Seismic signal adaptive amplitude compensation method based on multi-domain joint parameter calculation

Abstract

The invention discloses a seismic signal adaptive amplitude compensation method based on multi-domain joint parameter calculation. The method relates to the technical field of amplitude compensation, and comprises the following steps: preprocessing an original seismic trace set and extracting multi-domain basic information, cooperatively acquiring compensation core parameters, and performing staged adaptive amplitude compensation and verifying and iteratively optimizing a compensation effect. The method comprises the steps of preprocessing an original seismic trace set, extracting multi-domain basic information, cooperatively obtaining multi-domain compensation core parameters, compensating seismic signal attenuation and energy difference in stages based on the parameters, and finally ensuring that data is adaptive to subsequent geological interpretation and reservoir prediction through compensation effect verification and iterative optimization. The accuracy of seismic signal self-adaptive amplitude compensation is improved, and the problem that in the prior art, the accuracy of seismic signal self-adaptive amplitude compensation is low is solved.

Description

Technical Field

[0001] This invention relates to the field of amplitude compensation technology, and in particular to an adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter calculation. Background Technology

[0002] First, existing technologies mostly rely on single-domain parameter estimation (such as parameter calculation based only on the time or frequency domain), which severs the correlation of multi-domain information during seismic wave propagation. Furthermore, they do not fully account for the physical mechanism of seismic wave attenuation, which is the result of the coupling of multiple factors such as geometric diffusion, viscoelastic absorption, and scattering. At the same time, the model assumptions are too idealistic and seriously inconsistent with the actual geological propagation scenarios. Specifically, the Q-value estimation has significant multiple solutions due to the lack of multi-domain constraints such as time-domain travel time variation and spatial domain geometric parameters. The calculation of the spherical diffusion coefficient ignores the actual situation of complex terrain and lateral unevenness of near-surface structures, and only extrapolates according to the ideal spherical propagation model. This results in problems such as insufficient deep energy and energy imbalance between tectonic high points and low points even after compensation, and cannot truly reflect the reflection characteristics of underground geological bodies.

[0003] Secondly, the reason why surface consistency compensation technology is insufficient in compensating for frequency bands with severe energy loss (such as deep high-frequency signals) is fundamentally because it relies solely on the energy statistics of the dominant frequency band for compensation parameter inversion. It fails to design differentiated compensation logic for the energy attenuation characteristics of different frequency bands and does not consider the unique energy attenuation mechanism of deep high-frequency signals due to their long propagation paths and strong viscoelastic absorption. Ultimately, this results in a "beaded" distribution of lateral energy in the compensated profile, severely impacting reservoir prediction accuracy. Simultaneously, existing compensation technologies suffer from poor compensation stability and susceptibility to noise or artifacts. This problem stems from inherent flaws in algorithm design and the lack of adaptive mechanisms. Traditional Q-compensation uses an exponential compensation function, inherently risking numerical divergence in high-frequency bands. Even with the addition of empirical stabilization factors, the design relies solely on human experience and lacks an adaptive adjustment mechanism based on signal time-frequency characteristics, making it difficult to adapt to the attenuation characteristics of all seismic signals in the work area. This leads to artifacts such as linear noise and amplitude abrupt changes in the compensated profile, reducing data continuity.

[0004] However, fixed gain compensation technology fails to fully recognize the core physical characteristic of the difference in signal and noise distribution in the time and frequency domains. Its algorithm design lacks signal-noise separation and dynamic gain control logic, resulting in overcompensation of noise in low signal-to-noise ratio regions and undercompensation in high signal-to-noise ratio regions, which further deteriorates data quality.

[0005] Finally, existing compensation techniques suffer from weak adaptability and limited effectiveness under complex geological conditions. The root cause lies in the fact that the parameter model design is not adapted to the propagation patterns of seismic waves in complex geological scenarios and ignores the influence of spatial domain parameters. In scenarios with complex surfaces (such as mountains and loess plateaus) and highly heterogeneous strata (such as igneous rocks and salt domes), the propagation paths of seismic waves are complex, and the attenuation characteristics in the longitudinal and lateral directions vary drastically. The single-parameter models used in existing technologies (such as fixed Q values ​​and uniform surface consistency factors) cannot adapt to such multi-dimensional and highly variable attenuation characteristics, leading to compensation failure. At the same time, in the pre-stack gather processing, traditional methods ignore the influence of spatial parameters such as shot-receiver distance and azimuth on seismic wave amplitude and fail to coordinate spatial domain parameters with time and frequency domain parameters. This results in the destruction of the inherent AVO (Amplitude Versus Offset) characteristics of the gather during the compensation process, causing AVO characteristic distortion, which affects the accuracy of lithology and fluid identification, and consequently leads to low accuracy of adaptive amplitude compensation for seismic signals. Summary of the Invention

[0006] To address the low accuracy of existing adaptive amplitude compensation methods for seismic signals, this invention provides an adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter calculation. The technical solution is as follows: On the one hand, an adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition is provided. This method includes: Step 101, preprocessing the original seismic gathers obtained from seismic exploration to avoid interference from acquisition errors and near-surface propagation static time differences on the seismic signal, obtaining preprocessed seismic gathers, and simultaneously extracting multi-domain basic information from the preprocessed seismic gathers; Step 102, based on the extracted multi-domain basic information, collaboratively acquiring the core parameters required for seismic signal amplitude compensation to reduce the ambiguity and bias of single-domain parameter estimation, ensuring the accuracy and adaptability of the compensation core parameters. The core parameters include, but are not limited to, […]. Limited to the common shot point domain, common receiver domain, and common offset domain; Step 103, based on the core parameters, performs staged adaptive amplitude compensation on the preprocessed seismic gathers to achieve accurate compensation for geometric attenuation, viscoelastic absorption attenuation, and lateral energy differences of the seismic signal, taking into account both compensation accuracy and data stability. The staged adaptive amplitude compensation includes diffusion compensation, absorption compensation, and adaptive gain compensation; Step 104, performs effect verification and iterative optimization on the seismic gathers after staged adaptive amplitude compensation to ensure that the compensated seismic data meets the needs of subsequent geological interpretation and reservoir prediction, and to ensure the reliability and adaptability of the compensation effect.

[0007] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By employing multi-domain parallel analysis across time, frequency, spatial, and imaging domains, accurate extraction of fundamental information is achieved. Combining Hilbert transform, generalized S-transform, trace head information analysis, and pre-stack time migration, multi-dimensional information such as seismic signal time-frequency characteristics, spatial distribution, and stratigraphic structure is simultaneously acquired and accurately matched and correlated, overcoming the limitations of single-domain information extraction. Based on the collaborative calculation of multi-domain fundamental information, the core parameters for amplitude compensation are effectively reduced, minimizing the ambiguity and numerical bias of single-domain parameter estimation. This ensures that core parameters such as common shot point domain, common receiver domain, and common offset domain are highly compatible with the preprocessed seismic gathers, providing accurate and reliable parameter support for subsequent staged amplitude compensation. This guarantees the scientific and rational nature of amplitude compensation from the source, avoiding undercompensation or overcompensation due to parameter errors.

