Attenuation plays a fundamental role in the study of stratiform media. The conventional method including amplitude versus offset (AVO) and frequency-dependent AVO (FAVO) based on Zoeppritz equation can account for viscoelastic effects, but the assumption is the reflection and transmission at the single interface, neglecting the interlayer attenuation of seismic waves. Based on the theory of wave propagation in the stratiform media, a viscoelastic prestack waveform inversion (PWI) is proposed to predict the attenuative parameters. The viscoelastic reflection matrix method (VRMM) incorporates the complex velocity to simulate the full waveform seismic responses capturing the interlayer attenuation and other propagation effects in the stratiform media. During the inversion process, an objective function under Bayesian framework is constructed to estimate the multiparameter by incorporating prior information. Modeling tests demonstrate that prestack angle gathers based on VRMM can account for both propagation effects and attenuative characteristics. Compared to elastic PWI, viscoelastic PWI can provide better predictions of model parameters. Synthetic inversion tests demonstrate that the feasibility and accuracy of the proposed method, which is robust to the initial models and resistant to noise. Field test results show that our method can successfully invert P-wave and S-wave quality factors, providing strong support for reservoir characterization.

The absence of low-frequency (LF) component in seismic records leads to non-uniqueness in inversion, making the building of a reasonable LF model crucial for achieving high-precision acoustic impedance inversion. Artificial intelligence (AI) techniques represented by Gated Recurrent Unit (GRU) have shown favorable performance in the building of LF model. However, the problems of neural network overfitting and poor generalization due to small sample sizes remain challenging. We propose a neural network framework for LF model prediction towards small sample sizes. The framework utilizes the GRU as the basic unit and fully considers the long-term dependence in the time series of the LF model to ensure the integrity of vertical long-wavelength information. In order to improve the horizontal continuity of the LF model, a spatio-temporal feature fusion module is designed to convert the multi-trace data into potential vectors containing spatial information. Moreover, the seismic envelope and the initial model serve as prior information to guide the neural network, which is helpful for the network to yield stable predictions. In the synthetic and field data testing phase, spatial perturbations are introduced on the original data, it can generate more diverse labeled data. Experimental results demonstrate the predicted model on the basis of the initial model exhibit richer frequency components, more complete geological structures, and greater consistency with seismic records, and the predicted LF model of acoustic impedance not only aligns with the structural and depositional patterns of the area but also matches the measurements from a validation well.

Full-waveform inversion (FWI) is a powerful tool for time-lapse seismic analysis, enabling high-resolution imaging of subsurface physical properties to monitor reservoir changes during injection, production, and long-term CO2 storage. However, conventional time-lapse FWI, which relies on a parallel inversion strategy, suffers from significant artifacts due to survey non-repeatability, disrupting convergence consistency between baseline and monitor inversions. Additionally, the high computational cost remains a major challenge. To address these limitations, we propose a novel time-lapse FWI strategy—the target-oriented (TO) common-model strategy (CMS)—which strategically integrates multiple approaches. Our method combines TO FWI, which enhances model convergence in the target region to improve time-lapse accuracy, with CMS, which reduces artifacts by using an optimized starting model to guide baseline and monitor inversions toward similar convergence paths. Additionally, we employ an amplitude-encoding multi-source strategy, significantly reducing computational costs without compromising inversion accuracy. Through extensive elastic tests, we validate the robustness and effectiveness of TO CMS, demonstrating superior performance over both the conventional parallel strategy and standard CMS across various challenging scenarios—including non-repeated source positions, random noise, seawater velocity variations, and biased initial models. Notably, strong noise and seawater velocity variations can significantly impact time-lapse FWI results, highlighting the need for further investigation. Ensuring consistent multi-source parameters in time-lapse FWI can help minimize artifacts.

We present a study of marine controlled-source electromagnetic (CSEM) inversion using joint constraints based on structural and petrophysical properties. Marine CSEM can directly evaluate the oil and gas distribution within a reservoir and improve drilling success rates. However, marine CSEM inversion suffers from significant non-uniqueness. Constrained inversion is an effective method to reduce non-uniqueness and improve resolution. There are two main approaches to constrained inversion: one involves applying constraints based on subsurface structural information, and the other relies on known petrophysical information. Current constrained inversion techniques typically utilize only one of these methods in isolation. In this study, we develop a joint constraint method that combines structural constraints with petrophysical property constraints for marine CSEM inversion. First, we obtain a high-precision velocity structure using seismic full-waveform inversion and introduce the velocity structure constraint into the objective function of marine CSEM inversion using a cross-gradient function. This enables marine CSEM inversion based on seismic velocity structure constraints. Additionally, during the iterative inversion process, we apply petrophysical property constraints using the fuzzy C-means clustering method. The joint constraint method integrates the advantages of both types of constraints, effectively improving inversion accuracy. Synthetic model analyses and field data applications demonstrate that marine CSEM inversion, guided by joint constraints from seismic velocity structures and petrophysical properties, not only enhances the imaging accuracy of resistivity structures but also recovers realistic resistivity properties. Furthermore, the fuzzy C-means clustering method sharpens the boundaries of inversion results, making them clearer and more interpretable for geological analysis.

