Habimana Emmanuel, Jaehyung Yu, Lei Wang, Sung Hi Choi, Gilljae Lee, Digne E. Rwabuhungu R
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引用次数: 0
摘要
本研究旨在开发一种基于从 SRTM DEM 提取的形态参数的自动撞击坑分类机器学习(ML)方法。训练和测试数据集包括 52 个已确认的、保存完好的和中度侵蚀的撞击坑数据,以及韩国最近发现的一个撞击坑--Jeokjung Chogye Basin(JCB)的数据。对复杂陨石坑和简单陨石坑的边缘直径、底面直径和壁宽等形态参数进行了曼-惠特尼 U 检验和单样本 Wilcoxon 符号秩检验。检验结果表明,这些参数能在统计学上将两类陨石坑区分开来。随机森林模型对它们进行分类的准确率为 88.6%,卡帕系数为 0.67,其中边缘直径、底面直径和壁宽被认为是基尼指数最高的变量。与具有抛物线基底的简单陨石坑相比,复杂陨石坑的特点是平面直径大、壁宽。造成这种差异的原因是陨石坑形成时的撞击能量。研究证实,利用机器学习方法,通过检查 SRTM 高程模型,可以将复杂陨石坑和简单陨石坑区分开来。通过统计检验和机器学习算法,JCB 撞击坑的形态参数表明该撞击坑属于高度复杂的撞击坑。
Classification of terrestrial impact craters based on morphometric parameters using remote sensing data: a case study of Jeokjung-Chogye impact crater, South Korea
This study aims to develop an automated impact crater classification machine learning (ML) method based on the morphometric parameters extracted from SRTM DEM. The training and testing dataset comprises data from 52 confirmed, well preserved, and moderately eroded impact craters and a recently discovered impact crater in Korea, Jeokjung Chogye Basin (JCB). The morphometric parameters including rim diameter, floor diameter, and wall width of complex craters and simple craters were tested by Mann Whitney U test and One Sample Wilcoxon signed rank test. The tests showed that those parameters can statistically separate the two types of craters. The Random Forest model classified them with an accuracy of 88.6% and a Kappa coefficient of 0.67, where rim diameter, floor diameter, and wall width were identified as variables with the highest Gini indices. Complex craters are characterized by a large flat diameter and wide wall width compared to simple craters with parabolic bases. The difference is caused by the impact energy when the craters were formed. The study confirmed that using machine learning, the complex craters and simple craters can be separated by checking the SRTM elevation model with machine learning methods. The morphometric parameters of JCB impact crater indicated that the crater is highly a complex crater concluded by both statistical tests and machine learning algorithm.
期刊介绍:
Geosciences Journal opens a new era for the publication of geoscientific research articles in English, covering geology, geophysics, geochemistry, paleontology, structural geology, mineralogy, petrology, stratigraphy, sedimentology, environmental geology, economic geology, petroleum geology, hydrogeology, remote sensing and planetary geology.