[0008] 2. By implementing diffusion compensation, absorption compensation, and adaptive gain compensation in stages, precise and layered correction of various attenuations and energy differences in seismic signals was achieved, balancing compensation accuracy with the stability of seismic gather data. Specifically, diffusion compensation dynamically adjusts the instantaneous frequency based on the instantaneous amplitude mean square error to specifically compensate for high-frequency losses, while limiting the frequency adjustment in areas of strong noise / anomalies to avoid unstable frequency adjustments. Absorption compensation achieves precise windowed compensation of viscoelastic absorption attenuation by dividing the gather's effective time window into sub-windows and dynamically adjusting the maximum allowable phase rotation based on the viscoelastic attenuation coefficient, adapting to the absorption attenuation characteristics of different formations. Adaptive gain compensation dynamically adjusts the effective gain amplitude threshold based on the gain sliding time window length, enabling differentiated gain determination for shallow, medium, and deep layers, accurately separating weak signals from noise, effectively correcting lateral energy differences in seismic signals, and avoiding noise amplification by stopping gain boosting in noise segments, resulting in a more balanced energy distribution and stronger continuity of reflection phase axes in the compensated seismic gather.

[0009] 3. This method uses the upper and lower limits of the dominant frequency of the compensated gather as the core basis. By calculating the actual bandwidth, it dynamically adjusts the energy balance threshold of the gather, ensuring that the energy balance judgment standard is highly adapted to the spectral characteristics of the gather. Narrow-band gathers are adapted to higher balance thresholds, and wide-band gathers are adapted to lower balance thresholds. This achieves dynamic and precise judgment of the compensation effect, breaking through the limitations of fixed threshold judgment and making the judgment results more consistent with the actual situation of gathers with different spectral characteristics. Using the dynamically adjusted target gather energy balance threshold as the core judgment basis, the energy balance of the compensated gather is verified. If the standard is not met, the iterative optimization process is immediately initiated, forming a closed-loop processing mechanism of compensation execution-effect verification-iterative optimization. This effectively ensures that the key indicators such as energy balance, signal-to-noise ratio, and bandwidth of the compensated seismic gather meet the needs of subsequent geological interpretation and reservoir prediction, making the amplitude compensation effect reliable and adaptable. The final output seismic gather can more realistically and clearly reflect the actual characteristics of the underground strata and reservoirs. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart of an adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition provided in this application embodiment; Figure 2 The flowchart illustrates the effect verification and iterative optimization of the adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition provided in the embodiments of this application. Detailed Implementation

[0012] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0013] To facilitate understanding of the embodiments of this application, the following points will be explained first: First, in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but it does not exclude the possibility of indicating an "and" relationship; the specific meaning can be understood in context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0014] Second, the use of prefixes such as "first" and "second" in this application is solely for the purpose of distinguishing and describing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0015] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0016] like Figure 1 The diagram shows a flowchart of an adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter calculation provided in this application. The method includes the following steps: Step 101: Preprocess the raw seismic gathers obtained from seismic exploration to avoid interference from acquisition errors and near-surface propagation static time difference on the seismic signal, and obtain the preprocessed seismic gathers. Simultaneously, extract multi-domain basic information from the preprocessed seismic gathers.

[0017] It should be understood that multiple domains include the time domain, frequency domain, spatial domain, and imaging domain; A multi-domain parallel analytical algorithm is used to extract multi-domain basic information from the preprocessed seismic gathers. In the time domain, the amplitude envelope, instantaneous frequency, and instantaneous phase characteristics of the seismic signal are extracted by Hilbert transform. The time-varying energy decay law is statistically analyzed by using a sliding time window. In the frequency domain, the preprocessed seismic gathers are decomposed into time-frequency components by generalized S-transform to obtain energy distribution characteristics, including but not limited to full-band amplitude spectrum and phase spectrum. In the spatial domain, the seismic gather head information is analyzed to extract the shot-receiver offset, azimuth, common center point coordinates, common shot point identifier, and common receiver point identifier. The lateral energy variation of the seismic signal in different spatial domains is statistically analyzed. Different spatial domains represent the common shot point domain, common receiver point domain, common offset domain, etc. In the imaging domain, preliminary migration imaging is performed using the pre-stack time migration method to invert and obtain information on the velocity field of the subsurface strata and the structural morphology of the reflection interface. This enables the simultaneous extraction of basic information in the time domain, frequency domain, spatial domain, and imaging domain, achieving accurate matching and association of information across multiple domains.

[0018] In this embodiment, firstly, in the time-domain processing stage, Hilbert transform is used to analyze the signal features of the preprocessed seismic gathers. This accurately extracts the three core features of the seismic signal: amplitude envelope, instantaneous frequency, and instantaneous phase. Among these, the amplitude envelope clearly reflects the trend of signal energy strength changes, the instantaneous frequency effectively captures the dynamic fluctuation characteristics of signal frequency over time, and the instantaneous phase accurately depicts the instantaneous change law of signal phase, laying the foundation for subsequent analysis of the time-varying characteristics of the signal. Simultaneously, by setting a sliding time window to perform segment-by-segment statistical analysis of the signal, the time-varying energy attenuation law of the seismic signal can be efficiently obtained. This law can intuitively reflect the absorption and attenuation characteristics of seismic wave energy by the underground medium, providing a key basis for subsequent inversion of medium properties. Secondly, in the frequency domain processing stage, the generalized S-transform is used to perform high-precision time-frequency decomposition on the preprocessed seismic gathers. This transform has both good time and frequency resolution, which can effectively overcome the limitations of traditional time-frequency analysis methods. Through decomposition, the energy distribution characteristics of seismic signals can be fully obtained, including core information such as the full-band amplitude spectrum and phase spectrum. The full-band amplitude spectrum can clearly show the energy intensity distribution of different frequency components, while the phase spectrum can accurately reflect the phase relationship of each frequency component, providing reliable frequency domain support for subsequent identification of underground reservoir characteristics and judgment of stratigraphic interface information. Furthermore, in the spatial domain processing stage, the focus is on analyzing the head information of the seismic gathers, accurately extracting key spatial parameters such as shot-receiver offset, azimuth, common center point coordinates, common shot point identifier, and common receiver point identifier. Based on this, statistical analysis is performed on the lateral energy variation of seismic signals in different spatial domains, such as the common shot point domain, common receiver point domain, and common offset domain. The analysis results can effectively reflect the spatial distribution characteristics of underground structures, and accurately capture the energy differences of seismic signals at different spatial locations, providing important spatial feature basis for subsequent spatial domain signal denoising and imaging accuracy optimization. Finally, in the imaging domain processing stage, pre-stack time migration is used to perform preliminary migration imaging on the preprocessed seismic gathers. This method can effectively correct the influence of offset during seismic wave propagation and improve the imaging accuracy of subsurface reflection interfaces. Through imaging inversion, the subsurface stratum velocity field and reflection interface structural morphology information can be accurately obtained. Among them, the stratum velocity field is the core basic data for subsequent time-depth conversion and reservoir prediction, while the reflection interface structural morphology information can intuitively present the undulations and structural characteristics of the subsurface strata. Through the synchronous processing of the above four domains, the synchronous extraction of basic information in the time domain, frequency domain, spatial domain, and imaging domain is realized. The information in each domain can be mutually verified and complemented, effectively improving the completeness and accuracy of seismic information extraction. Finally, accurate matching and correlation of multi-domain information is achieved, providing comprehensive and reliable technical support for subsequent seismic data interpretation, reservoir prediction, and other work.

