Seismic impedance inversion is a fundamental task in exploration geophysics. With the development of artificial intelligence, deep learning has been increasingly adopted for post-stack seismic impedance inversion. This paper proposes a frequency-aware deep hybrid framework that integrates continuous wavelet transform (CWT) representations, a transformer encoder, and atrous spatial pyramid pooling (ASPP) to improve both impedance fidelity and resolution. The CWT provides time–frequency components of seismic traces; the transformer aggregates non-local context to stabilize structural consistency; and ASPP captures local multi-scale features. In addition, we introduce a frequency-aware loss that rebalances optimization toward components that are more difficult to reconstruct, improving detail recovery in thin beds. Network training used a semi-supervised strategy. At well locations, well-log impedance values supervised the network prediction; at non-well locations, synthetic seismic data were generated by seismic forward modeling and compared with the observed seismic records. Validation on the Marmousi II model and a field dataset indicated that the proposed method achieves consistently higher accuracy than representative deep-learning baselines and a commercial post-stack inversion workflow.
Suppressing random noise is a critical component in seismic data processing, exerting a pivotal role in enhancing the signal-to-noise ratio and the overall quality of seismic data. To effectively attenuate noise and retain fine details, a novel non-local weighted structure tensor total variation (NLWSTV) model is presented for seismic data noise reduction. This model effectively combined the local structural regularity and non-local self-similarity characteristics of seismic data. Specifically, we developed an anisotropic weighted matrix that allocated different weights to the discrete gradients in the horizontal and vertical directions to accurately grasp local features of seismic data. In parallel, we integrated a non-local version of the structure tensor total variation model harmoniously with the anisotropic weighted matrix to thoroughly investigate non-local feature information across the entire seismic data. To efficiently tackle the NLWSTV model, we adopted an alternating direction method of multipliers-based optimization algorithm. Comparative experiments clearly showed the effectiveness and superiority of the presented approach, especially in eliminating random noise while better preserving the fine details of geological structures.
In multi-parameter anisotropic full-waveform inversion (FWI) for surface seismic data, both the far-offset and near-offset data are required to obtain accurate estimations of anisotropic properties. However, in acoustic transversely isotropic media with a vertical symmetry axis (vertical transverse isotropy [VTI]), the near-offset data are insensitive to the high-resolution features of the two anisotropy parameters ε and δ in the typical parameterization (vp, ε, δ), which results in the corresponding low-resolution reconstruction in acoustic anisotropic FWI. To improve performance in surface seismic FWI, we propose a novel parameterization for acoustic VTI media using the Thomsen parameters ε and δ, along with a relevant parameter Cs (associated with the combination of vertical velocity and the Thomsen parameters). Using the Born approximation, we derive analytical expressions for the radiation patterns under this new parameterization and compare them with those under different parameterizations. The radiation patterns are imperative for indicating the angular influence of the perturbation on the parameters. This indicates that introducing the new parameter Cs is crucial for improving the contributions of ε and δ to near-offset data. We also derive the corresponding three-dimensional gradients and adjoint wave equations for parameterization (Cs, ε, δ) in acoustic VTI media. Furthermore, we develop multi-parameter point spread functions to numerically evaluate sensitivity during inversion. As a result, we can perform acoustic VTI FWI using the parameterization (Cs, ε, δ), which is optimal for inverting both the low- and high-wavenumber components of the parameters (Cs, ε, and δ). Finally, we apply the synthetic data to test the accuracy and effectiveness of our multi-parameter acoustic VTI FWI algorithm. The results show that the proposed parameterization has the potential to invert near-offset records to high-resolution anisotropy parameters in an active-source seismic experiment.
