地球科学进展 doi: 10.11867/j.issn.1001-8166.2026.028

   

盆山耦合物理模拟中的材料、模型与技术融合
姚熙强,戴霜*,张煦,张宇帆,田胜强   
  1. (兰州大学 地质科学与矿产资源学院,甘肃 兰州 730000)
  • 基金资助:
    国家重点研发计划项目(编号:2022YFC2903305-05);甘肃省科技计划项目(编号:25ZYJA023)资助.

Materials, Models, and Technology Integration in Physical Modeling of Basin-Range Coupling

Yao Xiqiang, Dai Shuang*, Zhang Xu, Zhang Yufan, Tian Shengqiang   

  1. (School of Earth Sciences, Lanzhou University, Lanzhou 730000, China)
  • About author:Yao Xiqiang, research area includes tectonic modeling. E-mail: yxq315666@163.com
  • Supported by:
    Project supported by the National Key Research and Development Program of China (Grant No. 2022YFC2903305-05);The Gansu Provincial Science and Technology Program (Grant No. 25ZYJA023).
盆山耦合是大陆岩石圈能量与物质交换的关键过程,对理解板块构造、资源分布及地质灾害具有重要科学意义。其相关物理模拟研究经历了从几何相似到动力相似、从单一构造模拟到多过程耦合的显著演进。通过回顾该领域的发展历程,指出阶段划分差异实质上是理论突破与技术革新共同驱动的结果。在实验材料方面,模拟材料已从石英砂和硅胶等基础材料,发展为能够精准匹配地质原型流变行为的复合功能材料;进一步对比了脆性、韧性和塑性3 类材料的密度、摩擦 角/黏度及关键无量纲参数,探讨了超重力条件下材料行为的差异及动力学相似性要求。根据动力源类型,实验模型可分为挤压、伸展和走滑3 类驱动模型,它们分别揭示了前陆盆地、裂谷盆地及走滑转换带形成的动力学机制,并一致强调了先存构造与流变分层的控制作用;通过选取3个代表性盆山体系进行同维度对比,揭示了参数实验与实例研究之间的互补性及物理模拟的固有局限。当前,超重力离心、CT扫描、数字体积相关与数值模拟等前沿技术的融合,正推动物理模拟向实时、三维、定量化方向深刻转型。针对尺度效应、时间压缩、实验设备影响及温度缺失等局限,提出了“高分辨率观测与精细建模、智能化连续监测与参数反演、全过程多场耦合模拟、多源数据同化”四类技术发展方向,并构建了“3D打印初始模型—超重力离心提供应力环境—数字体积相关实现四维应变场捕捉—人工智能自动提取构造要素—数值模拟进行参数反演与演化趋势预测”的闭环研究范式。未来该领域将更注重“构造—地貌—沉积”全过程模拟与人工智能识别技术深度融合,从而为深入揭示地球动力系统的运行机制提供关键的实验支撑与理论框架。
Abstract:Basin-range coupling is a key process for energy and material exchange within the continental lithosphere, fundamental to understanding plate tectonics, resource distribution, and geological hazards. Its related physical modeling research has undergone a notable evolution from geometric similarity to dynamic similarity, and from single tectonic simulation to multi-process coupling. By reviewing the development of this field, it is pointed out that the differences in stage division are essentially driven by theoretical breakthroughs and technological innovations. Regarding experimental materials, modeling materials have evolved from basic substances such as quartz sand and silicone to composite functional materials capable of accurately matching the rheological behavior of geological prototypes. This paper further compares the density, friction angle/viscosity, and key dimensionless parameters of brittle, ductile, and plastic materials, and discusses the differences in material behavior under hypergravity conditions as well as the requirements for dynamic similarity. Based on the type of driving force, experimental models can be classified into three categories: compression, extension, and strike-slip. These models respectively elucidate the dynamic mechanisms of foreland basins, rift basins, and strike-slip transfer zones, consistently highlighting the controlling roles of pre-existing structures and rheological layering. By comparing three representative basin-mountain systems under the same dimensions, this paper reveals the complementarity between parametric experiments and case studies, along with the inherent limitations of physical modeling. Currently, the integration of cutting-edge technologies such as hypergravity centrifugation, CT scanning, digital volume correlation, and numerical modeling is driving a profound transformation of physical modeling towards real-time, three-dimensional, and quantitative analysis. Addressing the limitations of scale effects, time compression, laboratory apparatus influences, and lack of thermal effects, this paper proposes four technological development directions: high-resolution observation and fine modeling, intelligent continuous monitoring and parameter inversion, full-process multi-field coupled simulation, and multi-source data assimilation. Accordingly, a closed-loop research paradigm is constructed: “3D printing of initial models— hypergravity centrifugation for stress environment—digital volume correlation for four-dimensional strain field capture—artificial intelligence for automatic extraction of structural elements—numerical modeling for parameter inversion and evolutionary trend prediction”. In the future, this field will pay more attention to fullprocess simulation of “tectonics-geomorphology-sedimentation” and deep integration of artificial intelligence recognition technologies, thereby providing critical experimental support and theoretical frameworks for a deeper understanding of the operating mechanisms of Earth’s dynamic systems.

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