[0019] Step 102: Based on the extracted multi-domain basic information, collaboratively obtain the core parameters required for seismic signal amplitude compensation in order to reduce the ambiguity and bias of single-domain parameter estimation and ensure the accuracy and adaptability of the compensation core parameters. The core parameters include, but are not limited to, the common shot point domain, the common receiver point domain, and the common offset domain.

[0020] It should be understood that, firstly, the multi-domain basic information extracted in the early stage is preprocessed, filtered and integrated. Among them, the amplitude envelope and time-varying energy decay law in the time domain can provide the basis for the dynamic change of signal energy over time; the full-band amplitude spectrum and phase spectrum in the frequency domain can support the energy compensation requirements of different frequency components; the shot-receiver distance, azimuth angle and the lateral energy variation law of each domain (common shot point domain, common receiver point domain, common offset domain) in the spatial domain can reflect the influence characteristics of spatial position on amplitude; and the formation velocity field and reflection interface structure information in the imaging domain can help correct the amplitude distortion caused by the difference in the underground medium. Through the systematic integration of multi-domain information, a comprehensive basic dataset for parameter estimation can be constructed, effectively avoiding the problem of one-sided parameter estimation caused by insufficient dimensions of information in a single domain. Secondly, based on the integrated multi-domain basic information, collaborative estimation is carried out for the core parameters of amplitude compensation (focusing on parameters related to the common shot point domain, common receiver point domain, and common offset domain). A multi-domain constrained iterative algorithm is adopted, with the energy attenuation law in the time domain as the time dimension constraint, the energy distribution characteristics in the frequency domain as the frequency dimension constraint, and the lateral energy variation law in each spatial domain as the spatial dimension constraint. At the same time, the influence of medium attenuation on the parameters is corrected by the formation velocity field information in the imaging domain, so as to achieve cross-validation and collaborative optimization of each core parameter. In this process, through the mutual constraint of multi-domain information, the problem of multiple solutions that are prone to occur in single-domain parameter estimation can be significantly reduced. For example, relying solely on spatial domain information is prone to parameter deviation due to structural complexity, and relying solely on time domain information is difficult to take into account the compensation requirements of different frequency components. Multi-domain collaborative estimation can accurately lock the optimal value of each core parameter through complementary verification of information in each dimension, while effectively reducing the systematic and random biases of parameter estimation. Finally, the compatibility and accuracy of the core amplitude compensation parameters obtained from the collaborative estimation were verified and optimized. The estimated parameters were applied to a small-scale seismic gather amplitude compensation trial. The parameters were fine-tuned by combining the time-domain amplitude envelope correction effect, the frequency-domain energy balance, the lateral consistency of energy in each spatial domain (common shot domain, common receiver domain, common offset domain), and the imaging clarity of the reflection interface in the imaging domain. Through this verification and optimization step, it can be ensured that the core parameters not only have accuracy but also adapt to seismic gathers with different geological conditions and signal characteristics. This avoids the problem of poor compensation effect caused by the mismatch between parameters and actual seismic signals, ultimately ensuring the accuracy and compatibility of the core amplitude compensation parameters. This lays a solid foundation for the efficient development of subsequent seismic signal amplitude compensation work, thereby improving the resolution and reliability of the entire seismic data processing.

[0021] Step 103: Perform phased adaptive amplitude compensation on the preprocessed seismic gather based on the core parameters to achieve accurate compensation for geometric attenuation, viscoelastic absorption attenuation and lateral energy difference of seismic signals, while taking into account both compensation accuracy and data stability. The phased adaptive amplitude compensation includes diffusion compensation, absorption compensation and adaptive gain compensation.

[0022] It should be noted that the specific steps for diffusion compensation are as follows: For each data point in the seismic trace set, the instantaneous amplitude envelope and instantaneous frequency are obtained. For the data point to be analyzed, its instantaneous amplitude envelope is compared with the amplitude envelope reference value to obtain the instantaneous amplitude mean square error. A mapping relationship is established between the instantaneous amplitude mean square error and the instantaneous frequency adjustment. The instantaneous frequency of the seismic signal is dynamically adjusted based on the instantaneous amplitude mean square error, specifically as follows: If the instantaneous amplitude mean square error is less than the critical lower limit of the mean square error, it indicates that the amplitude decay is normal, and no instantaneous frequency adjustment is performed. If the instantaneous amplitude mean square error is within the critical range of mean square error, then the instantaneous amplitude mean square error and the instantaneous frequency adjustment amount are positively correlated and monotonically increasing. The current instantaneous amplitude mean square error is input into the mapping relationship between the instantaneous amplitude mean square error and the instantaneous frequency adjustment amount, and the instantaneous frequency gain is output to increase the instantaneous frequency to compensate for high frequency loss. The critical range of mean square error represents the closed interval formed by the critical lower limit and the critical upper limit of mean square error.

[0023] Dynamically adjusting the instantaneous frequency of seismic signals based on the mean square error of instantaneous amplitude also includes: If the instantaneous amplitude mean square error is greater than the critical upper limit of the mean square error, the instantaneous frequency adjustment amount is limited to prevent excessive and unstable frequency adjustment in strong noise or abnormal areas. The current instantaneous amplitude mean square error is input into the mapping relationship between the instantaneous amplitude mean square error and the instantaneous frequency adjustment amount, and the instantaneous frequency reduction amount is output. Based on the instantaneous frequency adjustment, the instantaneous frequency of the current seismic signal is adjusted, specifically as follows: If the instantaneous frequency gain is output, the instantaneous frequency of the current seismic signal is superimposed with the instantaneous frequency gain to obtain the instantaneous frequency of the target seismic signal; If the instantaneous frequency reduction is output, the instantaneous frequency of the current seismic signal is superimposed with the instantaneous frequency reduction to obtain the instantaneous frequency of the target seismic signal.

