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

STTR 第一阶段：深度迁移学习支持的机器视觉检测及其在勘探地球物理中的应用

• 批准号: 1746824
• 负责人: Wenyi Hu
• 金额: $ 22.5万
• 依托单位: Advanced Geophysical Technology Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1746824&HistoricalAwards=false
• 关键词: STTR; Phase; Deep; Transfer; Learning

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.

小企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力将直接受益于美国经济的能源部门，因为地震勘探将在满足日益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。该项目的目标是开发一个自动模式识别软件包，地震处理公司可使用该软件包从地震数据中自动挑选地质特征。随着地震勘探行业扩大其勘测范围，地震数据量在过去三十年中呈指数级增长。人工拾取和地质模式识别工作依赖于目视检查，劳动密集型，无法跟上地震勘探产生的数据的增长。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识别工具。在训练用于模式识别的高效深度学习网络方面所获得的经验教训在其他领域（如医学图像分析）中有着广泛的应用。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培训。这个小企业技术转让（STTR）第一阶段项目旨在开发一个深度学习网络模型，以识别嵌入地震数据中的独特模式，这些模式是相关地质结构的特征。具体来说，该项目将证明提供机器视觉检测工具的可行性，以减轻领域专家的劳动密集型视觉检查活动。目前存在各种自动拣选方法，具有不同程度的成功。尽管如此，这些工具所涉及的不确定性仍然太高，无法被业界广泛采用。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的自学深度迁移学习方法来克服与数据相关的专有权导致的数据短缺问题。新的训练流程是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，并为新的和看不见的数据集提供一个具有保证性能的强大系统。

项目成果

Seismic data denoising by deep-residual networks

• DOI: 10.1190/segam2018-2998619.1
• 发表时间: 2018-08
• 期刊: SEG Technical Program Expanded Abstracts 2018
• 作者: Yuchen Jin;Xuqing Wu;Jiefu Chen;Zhu Han;Wenyi Hu

Learn low-wavenumber information in FWI via deep inception-based convolutional networks

• DOI: 10.1190/segam2018-2997901.1
• 发表时间: 2018-08
• 期刊: SEG Technical Program Expanded Abstracts 2018
• 作者: Yuchen Jin;Wenyi Hu;Xuqing Wu;Jiefu Chen

First-break automatic picking with deep semisupervised learning neural network

首次突破深度半监督学习神经网络自动拣选

• DOI: 10.1190/segam2018-2998106.1
• 发表时间: 2018
• 期刊: SEG Technical Program Expanded Abstracts 2018
• 作者: Tsai, Kuo Chun;Hu, Wenyi;Wu, Xuqing;Chen, Jiefu;Han, Zhu
• 通讯作者: Han, Zhu