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在切尔诺贝利野火发生之前使用深度神经网络进行时空源术语估计,并将其应用于切尔诺贝利野火.

Antonie Brožová¹, Václav Šmídl², Ondřej Tichý²

¹Institute of Information Theory and Automation, Czech Academy of Sciences, Pod Vodárenskou věží 4, Prague 18200, Czech Republic; Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, Prague 11200, Czech Republic.

Journal of hazardous materials

|February 8, 2025

概括

这项研究引入了一种新的深度学习方法,用于精确确定大气排放中的危险物质来源. 该方法提高了跟踪空气中的放射性物质的准确性,这对环境安全至关重要.

关键词:

大气的反转是大气的反转.切尔诺贝利的野火发生在野外.深度图像之前的图像.深度神经网络是一种深度神经网络.时间空间源是什么?

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科学领域:

环境科学与工程环境科学与工程
大气科学大气科学
核安全问题核安全问题

背景情况:

对大气危险物质排放的准确源期估计对于分析意外释放至关重要,特别是对于野火造成的放射性污染.
现有的大气逆向问题往往是错误的,没有足够的测量数据来完全描述5D发射张量 (空间位置,时间,高度,粒子大小).

研究的目的:

提出一种用于规范大气反向问题的新方法,用于危险物质排放分析.
利用深度学习,特别是深度图像预测,以在放射性野火等复杂场景中改进源术语估计.

主要方法:

引入深度图像先验方法,使用随机初始化的深度神经网络结构,没有事先的数据集训练.
在调整反转过程之前,将变化优化与深度图像集成在一起.
在源项中强制执行先前的协差结构,以减少未知数和增强空间平滑性.

主要成果:

提出的方法成功地在排放估计中引入了空间平滑性.
该方法通过结合先前的结构信息,有效地减少了未知数.
在2020年切尔诺贝利野火期间估计-137 (¹³⁷Cs) 排放的有效性.

结论:

深度图像预先结合变异优化为大气源术语估计提供了强大的,数据效率高的规范化技术.
这种方法对改善空气中放射性物质传播的分析具有显著的前景,特别是在野火后的污染事件中.