[0024] In this embodiment, firstly, for each data point in the seismic trace set, its instantaneous amplitude envelope and instantaneous frequency are extracted one by one. This step provides core basic data support for subsequent dynamic frequency adjustment, ensuring that the adjustment process can accurately correspond to each signal sampling point and avoid adjustment deviations caused by insufficient data granularity. At the same time, the accurate extraction of instantaneous amplitude envelope and instantaneous frequency can effectively preserve the original characteristics of the seismic signal, providing a reliable data basis for subsequent error judgment and frequency adjustment. Subsequently, for each data point to be analyzed, its extracted instantaneous amplitude envelope is compared and calculated with a preset amplitude envelope benchmark value to obtain the instantaneous amplitude mean square error corresponding to the data point. By comparing with the benchmark value, the deviation of the amplitude of the current data point can be quantified, accurately identifying signal points with abnormal amplitude attenuation, providing a clear judgment indicator for triggering subsequent frequency adjustment, and effectively avoiding the signal distortion problem caused by indiscriminate frequency adjustment.Based on this, a mapping relationship is established between the instantaneous amplitude mean square error and the instantaneous frequency adjustment. This mapping relationship is the core logic carrier for realizing dynamic frequency adjustment. It can adaptively match the corresponding frequency adjustment strategy according to the degree of amplitude deviation, breaking the limitations of the traditional fixed frequency adjustment mode and improving the targeting and flexibility of frequency adjustment. Based on this mapping relationship, the instantaneous frequency of the seismic signal is dynamically adjusted. The specific process and technical effects are as follows: If the calculated instantaneous amplitude mean square error is less than the critical lower limit of the mean square error, it means that the amplitude attenuation of the seismic signal corresponding to the data point is within the normal range, and no instantaneous frequency adjustment is required. This judgment logic can effectively avoid the unintended consequences of adjusting normal signals. Effective intervention preserves the original authenticity of seismic signals to the greatest extent possible, preventing signal redundancy or characteristic distortion caused by over-adjustment. If the instantaneous amplitude mean square error is within the critical range of mean square error (i.e., within the closed interval formed by the lower and upper critical limits of mean square error), then the instantaneous amplitude mean square error and the instantaneous frequency adjustment are positively correlated and monotonically increasing. By inputting the current instantaneous amplitude mean square error into a preset mapping relationship, the corresponding instantaneous frequency gain can be output. By increasing the instantaneous frequency, high-frequency losses generated during seismic signal transmission are compensated. This adjustment method can accurately compensate for signal points with moderate amplitude attenuation, effectively restoring high-frequency signals. The signal component improves the resolution of the seismic signal, providing clearer signal support for subsequent geological interpretation. If the instantaneous amplitude mean square error exceeds the critical upper limit of the mean square error, it indicates that the data point may be in a high-noise environment or a signal anomaly area. In this case, the instantaneous frequency adjustment needs to be limited. This limiting design can effectively prevent excessive and unstable frequency adjustment in high-noise or anomaly areas, avoid increased signal distortion after adjustment, and ensure the stability and reliability of the frequency adjustment process. Then, the current instantaneous amplitude mean square error is input into the mapping relationship, and the instantaneous frequency reduction is output. Finally, based on the instantaneous frequency adjustment (gain or reduction) output in the above steps, the current seismic signal is adjusted. The instantaneous frequency is adjusted in a targeted manner: if the output is an instantaneous frequency gain, the current instantaneous frequency of the seismic signal is superimposed with the gain to obtain the instantaneous frequency of the target seismic signal, thereby achieving high-frequency compensation for normally attenuated signals and optimizing signal quality; if the output is an instantaneous frequency reduction, the current instantaneous frequency of the seismic signal is superimposed with the reduction to obtain the instantaneous frequency of the target seismic signal, thereby achieving stable adjustment of anomalous signals and avoiding noise interference. The overall process, through a dynamic adaptive frequency adjustment strategy, not only ensures the original characteristics of the seismic signal but also effectively improves signal resolution and stability, laying a solid technical foundation for subsequent geological exploration and interpretation work.

[0025] It should be further explained that the specific steps for absorption and compensation are as follows: The effective time window of the preprocessed seismic gather is extracted as a time interval. The time interval represents the closed interval formed by the start time point corresponding to the end of the first arrival wave in the gather and the end time point corresponding to the end of the effective deep reflection wave in the gather. The effective time window of the gather is divided into continuous and non-overlapping sub-effective time windows according to a preset time step. The viscoelastic attenuation coefficient corresponding to each sub-effective time window is obtained. The initial maximum allowable phase rotation amount within the effective time window of the gather is set. The initial maximum allowable phase rotation amount is the basic phase rotation threshold in the viscoelastic absorption attenuation compensation process of seismic waves. Establish a mapping relationship between the viscoelastic decay coefficient and the phase rotation correction coefficient for each sub-effective time window; If the viscoelastic decay coefficient of each sub-effective time window is less than the critical lower limit of the reduction coefficient, then the current viscoelastic decay coefficient is input into the mapping relationship between the viscoelastic decay coefficient and the phase rotation correction coefficient of each sub-effective time window, and the phase rotation reduction coefficient is output. If the viscoelastic decay coefficient of each sub-effective time window is within the critical range of the reduction coefficient, then the phase rotation correction reference coefficient is output. The critical range of the reduction coefficient represents the closed interval formed by the lower critical limit and the upper critical limit of the reduction coefficient.

[0026] Absorption compensation also includes: If the viscoelastic decay coefficient of each sub-effective time window is greater than the critical upper limit of the reduction coefficient, then the current viscoelastic decay coefficient is input into the mapping relationship between the viscoelastic decay coefficient and the phase rotation correction coefficient of each sub-effective time window, and the phase rotation gain coefficient is output. The maximum allowable phase rotation is dynamically adjusted based on the phase rotation correction coefficient, specifically as follows: If the output phase rotation reduction coefficient is used, then the phase rotation reduction coefficient is combined with the maximum permissible phase rotation reference value to obtain the target maximum permissible phase rotation value. If the output phase rotation correction reference coefficient is used, then the phase rotation correction reference coefficient is combined with the maximum permissible phase rotation reference value to obtain the target maximum permissible phase rotation value. If the output phase rotation gain coefficient is used, then the phase rotation gain coefficient is combined with the maximum allowable phase rotation reference value to obtain the target maximum allowable phase rotation value.

