a single-channel dynamic value, and the corresponding K value varies for different weak energy channels due to different Ar1 values. The amplitude values ​​of all sampling points in the weak energy channel are multiplied by the calculated dynamic gain coefficient K to complete the overall improvement of the single-channel amplitude.

[0060] In another example, step 103, which involves removing seismic traces whose relationship between the first and second eigenvalues ​​satisfies a third preset condition, may specifically include: For each seismic trace, calculate the ratio of its first eigenvalue to its second eigenvalue; The seismic trace is removed from the shot gather data when the ratio meets the third preset condition, which is that the ratio is less than a preset threshold.

[0061] Specifically, the first feature value of each current seismic trace is extracted and denoted as Ar1, and the second feature value of each seismic trace after amplitude enhancement is extracted and denoted as Ar2. The ratio of the first feature value Ar1 to the second feature value Ar2 is calculated and denoted as Tr. Seismic traces with Tr less than a preset threshold are removed. In this embodiment, the preset threshold ranges from 0.2 to 0.3, which can be determined based on the statistical results of the work area data. This embodiment does not limit its specific value.

[0062] Compared with related technologies, this embodiment makes up for the limitations of single energy feature screening by quantitatively determining the ratio of feature values. It ensures that invalid channels that cannot be recovered by amplitude enhancement are eliminated. This avoids invalid channels occupying computing resources and affecting processing efficiency, and also ensures the purity of the remaining shot gather data. This allows subsequent amplitude compensation to focus on the energy balance of the effective signal, further improving the reliability of the entire weak energy noise suppression process.

[0063] Example 5 The fifth embodiment of the present invention relates to a low-energy noise suppression system for seismic data, such as... Figure 4 As shown, it specifically includes: The data filtering module 401 is used to acquire shot gather data of weak energy noise to be suppressed, perform frequency characteristic value statistics on each seismic trace signal in the shot gather data, and filter out seismic traces whose frequency characteristic values ​​do not meet the first preset condition. The energy enhancement module 402 is used to calculate the root mean square amplitude of each seismic trace as the first feature value, screen seismic traces with root mean square amplitude below the energy threshold as weak energy traces, and enhance the amplitude of the weak energy traces to meet the second preset condition. Amplitude compensation module 403: used to recalculate the root mean square amplitude of each seismic trace as the second characteristic value, remove seismic traces whose relationship between the first characteristic value and the second characteristic value meets the third preset condition, and perform amplitude compensation on the remaining seismic trace data after removal.

[0064] In this embodiment, the data filtering module 401 is also used for: Acquire seismic single-shot data, perform initial denoising on the seismic single-shot data, and obtain amplitude-preserving shot gather data; After surface uniformity amplitude compensation is performed on the amplitude-preserving shot gather data, data sorting and common reflection point stacking are performed to obtain the seismic stacking profile. If abnormal amplitudes are found in the earthquake stack profile, the amplitude-preserving shot gather data will be used as the shot gather data for suppressing weak energy noise.

[0065] In summary, it is not difficult to see that this embodiment is a system embodiment corresponding to the aforementioned method embodiments. Other unmentioned technical details recorded in the aforementioned method embodiments are still valid in this embodiment, and will not be repeated here.

[0066] Example 6 The sixth embodiment of the present invention relates to an electronic device, such as... Figure 5 As shown, it includes at least one processor 502; and a memory 501 communicatively connected to at least one processor 502; wherein the memory 501 stores instructions executable by at least one processor 502, the instructions being executed by at least one processor 502 to enable at least one processor 502 to perform any of the above method embodiments.

[0067] The memory 501 and processor 502 are connected via a bus, which may include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 502 and memory 501 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 502 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 502.

[0068] Processor 502 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 501 can be used to store data used by processor 502 during operation.

[0069] Example 7 The seventh embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements any of the above-described method embodiments.

[0070] Example 8 The eighth embodiment of the present invention relates to a computer program product, including computer instructions that, when executed by a processor, implement any of the above-described method embodiments.

[0071] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0072] 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.

