Application of a CNN to the Boda Claystone Formation for high-level radioactive waste disposal

Author:

Lovász Virág,Halász Amadé,Molnár Péter,Karsa Róbert,Halmai Ákos

Abstract

AbstractNations relying on nuclear power generation face great responsibilities when designing their firmly secured final repositories. In Hungary, the potential host rock [the Boda Claystone Formation (BCF)] of the deep geological repository is under extensive examination. To promote a deeper comprehension of potential radioactive isotope transport and ultimately synthesis for site evaluation purposes, we have efficiently tailored geospatial image processing using a convolutional neural network (CNN). We customized the CNN according to the intricate nature of the fracture geometries in the BCF, enabling the recognition process to be particularly sensitive to details and to interpret them in the correct tectonic context. Furthermore, we set the highest processing scale standards to measure the performance of our model, and the testing circumstances intentionally involved various technological and geological hindrances. Our presented model reached ~ 0.85 precision, ~ 0.89 recall, an ~ 0.87 F1 score, and a ~ 2° mean error regarding dip value extraction. With the combination of a CNN and geospatial methodology, we present the description, performance, and limits of a fully automated workflow for extracting BCF fractures and their dipping data from scanned cores.

Funder

University of Pécs

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference74 articles.

1. Witherspoon, P. A., Bodvarsson, G. S. (eds). Geological Challenges in Radioactive Waste isolation: Third worldwide review. Report number: LBNL-49767. 335 p. (Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA [United States], 2001).

2. Nuclear Energy Agency. Management and Disposal of High-Level Radioactive Waste: Global Progress and Solutions. OECD NEA No. 7532, 51 p. OECD Publishing, Paris Available: https://www.oecd-nea.org/upload/docs/application/pdf/2020-07/7532-dgr-geological-disposal-radioactive-waste.pdf (2020). Accessed 23 January 2023.

3. Nős, B. Needs of countries with longer timescale for deep geological repository implementation. EPJ Nucl. Sci. Technol. 6(22), 7. https://doi.org/10.1051/epjn/2019042 (2020).

4. Warner P. J. United States of America Activities Relative to the International Atomic Energy Agency (IAE) Initiative: Records Management for Deep Geological Repositories [conference paper—1997 Waste Management Conference, Tucson Arizona] 18 p. https://www.osti.gov/biblio/451245 (1997). Accessed 22 February 2022.

5. Apted, M., Ahn, J. (eds.) Geological Repository Systems for Safe Disposal of Spent Nuclear Fuels and Radioactive Waste. 802 p. ISBN: 9780081006429 (Woodhead Publishing Series in Energy, 2017).

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3