This study presents an innovative mathematical framework operating entirely in the time–space (t-x) domain for pre-stack seismic noise attenuation and wavefield separation, establishing a modified Ladjadj method tailored for automated, high-fidelity wavelet extraction. Driven by a non-destructive matrix optimization algorithm pairing spatial trace windows with an empirical comparison rate consensus, the technique successfully addresses severe multi-modal noise fields and complex wavefield interferences. Methodological validation on highly contaminated synthetic datasets demonstrates the framework’s capability to isolate overlapping primary reflections and suppress complex noise without distorting the desired signal. When applied to conventional real production data, this empirical comparison rate-conditioned gating mask suppresses random and coherent noise while preserving structural horizon integrity and relative amplitude signatures, thereby eliminating the operational reliance on user-dependent manual top muting, structural trace re-sorting, or cumulative multi-domain transformation errors. Crucially, by demonstrating its robustness in handling overlapping signals, this framework establishes a fully automated approach for seismic wavefield deblending. While validated on conventional surveys, this framework provides a potential pathway for future seismic processing challenges, including multi-source high-productivity vibroseis acquisitions, spatial acquisition footprint reduction, and complex three-dimensional azimuthal imaging. By preserving subtle anisotropic variations in amplitude-versus-offset and amplitude-versus-azimuth responses that are relevant to fractured reservoir characterization, this research connects applied mathematics and advanced exploration geophysics, offering a potential tool for next-generation industrial seismic processing.
Structural stress perturbation in salt formations is important for salt-cavern site selection, cavern stability, and underground storage safety, but direct stress measurements are usually sparse and numerical geomechanical modeling depends strongly on model parameters and boundary conditions. This study proposes a seismic curvature-based method for estimating local structural stress perturbation in finite-thickness salt layers. The method uses interpreted top and bottom salt-layer horizons to construct the middle surface and thickness field, calculates the maximum and minimum principal curvatures, and introduces Poisson coupling and a thickness-dependent correction factor to build a normalized curvature-derived structural stress index. Synthetic model tests show that the proposed index is controlled not only by curvature anomalies, but also by principal-curvature coupling, salt-layer thickness, and finite-thickness correction. Mechanical validation indicates that the index is spatially consistent with the main stress concentration patterns obtained from theoretical and elastic solutions, while sensitivity analysis shows that the major high-value zones remain relatively stable under parameter variations. Field applications demonstrate that the proposed method can generate continuous stress concentration zoning maps from seismic salt-layer horizons. The method provides a rapid seismic–geometry-constrained tool for identifying potential stress-sensitive zones and supporting salt-cavern site evaluation, cavern layout optimization, monitoring deployment, and subsequent geomechanical modeling.
The controlled accurate seismic source (CASS), with its advantages of minimal impact on the deployment site and high repeatability, provides a possible technical approach for high-precision dynamic monitoring of regional-scale crustal structure and physical parameters. However, the continuous signal generated by CASS rapidly attenuates with increasing propagation distance. Unlike processing waveforms from impulsive events such as earthquakes or explosive sources, identifying seismic phases from CASS sources remains a challenging task. This challenge prevents the wide application of CASS sources. In this paper, we design a time-varying narrowband filter and apply it to the CASS source frequency correction model. From the filtering results, the model can effectively correct the frequency sweep curve. In addition, we develop a new global seismic phase scanning (GSPS) algorithm to identify seismic phases propagating through the lithosphere. We processed the seismic data from the 40-ton CASS during the field experiment around the Xinfengjiang reservoir. The results obtained with the GSPS method are compared with theoretical phase travel times, and the proposed algorithm clearly yields the seismic phase distribution. Meanwhile, leveraging the narrowband characteristics of the designed time-varying filter, we innovatively achieve effective separation of seismic phases. The proposed GSPS algorithm achieves O (N×M) time complexity, where N is the number of time shifts, and M is the processing window length, providing efficient seismic phase detection with linear scaling in analysis duration. Furthermore, the algorithm applied to CASS data offers valuable insights for processing linear frequency modulation (LFM) signals in other research fields.
