The combination of lidar and digital photography provides a new technology for creating a high-resolution 3D digital outcrop model. The digital outcrop model can accurately and conveniently depict the surface 3D properties of an outcrop profile, making up for the shortcomings of traditional outcrop research techniques. However, the advent of digital outcrop poses additional challenges to the 3D spatial analysis of virtual outcrop models, particularly in the interpretation of geological characteristics. In this study, the detailed workflow of automated interpretation of geological characteristics of fractures and cavities on a 3D digital outcrop texture model is described. Firstly, advanced automatic image analysis technology is used to detect the 2D contour of the fractures and cavities in the picture. Then, to obtain an accurate representation of the 3D structure of the fractures and cavities on the digital outcrop model, a projection method for converting 2D coordinates to 3D space based on geometric transformations such as affine transformation and linear interpolation is proposed. Quantitative data on the size, shape, and distribution of geological features are calculated using this information. Finally, a novel and comprehensive automated 3D quantitative characterization technique for fractures and cavities on the 3D digital outcrop texture model is developed. The proposed technology has been applied to the 3D mapping and quantitative characterization of fractures and cavities on the outcrop profile for the Dengying Formation (second member), providing a foundation for profile reservoir appraisal in the research region. Furthermore, this approach may be extended to the 3D characterization and analysis of any point, line, and surface objects derived from outcrop photos, hence increasing the application value of the 3D digital outcrop model.

Aiming at the problems of low matching accuracy and slow matching speed of high-frequency trajectory data in complex urban road networks, this paper proposes a matching method based on path increment. This method consists of two parts: combined filtering and incremental matching. Firstly, the road network is simplified through combined filtering, and then the incremental matching is carried out by taking the paths as increments. In the matching procedure, a comprehensive evaluation scheme of similarity based on distance factor and curvature is adopted. The above measures effectively reduce the impact of complex road segments on the matching results, while the path increment method enables the matching process to be executed more rapidly and accurately. The experiments were conducted using the Geolife datasets. The results show that our algorithm has obvious advantages over similar algorithms in terms of matching accuracy and efficiency, and shows good stability in road matching tests with different complexity.

The Longdong area in the Ordos basin is a typical fluvial reservoir with strong heterogeneity. In order to clarify the distribution law of underground reservoirs in the Longdong area, it is necessary to establish and optimize a 3D geological model to characterize the heterogeneity of reservoirs. This is of great significance for accelerating the exploitation of tight sandstone gas in the southwest of the Ordos basin. This study takes the P₂h₈ member of the Ct3 research area in the Longdong area as an example, analyzes the core and logging curve shape to divide the sedimentary microfacies, and establishes the facies model. In particular, in view of the difficulty in obtaining 3D training images under the existing conditions in the study area, we use the multi-point geostatistics method combining sequential two-dimensional condition simulation and the direct sampling method to establish the facies model. This method can simulate the 3D geological model by using the 2D training images composed of the digital plane facies diagrams and the well-connection facies diagrams. In addition, we choose the object-based method and sequential indicator method for comparative experiments to verify the feasibility of this method (sequential two-dimensional condition simulation combined with the direct sampling method) from many aspects. The results show that the multi-point geostatistics method based on 2D training images can not only match the well data, but also show the geometric characteristics and contact relationship of the simulation object. The distribution characteristics of sandbody thickness and modeling results are consistent with the actual geological conditions in the study area. This study explores the feasibility of this method in the 3D geological simulation of large-scale fluvial facies tight sandstone reservoirs. Additionally, it also provides a new idea and scheme for the modeling method of geologists in similar geological environments.

Collecting information about fracture attributes through outcrops measurement is crucial for analyzing the scale, distribution, orientation, and spatial arrangement of fractures. The emergence of digital outcrop models (DOMs) provides a new technology for quantitative interpretation of fractures. However, large-scale DOMs pose additional challenges to the practical application, particularly in the interpretation of geological elements (e.g. fractures). This research proposes a workflow for fracture characteristics interpretation based on DOMs. First, DOMs are generated using light detection and ranging scanning technology. Then, a 3D visualization platform is developed based on OpenSceneGraph. We use level-of-detail technology to reconstruct DOMs for multiscale fast visualization of large-scale models. Finally, in order to realize the quantitative interpretation of fractures, we propose the best-plane fitting and the feature information (orientation, length, spacing, etc.) extraction methods for two types of fractures (exposed fracture walls and fracture traces). The proposed methods are applied to extract attributes of fractures in Dengying Formation (second member), Ebian, Xianfeng, southwest Sichuan Basin, China. The results provide the basis for reservoir evaluation in this area.

To clarify the main controlling factors of effective reservoirs in the sandy braided river delta plain, the eighth member of Xiashihezi Formation in the Ct3 area, Ordos Basin, is taken as an example. We describe and analyze approximately 600 cores in detail and correspond the cores on the logging curve. We summarize the logging facies mode suitable for the Ct3 area, and finally investigate the types and spatial distribution characteristics of sedimentary microfacies. The P2h8 member of Ct3 area in Ordos Basin is braided river delta plain subfacies. Sedimentary microfacies are divided into three types: distributary channel, channel bar, and distributary interchannel. The sand body in the h84 of the P2h8 member is the thickest, and the number of channel bar is the largest. All gas reservoirs (effective reservoirs) are developed on the sand body of the channel bar, which is the most favorable microfacies type. Poor gas reservoirs are mostly developed in distributary river channel sand body, whereas gas-bearing reservoirs (gas reservoirs and poor gas reservoirs) are undeveloped in distributary interchannel. According to the hydrodynamic conditions and sedimentary mechanism, the channel bar is divided into the bar head, the bar tail, the bar main body, and the bar wing. It is found that 63% of the gas reservoirs in the channel bar are developed in the bar main body, followed by the bar tail. The number of gas reservoirs developed at the bar head and the bar wing is the least. The results found that the most effective reservoirs are developed in the bar main body. Sedimentary microfacies, sand-body thickness, and grain size are the main controlling factors for effective reservoirs. The research results can guide the gas field exploration and later well location deployment in the sedimentary environment similar to the Ct3 area.

High-resolution LiDAR-derived 3D digital outcrop models are essential for detailed geological analysis, but their massive data volumes often exceed the rendering and memory capacities of standard computer systems, posing significant visualization challenges. Although Level of Detail (LOD) techniques are well-established in GIS and computer graphics, they are not directly suitable for the elongated, cliff-face morphology of typical outcrops. This paper presents an automated method for constructing and visualizing LOD models specifically tailored to high-resolution LiDAR outcrops. The workflow begins by segmenting the monolithic model based on texture coverage, followed by building an adaptive LOD tile pyramid for each segment using a pseudo-quadtree approach. A feature-preserving Quadric Error Metric (QEM) simplification algorithm, incorporating boundary freezing and fallback strategies, is employed to retain critical geological structures while significantly reducing geometric complexity. For visualization, a dynamic multi-scale loading and rendering mechanism is implemented using an LOD index with the OpenSceneGraph (OSG) engine. Results demonstrate that the proposed method effectively overcomes the bottleneck of rendering massive outcrop models, enabling smooth, interactive visualization and providing a robust foundation for multi-scale geological interpretation.