But cave entrances are difficult to spot, especially from orbit, as they blend in with the dusty background. A new machine learning algorithm has been developed to quickly scan images of the Martian surface, searching for potential cave entrances.

Researchers Thomas Watson and James Baldini from Durham University in the U.K. used a convolutional neural network (CNN), trained to identify potential cave entrances (PCEs) from images of the Martian surface to locate new potential caves. It was able to identify 61 new cave entrances from images in four different regions on Mars.

Previously, most detections of Martian PCEs have come from a manual review of visible satellite imagery, with images taken by the Mars Reconnaissance Orbiter's (MRO) Context Camera (CTX) and High-Resolution Imaging Science Experiment (HiRISE) cameras. A database from that manual review called the Mars Global Candidate Cave Catalogue (MGC3) contains the coordinates and brief descriptions of more than 1,000 identified PCEs on Mars.

"Manual review of satellite imagery for Martian cave detection is far from efficient on a planet-wide scale," wrote Watson and Baldini in their paper, published in the journal Icarus, "due to the time constraints associated with reviewing such a large dataset. Machine learning presents an intriguing solution to this problem, reducing the dataset to only include imagery computationally determined to contain a PCE."