[0027] In this embodiment, the effective time window of the preprocessed seismic gather is first precisely extracted. A closed time interval is defined as the effective time window, with the start time corresponding to the end of the first arrival wave within the gather as the left endpoint and the end time corresponding to the termination of the effective deep reflected wave within the gather as the right endpoint. This step accurately defines the core time range carrying the effective reflected wave information within the seismic gather, eliminating interference segments from the first arrival wave and invalid noise segments from the deep layers, avoiding interference from irrelevant time intervals on subsequent viscoelastic absorption compensation, and significantly improving the targeting and effectiveness of absorption compensation. Next, the extracted effective time window is divided into several continuous and non-overlapping sub-effective time windows according to a preset time step, and the viscoelastic attenuation coefficient corresponding to each sub-effective time window is accurately obtained. The method involves calculating the viscoelastic attenuation coefficient and setting an initial maximum allowable phase rotation for the effective time window of the gather. This initial maximum allowable phase rotation serves as the fundamental phase rotation threshold in the viscoelastic absorption attenuation compensation process. This step, through refined windowing of the effective time window, achieves precise time-segmented solution of the viscoelastic attenuation coefficient, ensuring a high degree of match between the attenuation coefficient and the actual viscoelastic attenuation characteristics of the seismic wave at different time periods. The setting of the initial maximum allowable phase rotation sets the basic boundary for subsequent phase rotation control, avoiding phase distortion caused by overcompensation from the source. Subsequently, a specific mapping relationship is established between the viscoelastic attenuation coefficient and the phase rotation correction coefficient for each sub-effective time window. This step achieves precise correlation and matching between the viscoelastic attenuation coefficient and the phase rotation control parameter, allowing the phase rotation... The conversion correction can be dynamically adapted and adjusted according to the actual viscoelastic attenuation of the seismic wave, ensuring the scientific, reasonable, and accurate nature of subsequent phase rotation correction operations. Then, a three-level threshold judgment is performed on the viscoelastic attenuation coefficient of each sub-effective time window. If the viscoelastic attenuation coefficient of the sub-effective time window is less than the critical lower limit of the reduction coefficient, the viscoelastic attenuation coefficient is input into the established mapping relationship, and the corresponding phase rotation reduction coefficient is output. This operation can specifically reduce the phase rotation during periods of low viscoelastic attenuation by using the reduction coefficient, avoiding excessive phase rotation under low attenuation conditions and effectively protecting the original phase characteristics and waveform information of the seismic wave. If the viscoelastic attenuation coefficient of the sub-effective time window is between the critical lower limit of the reduction coefficient and the reduction coefficient... Within the critical range of the reduction coefficient defined by the upper limit of the number of critical values, the phase rotation correction reference coefficient is directly output. This operation maintains stable control of the phase rotation with the reference coefficient during periods when the viscoelastic attenuation is within a reasonable and normal range, ensuring accurate compensation of the viscoelastic absorption attenuation of seismic waves during this period, while ensuring that the phase rotation is always within a controllable range. If the viscoelastic attenuation coefficient of the sub-effective time window is greater than the upper limit of the reduction coefficient, the viscoelastic attenuation coefficient is input into the established mapping relationship, and the corresponding phase rotation gain coefficient is output. This operation can specifically increase the phase rotation during periods of high viscoelastic attenuation by using the gain coefficient, achieving full compensation of seismic wave energy under high attenuation conditions and effectively recovering the effective information of deep reflected waves.Finally, the maximum allowable phase rotation is dynamically adjusted based on the phase rotation correction coefficients output by each sub-effective time window. Specifically, for different cases of output phase rotation reduction coefficient, phase rotation correction reference coefficient, and phase rotation gain coefficient, the corresponding coefficients are combined with the maximum allowable phase rotation reference value to obtain the target maximum allowable phase rotation value for each sub-effective time window. This step achieves time-segmented and differentiated dynamic control of the maximum allowable phase rotation value, ensuring that the phase rotation threshold for viscoelastic absorption compensation is highly adapted to the actual viscoelastic attenuation level of each sub-time window. This achieves sufficient energy compensation during high attenuation periods, effectively avoids phase distortion during low attenuation periods, and ensures the stability of compensation during normal attenuation periods. Ultimately, it achieves accurate, adaptable, and controllable compensation for viscoelastic absorption attenuation within the entire effective time window of the seismic gather, significantly improving the waveform quality, energy consistency, and phase accuracy of the seismic gather data, providing a high-quality data foundation for subsequent seismic data interpretation and reservoir prediction.

[0028] It should be further explained that the specific steps of adaptive gain compensation are as follows: The length of the gain sliding window adapted after diffusion compensation and absorption compensation of the preprocessed seismic gathers is extracted, and the mean effective signal amplitude of the seismic gathers is obtained. The mean effective signal amplitude is used as the reference value of the effective gain amplitude threshold. The pre-defined gain sliding window length is divided into a graded critical range, including a first critical length and a second critical length. The first critical length is the window length threshold adapted to the shallow short reflection in-phase axis, and the second critical length is the window length threshold adapted to the deep long reflection in-phase axis. A mapping relationship is established between the gain sliding window length and the effective amplitude threshold correction coefficient. If the gain sliding window length is less than or equal to the first critical length, the gain sliding window length is input into the mapping relationship between the gain sliding window length and the effective amplitude threshold correction coefficient, and the effective amplitude threshold reduction coefficient is output. The effective amplitude threshold reduction coefficient and the effective gain amplitude threshold are combined to obtain the target effective gain amplitude threshold, so as to adapt to the weak signal and noise separation requirements of shallow short windows.

[0029] Adaptive gain compensation also includes: If the length of the gain sliding window is within the critical length range, the effective amplitude threshold reference coefficient is maintained. The effective amplitude threshold reference coefficient and the effective amplitude threshold of the gain are combined to obtain the target effective amplitude threshold, so as to adapt to the effective signal amplitude characteristics of the medium-length window in the middle layer. The critical length range represents the open interval formed by the first critical length and the second critical length. If the gain sliding window length is greater than or equal to the second critical length, the gain sliding window length is input into the mapping relationship between the gain sliding window length and the effective amplitude threshold correction coefficient, and the effective amplitude threshold gain coefficient is output. The effective amplitude threshold gain coefficient and the effective amplitude threshold are combined to obtain the target effective amplitude threshold, so as to adapt to the strong attenuation signal energy characteristics of deep long windows. After dynamically assigning the effective gain amplitude threshold under different gain sliding window lengths, the dynamically adjusted target effective gain amplitude threshold is used as the effective execution criterion for the gain boosting operation within the corresponding gain sliding window. When the signal amplitude within the gain sliding window is lower than the target effective gain amplitude threshold corresponding to that window, the gain boosting operation for that window is stopped.

[0030] In this embodiment, firstly, key parameters are extracted from the seismic gathers after diffusion compensation and absorption compensation preprocessing to accurately determine the length of the gain sliding window that is suitable for the characteristics of the current gather. At the same time, the mean effective signal amplitude of the seismic gather is calculated and obtained, and this mean effective signal amplitude is used as the benchmark value of the effective gain amplitude threshold. This step lays the core parameter foundation for the subsequent precise control of gain compensation. The determination of the mean effective signal amplitude can fit the amplitude level of the actual effective signal of the gather, avoiding problems such as excessive gain boosting of noise or insufficient gain failing to highlight the effective signal due to setting the benchmark threshold too high or too low, thus ensuring the adaptability of the parameters in the early stage of gain compensation. Next, a graded critical interval for the gain sliding window length is preset, clearly defining a first critical length (window length threshold) adapted to shallow short reflection phase axes and a second critical length (window length threshold) adapted to deep long reflection phase axes. A specific mapping relationship is established between the gain sliding window length and the effective amplitude threshold correction coefficient. The division of the graded critical intervals can adapt to the length differences of reflection phase axes at shallow, intermediate, and deep layers in the seismic gather. The establishment of the mapping relationship achieves a precise correlation between the window length and the amplitude threshold correction parameter, allowing the amplitude threshold to be dynamically adjusted based on the window length, providing a scientific basis for gain compensation at different layers. Subsequently, a graded judgment and target amplitude threshold calculation are performed on the extracted gain sliding window length. If the gain sliding window length is less than or equal to the first critical length, it indicates that the window is suitable for shallow short-reflection in-phase axes. The window length is then input into the established mapping relationship, and an effective amplitude threshold reduction coefficient is output. This reduction coefficient is then combined with the effective gain amplitude threshold reference value to obtain the target effective gain amplitude threshold. This operation can reduce the amplitude judgment threshold of shallow short windows through the reduction coefficient, accurately adapting to the separation requirements of weak signals and noise in shallow short windows, effectively highlighting shallow weak reflection signals while avoiding misjudging shallow high-frequency noise as effective signals. Gain is boosted to improve the signal-to-noise ratio of shallow signals. If the gain sliding window length is in the open interval (i.e., the critical length interval) formed by the first critical length and the second critical length, it means that the window is adapted to the middle layer medium-length reflection in-phase axis. The effective amplitude threshold reference coefficient is maintained. The reference coefficient is combined with the effective amplitude threshold reference value to obtain the target effective amplitude threshold. This operation maintains the stability of the amplitude threshold with the reference coefficient, which can accurately adapt to the effective signal amplitude characteristics of the middle layer medium-length window, and ensure the appropriateness of the effective signal gain compensation in the middle layer. This avoids both insufficient gain leading to low signal amplitude and excessive gain causing signal distortion.If the gain sliding window length is greater than or equal to the second critical length, it indicates that the window is adapted to the deep long-reflection in-phase axis. This window length is input into the mapping relationship, and the effective amplitude threshold gain coefficient is output. This gain coefficient is then combined with a reference value to obtain the target gain effective amplitude threshold. This operation can increase the amplitude determination threshold of the deep long window through the gain coefficient, accurately adapting to the energy characteristics of the strongly attenuated signal in the deep long window. It can specifically boost the gain of the effective signal with severe attenuation in the deep, effectively restoring the energy amplitude of the deep long-reflection signal and solving the problem of weak amplitude caused by the long propagation distance and severe attenuation of the deep signal. Finally, after completing the dynamic assignment of the effective amplitude threshold under different gain sliding window lengths, the dynamic adjustment corresponding to each window is... The adjusted target gain effective amplitude threshold serves as the criterion for determining the effective execution of gain boosting operations within the corresponding gain sliding window. When the signal amplitude within a certain gain sliding window falls below the target gain effective amplitude threshold corresponding to that window, the gain boosting operation for that window is immediately stopped. This final step achieves adaptive start-stop control of the gain boosting operation, ensuring that gain compensation for each window is performed based on precise amplitude determination criteria. This enables differentiated and precise adaptive gain compensation for shallow, intermediate, and deep seismic gathers with different reflection phase axes of varying lengths. Ultimately, this significantly improves the amplitude consistency and signal-to-noise ratio of the effective signal across the entire gather, optimizes the overall quality of the seismic gather, and provides high-quality data support for subsequent work such as detailed interpretation of seismic data and reservoir prediction.

