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深度学习和基于直方图的图像粒度分析

Wei Wei¹, Xiaohong Xu¹, Guangming Hu¹

¹School of Geosciences, Yangtze University, Wuhan 430100, China.

Sensors (Basel, Switzerland)

|August 10, 2024

概括

一个新的深度学习模型使用直方图层准确地分析沉积物模拟实验 (SSEs) 中的粒度大小. 这种方法提高了图像的效率和精度,有不规则的粒度分布.

科学领域:

地质科学地质科学
沉积物学的沉积物学
计算方法计算方法

背景情况:

在沉积体模拟实验 (SSE) 中,粒径大小分析对于理解沉积环境和水力动力学条件至关重要.
传统的基于图像的颗粒大小分析方法与SSE图像中常见的模糊的颗粒边缘和不规则的排列作斗争.

研究的目的:

开发一种深度学习模型,用于SSE图像的精确粒度分析,解决现有方法的局限性.
在SSEs中提高粒度大小分析的量化和自动化.

主要方法:

提出了一个深度学习模型,将ResNet18特征提取与直方图层结合起来.
提取了局部历史图特征并连接起来,形成了全面的图像历史图特征.
这些特征被用来估计与累计体积百分比相对应的颗粒大小.

主要成果:

与其他八种模型相比,拟议的深度学习方法实现了更高的准确性.
结果显示与手动谷粒大小分析具有很高的一致性.
该模型有效地处理具有不规则粒度分布和模糊边缘的图像.

结论:

开发的方法显著提高了SSE中谷粒大小分析的效率和准确性.
这种方法提高了复杂的沉积物图像的粒度分析的量化和自动化.
该方法在土壤科学和地质工程方面具有潜在的应用.

关键词:

深沉的倾斜在深沉的倾斜.谷粒大小分析分析谷粒大小累积曲线的累积曲线图形图表层图形图表层图形图表层模拟实验实验的模拟

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