Counting craters: 1, 2, 3, 4 billion +
Crater counting has been used since the 1960s to determine ages for planetary surfaces. The more craters a planetary body has means the older the surface. But it’s always been a laborious, manual effort.
We’ve developed an advanced machine learning algorithm to automate this process and validated it against the manually counted datasets. With this algorithm, we analysed a 3.9TB mosaicked[1] (5m/pixel) image of Mars. The previous (definitive) manually counted dataset was 380,000 craters. This dataset stopped at craters 1km in diameter. Our algorithm returned 94 million craters down to 50m in diameter in 24 hours. This is now the world’s largest database of Martian craters. It allows us to see age variation across Mars at unprecedented resolution.
This is the first time a machine learning surface age tool has been applied to discover new knowledge of our solar system. We have used it to address multiple science questions, including pinpointing locations on Mars that are the sources of meteorites we have here on Earth, effectively the first Mars sample return.
We are now able to count down to the smallest sizes that we can see—as small as 10m across. We can do this in a robust, quantitative and reproducible way. We can measure all the craters across the entire surface of a planet or a moon and create a complete age map for that body at ultimate resolution. Our latest focus is the Moon, where we now have a quantitative dataset for over 240 million craters. This is a principal area of focus SSTC collaboration with NASA in the Artemis mission.
This work is led by Professor Gretchen Benedix.
[1] This is a form of astrophotography that captures multiple images and layers them together to create a composite image.