Real-Time Dual-Parameter Full-Waveform Inversion of GPR Data Based on Robust Deep Learning

Jiyan Xue, Qinghua Huang, Sihong Wu, Li Zhao, Bowen Ma
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Abstract

Ground penetrating radar (GPR) is becoming an increasingly important tool for understanding the shallow electrical structures of the earth and planets due to its adaptability to harsh detection environments, efficient data acquisition and accurate detection results. GPR full-waveform can simultaneously constrain the permittivity and resistivity of the medium, providing more comprehensive geophysical information and reducing the non-uniqueness of inversion. However, given the highly non-linear inverse problem and the massive data resulted from high temporal and spatial samplings, traditional full-waveform inversion algorithms are prohibitively costly. Inspired by Google's vision semantic segmentation system, we develop a robust deep learning-guided network that integrates geology and geophysics knowledge to support the real-time translation of zero-offset GPR data into dual-parameter electrical structures. We test our proposed network using synthetic data, which demonstrates that the algorithm can provide an accurate dual-parameter electrical model from a GPR sounding in milliseconds on a common laptop PC, exhibiting high robustness and adaptability to noise interference and extreme values of model parameters. We also apply our network to field data gathered for pollutant investigation in the US. The resulting dual-parameter structure provides a more comprehensive and realistic depiction of subsurface electrical properties and reveals the migration and aging of pollutants. Our algorithm's real-time and accurate advantages are expected to further unleash the potential of GPR technology and enable it to play a more significant role in earth and planetary exploration.
基于鲁棒深度学习的 GPR 数据实时双参数全波形反演
地面穿透雷达(GPR)因其对恶劣探测环境的适应性、高效的数据采集和精确的探测结果,正日益成为了解地球和行星浅层电结构的重要工具。GPR 全波形可以同时约束介质的介电常数和电阻率,提供更全面的地球物理信息,减少反演的非唯一性。然而,考虑到高度非线性反演问题和高时空采样带来的海量数据,传统的全波形反演算法成本过高。受谷歌视觉语义分割系统的启发,我们开发了一种强大的深度学习引导网络,该网络集成了地质学和地球物理学知识,支持将零偏移 GPR 数据实时转换为双参数电气结构。我们使用合成数据测试了我们提出的网络,结果表明,该算法可以在普通笔记本电脑上,在几毫秒内从 GPR 探测数据中提供准确的双参数电气模型,对噪声干扰和模型参数的极端值表现出很高的鲁棒性和适应性。我们还将我们的网络应用于在美国进行污染物调查时收集的实地数据。由此产生的双参数结构更全面、更真实地描述了地下电特性,并揭示了污染物的迁移和老化过程。我们的算法具有实时和精确的优势,有望进一步释放 GPR 技术的潜力,使其在地球和行星探测中发挥更重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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