[0031] Step 104 involves verifying and iteratively optimizing the seismic gathers after phased adaptive amplitude compensation to ensure that the compensated seismic data meets the requirements of subsequent geological interpretation and reservoir prediction, and to guarantee the reliability and adaptability of the compensation effect.

[0032] It should be understood that, such as Figure 2 The diagram shows the effect verification and iterative optimization flowchart of the adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition provided in this application embodiment. The specific process is as follows: Starting from "Start Processing," the spectral characteristics of the seismic gather after staged adaptive amplitude compensation are first extracted, and the upper and lower limits of its dominant frequency are determined to calculate the actual bandwidth. Next, based on the calculated actual bandwidth range (narrow band, mid-band, or wide band), corresponding threshold adjustment strategies are adopted: for the narrow band, the gain coefficient is obtained by looking up a table to amplify the original threshold; for the mid-band, the original threshold remains unchanged; for the wide band, a reduction coefficient is obtained by looking up a table to lower the threshold. After the above adjustments, a final target gather energy balance threshold is obtained. Then, the verification stage is entered, comparing the actual energy balance of the compensated gather with the target threshold: if the actual balance is less than the target threshold, the energy balance is deemed satisfactory, and the process ends; otherwise, it is deemed unsatisfactory, and the iterative optimization process is restarted for reprocessing until the conditions are met.

[0033] It should be noted that the specific steps for verifying and iteratively optimizing the seismic gathers after staged adaptive amplitude compensation are as follows: The effective signal spectrum characteristics of the seismic gather after staged adaptive amplitude compensation are extracted to determine the upper and lower limits of the dominant frequency of the compensated gather. The lower limit of the dominant frequency of the compensated gather is the low-frequency cutoff frequency of the effective signal of the compensated gather, and the upper limit of the dominant frequency of the compensated gather is the high-frequency cutoff frequency of the effective signal of the compensated gather. The degree of deviation between the upper limit of the main frequency and the lower limit of the main frequency of the compensated gather is recorded as the actual bandwidth of the compensated gather, and a mapping relationship is established between the actual bandwidth of the compensated gather and the energy balance threshold adjustment coefficient of the gather. If the actual bandwidth of the compensated gather is less than or equal to the first critical bandwidth, then the current actual bandwidth of the compensated gather is input into the mapping relationship between the actual bandwidth of the compensated gather and the gather energy balance threshold adjustment coefficient, and the gather energy balance threshold gain coefficient is output. The gather energy balance threshold gain coefficient and the gather energy balance threshold are combined to obtain the target gather energy balance threshold, so as to adapt to the energy balance determination requirements of narrow band gathers.

[0034] The process of validating and iteratively optimizing the results also includes: If the actual bandwidth of the compensated gather is within the critical bandwidth range, the current gather energy balance threshold is maintained to meet the conventional energy balance determination requirements of mid-band gathers. The critical bandwidth range represents the open interval between the first critical bandwidth and the second critical bandwidth. If the actual bandwidth of the compensated gather is greater than or equal to the second critical bandwidth, then the current actual bandwidth of the compensated gather is input into the mapping relationship between the actual bandwidth of the compensated gather and the gather energy balance threshold adjustment coefficient, and the gather energy balance threshold reduction coefficient is output. The gather energy balance threshold reduction coefficient and the gather energy balance threshold are combined to obtain the target gather energy balance threshold, so as to adapt to the high-resolution energy balance determination requirements of wideband gathers. The target gather energy balance threshold is used as the basis for judging the energy balance in the compensation effect verification. If the actual energy balance of the gather after compensation is less than the target gather energy balance threshold, the energy balance is judged to meet the standard; otherwise, it is judged to not meet the standard and the iterative optimization process is started.

