arXiv subjects

Xingyu Luo

Publications and source records attributed to Xingyu Luo.

3 recordsLinked to original sources

arXiv2026

Seismic P-wave attenuation estimation based on frequency-dependent AVO using Kramers-Kronig relations for gas reservoir prediction

Estimation of seismic attenuation (inverse quality factor) is important for gas reservoir prediction. Two key issues in seismic attenuation estimation are the development of a physically consistent reflection coefficient equation and stable estimation of seismic attenuation from seismic data. To address these issues, this study, within the framework of isotropic linear viscoelastic media, starts from the Kramers-Kronig relations and expresses the viscoelastic stiffness matrix as a function of seismic attenuation. Under the assumptions of weak attenuation and small elastic and attenuation contrasts across the interface, a frequency-dependent PP-wave reflection coefficient equation explicitly containing seismic attenuation terms is derived using scattering theory. The derived reflection coefficient equation not only satisfies the causality constraint, but also preserves a compact mathematical form. Based on this reflection coefficient equation, seismic attenuation is estimated within the framework of frequency-dependent AVO inversion. Synthetic seismic data tests show that the estimated P-wave attenuation attribute is sensitive to variations in reservoir gas saturation, with reservoirs of higher gas saturation exhibiting stronger P-wave attenuation anomalies. Application to field seismic data further demonstrates that the P-wave attenuation anomalies agree well with the gas saturation log and effectively identify high gas saturation reservoirs. This study provides a new approach for extracting P-wave attenuation information from seismic data and achieving high resolution prediction of gas reservoirs.

physics.geo-ph↗

arXiv2026

GLAD: Generative Language-Assisted Visual Tracking for Low-Semantic Templates

Vision-language tracking has gained increasing attention in many scenarios. This task simultaneously deals with visual and linguistic information to localize objects in videos. Despite its growing utility, the development of vision-language tracking methods remains in its early stage. Current vision-language trackers usually employ Transformer architectures for interactive integration of template, search, and text features. However, persistent challenges about low-semantic images including prevalent image blurriness, low resolution and so on, may compromise model performance through degraded cross-modal understanding. To solve this problem, language assistance is usually used to deal with the obstacles posed by low-semantic images. However, due to the existing gap between current textual and visual features, direct concatenation and fusion of these features may have limited effectiveness. To address these challenges, we introduce a pioneering Generative Language-AssisteD tracking model, GLAD, which utilizes diffusion models for the generative multi-modal fusion of text description and template image to bolster compatibility between language and image and enhance template image semantic information. Our approach demonstrates notable improvements over the existing fusion paradigms. Blurry and semantically ambiguous template images can be restored to improve multi-modal features in the generative fusion paradigm. Experiments show that our method establishes a new state-of-the-art on multiple benchmarks and achieves an impressive inference speed. The code and models will be released at: https://github.com/Confetti-lxy/GLAD

cs.CV↗

arXiv2025

DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model

Although significant advances have been achieved in SAR land-cover classification, recent methods remain predominantly focused on supervised learning, which relies heavily on extensive labeled datasets. This dependency not only limits scalability and generalization but also restricts adaptability to diverse application scenarios. In this paper, a general-purpose foundation model for SAR land-cover classification is developed, serving as a robust cornerstone to accelerate the development and deployment of various downstream models. Specifically, a Dynamic Instance and Contour Consistency Contrastive Learning (DI3CL) pre-training framework is presented, which incorporates a Dynamic Instance (DI) module and a Contour Consistency (CC) module. DI module enhances global contextual awareness by enforcing local consistency across different views of the same region. CC module leverages shallow feature maps to guide the model to focus on the geometric contours of SAR land-cover objects, thereby improving structural discrimination. Additionally, to enhance robustness and generalization during pre-training, a large-scale and diverse dataset named SARSense, comprising 460,532 SAR images, is constructed to enable the model to capture comprehensive and representative features. To evaluate the generalization capability of our foundation model, we conducted extensive experiments across a variety of SAR land-cover classification tasks, including SAR land-cover mapping, water body detection, and road extraction. The results consistently demonstrate that the proposed DI3CL outperforms existing methods. Our code and pre-trained weights are publicly available at: https://github.com/SARpre-train/DI3CL.

cs.CV↗