Bayesian amplitude variation with angle (AVA) inversion provides a probabilistic way to estimate elastic parameters and quantify uncertainty, but conventional Metropolis–Hastings Markov chain Monte Carlo may mix slowly in multimodal posterior distributions. This study develops a nonlinear AVA inversion workflow combining an improved parallel tempering Markov chain Monte Carlo scheme with the exact Zoeppritz equations. Multiple temperature chains explore the posterior distribution, and probabilistic state exchanges improve communication among chains. The exact Zoeppritz equations serve as the forward operator, reducing errors from linearized approximations, especially at relatively large incidence angles or strong elastic contrasts. Synthetic tests, including noisy angle gathers and a Marmousi model, show that the proposed workflow recovers P-wave velocity, S-wave velocity, and density with higher correlation coefficients than conventional inversion. A field application to a sandstone–mudstone reservoir in the Tarim Basin, northwestern China, further demonstrates improved lateral continuity, with correlation coefficients between the inverted and well-log P-wave velocity, S-wave velocity, and density increasing from 0.79, 0.76, and 0.74 for conventional inversion to 0.88, 0.84, and 0.81, respectively. These results suggest that the proposed method provides a useful probabilistic framework for nonlinear elastic-parameter inversion, although its computational cost remains higher than that of deterministic inversion methods.
Marine deep seismic exploration is a core technique for imaging deep submarine structures and supporting marine resource exploration. This paper reviews recent advances in the acquisition, processing, and integrated interpretation of marine deep seismic exploration. Major acquisition technologies include large-source long-streamer, wide-line, ocean bottom seismometer (OBS), dual-ship acquisition, and marine distributed acoustic sensing. These methods differ in penetration depth, resolution, operational efficiency, and cost: streamer systems provide high-resolution shallow-to-mid-crust imaging, while OBS enables robust wide-angle refraction recording for deep-velocity modeling. Processing advances focus on noise and multiple suppression, ghost elimination, broadband reconstruction, high-precision velocity modeling, and Moho imaging. Inversion methods include traveltime tomography, full-waveform inversion, and multi-parameter joint inversion, which provide quantitative constraints on crustal velocity and lithology. Integrated interpretation combines seismic with gravity, magnetic, magnetotelluric, and geological data to reduce non-uniqueness. This review clarifies technical strengths, limitations, and suitable scenarios of each method. Future development will move toward broadband, intelligent, autonomous, and multi-physics integrated systems for deeper, higher-precision imaging of oceanic lithospheric structures.
Seismic random noise degrades data quality and obscures reflection events critical for exploration interpretation. Existing convolutional neural network-based denoisers, including ADNet, suffer from limited receptive fields and single-scale attention, causing poor long-range reflection continuity and signal leakage at low signal-to-noise ratio (SNR). To address these challenges, we propose multi-feature-enhanced (MFE)-ADNet, a novel framework that integrates a pyramid spatial attention (PSA) module into ADNet. To the best of our knowledge, this is the first application of PSA for seismic random noise attenuation. The PSA module enables multi-scale spatial feature extraction and captures long-range channel dependencies, complementing ADNet’s attention-guided denoising mechanism to preserve weak reflections while suppressing incoherent noise. Experimental results on synthetic and field datasets demonstrated that MFE-ADNet gains of 12–15 dB relative to the original noisy data. Compared with ADNet, it provides an additional 3 dB SNR improvement and reduces the mean squared error by 0.0015, indicating substantially lower residual signal energy and reduced signal leakage. The method also attained a local similarity of 0.86–0.98, outperforming wavelet denoising, time–frequency peak filtering, and ADNet by substantial margins. Residual analysis confirms minimal signal leakage, validating the method’s reliability for practical seismic processing and subsequent geological interpretation.
