Thielsen, Chris2022-02-152022-02-152021-05https://hdl.handle.net/11299/226351University of Minnesota M.S. thesis. May 2021. Major: Geological Engineering. Advisor: Randal Barnes. 1 computer file (PDF); xviii, 171 pages.We live in an era of big data, brought on by the advent of automatic large-scale data acquisition in many industries. Machine learning can be used to take advantage of large data sets, predicting otherwise unknown information from them. The Minnesota County Well Index (CWI) database contains information about wells and borings in Minnesota. While a plethora of information is recorded in CWI, some objective codes are missing. A random forest classifier is used to predict aquifer and stratigraphy codes in CWI based on the data provided in drillers’ logs; i.e., before the strata are interpreted by a geologist. We find that by learning from the information written down by the well driller, stratigraphic codes can be predicted with an accuracy of 92.15%. There are 2,600,000 strata recorded in CWI; these codes are not only useful in understanding the geologic history of Minnesota, but also directly inform groundwater models.enaquifermachine learningMinnesota County Well Indexrandom foreststratigraphywell databaseThesis or Dissertation