Machine Learning Reveals Hidden Geology
Leila Donn, a doctoral student at the University of Texas at Austin studying environmental geoscience, wasn’t necessarily looking for a computer model to help her find the location of ancient Mayan caves last year. Mostly, she just was hot and tired and the work was going slowly.
“In summer the of 2018 I went to the ancient Maya site of El Zotz in the Petén Department of Guatemala to help a colleague look for caves. Each evening we’d sit down and take a look at the LIDAR hillside, looking for depressions surrounded by steep areas,” she said.
“While hard tropical forest hikes actually are my idea of fun, looking for caves in this manner wasn’t particularly efficient,” she said.
She had an idea.
“It occurred to me that it would be tremendously useful if we could develop a computational method of predicting likely locations of caves,” she said.
...she was surprised to find that no one had actually applied machine learning for searches like hers.
She did this by using Python and ArcGIS to make raster layers to capture morphologic characteristics that were associated with cave entrances, such as the fact that they are frequently located in areas of very steep topography.
Donn, whose master thesis at UT Austin was on the “Human-Environment Impact on Geomorphology and Flood Recurrence of the Belize Rover Valley,” now wants to expand her work to predict caves more accurately.
“I’d like my program to identify only caves versus small cave-like voids. It would also be tremendously helpful if I were able to acquire an additional training dataset from an area of similar geomorphology,” she said.
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