论文标题
深度学习替代互动马尔可夫链蒙特卡洛基于材料量化性质的全波反倒置方案
Deep learning surrogate interacting Markov chain Monte Carlo based full wave inversion scheme for properties of materials quantification
论文作者
论文摘要
全波反转（FWI）成像方案在工程，地球科学和医学科学方面有许多应用。在本文中，提出了一种替代深度学习的FWI方法，以使用应力波量化材料的性质。通常，这种反问题是不适合和非概念的，尤其是在解决方案表现出冲击，异质性，不连续性或大梯度的情况下。在这些情况下，提出的方法有效地获得了全球最小响应。基于随机采样的材料特性集和围绕局部最小值的采样试验进行了训练，因此，它需要进行正向模拟可以处理高异质性，不连续性和较大的梯度。高分辨率Kurganov-Tadmor（KT）中央有限体积方法用作前向波传播操作员。使用所提出的框架，在几种不同的情况下对2D介质的材料特性进行了量化。结果表明，使用深度学习方法估算高精度的材料的机械性能的拟议方法的可行性。
Full Wave Inversion (FWI) imaging scheme has many applications in engineering, geoscience and medical sciences. In this paper, a surrogate deep learning FWI approach is presented to quantify properties of materials using stress waves. Such inverse problems, in general, are ill-posed and nonconvex, especially in cases where the solutions exhibit shocks, heterogeneity, discontinuities, or large gradients. The proposed approach is proven efficient to obtain global minima responses in these cases. This approach is trained based on random sampled set of material properties and sampled trials around local minima, therefore, it requires a forward simulation can handle high heterogeneity, discontinuities and large gradients. High resolution Kurganov-Tadmor (KT) central finite volume method is used as forward wave propagation operator. Using the proposed framework, material properties of 2D media are quantified for several different situations. The results demonstrate the feasibility of the proposed method for estimating mechanical properties of materials with high accuracy using deep learning approaches.
