@article{eage:/content/journals/10.3997/1365-2397.n0056, author = "Fielding, Samuel R. and Davies, Lorin", title = "Drivers of imbalance in machine learning uptake across geology and geophysics", journal= "First Break", year = "2019", volume = "37", number = "9", pages = "59-63", doi = "https://doi.org/10.3997/1365-2397.n0056", url = "https://www.earthdoc.org/content/journals/10.3997/1365-2397.n0056", publisher = "European Association of Geoscientists & Engineers", issn = "1365-2397", type = "Journal Article", abstract = "Abstract Machine learning (ML) is being used to process and understand data more and more throughout geology and geophysics in oil and gas exploration. However, its uptake has been slower than it has been for other industries and within exploration has been much stronger for some types of data and disciplines than it has for others. For instance, there has been significant ML focus on seismic data, while other data, such as those generated by geochemistry, have received relatively little. This imbalance in ML uptake leaves much opportunity for the growth of ML applications in these undersaturated fields.", }