STTR Phase I: Deep Transfer Learning Enabled Machine Vision Inspection and Its Applications in Exploration Geophysics

由于地震勘探将在满足不断增长的能源需求和保持健康的石油和天然气产量方面发挥越来越重要的作用，因此，这个小型企业技术转让（STTR）第一阶段项目的广泛影响和商业潜力将直接惠及美国经济的能源部门。该项目的目标是开发一个用于自动模式识别的软件包，该软件包可用于地震处理公司从地震数据中自动选择地质特征。在过去的30年里，随着地震勘探行业扩大了勘探范围，地震数据量呈指数级增长。人工采集和地质模式识别工作依赖于目测，是劳动密集型的工作，无法跟上地震调查数据的增长。在这个项目中，该公司将开发一种由深度学习网络训练的机器视觉采摘和识别工具。训练高效的深度学习网络用于模式识别的经验教训在医学图像分析等其他领域具有广泛的应用。该项目将支持地震勘探、机器学习和高性能计算领域的研究生和本科生的培训。这项小型企业技术转移（STTR）第一阶段项目旨在开发一种深度学习网络模型，以识别地震数据中嵌入的独特模式，这些模式是相关地质结构的特征。具体来说，该项目将展示交付机器视觉检测工具的可行性，从而将领域专家从劳动密集型的视觉检查活动中解脱出来。目前存在各种自动采摘方法，取得了不同程度的成功。尽管如此，这些工具的不确定性仍然太高，无法被行业广泛采用。深度学习领域的最新进展使得在某些应用中超越人类水平的视觉识别性能成为可能。然而，高性能的深度学习网络模型需要大量高质量的训练数据。在这个项目中，该公司提出使用一种新颖的自学深度迁移学习方法来克服由于与数据相关的专有权利而导致的数据短缺问题。新的训练流程适应于地震数据处理领域。它还将最大限度地减少训练工作量，并为新的和未见过的数据集提供具有保证性能的健壮系统。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will result from a direct benefit to the energy sector of the U.S. economy, since seismic exploration will play an increasingly important role in meeting increasing energy demands and maintaining healthy oil and gas output. The goal of this project is to develop a software package for automated pattern recognition that can be used by seismic processing companies to automatically pick geological features from seismic data. Seismic data volumes have grown exponentially over the last three decades as the seismic exploration industry increases its survey coverage. Manual picking and geological pattern identification jobs, which depend on visual inspection, are labor intensive and cannot keep up with the growth in data generated by seismic surveys. In this project, the company will develop a machine vision enabled picking and identification tool trained by a deep learning network. Lessons learned in training an efficient deep learning network for pattern recognition have wide applications in other areas such as medical image analysis. This project will support the training of both graduate and undergraduate students in the areas of seismic exploration, machine learning and high-performance computing. This Small Business Technology Transfer (STTR) Phase I project aims to develop a deep learning network model to recognize unique patterns embedded in seismic data, which patterns are characteristic of the associated geological structures. Specifically, the project will demonstrate the feasibility of delivering a machine vision enabled inspection tool to relieve domain experts from labor-intensive visual examination activities. Various automatic picking approaches currently exist, with differing degrees of success. Nonetheless, the uncertainty involved in these tools is still too high for them to be widely adopted by the industry. Recent advances in the area of deep learning make it possible to surpass human-level visual recognition performance in some applications. High performance deep learning network models, however, require a large amount of high quality training data. In this project, the company proposes to use a novel self-taught deep transfer learning approach to overcome the data shortage problem resulting from proprietary rights associated with the data. The new training workflow is adaptive to the domain of seismic data processing. It will also minimize the training effort and deliver a robust system with guaranteed performance for new and unseen datasets.