With the continuous advancement of oil and gas exploration, the focus has gradually shifted from conventional structural reservoirs to lithologic reservoirs. However, lithologic reservoirs are often deeply buried and thinly layered, exhibiting weak seismic responses. In addition, strong lateral heterogeneity in their spatial distribution makes them increasingly subtle and difficult to characterize, posing significant challenges for reservoir detection. To address the problem of accurately detecting subtle reservoirs, this study proposes a subtle reservoir detection workflow based on the fluid mobility attribute derived from the frequency-corrected generalized S-transform (FCGST). The workflow utilizes the high-resolution and high-precision time–frequency spectrum generated by FCGST to extract fluid mobility attributes from seismic data, thereby effectively characterizing the spatial distribution of subtle reservoirs. The effectiveness of the proposed workflow is validated using both synthetic models and field seismic data from the Permian Maokou Formation in the Sichuan Basin. Compared with conventional generalized S-transform-based approaches, the proposed method reduces the dominant-frequency estimation error from approximately 30% to less than 3% under different signal-to-noise ratio (SNR) conditions in synthetic tests, while significantly improving the spatial focusing of reservoir-related anomalies. In synthetic tests under noisy conditions (SNR as low as 5 dB), the FCGST-based attribute exhibits enhanced robustness and maintains clearer delineation of reservoir boundaries. The proposed workflow demonstrates strong potential for practical exploration applications, providing effective support for subtle reservoir characterization, well placement, and horizontal well trajectory optimization.
Accurate characterization of low-order faults and sand bodies in complex faulted basins remains a core challenge in the exploration of subtle oil and gas reservoirs. Conventional seismic techniques are constrained by resolution bottlenecks and cannot meet exploration demands. The advancement of optical fiber distributed acoustic sensing (DAS) technology provides a new avenue for high-density surface–borehole joint acquisition. This study presents a bidirectional-driven high-resolution processing workflow optimized for DAS-enabled surface–borehole joint seismic data acquired in the complex fault systems of faulted basins. The proposed technology establishes an “Eight Unifications” surface-processing framework, defines the “Four Determinacies” core processing steps, and builds a “Three Synchronous Joint” surface–borehole joint bidirectional-driven processing flow to enable interactive constraints and iterative optimization of borehole and surface seismic data. Applied to the Cenozoic rift basin in eastern China, this workflow simultaneously delivered three-dimensional vertical seismic profile (3D VSP) and surface seismic imaging data. The 3D VSP imaging achieved a dominant frequency of 45 Hz and a bandwidth of 3–85 Hz, enabling identification of low‑order faults with throws ≥5 m within 4 km of the well. Surface seismic imaging yielded a dominant frequency of 35 Hz and a bandwidth of 4–68Hz, supporting continuous tracking of a 10–20 m throw fault in the work area. Field applications demonstrated that this technology significantly improves the precision of complex fault system characterization, establishes a high‑resolution surface–borehole joint exploration model tailored for rift basins, and provides a replicable technical template for oil and gas exploration in structurally complex areas.
Acoustic emission (AE) monitoring is an effective means of investigating the characteristics of fracture surfaces in rocks, and accurate clustering analysis is essential for reliable extraction of fracture-surface information. Rock AE events cluster strongly in space and time, but physically distinct event populations—such as a main fracture and its co-located microcracking—often overlap spatially and differ chiefly in magnitude, so conventional clustering methods constrained only by space and time cannot separate them. In this study, a magnitude-constrained spatio-temporal hierarchical clustering algorithm (MSTC) is proposed. The algorithm introduces a magnitude weighting factor into the spatio-temporal distance metric of an agglomerative hierarchical clustering procedure, so that events close in space and time and similar in magnitude are preferentially grouped, and a minimum-cluster-size filter suppresses background noise. Validation on synthetic data shows that MSTC recovers the correct clusters with an adjusted Rand index of 0.97 over a broad, stable parameter range, whereas the magnitude-free algorithm and the established spatio-temporal density-based spatial clustering of applications with noise and nearest-neighbor methods achieved an adjusted Rand index of 0.82 and fail to separate spatially interleaved, magnitude-distinct populations. Applied to AE data from a triaxial-compression experiment on a partly water-saturated tight sandstone, the algorithm resolves the main fracture, its secondary branch, the terminal fracture network, and off-fault damage; the extracted clusters agree well with the fracture morphology revealed by X-ray computed tomography (CT) scanning, and the strike of the fracture surface fitted from the main-fracture cluster shows only a small deviation from the CT-derived measurement. This study provides a new method for laboratory-scale rock-fracture analysis, and because the algorithm operates on a standard time–location–magnitude event catalog, the workflow extends directly to microseismic monitoring of hydraulic fracturing in unconventional reservoirs—supporting fracture-plane identification, fracture network delineation, and noise rejection—and to rock-mass stability monitoring in underground engineering.
