标题： 使用物理信息神经网络的直流电阻率数据反演 显示英文标题

标题： Inversion of DC Resistivity Data using Physics-Informed Neural Networks

摘要： 直流电阻率数据的反演是一种广泛用于近地表特征描述的方法。 最近，基于深度学习的反演技术由于其能够阐明地球物理数据与模型参数之间复杂的非线性关系而受到广泛关注。 然而，这些方法面临诸如训练数据可用性有限和生成地质上不一致的解决方案等挑战。 通过引入物理信息的方法可以缓解这些担忧。 此外，预测不确定性的量化在基于深度学习的反演方法中至关重要，但常常被忽视。 在本研究中，我们利用基于卷积神经网络的物理信息神经网络（PINNs）对合成和现场施尔伯格测深数据进行反演，并通过蒙特卡洛丢弃法估计预测不确定性。 对于合成和现场案例研究，PINNs估计的中位剖面与现有文献的结果相当，同时提供了不确定性估计。 因此，PINNs在近地表特征描述中的广泛应用显示出巨大的潜力。 显示英文摘要

摘要： The inversion of DC resistivity data is a widely employed method for near-surface characterization. Recently, deep learning-based inversion techniques have garnered significant attention due to their capability to elucidate intricate non-linear relationships between geophysical data and model parameters. Nevertheless, these methods face challenges such as limited training data availability and the generation of geologically inconsistent solutions. These concerns can be mitigated through the integration of a physics-informed approach. Moreover, the quantification of prediction uncertainty is crucial yet often overlooked in deep learning-based inversion methodologies. In this study, we utilized Convolutional Neural Networks (CNNs) based Physics-Informed Neural Networks (PINNs) to invert both synthetic and field Schlumberger sounding data while also estimating prediction uncertainty via Monte Carlo dropout. For both synthetic and field case studies, the median profile estimated by PINNs is comparable to the results from existing literature, while also providing uncertainty estimates. Therefore, PINNs demonstrate significant potential for broader applications in near-surface characterization.