[0035] In this embodiment, firstly, the effective signal spectral features are extracted from the seismic gathers after staged adaptive amplitude compensation. Spectral analysis techniques are used to accurately capture the frequency distribution range of the effective signal in the compensated gathers, thereby determining the upper and lower limits of the dominant frequency of the compensated gathers. The lower limit of the dominant frequency corresponds to the low-frequency cutoff frequency of the effective signal, and the upper limit corresponds to the high-frequency cutoff frequency of the effective signal. This achieves precise definition of the frequency boundaries of the compensated effective signal, providing a reliable frequency dimension basis for subsequent bandwidth calculation and energy balance determination, avoiding deviations in subsequent determination results due to ambiguity in the frequency range. Subsequently, the degree of deviation between the upper and lower limits of the dominant frequency of the compensated gathers is calculated, and this deviation is... The difference is defined as the actual bandwidth of the compensated gather. A mapping relationship is established between the actual bandwidth of the compensated gather and the gather energy balance threshold adjustment coefficient. This mapping relationship enables precise correlation between bandwidth and threshold adjustment, allowing for adaptive matching of corresponding threshold adjustment strategies based on different bandwidth characteristics. This breaks the limitations of traditional fixed threshold determination and improves the adaptability of energy balance determination. Next, based on the comparison between the actual bandwidth of the compensated gather and the preset critical bandwidth, a differentiated target gather energy balance threshold determination process is executed: if the actual bandwidth of the compensated gather is less than or equal to the first critical bandwidth, it indicates that the gather belongs to the narrow bandwidth type. The current actual bandwidth is input into the above mapping relationship, and the corresponding gather energy balance threshold gain coefficient is output. This gain coefficient is combined with the initial gather energy balance threshold (e.g., multiplication and superposition) to obtain the target gather energy balance threshold. The gain is adjusted to adapt the threshold to the energy distribution characteristics of the narrow-band gather, ensuring the accuracy of the narrow-band gather energy balance determination and avoiding misjudgments due to excessively high or low thresholds. If the actual bandwidth of the compensated gather is in the open interval between the first and second critical bandwidths (i.e., the critical bandwidth interval), it indicates that the gather belongs to the mid-band type. In this case, the current gather energy balance threshold is kept unchanged, and its technical effect is... If the energy distribution of the mid-band gather is relatively stable, no additional threshold adjustment is needed to meet the requirements of conventional energy balance determination, balancing determination efficiency and accuracy; if the actual bandwidth of the gathered after compensation is greater than or equal to the second critical bandwidth, it indicates that the gather belongs to the wideband type. At this time, the current actual bandwidth is input into the mapping relationship, and the corresponding gather energy balance threshold reduction coefficient is output. The reduction coefficient is combined with the initial threshold to obtain the target gather energy balance threshold. By reducing and adjusting, the threshold is adapted to the high-resolution energy distribution requirements of the wideband gather, accurately identifying the energy imbalance region in the wideband gather, and ensuring the reliability of energy balance determination in high-resolution processing scenarios.Finally, the determined target gather energy balance threshold is used as the core criterion for judging energy balance in the compensation effect verification. The actual energy balance of the compensated gather is detected. If the actual energy balance of the compensated gather is less than the target gather energy balance threshold, the gather's energy balance is deemed to meet the standard, indicating that the phased adaptive amplitude compensation effect meets expectations. Conversely, if the actual energy balance is greater than or equal to the target threshold, the energy balance is deemed to have failed to meet the standard, and an iterative optimization process is immediately initiated to adjust and optimize the parameters of the phased adaptive amplitude compensation. This iterative verification mechanism forms a closed-loop processing flow of "compensation-judgment-optimization," continuously improving the amplitude compensation effect and energy balance of seismic gathers, providing high-quality data support for subsequent seismic data interpretation (such as reservoir prediction and structural identification).

[0036] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0037] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0038] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0039] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, one or more processors can also be operated to support the performance of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors), where these operations are accessible via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application programming interfaces (APIs)).

[0040] The performance of certain operations can be distributed across processors, residing not only within a single machine but also deployed across multiple machines. In some example embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., within a home environment, office environment, or server cluster). In other example embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0041] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0042] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0043] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. An adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition, characterized in that, Includes the following steps: Step 101: Preprocess the raw seismic gathers obtained from seismic exploration to avoid interference from acquisition errors and near-surface propagation static time difference on the seismic signal, and obtain the preprocessed seismic gathers. Simultaneously, extract multi-domain basic information from the preprocessed seismic gathers. Step 102: Based on the extracted multi-domain basic information, collaboratively obtain the core parameters required for seismic signal amplitude compensation in order to reduce the ambiguity and bias of single-domain parameter estimation and ensure the accuracy and adaptability of the compensation core parameters. The core parameters include, but are not limited to, the common shot point domain, the common receiver point domain, and the common offset domain. Step 103: Perform phased adaptive amplitude compensation on the preprocessed seismic gather based on the core parameters to achieve accurate compensation for geometric attenuation, viscoelastic absorption attenuation and lateral energy difference of seismic signal, while taking into account both compensation accuracy and data stability. The phased adaptive amplitude compensation includes diffusion compensation, absorption compensation and adaptive gain compensation. Step 104 involves verifying and iteratively optimizing the seismic gathers after phased adaptive amplitude compensation to ensure that the compensated seismic data meets the requirements of subsequent geological interpretation and reservoir prediction, and to guarantee the reliability and adaptability of the compensation effect.

2. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 1, characterized in that, The multiple domains include the time domain, frequency domain, spatial domain, and imaging domain; A multi-domain parallel analytical algorithm is used to extract multi-domain basic information from the preprocessed seismic gathers. In the time domain, the amplitude envelope, instantaneous frequency, and instantaneous phase characteristics of the seismic signal are extracted by Hilbert transform. The time-varying energy decay law is statistically analyzed by using a sliding time window. In the frequency domain, the preprocessed seismic gather is decomposed into time-frequency characteristics by generalized S-transform to obtain energy distribution features, including but not limited to full-band amplitude spectrum and phase spectrum. In the spatial domain, the seismic gather head information is analyzed to extract the shot-receiver distance, azimuth, common center point coordinates, common shot point identifier, and common receiver point identifier, and the energy lateral variation law of seismic signals in different spatial domains is statistically analyzed. Preliminary migration imaging is performed in the imaging domain using the pre-stack time migration method, and the underground stratum velocity field and reflective interface morphology information are obtained by inversion. This enables the synchronous extraction of basic information in the time domain, frequency domain, spatial domain, and imaging domain, and achieves accurate matching and association of information in multiple domains.

3. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 1, characterized in that, The specific steps of the diffusion compensation are as follows: For each data point in the seismic trace set, the instantaneous amplitude envelope and instantaneous frequency are obtained. For the data point to be analyzed, its instantaneous amplitude envelope is compared with the amplitude envelope reference value to obtain the instantaneous amplitude mean square error. A mapping relationship is established between the instantaneous amplitude mean square error and the instantaneous frequency adjustment. The instantaneous frequency of the seismic signal is dynamically adjusted based on the instantaneous amplitude mean square error, specifically as follows: If the instantaneous amplitude mean square error is less than the critical lower limit of the mean square error, it indicates that the amplitude decay is normal, and no instantaneous frequency adjustment is performed. If the instantaneous amplitude mean square error is within the critical range of mean square error, then the instantaneous amplitude mean square error and the instantaneous frequency adjustment amount are positively correlated and monotonically increasing. The current instantaneous amplitude mean square error is input into the mapping relationship between the instantaneous amplitude mean square error and the instantaneous frequency adjustment amount, and the instantaneous frequency gain is output to increase the instantaneous frequency to compensate for high frequency loss. The critical range of mean square error represents the closed interval formed by the critical lower limit of mean square error and the critical upper limit of mean square error.

4. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 3, characterized in that, The method of dynamically adjusting the instantaneous frequency of seismic signals based on the mean square error of instantaneous amplitude also includes: If the instantaneous amplitude mean square error is greater than the critical upper limit of the mean square error, the instantaneous frequency adjustment amount is limited to prevent excessive and unstable frequency adjustment in strong noise or abnormal areas. The current instantaneous amplitude mean square error is input into the mapping relationship between the instantaneous amplitude mean square error and the instantaneous frequency adjustment amount, and the instantaneous frequency reduction amount is output. Based on the instantaneous frequency adjustment amount, the instantaneous frequency of the current seismic signal is adjusted, specifically as follows: If the instantaneous frequency gain is output, the instantaneous frequency of the current seismic signal is superimposed with the instantaneous frequency gain to obtain the instantaneous frequency of the target seismic signal; If the instantaneous frequency reduction is output, the instantaneous frequency of the current seismic signal is superimposed with the instantaneous frequency reduction to obtain the instantaneous frequency of the target seismic signal.

5. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 1, characterized in that, The specific steps of the absorption compensation are as follows: The effective time window of the preprocessed seismic gather is extracted as a time interval. The time interval represents the closed interval formed by the start time point corresponding to the end of the first arrival wave in the gather and the end time point corresponding to the end of the effective deep reflection wave in the gather. The effective time window of the gather is divided into continuous and non-overlapping sub-effective time windows according to a preset time step. The viscoelastic attenuation coefficient corresponding to each sub-effective time window is obtained. The initial maximum allowable phase rotation amount within the effective time window of the gather is set. The initial maximum allowable phase rotation amount is the basic phase rotation threshold in the viscoelastic absorption attenuation compensation process of seismic waves. Establish a mapping relationship between the viscoelastic decay coefficient and the phase rotation correction coefficient for each of the sub-effective time windows; If the viscoelastic decay coefficient of each sub-effective time window is less than the critical lower limit of the reduction coefficient, then the current viscoelastic decay coefficient is input into the mapping relationship between the viscoelastic decay coefficient and the phase rotation correction coefficient of each sub-effective time window, and the phase rotation reduction coefficient is output. If the viscoelastic decay coefficient of each of the sub-effective time windows is within the critical range of the reduction coefficient, then the phase rotation correction reference coefficient is output. The critical range of the reduction coefficient represents the closed interval formed by the lower critical limit of the reduction coefficient and the upper critical limit of the reduction coefficient.

6. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 5, characterized in that, The absorption compensation also includes: If the viscoelastic decay coefficient of each sub-effective time window is greater than the critical upper limit of the reduction coefficient, then the current viscoelastic decay coefficient is input into the mapping relationship between the viscoelastic decay coefficient and the phase rotation correction coefficient of each sub-effective time window, and the phase rotation gain coefficient is output. The maximum allowable phase rotation is dynamically adjusted based on the phase rotation correction coefficient, specifically as follows: If the output phase rotation reduction coefficient is used, then the phase rotation reduction coefficient is combined with the maximum permissible phase rotation reference value to obtain the target maximum permissible phase rotation value. If the output phase rotation correction reference coefficient is used, then the phase rotation correction reference coefficient is combined with the maximum permissible phase rotation reference value to obtain the target maximum permissible phase rotation value. If the output phase rotation gain coefficient is used, then the phase rotation gain coefficient is combined with the maximum allowable phase rotation reference value to obtain the target maximum allowable phase rotation value.

7. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 1, characterized in that, The specific steps of the adaptive gain compensation are as follows: The length of the gain sliding window adapted after diffusion compensation and absorption compensation of the preprocessed seismic gather is extracted, and the mean effective signal amplitude of the seismic gather is obtained. The mean effective signal amplitude is used as the reference value of the effective gain amplitude threshold. The pre-defined gain sliding window length is divided into a graded critical range, including a first critical length and a second critical length. The first critical length is the window length threshold adapted to the shallow short reflection in-phase axis, and the second critical length is the window length threshold adapted to the deep long reflection in-phase axis. A mapping relationship is established between the gain sliding window length and the effective amplitude threshold correction coefficient. If the gain sliding window length is less than or equal to the first critical length, the gain sliding window length is input into the mapping relationship between the gain sliding window length and the effective amplitude threshold correction coefficient, and the effective amplitude threshold reduction coefficient is output. The effective amplitude threshold reduction coefficient and the effective gain amplitude threshold are combined to obtain the target effective gain amplitude threshold, so as to adapt to the weak signal and noise separation requirements of shallow short windows.

8. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 7, characterized in that, The adaptive gain compensation also includes: If the length of the gain sliding window is within the critical length range, the effective amplitude threshold reference coefficient is maintained, and the effective amplitude threshold reference coefficient is combined with the effective amplitude threshold of the gain to obtain the target effective amplitude threshold, so as to adapt to the effective signal amplitude characteristics of the medium-length window in the middle layer. The critical length range represents the open interval formed by the first critical length and the second critical length. If the gain sliding window length is greater than or equal to the second critical length, the gain sliding window length is input into the mapping relationship between the gain sliding window length and the effective amplitude threshold correction coefficient, and the effective amplitude threshold gain coefficient is output. The effective amplitude threshold gain coefficient and the effective amplitude threshold are combined to obtain the target effective amplitude threshold, so as to adapt to the strong attenuation signal energy characteristics of deep long windows. After dynamically assigning the effective gain amplitude threshold under different gain sliding window lengths, the dynamically adjusted target effective gain amplitude threshold is used as the effective execution criterion for the gain boosting operation within the corresponding gain sliding window. When the signal amplitude within the gain sliding window is lower than the target effective gain amplitude threshold corresponding to the window, the gain boosting operation for that window is stopped.

9. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 1, characterized in that, The specific steps for verifying and iteratively optimizing the seismic gathers after staged adaptive amplitude compensation are as follows: The effective signal spectrum features of the seismic gather after staged adaptive amplitude compensation are extracted to determine the upper and lower limits of the dominant frequency of the compensated gather. The lower limit of the dominant frequency of the compensated gather is the low-frequency cutoff frequency of the effective signal of the compensated gather, and the upper limit of the dominant frequency of the compensated gather is the high-frequency cutoff frequency of the effective signal of the compensated gather. The degree of deviation between the upper limit of the main frequency and the lower limit of the main frequency of the compensated gather is recorded as the actual bandwidth of the compensated gather, and a mapping relationship is established between the actual bandwidth of the compensated gather and the energy balance threshold adjustment coefficient of the gather. If the actual bandwidth of the compensated gather is less than or equal to the first critical bandwidth, then the current actual bandwidth of the compensated gather is input into the mapping relationship between the actual bandwidth of the compensated gather and the gather energy balance threshold adjustment coefficient, and the gather energy balance threshold gain coefficient is output. The gather energy balance threshold gain coefficient and the gather energy balance threshold are combined to obtain the target gather energy balance threshold, so as to adapt to the energy balance determination requirements of narrow band gathers.

10. The adaptive amplitude compensation method for seismic signals based on multi-domain joint parameter acquisition as described in claim 9, characterized in that, The process of verifying the effects and iteratively optimizing also includes: If the actual bandwidth of the compensated gather is within the critical bandwidth range, the current gather energy balance threshold is maintained to meet the conventional energy balance determination requirements of mid-frequency gathers. The critical bandwidth range refers to the open interval between the first critical bandwidth and the second critical bandwidth. If the actual bandwidth of the compensated gather is greater than or equal to the second critical bandwidth, then the current actual bandwidth of the compensated gather is input into the mapping relationship between the actual bandwidth of the compensated gather and the gather energy balance threshold adjustment coefficient, and the gather energy balance threshold reduction coefficient is output. The gather energy balance threshold reduction coefficient and the gather energy balance threshold are combined to obtain the target gather energy balance threshold, so as to adapt to the high-resolution energy balance determination requirements of wideband gathers. The target gather energy balance threshold is used as the basis for judging the energy balance in the compensation effect verification. If the actual energy balance of the gather after compensation is less than the target gather energy balance threshold, the energy balance is judged to meet the standard; otherwise, it is judged to not meet the standard and the iterative optimization process is started.

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