论文标题
多物理特性的极弱监督反转
Extremely Weak Supervision Inversion of Multi-physical Properties
论文作者
论文摘要
多物理反演在地球物理学中起着至关重要的作用。它已被广泛用于推断各种物理特性〜（例如速度和电导率）。在这些反转问题中，有些是由部分微分方程（PDE）明确控制的，而另一些则不是。如果没有明确的管理方程式，传统的多物理反演技术将是不可行的，并且数据驱动的反演需要昂贵的完整标签。为了克服这个问题，我们开发了一种新的数据驱动的多物理倒置技术，并具有极为弱的监督。我们的关键发现是，可以通过在非常稀疏的良好地点位置学习地球物理特性之间的局部关系来构建伪标签。我们探索了一个多物理倒置问题，从两个不同的测量值〜（地震和EM数据）到三个地球物理特性〜（速度，电导率和CO $ _2 $ $饱和）。我们的结果表明，我们能够在不明确管理方程式的情况下倒入属性。此外，有关三种地球物理特性的标签数据可以显着降低50倍〜（从100下降到只有2个位置）。
Multi-physical inversion plays a critical role in geophysics. It has been widely used to infer various physical properties~(such as velocity and conductivity). Among those inversion problems, some are explicitly governed by partial differential equations~(PDEs), while others are not. Without explicit governing equations, conventional multi-physical inversion techniques will not be feasible and data-driven inversion requires expensive full labels. To overcome this issue, we develop a new data-driven multi-physics inversion technique with extremely weak supervision. Our key finding is that the pseudo labels can be constructed by learning the local relationship among geophysical properties at very sparse well-logging locations. We explore a multi-physics inversion problem from two distinct measurements~(seismic and EM data) to three geophysical properties~(velocity, conductivity, and CO$_2$ saturation). Our results show that we are able to invert for properties without explicit governing equations. Moreover, the label data on three geophysical properties can be significantly reduced by 50 times~(from 100 down to only 2 locations).
