[M-IS21] Planetary Volcanology

[MIS21-P08] Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

*Rina Noguchi¹, Daigo Shoji², Fujimoto Keiichiro², Nobuo Geshi³, Motomaro Shirao, Junichi Haruyama² (1.Faculty of Science, Niigata University, 2.JAXA, 3.AIST)

Keywords:stratigraphic exposure, machine learning, geological survey, Mars

The stratigraphic column is one of the description methods which summarize fundamental geological data such as layer thickness and constituent materials at a certain outcrop. Identification and discrimination of stratigraphic exposures are preliminary and fundamental work in the geological survey. Automation of such basic investigations is not as easy as it seemed at first sight because they are often obscured by talus and vegetation. In this study, we performed supervised machine learning to extract areas of stratigraphic exposures in visible images using u-net. We input augmented 14024 terrestrial outcrop images and those masked images to train the machine. As a result, we obtained 92% of accuracy for validation data. The trained model can extract stratigraphic exposures from inputted images, though there are some difficulties in color and untrained situations such as snow coverage. This autonomous detection of exposed stratigraphic structures will increase output from the huge storage of high-resolution images taken on terrestrial bodies. Autonomous detection of exposed stratigraphic structures from outcrop images will contribute to the remote-geological survey on the red planet.