Stratigraphic information extraction from landsat data based on machine learning—a case study of the Keping area of southwest Tianshan Mountains

In the current era of artificial intelligence, machine learning methods are constantly innovating and developing.It provides a good platform for effectively analyzing and utilizing of remote sensing data.Combined with the typical geological characteristics of sedimentary rocks in Keping area of southwest Tianshan Mountains, in this paper, remote sensing data from 9 bands of Landsat 8 data were used for machine learning interpretation.In order to enhance the number of variables involved in the process of machine learning, ratio method and principal component analysis method were used to enhance the data superposition based on the original 9-band data.In order to weaken the internal texture information of geological bodies without affecting the boundary between geological bodies, bilateral filtering was used to further process remote sensing data.Three machine learning methods, Extra Trees, Hist Gradient Boosting and Random Forest, were selected.Through experiments, the overall recognition accuracy exceeded 93%, especially 94.18% for the Extra Trees method.It is worth popularizing the research method of this paper in other geological information interpretations and geological mapping.