基于深度学习的澳大利亚大陆尺度概率电阻率成像：对地质、地下水和关键矿产的意义
Continental-scale probabilistic resistivity imaging of Australia using deep learning: Implications for geology, groundwater, and critical minerals
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中文总结 AI 辅助
该研究利用可逆神经网络对澳大利亚2660万个航空电磁测深数据进行概率反演，构建了深度约650米的大陆尺度电阻率模型，仅用4.37个GPU小时完成，为地质、地下水和关键矿产勘探提供新约束。
中文摘要 AI 辅助
电阻率是一种基本物理属性，可为地下结构、岩性、流体分布和矿化提供强有力的约束。我们提出了一个具有量化不确定性的澳大利亚浅地壳大陆尺度电阻率模型，其深度延伸至约650米。该模型基于对AusAEM计划沿352,600测线公里采集的超过2660万个航空电磁（AEM）测深数据进行概率反演而得出。我们开发了一种基于深度学习的概率反演框架，使用可逆神经网络（INNs）。为适应不同AEM测量之间的差异，我们使用一组共同的合成电阻率模型和相同的训练策略训练了11个网络，从而能够一致地整合澳大利亚各地的反演结果。整个反演仅用时4.37个GPU小时便完成，克服了大陆尺度概率地球物理成像长期存在的计算障碍。所得电阻率模型呈现出清晰的空间格局，与澳大利亚主要地质省、沉积盆地、含水层和成矿系统密切吻合。我们进一步利用后验电阻率分布定义调查深度，以支持不确定性知情的 geologic 解释。该电阻率模型连同量化不确定性为澳大利亚各地的风化层厚度、盆地构造、古河道和成矿系统提供了新的物理约束，为解释大陆尺度电性结构及其地质、水文和成矿系统意义奠定了基础。
英文摘要
Electrical resistivity is a fundamental physical property that provides powerful constraints on subsurface structure, lithology, fluid distribution, and mineralization. We present a continental-scale electrical resistivity model of Australia's shallow crust with quantified uncertainty, extending to depths of up to ~650 m. The model is derived from probabilistic inversion of more than 26.6 million airborne electromagnetic (AEM) soundings acquired along 352,600 line-kilometers from the AusAEM program. We develop a deep learning-based probabilistic inversion framework using invertible neural networks (INNs). To accommodate differences among AEM surveys, we trained 11 networks using a common set of synthetic resistivity models and the same training strategy, enabling consistent integration of inversion results across Australia. The entire inversion was completed in only 4.37 GPU hours, overcoming long-standing computational barriers to continental-scale probabilistic geophysical imaging. The resulting resistivity model exhibits clear spatial patterns that closely align with Australia's major geological provinces, sedimentary basins, aquifer and mineralizing systems. We further define the depth of investigation using posterior resistivity distributions to support uncertainty-informed geological interpretation. The resistivity model together with quantified uncertainty provides new physical constraints on regolith thickness, basin architecture, paleochannel and mineralized systems across Australia, serving as a foundation for interpreting continental-scale electrical structure and its geological, hydrological, and mineral system implications.
发表机构
- University of Houston（休斯顿大学）
- Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
