Abstract Cross-dipole acoustic logging is widely used to determine the maximum horizontal stress azimuth and stress imbalance in vertical boreholes within isotropic formations. However, theoretical analysis of dipole flexural dispersion under imbalanced stresses in transversely isotropic formations with a vertical symmetry axis (VTI) remains limited. This study integrates borehole stress effects into an acoustoelastic model, deriving flexural dispersion curves via the perturbation method (PM). We identify four distinct dispersion signatures related to the fast and slow dispersion curves. Analysis of 33 rock samples from cited references reveals that flexural dispersion crossovers reliably indicate stress imbalance in isotropic formations for most rock samples. Furthermore, alignment between the fast shear-wave azimuth and the maximum horizontal stress azimuth is consistent for most rock samples. Given that radial variations in axial-shear velocities are related to flexural dispersion curves, we find that the elastic constants (c11, c44, c66, c144, and c155) critically influence these four types. We demonstrate that for intersecting flexural dispersion curves, the fast and slow dispersion curves typically exhibit a single crossover at sonic frequencies. Synthetic examples confirm that the PM results are consistent with finite-difference time-domain (FDTD) simulations. Consequently, we propose a classification method utilizing c11, c44, c66, c144, and c155
Abstract Seismic waves are attenuated and distorted during the propagation. The amplitude attenuation can decrease the energy of the wavefields, and dispersion effect distort the phase of seismic waves, thereby degrading the imaging resolution. In nearly constant-Q reverse time migration (Q-RTM), it is essential to decouple the dispersion and dissipation components of the viscoacoustic wave equation (WE) to facilitate attenuation compensation and phase correction. Conventional decoupled viscoacoustic WEs are typically formulated with fractional Laplacian operators, requiring the computationally expensive pseudo-spectral method (PSM). Although viscoacoustic WEs derived from the generalized standard linear solid (GSLS) model has widely applied due to high computational efficiency, their viscous terms are usually coupled, rendering them unsuitable for Q-RTM. To address this limitation, a new GSLS-based viscoacoustic decoupled WE is derived for the Q-RTM framework. The proposed WE offers three main advantages: Firstly, when the number of SLS is greater than one, the dispersion and dissipation terms are explicitly decoupled under various reference frequencies, making it well-suited for Q-RTM. Secondly, the proposed WEs can be efficiently solved using finite-difference method (FDM), thereby avoiding the high computational cost. Thirdly, by using a regularization method to suppress high-frequency components in the seismic wavefield, the proposed WE enables stable and effective Q-RTM. The modified Marmousi and BP gas models demonstrates superior computational efficiency and imaging accuracy. Furthermore, application to field seismic data shows that the new method enhances the imaging resolution, particularly in deeper sections, thereby confirming its potential for practical exploration scenarios.
Abstract Considering the effect of remanence, the magnetization vector inversion method can recover three orthogonal components of total magnetization vector using the magnetization intensity as a constraint, which makes is a more practical approach for the inversion of magnetic. However, inversion precision is relatively low when the magnetization directions of adjacent geological bodies differ significantly. Based on the characteristic that the normalized source strength (NSS) is weakly sensitive to the magnetization direction, NSS is adopted instead of magnetization intensity to constrain the three orthogonal components. To address magnetic anomalies arising from the superposition of multiple magnetization directions, a geometric-shape-based partitioning method is further developed to realize high-precision magnetization vector inversion constrained by NSS; this approach is termed the NSS-GSC inversion method. Synthetic tests show that the NSS-GSC inversion method yields more accurate inversion results compared with the magnetization intensity method, and exhibits good anti-noise performance. The NSS-GSC method is applied to the Huanggangliang-Ganzhuermiao metallic ore cluster in China to obtain the distribution of highly magnetic bodies located near NE- and NW-trending faults, with top depths ranging from 178 m to 384 m. This provides robust evidence for further mineral exploration.
Abstract The five-dimensional (5D) Radon transform holds high potential to effectively address the significant data irregularity and noise challenges presented by 5D seismic surveys. However, its direct application is impeded by prohibitive memory and computational demands. To mitigate this, we present a robust and memory-efficient 5D seismic data interpolation framework based on strong group sparsity regularization in the Radon domain. Specifically, to reconstruct missing traces and attenuate strong erratic noise, we formulate the inversion as an ℓ1-ℓ1 minimization problem that simultaneously promotes coefficient sparsity and enhances robustness against outliers. Unlike conventional sparse Radon methods, we explicitly exploit the structured coherence of seismic signals by enforcing strong group sparsity: Radon coefficients are partitioned into non-overlapping groups along the slowness axes, and an energy-based greedy selection strategy is applied to identify a small subset of active groups. A key contribution of this work is the development of a memory-efficient 5D Radon transform implementation, which avoids constructing the full 5D Radon cube by sequentially accumulating group energies across frequency slices and restricting coefficient updates to the selected groups only. The Radon transform is carried out in the frequency-slowness domain, enabling sharper focusing of coherent events and further improving computational efficiency. Synthetic experiments quantitatively demonstrate improved reconstruction accuracy relative to widely used interpolation approaches such as projection onto convex sets (POCS) and damped rank-reduction (DRR), while the field-data example shows improved apparent continuity and limited overall spectral distortion. The proposed implementation also drastically reduces the memory required for the 5D Radon representation and remains computationally practical for large 5D datasets.
Abstract Deepwater basins have become major targets for commercial oil and gas exploration. However, as exploration advances into ultra-deepwater basins, technical challenges become increasingly severe, as exemplified by the Liwan Sag in the northern South China Sea (SCS). The rugged seafloor terrain and great water depth degrade seismic imaging quality and increase uncertainties in drilling and geological interpretation. Conventional towed streamer reflection seismic data provide dense reflection information but are limited by relatively narrow observation angles and limited offset coverage. In contrast, ocean-bottom seismometer (OBS) data provide first-arrival traveltimes at larger offsets, thereby complementing the limited offset coverage of streamer data. In this study, we collate and preprocess joint OBS and towed streamer observations from the Liwan Sag to obtain seismic data with an improved signal-to-noise ratio for velocity model building over the full depth range. We establish an integrated workflow with three stages: seawater velocity model building, subseafloor background velocity model building, and subseafloor velocity refining. The resulting velocity models are evaluated using traveltime fitting, migration image quality, and common image gather (CIG) flattening, and are then used for imaging in the depth domain and geological interpretation of the survey line. This case study provides a practical workflow that integrates seismic data preprocessing, full-depth velocity model building, and stratigraphic imaging for ultra-deepwater seismic exploration.