[0073] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method for suppressing weak energy noise in seismic data, characterized in that, include: Acquire shot gather data of weak energy noise to be suppressed, perform frequency characteristic value statistics on each seismic trace signal in the shot gather data, and filter out seismic traces whose frequency characteristic values ​​do not meet the first preset condition. The root mean square amplitude of each seismic trace is calculated as the first feature value. Seismic traces with root mean square amplitudes lower than the energy threshold are selected as weak energy traces. The amplitude of the weak energy traces is increased to meet the second preset condition. The root mean square amplitude of each seismic trace is recalculated as the second characteristic value. Seismic traces whose relationship between the first characteristic value and the second characteristic value satisfies the third preset condition are removed. After removal, amplitude compensation is performed on the remaining seismic trace data.

2. The method according to claim 1, characterized in that, Before acquiring the shot gather data for the weak energy noise to be suppressed, the method includes: Seismic single-shot data is acquired, and initial denoising is performed on the seismic single-shot data to obtain amplitude-preserving shot gather data; After performing surface uniform amplitude compensation on the amplitude-preserving shot gather data, data sorting and common reflection point stacking are performed to obtain the seismic stacking profile. If abnormal amplitudes appear in the earthquake stack profile, the amplitude-preserving shot gather data will be used as the shot gather data for suppressing weak energy noise.

3. The method according to claim 2, characterized in that, The initial denoising of the single-shot seismic data includes: The seismic single-shot data is subjected to preset denoising processing to suppress noise; The preset denoising process includes at least: abnormal noise attenuation and Fx domain coherent noise suppression.

4. The method according to claim 1, characterized in that, The step of statistically analyzing the frequency characteristic values ​​of each seismic trace signal in the shot gather data and filtering out seismic traces whose frequency characteristic values ​​do not meet the first preset condition includes: Spectral analysis was performed on each seismic trace signal, and the dominant frequency, the number of zero-value sampling points, and the number of non-zero-value sampling points of each seismic trace signal were extracted as frequency characteristic values ​​based on the spectral analysis results. Seismic traces whose dominant frequency, number of zero-value sampling points, and number of non-zero-value sampling points do not meet the first preset condition are filtered out; the first preset condition is: the dominant frequency is zero, or the number of non-zero-value sampling points is less than half of the total number of sampling points in the current seismic trace signal.

5. The method according to claim 1, characterized in that, The steps for determining the energy threshold include: The width of the median filter is preset based on the spatial distribution width of the upper wave recorded by a single shot. Based on the width window, a neighborhood range of half the width of the window corresponding to each seismic trace is determined, and the median of the root mean square amplitude of the seismic trace within the neighborhood range is used as the normal amplitude energy reference. The energy threshold is set based on the amplitude energy reference.

6. The method according to claim 5, characterized in that, The enhancement of the amplitude of the weak energy channel to satisfy the second preset condition includes: Multiply the amplitude value of each sampling point in the weak energy channel by the dynamic gain coefficient so that the root mean square amplitude of the weak energy channel is increased to meet the second preset condition. The second preset condition is that the root mean square amplitude of the enhanced weak energy channel is the normal amplitude energy reference.

7. The method according to claim 1, characterized in that, The seismic traces whose relationship between the first feature value and the second feature value satisfies a third preset condition are removed, including: For each of the aforementioned seismic traces, calculate the ratio of its first characteristic value to its second characteristic value; When the ratio meets the third preset condition, the seismic trace is removed from the shot gather data. The third preset condition is that the ratio is less than a preset threshold.

8. A low-energy noise suppression system for seismic data, characterized in that, include: The data filtering module is used to acquire shot gather data of weak energy noise to be suppressed, perform frequency characteristic value statistics on each seismic trace signal in the shot gather data, and filter out seismic traces whose frequency characteristic values ​​do not meet the first preset condition. An energy enhancement module is used to calculate the root mean square amplitude of each seismic trace as a first feature value, screen seismic traces with root mean square amplitudes lower than an energy threshold as weak energy traces, and enhance the amplitude of the weak energy traces to meet a second preset condition. The amplitude compensation module is used to recalculate the root mean square amplitude of each seismic trace as the second characteristic value, remove seismic traces whose relationship between the first characteristic value and the second characteristic value satisfies a third preset condition, and then perform amplitude compensation on the remaining seismic trace data after removal.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the weak energy noise suppression method for seismic data as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for suppressing weak energy noise in seismic data as described in any one of claims 1 to 7.

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