主动学习提高地震资料质量控制水平
Improving the quality control of seismic data through active learning
摘要：
在图像去噪问题中，可用图像的密度不断增加使得彻底的视觉检查变得不可能，因此必须为此目的部署基于机器学习的自动化方法。这在地震信号处理中尤其如此。工程师/地震学家必须处理数以百万计的地震时间序列。寻找对石油工业有用的地下特性可能需要长达一年的时间，并且在计算/人力资源方面非常昂贵。特别是，数据必须经过不同的噪声衰减步骤。每个去噪步骤之后理想地是通过人类专业知识执行的质量控制（QC）阶段。为了以监督的方式学习质量控制分类器，必须提供标记的训练数据，但是从人类专家那里收集标签非常耗时。因此，我们提出了一种新的主动学习方法来顺序选择最相关的数据，然后将其返回给人类专家进行标记。除了在地球物理学中的应用，我们在本文中推广的技术，基于局部误差及其不确定性的估计，是通用的。它的性能得到了强有力的经验证据的支持，如本文中提出的数值实验所示，在合成和真实地震数据集上将其与替代主动学习策略进行比较。
英文摘要：
In image denoising problems, the increasing density of available images makes an exhaustive visual inspection impossible and therefore automated methods based on machine-learning must be deployed for this purpose. This is particulary the case in seismic signal processing. Engineers/geophysicists have to deal with millions of seismic time series. Finding the sub-surface properties useful for the oil industry may take up to a year and is very costly in terms of computing/human resources. In particular, the data must go through different steps of noise attenuation. Each denoise step is then ideally followed by a quality control (QC) stage performed by means of human expertise. To learn a quality control classifier in a supervised manner, labeled training data must be available, but collecting the labels from human experts is extremely time-consuming. We therefore propose a novel active learning methodology to sequentially select the most relevant data, which are then given back to a human expert for labeling. Beyond the application in geophysics, the technique we promote in this paper, based on estimates of the local error and its uncertainty, is generic. Its performance is supported by strong empirical evidence, as illustrated by the numerical experiments presented in this article, where it is compared to alternative active learning strategies both on synthetic and real seismic datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
