走向使用集成的多维外数据进行三维离散断裂网络建模.
Graciela Racolte1,2, Ademir Marques3,4, Vinicius Sales3,4
1Vizlab - Center of Excellence in Geoinformatics and Visual Computing, Unisinos, Avenida Unisinos, 950, São Leopoldo, Rio Grande do Sul, 93022-750, Brazil. gracielarr@unisinos.br.
Scientific reports
|February 21, 2026
概括
人工智能增强了数字外模型中的离散断裂网络 (DFN) 分析. 这种方法通过减轻偏差和改进参数估计来提高断裂特征和建模精度.
科学领域:
- 地质科学 地质科学
- 人工智能的人工智能
- 计算地质学的计算地质学
背景情况:
- 模拟突出提供了超越地震和井日志分辨率的离散断裂网络 (DFN) 数据.
- 断裂的表征需要对几何,拓和地质力学属性的专业解释.
- 露出数据可能是有限的,可能会影响DFN模型的准确性.
研究的目的:
- 开发一种人工智能驱动的方法,用于使用数字突出数据进行断裂特征和DFN建模.
- 通过人工智能减轻DFN分析和参数估计中的偏差.
- 创建包含断裂持久性的决定性和随机体积模型.
主要方法:
- 用修改的K-Means算法对3D断裂数据进行球形聚类 (strike/dip).
- 随机梯度下降用于权力定律估计,考虑到碎形断裂传播.
- 应用于Potiguar盆地的Jandaíra形成碳酸盐岩,用于案例研究.
主要成果:
- 人工智能集成成功地减轻了DFN分析和参数估计中的偏差.
- 产生了具有骨折持久性的决定性和随机体积模型.
- 在连接性,骨折强度和长度分布方面观察到2D和3D数据之间的更强的相关性.
结论:
- 拟议的人工智能方法提高了DFN模拟从数字浮标数据的准确性.
- 该方法有效地整合了各种折断属性,以产生强大的模型.
- 人工智能驱动的断裂表征为地下地质建模提供了更可靠的基础.
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