Time-lapse seismic monitoring plays a critical role in tracking the evolution of carbon dioxide (CO2) plumes in geological storage. Most existing studies focus primarily on P-wave velocity and attenuation, while the contribution of S-wave responses is often neglected due to their relatively weak sensitivity to fluid saturation. However, seismic-wave propagation in viscoelastic media involves coupled compressional- and shear-wave responses, suggesting that S-wave–related effects may still influence time-lapse imaging and interpretation. In this study, we developed an integrated framework for CO2 time-lapse monitoring by combining rock-physics modeling, viscoelastic wavefield simulation, and multicomponent reverse time migration. The framework accounts for both velocity and attenuation effects and enables a systematic investigation of S-wave responses, including S-wave velocity and attenuation (QS). To accurately describe wave propagation in such media, we derived a generalized standard linear solid-based viscoelastic wave equation with an explicit representation of the quality factor (Q), and employed reverse time migration to generate time-lapse seismic images. Using synthetic models, we analyzed the sensitivity of seismic attributes to CO2 saturation and evaluated the impact of different QS modeling strategies on time-lapse imaging results. The results show that different QS assumptions lead to noticeable variations in amplitude and phase behavior, which can significantly affect the interpretation of CO2-induced changes. These findings demonstrate that S-wave responses, particularly S-wave attenuation, should not be neglected in viscoelastic time-lapse seismic analysis. The proposed framework provides a physically consistent approach to improving the reliability of CO2 monitoring.
Irregular topography can generate out-of-plane signals (OPS) on seismic sections, interfering with the imaging of the true seafloor directly beneath the survey line. While acquiring three-dimensional data or using specialized sensors can mitigate this, these options are often costly or unavailable, especially for legacy surveys. To efficiently remove OPS from two-dimensional (2D) data, this study investigates the validity of using a neural network (NN) for picking and muting. First, we demonstrate the limitation of conventional frequency-wavenumber domain directional filtering due to the kinematic similarity between OPS and true seafloor reflections. Then, we present a workflow that employs a cascade-correlation learning algorithm to identify and mute OPS arrivals before the first break. Unlike data-intensive deep learning techniques that require large training datasets, this lightweight NN is trained on userpicked examples of true seafloor reflections, enabling it to distinguish OPS events arriving from outside the vertical survey plane. Application of this technique to a 2D line acquired near irregular seafloor topography in the Ulleung Basin demonstrates the true seafloor reflector and the removal of false offline signals. Qualitative and quantitative validation against an independent external bathymetric reference both showed a reduction in travel time error compared to the raw data, confirming the effectiveness of the picking results. The results highlight that a cascade-correlation NN-based picking and muting can efficiently suppress OPS in cases of irregular topography on 2D seismic data.
