利用物理信息神经网络反演直流电阻率数据

Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh
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引用次数: 0

摘要

直流电阻率数据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因其能够揭示地球物理数据与模型参数之间错综复杂的非线性关系而备受关注。然而,这些方法面临着一些挑战,如训练数据可用性有限以及生成的地质解决方案不一致。这些问题可以通过整合物理学方法来解决。此外,预测不确定性的量化至关重要,但在基于深度学习的反演方法中往往被忽视。在这项研究中,我们利用基于卷积神经网络(CNNs)的物理信息神经网络(PINNs)对合成和现场斯伦贝谢探测数据进行反演,同时还通过蒙特卡洛漏失估算预测的不确定性。对于合成和现场案例研究,PINNs 估算的中值剖面与现有文献的结果相当,同时还提供了不确定性估算。因此,PINN 在近地表特征描述的更广泛应用方面具有巨大潜力。
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Inversion of DC Resistivity Data using Physics-Informed Neural Networks
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.
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