Accurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep-learning-based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non-linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple-condition fusion network (MCF-Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple-source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple-condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF-Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF-Net in applications of hydrogeological modeling.

中文翻译：

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使用多条件融合神经网络从硬数据和软数据中表征地下结构


由于形态对流动和传输行为的影响，准确推断真实的地下结构带来了相当大的挑战。传统的地下表征依赖于两种主要类型的数据：硬数据（来自直接地下测量）和软数据（包括遥感地球物理信息及其解释）。现有的基于深度学习的方法主要关注从多次观测到地下结构的过渡。然而，不同数据源之间的隐式非线性相关性往往仍未得到充分利用，从而导致潜在的偏差和错误。在本研究中，我们引入了一种多条件融合网络 （MCF-Net） 来表征基于硬数据和软数据的地下结构。为了充分利用多源地下观测的潜力，两个不同的神经网络从硬数据和软数据中提取隐含特征。这些特征的集成是通过多条件融合模块实现的，旨在捕获代表性特征。这些块还擅长重建异质结构和促进水文参数化。MCF-Net 在估计各种类型的地下观测的地下结构方面表现出准确性。实验结果强调了 MCF-Net 在水文地质建模应用中的实用性和优越性。