In recent years, traditional geophones for well seismic data acquisition have progressively been replaced by distributed acoustic sensing (DAS), a novel technique. The primary attributes of DAS are its extensive well coverage and robust adaptability to challenging acquisition situations. Unlike conventional geophones, vertical seismic profile (VSP) data obtained using DAS exhibit lower signal-to-noise ratios (SNRs) and more complex noise types. These complex and energetic perturbations pose challenges for further data analysis. Contemporary methods for mitigating noise in DAS-VSP data sometimes fail to yield complete and precise information, leading to inferior denoising quality and diminished signal recovery. We propose a hierarchical division encoder-decoder network utilizing a convolutional neural network to address this issue. This network employs spatial attention techniques for systematic reconstruction and facilitates hierarchical feature extraction according to while preserving signal integrity. It achieves this by comprehensively addressing features at all scales. Additionally, we generated the required training set by combining synthetic data with real noise, as no publicly available training sets are available for DAS-VSP data. The trained denoising network processes and analyzes both synthetic and real recordings. The experimental results demonstrate the efficacy of this technique in eliminating various types of DAS-VSP noise while preserving signal amplitude integrity and ensuring continuity of signal recovery.
Accurate localization of weak seismic sources remains challenging in complex media, and conventional reverse time imaging is often sensitive to noise and wavefield interference. To address these issues, we propose a two-dimensional time-domain contrast-source reverse time imaging method based on scattering theory. Using a smoothed background model as a reference, the medium perturbations were reformulated as equivalent contrast-source terms. This formulation established an explicit physical link between the scattered wavefield and medium heterogeneity, enabling efficient simulation of scattering responses. Conventional cross-correlation imaging conditions are frequently dominated by high-energy channels and amplitude imbalance among receivers. We therefore developed a grouped cross-correlation imaging condition with energy normalization. This strategy suppresses the dominance of strong-energy channels and enhances coherent stacking across different receiver azimuths, leading to improved focusing and more stable source localization. Numerical experiments were conducted on a simple scatterer model and the Marmousi velocity model. The proposed method was compared with conventional finite-difference reverse time imaging under different noise levels and multi-source scenarios. Results demonstrate that the contrast-source-based approach provided clear advantages in characterizing weak scattering signals, noise robustness, and energy focusing. The proposed grouped energy-normalized imaging condition further improved imaging resolution and source detectability. These results indicate that the proposed method is stable and adaptable in complex media, offering a promising imaging framework for microseismic and other passive-source localization. The method also shows potential for extension to three-dimensional elastic wave equations and real field data.
The mudstone in a specific exploration area of the Yinggehai Basin is rich in organic matter and serves as a paradigmatic example of a low-velocity mudstone formation in rock physics. This type of interval exhibits seismic response characteristics similar to those of hydrocarbon reservoirs, which complicates the identification of gas reservoirs and the prediction of gas-bearing zones in this area. In this study, the analysis and modeling of rock physical characteristics within the exploration area are investigated. Based on the actual drilling curve and coring analysis data,a rock physics model of the low-velocity mudstone reservoir is established using self-consistent approximations and the Ciz-Gassmann model, and the influence of organic matter content on the mudstone's elastic properties is analyzed. Additionally, a robust quantitative inversion procedure is introduced to test the feasibility of inverting for porosity, clay content, and water saturation to mitigate the risk of low-velocity mudstone. The application of actual data, including core samples, logging data, and seismic database, demonstrates the effectiveness of this method and provides technical support for seismic prediction of sand body identification and hydrocarbon detection.
Deep coalbed methane (CBM) reservoirs are characterized by low porosity and low permeability. The complexity of hydraulic fracture networks controls reservoir stimulation efficiency, while the in-situ stress field controls fracture initiation and propagation. To address the challenges posed by the insensitivity of in-situ stress to elastic parameters and the limited accuracy of conventional seismic inversion methods, this study proposes an integrated workflow comprising “rock mechanical parameter inversion—heterogeneous modeling—stress field simulation.” Taking deep coal seams in the northeastern Ordos Basin as the study area, spatially variable mechanical parameters, including Young’s modulus, Poisson’s ratio, and density, were obtained through pre-stack seismic direct inversion. These parameters were subsequently input into a finite element model to simulate the three-dimensional in-situ stress field of the No. 8 coal seam of the Taiyuan Formation. The results show good agreement between the simulated values and measured data from six wells, with relative errors for the maximum and minimum horizontal principal stresses below 4.6% and 6.9%, respectively. The study reveals the spatial heterogeneity characteristics of the in-situ stress field in the No. 8 coal seam and identifies areas with a horizontal stress difference of 3–8 MPa as favorable “sweet spots” for hydraulic fracturing. This method overcomes the limitation of conventional simulations that rely on well interpolation for mechanical parameter assignment, which inadequately captures heterogeneity, and achieves an integrated framework combining seismic exploration and geomechanical analysis, providing a reliable scientific basis for fracturing optimization and sweet spot prediction in deep CBM reservoirs.