The Gassmann fluid term, an important attribute for characterizing reservoir fluid variations, is widely used in seismic inversion for reservoir prediction and fluid identification. However, most existing inversion methods rely on first-order linear approximations of the reflection coefficient equation, ignoring nonlinear responses in complex geological settings, thereby limiting the accuracy of inversion results. To address this limitation, this study derives a quadratic reflection coefficient approximation equation that explicitly incorporates the Gassmann fluid term, by combining the Russell approximation with the quadratic PP-wave reflection coefficient equation. Based on this formulation, an inversion framework is developed using the quadratic approximation. The proposed method first uses the arctangent penalty function as a sparsity constraint, which enhances the overall convexity of the objective function. The Hadamard operator is then used to decompose the variables of the quadratic terms and reduce optimization complexity. Finally, the alternating direction method of multipliers (ADMM) algorithm is introduced to decompose the nonlinear optimization problem into multiple single-variable sub-problems, which are solved through alternating iterations. Model tests and field data applications show that the proposed approach enhances the accuracy of reservoir fluid identification and validates the effectiveness of the quadratic approximation strategy.

Acoustic impedance (AI) inversion method based on the assumption of spatially invariant seismic wavelets has been widely applied in reservoir prediction. In reality, seismic wavelets are relatively stable but also exhibit subtle spatial variations, particularly when inconsistencies arise between seismic data and reservoir heterogeneity. To address this issue, we propose an alternate iterative inversion method of AI and wavelet based on sparse constraints. This method assumes that both the wavelet matrix and the reflection coefficients are sparse. Based on the Lp norm, we establish objective functions for wavelet estimation and AI inversion with sparse regularization constraints of different forms, ensuring the stability of the inversion results during the iterative process. In the inversion process, the statistical wavelet is used as the initial wavelet input to obtain the AI; then, the AI results are used to update the wavelet, and the two parameters are iteratively updated in this way. Through this alternating iteration, spatial variations of the wavelet are effectively captured, eliminating discrepancies in waveform and frequency characteristics of seismic data in the horizontal direction due to nonimpedance variations. Finally, the effectiveness of the proposed method is validated through model testing and application to actual data. The proposed method can invert impedance results that are more consistent with logging data, and the estimated wavelets synthesized seismogram show a better match with the well bypass. In particular, the proposed method is more applicable to the case where it is difficult to use a single wavelet to calibrate all wells simultaneously during the well-seismic calibration in the work area with multiple well data.

Gravity and magnetic anomalies contain abundant geological information. However, redundant information complicates the study of exploration targets. Existing methods primarily rely on exploiting spectral differences between shallow and deep sources to separate anomalies of different depths. Nevertheless, spectral overlap limits these conventional methods to separating anomalies caused by significantly different depth sources. To reduce effects due to spectral overlap, we propose a novel method for potential field separation. This method capitalizes on the sparsity of gravity and magnetic data in both singular spectrum and model spaces and employs a single-layer equivalent source to represent anomalies induced by target sources. The anomalies caused by sources with different depths can be separated. After sparsely approximating single-layer equivalent sources, we obtain the local anomalies caused by sources within the same layer. Synthetic model experiments demonstrate that the proposed method achieves high separation accuracy, particularly with respect to effectively separating anomalies induced by models with small depth differences. In addition, when comparing the noise resistance of low-rank methods with existing potential field separation methods using synthetic data, the results show that low-rank methods can extract effective signals from signals contaminated by sparse noise and periodic noise. We then apply this method to extract local gravity anomalies caused by intrusive rocks in the Nanling region and effectively identify gravity anomalies associated with various intrusive rocks. This method facilitates the separation of gravity and magnetic anomalies originating from sources at both different and similar depths, thereby expanding the applicability of separation techniques and enhancing the resolution of gravity and magnetic detection.

Seismic wavefield forward modeling in anelastic (attenuating) media is a fundamental tool for both data processing and interpretation in modern seismic exploration. We propose a generalized recursive convolution (RC) formula to calculate the temporal convolutions directly, rather than solving many auxiliary partial differential equations of the memory variables when dealing with a viscoelastic medium. The new formula is obtained in terms of the Taylor series expansion and offers approximations of the convolutions to arbitrary order by the number of terms retained. We conduct theoretical and numerical comparisons of the new method with the commonly used memory variable method and other traditional RC methods. The comparisons show that the new method has the highest accuracy of all these RC methods and yields better performance with various stress relaxation times and time steps than the common leapfrog time-stepping scheme to solve the auxiliary partial differential equations of the memory variables. Our numerical examples verify the versatility and feasibility of the new method for viscoacoustic and viscoelastic wave modeling.

Today’s 3-D seismic surveys usually contain hundreds of inline and crossline vertical seismic slices. Seismic interpreters usually need to spend weeks or even months for manually picking horizons on vertical seismic slices. Researchers have developed algorithms to accelerate horizon picking and most algorithms employ the seismic reflector’s dip as the input. However, the computed seismic reflector’s dip is usually inaccurate near and across the discontinuities in the seismic images. Note that the time samples which belong to the same horizon should have approximate similar seismic instantaneous phase values. We propose to automatically track the seismic horizon simultaneously considering the seismic reflector’s dip and instantaneous phase attributes. Our algorithm aims to achieve three objectives: 1) minimizing the difference between the dip computed using tracked horizon and seismic dip attribute, 2) minimizing the difference among instantaneous phase value of the time samples on the tracked horizon, and 3) the tracked horizon exactly passes through user-defined control points (seeds). A constrained conjugate least-square algorithm is employed to solve our optimization problem. The applications show that the tracked horizon which only uses dip attribute would “jump” from one seismic event to another seismic event near the unconformity zone. However, the horizon tracked using the proposed method strictly follows the same seismic event over the whole seismic survey.