Ultra-shallow gas and hydrate-bearing systems in deep-water Quaternary strata are important exploration targets, but their geological controls and seismic responses remain difficult to quantify because the reservoirs are weakly consolidated, laterally heterogeneous, and commonly occur near the base of the gas-hydrate stability zone. This study presents an integrated geological and rock-physics seismic interpretation of the Lingshui 36-1 (LS36-1) ultra-shallow gas reservoir in the Qiongdongnan Basin. Regional geology, seismic interpretation, gas-source geochemistry, drilled sediment samples, and rock-physics modeling were combined into a case-specific interpretation workflow. The results indicate that shallow gas enrichment is controlled by the coupling of a Quaternary submarine-fan sand body, a fault-gas chimney-microfracture migration system, and three sealing elements: deep-water mud, mass-transport deposits, and hydrate-bearing intervals. The gas is methane-dominated dry gas related to mature to over-mature coal-measure source rocks of the Paleogene Yacheng Formation. Samples showed that the reservoirs are very weakly consolidated and highly porous; Ledong submarine-fan sandstone had porosities of 38.4%–42.5% and permeabilities of 48.4–598.4 mD, whereas a broader shallow-reservoir sample set showed porosities of 28.4%–43.6%. Laboratory velocity measurements were interpreted as trend-calibration data rather than pressure-equivalent reservoir velocities. In free-gas-bearing samples, replacing gas with water increased P- and S-wave velocities; in hydrate-bearing samples, decreasing water saturation increased hydrate saturation and therefore increased stiffness. A Hertz–Mindlin–Hashin–Shtrikman–Gassmann workflow, constrained by well logs and calibrated mineral/fluid parameters, predicted high P-wave impedance at the top of hydrate-bearing intervals and low P-wave impedance at the top of free-gas intervals. We therefore used P-wave impedance to identify sand-prone reservoirs, while the P-wave velocity ratio ranked high gas-saturation intervals. The inversion is consistent with LS36-1 well data but lacks independent blind-well validation because no additional verification well is available. The workflow provides a case-specific, uncertainty-aware approach for evaluating deep-water ultra-shallow gas and hydrate-associated reservoirs.
Residual static corrections in geologically complex areas remain challenging and strongly affect seismic imaging and inversion. Conventional methods rely on near-surface consistency and accurate normal moveout correction velocities, which often fail when ray paths deviate from normal incidence or velocities are inaccurate. Non-surface-consistency methods such as trim static correction may align noise and produce false events. We apply a new residual static correction method, which exploits the high-correlation nature of seismic events in moveout-corrected common depth gathers or common midpoint gathers. By treating seismic events as locally correlated signals over several traces, the globally optimal static correction can be found using a graph optimization approach. This formulation directly reconstructs the residual statics without accurate subsurface velocities or iterative correction cycles, improving both robustness and efficiency. Tests on both synthetic and field data show improved event continuity compared with conventional residual static correction methods.
To address the challenge of identifying five predefined categories of disturbance-related abnormal signals (lightning interference, artificial blasting, power-supply failure, collapse-related events, and meteorological anomalies) in fixed-point deformation monitoring, this paper proposes an identification framework integrating parameter-optimized variational mode decomposition (VMD), multiscale entropy analysis, and an intelligent classification model. Firstly, particle swarm optimization is adopted to adaptively determine the mode number and penalty factor for VMD to realize adaptive signal decomposition. Then, an adaptive sample entropy algorithm with self-adjustable similarity thresholds is applied to extract multiscale complexity-oriented features from each decomposed modal component. Finally, the ant lion optimizer optimizes the competitive-layer topology and training hyperparameters of a self-organizing map network to build the target classifier. Comparative experiments against 18 widely-used classification models (including AdaBoost ensemble learners, generalized regression neural networks, long short-term memory networks, and gated recurrent units) are carried out on the real-world observation dataset from the Yixian seismic station. The experimental results show that the proposed framework achieves competitive classification performance for the five anomaly categories. The mean test-set accuracy over 100 independent repeated-run experiments reaches 99.62%, with a standard deviation of 0.6361. This study offers an effective technical reference for identifying deformation-related abnormal waveforms under complex observational conditions.
Least-squares migration can improve image resolution and compensate for uneven illumination, but data-domain implementations require repeated modeling and migration. Image-domain methods based on point-spread functions (PSFs) reduce this cost by approximating the local Hessian response, yet their performance depends on the representativeness of local PSFs in structurally complex areas. We propose SPIRE-Net, a self-supervised migration image restoration framework constrained by spatially variant PSFs. The network predicts a restored image patch, which is then filtered by the PSF assigned to the corresponding spatial location. Training minimizes the mismatch between this PSF-filtered prediction and the input image patch without requiring supervised target images. Structure-aware adaptive PSF-paired sampling and a structure-enhanced consistency loss strengthen the constraint in complex structural regions. The Marmousi experiment demonstrates the ability of SPIRE-Net to solve the prescribed PSF-based image-domain inverse problem through self-supervised learning. Field-data comparisons show improved local reflector continuity and event separation relative to image-domain LSM in the examined regions, while preserving the main reflection geometry. These results suggest that spatially variant PSFs can serve as physical constraints for self-supervised migration image restoration.
Rayleigh-wave dispersion and seismic refraction observations constrain different but coupled aspects of near-surface velocity structure. This study evaluates a reverse-learning-enhanced sine cosine algorithm (ESCA) for jointly estimating layer thickness, shear-wave velocity, and P-wave velocity in a one-dimensional, horizontally layered medium. Reverse learning is added during Tent-map initialization, while the best-solution-centered exponential update follows an earlier sine cosine formulation. The dimensionless joint objective combines root-mean-square relative residuals of fundamental-mode phase velocities and first-arrival times. ESCA, the standard sine cosine algorithm (SCA), and particle swarm optimization (PSO) were evaluated in 30 independent runs using multimodal benchmark functions, synthetic data generated from a model comprising 4 horizontal layers, and previously published field datasets from the Beşirli and MAE sites in Türkiye. The mean final joint-objective values for ESCA, SCA, and PSO, respectively, were 0.0028, 0.0070, and 0.0068 for noise-free synthetic data; 0.0336, 0.0356, and 0.0382 for synthetic data with 10% noise; 0.0343, 0.0393, and 0.0404 for the Beşirli dataset; and 0.0717, 0.0740, and 0.0722 for the MAE dataset. The corresponding runtimes for noise-free synthetic data were 145.32 ± 3.46, 137.89 ± 5.64, and 210.33 ± 4.65 s, respectively. ESCA required 0.99% more objective-function evaluations than the baseline algorithms, although its improvement over PSO for the MAE dataset was small. The field-data results demonstrate consistency within the assumed one-dimensional model but do not account for dipping interfaces or lateral variations in velocity.