Full-waveform inversion (FWI) imaging is a high-resolution seismic imaging technique that directly produces subsurface images by inverting the full recorded wavefield. However, its reliability is often limited by numerical dispersion errors arising from finite-difference (FD) forward modeling. One key approach for reducing dispersion is to optimize the FD coefficients using an optimization algorithm. However, conventional methods for optimizing FD weights focus only on reducing spatial dispersion, which can weaken numerical stability, especially when using large time steps (i.e., high Courant-Friedrichs-Lewy [CFL] numbers). To address this issue, we introduce a new optimization approach that improves both simulation accuracy and stability. The proposed method combines error functions from both the time-space domain and the spatial domain into a single adaptive objective function. A dynamic weighting factor, which depends on the CFL number, facilitates a trade-off between accuracy and stability of the optimal FD weights. We also use the seismic wavelet spectrum as prior information to constrain the optimization. The optimization problem is solved by the least-squares method. In the theoretical test, the proposed weights significantly reduce wavefield simulation errors across a wide range of wavenumbers, with a higher CFL number than conventional approaches. When applied to FWI, this method reduces phase distortion and local minima in the objective function. In a test using the Marmousi model at 40 Hz, our approach produced clear and continuous deep structures, closely matching results from dispersion-free benchmarks. In contrast, conventional methods failed due to severe dispersion.This work provides a more robust numerical foundation for high-frequency FWI imaging by improving both accuracy and stability.
Node seismometer signals are often contaminated with substantial environmental noise due to the complex conditions encountered in geophysical exploration and seismic monitoring. This necessitates high precision in the pre-processing of ground motion signals, as inadequate processing may compromise subsequent operations, such as P-wave first-arrival time extraction, peak energy calculation, ground motion period determination, and magnitude estimation. To obtain more authentic seismic waveforms, a node seismometer signal denoising model is proposed. This model integrates grey relational analysis (GRA) with improved complete ensemble empirical modal decomposition adaptive noise (ICEEMDAN). This method first decomposes the noisy signal using ICEEMDAN to obtain multiple intrinsic mode functions (IMFs), which are then sequentially arranged and labeled. Subsequently, for each IMF, the correlation coefficient, mutual information, R2, adjusted R2, Jensen-Shannon divergence, cosine similarity, root mean squared error, mean absolute error, mean absolute percentage error, and sample entropy were calculated, forming an evaluation matrix for assessing the reliability ofall IMFs. Finally, using GRA, the correlation coefficients and degrees of association between each evaluation metric and different IMF components were calculated. The IMF components were ranked based on their association degrees to determine the relative effectiveness of their signal components. Linear reconstruction on the top-ranked IMF components was performed to complete the signal denoising process proposed. Experiments on denoising simulated seismic signals, recorded seismic event signals, and recorded ground motion signals all demonstrate that the GRA-ICEEMDAN model outperforms classical denoising methods. The comprehensive denoising scores for the three experiments were 100, 98.0180, and 93.9056, respectively, with signal-to-noise ratio improvements reaching 24.0049 dB, 20.8926 dB, and 16.3523 dB, respectively. The model effectively distinguishes noise components from effective components, with minimal reconstruction errors and signal loss after original signal decomposition, making it suitable for seismic monitoring and geophysical exploration involving small-to-medium sample sizes.
