Deep learning techniques are increasingly being considered for geological applications where -- much like in computer vision -- the challenges are characterized by high-dimensional spatial data dominated by multipoint statistics. In particular, a novel technique called generative adversarial networks has been recently studied for geological parametrization and synthesis, obtaining very impressive results that are at least qualitatively competitive with previous methods. The method obtains a neural network parametrization of the geology -- so-called a generator -- that is capable of reproducing very complex geological patterns with dimensionality reduction of several orders of magnitude. Subsequent works have addressed the conditioning task, i.e. using the generator to generate realizations honoring spatial observations (hard data). The current approaches, however, do not provide a parametrization of the conditional generation process. In this work, we propose a method to obtain a parametrization for direct generation of conditional realizations. The main idea is to simply extend the existing generator network by stacking a second inference network that learns to perform the conditioning. This inference network is a neural network trained to sample a posterior distribution derived using a Bayesian formulation of the conditioning task. The resulting extended neural network thus provides the conditional parametrization. Our method is assessed on a benchmark image of binary channelized subsurface, obtaining very promising results for a wide variety of conditioning configurations.
翻译:在地质应用方面,人们越来越多地考虑深层次的学习技术,在这种应用中,挑战的特点是以多点统计为主的高维空间数据。特别是,最近为地质对称和合成研究了一种叫作基因对抗网络的新技术,称为基因对抗网络,最近为地质对称和合成进行了研究,取得了非常令人印象深刻的成果,至少与以前的方法在质量上具有竞争力。该方法获得了地质学神经网络的匹配,即所谓的发电机,它能够产生非常复杂的地质模式,其维度降低几个数量级。随后的工程已经解决了调节任务,即利用发电机实现空间观测(硬数据)的实现。然而,目前的方法并没有提供有条件生成过程的配对。在这项工作中,我们提出了一种方法,为直接生成有条件的实现而获得配对称的配对。主要想法是仅仅通过堆放第二层推论网络来扩展现有的发电机网络,从而学会进行调节。这种推论网络是一个神经网络,经过训练,用来抽样后对从Bayesian制成的后台式配置(硬数据),因此,对我们的平质结构进行了扩展了一种基础调整。