Characterizing buried sedimentary structures through the use of seismic data is part of many geoscientific projects. The evolution of seismic acquisition and processing capabilities has made it possible to acquire ever-growing amounts of data, increasing the image resolution so that sedimentary objects (geobodies) can be imaged with greater precision within sedimentary layers. However, exploring and interpreting them in large datasets can be tedious work. Recent practice has shown the potential of automated methods to assist interpreters in this task. In this paper, a new semi-supervised methodology is presented for identifying multi-facies geobodies in three-dimensional seismic data, while preserving their internal facies variability and keeping track of the input uncertainty. The approach couples a nonlinear data-driven method with a novel supervised learning method. It requires a prior delineation of the geobodies on a few seismic images, along with a priori confidence in that delineation. The methodology relies on a learning of an appropriate data representation, and propagates the prior confidence to posterior probabilities attached to the final delineation. The proposed methodology was applied to three-dimensional real data, showing consistently effective retrieval of the targeted multi-facies geobodies mass-transport deposits in the present case.

中文翻译：

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地震数据中的半监督多相对象检索

通过使用地震数据来表征埋藏的沉积物结构是许多地球科学项目的一部分。地震采集和处理能力的发展使得采集越来越多的数据成为可能，从而提高了图像分辨率，从而可以在沉积层内以更高的精度对沉积物（地质体）成像。但是，在大型数据集中探索和解释它们可能是繁琐的工作。最近的实践表明，自动化方法可以帮助口译员完成这项任务。本文提出了一种新的半监督方法，用于识别三维地震数据中的多相地质体，同时保留其内部相的可变性并跟踪输入不确定性。该方法将非线性数据驱动方法与新颖的监督学习方法结合在一起。它需要在一些地震图像上对地物进行事先描述，并对该描述具有先验信心。该方法依赖于对适当数据表示的学习，并将先验置信度传播到附加到最终轮廓上的后验概率。所提出的方法已应用于三维真实数据，显示了在当前情况下对目标多相地质体传质矿床的持续有效检索。并将先验置信度传播到附加到最终轮廓上的后验概率。所提出的方法已应用于三维真实数据，显示了在当前情况下对目标多相地质体传质矿床的持续有效检索。并将先验置信度传播到附加到最终轮廓上的后验概率。所提出的方法已应用于三维真实数据，显示了在当前情况下对目标多相地质体传质矿床的持续有效检索。