SciLM-ai/SiliciclasticReservoirs

该数据集包含1,000,000个合成的3D硅质碎屑岩储层地质立方体，这些立方体基于基于规则的沉积学模拟生成（包括浊积岩叶状体、6种河流-河道结构和三角洲-扇体分布）。每个样本的体素化为(64, 64, 32)单元格，包含岩相、孔隙度、渗透率以及一组结构化的地质条件参数。该数据集旨在训练可解释物理条件下的地下地质条件生成模型（如流匹配、扩散模型等）。数据集提供了详细的文件结构、层类型、体素数组信息以及训练条件参数，适用于地质学、地球科学、储层建模等领域的研究和应用。

The dataset comprises 1,000,000 synthetic 3D siliciclastic-reservoir geology cubes generated from rule-based sedimentological simulations (turbidite lobes + 6 fluvial-channel architectures + delta-fan distributary). Each sample is voxelized at (64, 64, 32) cells and includes facies, porosity, permeability, and a structured set of geological conditioning parameters. Designed to train conditional generative models (flow matching, diffusion, etc.) of subsurface geology under interpretable physical conditioning, the dataset provides detailed file structure, layer types, voxel arrays, and training conditioning parameters, making it suitable for research and applications in geology, earth sciences, and reservoir modeling.