2026-10-11T23:01:10Z https://www.scidb.cn/oai

10.57760/sciencedb.17864 2024-04-17T11:17:05Z

2024-04-17 Supporting data and code for the manuscript "Virtual borehole formation prediction using stacked machine learning algorithms". doi:10.57760/sciencedb.17864 en We use drilling data from a certain area along the coast of China for stratigraphic coding and normalized data pro- cessing, and then use the previously mentioned method to train predictions and obtain better results. Section 4 discusses the similarity and error of 10-fold cross-validation of real borehole and predicted borehole data. Section 5 summarizes the full text. This study proposes a new prediction method based on machine learning to address existing prob- lems in the field of virtual borehole formation prediction, such as insufficient prediction accuracy, low automation, and low efficiency. virtual borehole; prediction; machine learning; drilling data from a certain area BINGNING GUO PUBLIC https://api.github.com/licenses/agpl-3.0 dataset Science Data Bank