The scientists, using data on 7,895 previously identified craters and 1,411 dated craters, were able to apply machine learning to train a deep neural network. With information from China’s first and second lunar orbiters (Chang’e 1 and Chang’e 2), the network identified 109,956 new craters. The two unmanned spacecraft were launched in 2007 and 2010, respectively.
“Impact craters (are) the most diagnostic features of the lunar surface. This contrasts sharply with the Earth’s surface. It is very difficult to trace the history of the Earth from being affected by asteroids and comets during the last 4 billion years. ‘years,’ said the study’s author Chen Yang, from Jilin University College of Earth Sciences and the Key Laboratory for Exploring Lunar and Deep Space at the Chinese Academy of Sciences.
“The earth and the moon have been hit by the same impactful population over time, but large lunar craters have experienced limited degradation for billions of years. Therefore, lunar impact craters can track the evolution of the Earth, “he said in an email.
The craters of the moon lack water, an atmosphere, and tectonic plate activity, three forces that erode the Earth’s surface, meaning not all meteorite impacts except the most recent are visible.
The latest study is not the first to deploy machine learning to detect lunar craters, Mohamad Ali-Dib told the University of Montreal’s Exoplanet Research Institute.
“Machine learning can be used to detect craters on the moon,” he said in an email. Craters are “a window into the dynamic history of the solar system.