Enhanced Electrical Resistivity Tomography With Prior Physical Information

Zhuo Jia;Meijia Huang;Zhijun Huo;Yabin Li
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Abstract

Electrical resistivity tomography (ERT) is a key geophysical technique that provides detailed information on subsurface structures by measuring the distribution of electrical resistivity underground. ERT suffers from limitations in electrode arrangement, interference from environmental and instrument noise, and existing data processing algorithms that fail to adequately consider geological heterogeneity and uncertainty, resulting in insufficient inversion resolution. Traditional ERT methods rely on simplified algorithms and a limited number of observation points, which smooths model details and further reduces resolution. To address the resolution issues in ERT, this article proposes a deep learning inversion method that integrates prior physical information. This method uses low-resolution inversion results as prior knowledge to provide the deep learning algorithm with a constrained initial model, thereby combining the physical basis of traditional methods with the data-driven advantages of deep learning. The method not only retains the strengths of traditional inversion but also enhances the resolution and imaging efficiency of the inversion model using deep learning technology. Synthetic data experiments demonstrate that integrating deep learning significantly improves the model’s ability to detail subsurface structures, especially in the transition zones of shallow structures and the recovery of deep anomalies. Results from measured data indicate that the proposed method not only achieves high-resolution inversion but also maintains good consistency with prior information.
利用先验物理信息增强电阻率层析成像技术
电阻率层析成像(ERT)是一项关键的地球物理技术,通过测量地下电阻率的分布来提供地下结构的详细信息。ERT受到电极布置的限制、环境和仪器噪声的干扰以及现有数据处理算法未能充分考虑地质非均质性和不确定性的影响,导致反演分辨率不足。传统的ERT方法依赖于简化的算法和有限数量的观测点,这使得模型细节变得平滑,进一步降低了分辨率。为了解决ERT中的分辨率问题,本文提出了一种集成先验物理信息的深度学习反演方法。该方法以低分辨率反演结果作为先验知识,为深度学习算法提供有约束的初始模型,从而将传统方法的物理基础与深度学习的数据驱动优势相结合。该方法既保留了传统反演的优点,又利用深度学习技术提高了反演模型的分辨率和成像效率。综合数据实验表明,集成深度学习显著提高了模型详细描述地下结构的能力,特别是在浅层结构过渡带和深层异常恢复方面。实测数据表明,该方法不仅实现了高分辨率反演,而且与先验信息保持了良好的一致性。
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