%0 Journal Article %1 Zhou_2019 %A Zhou, Cuiying %A Ouyang, Jinwu %A Ming, Weihua %A Zhang, Guohao %A Du, Zichun %A Liu, Zhen %D 2019 %I MDPI AG %J Applied Sciences %K 68t07-artificial-neural-networks-and-deep-learning 86a15-seismology-earthquakes 86a32-geostatistics %N 17 %P 3553 %R 10.3390/app9173553 %T A Stratigraphic Prediction Method Based on Machine Learning %U https://www.mdpi.com/2076-3417/9/17/3553 %V 9 %X Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as well as geoengineering planning and design. A traditional method depends on the guidance of expert experience, which is subjective and limited, thereby making the effective evaluation of a stratum simulation quite impossible. To solve this problem, this study proposes a machine learning method for a geostratigraphic series simulation. On the basis of a recurrent neural network, a sequence model of the stratum type and a sequence model of the stratum thickness is successively established. The performance of the model is improved in combination with expert-driven learning. Finally, a machine learning model is established for a geostratigraphic series simulation, and a three-dimensional (3D) geological modeling evaluation method is proposed which considers the stratum type and thickness. The results show that we can use machine learning in the simulation of a series. The series model based on machine learning can describe the real situation at wells, and it is a complimentary tool to the traditional 3D geological model. The prediction ability of the model is improved to a certain extent by including expert-driven learning. This study provides a novel approach for the simulation and prediction of a series by 3D geological modeling.

@article{Zhou_2019, abstract = {Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as well as geoengineering planning and design. A traditional method depends on the guidance of expert experience, which is subjective and limited, thereby making the effective evaluation of a stratum simulation quite impossible. To solve this problem, this study proposes a machine learning method for a geostratigraphic series simulation. On the basis of a recurrent neural network, a sequence model of the stratum type and a sequence model of the stratum thickness is successively established. The performance of the model is improved in combination with expert-driven learning. Finally, a machine learning model is established for a geostratigraphic series simulation, and a three-dimensional (3D) geological modeling evaluation method is proposed which considers the stratum type and thickness. The results show that we can use machine learning in the simulation of a series. The series model based on machine learning can describe the real situation at wells, and it is a complimentary tool to the traditional 3D geological model. The prediction ability of the model is improved to a certain extent by including expert-driven learning. This study provides a novel approach for the simulation and prediction of a series by 3D geological modeling.}, added-at = {2022-07-03T03:13:41.000+0200}, author = {Zhou, Cuiying and Ouyang, Jinwu and Ming, Weihua and Zhang, Guohao and Du, Zichun and Liu, Zhen}, biburl = {https://www.bibsonomy.org/bibtex/25f61ae20b326a5b8eb535123d068af9d/gdmcbain}, doi = {10.3390/app9173553}, interhash = {77cea42a6419f5de6dee3bbc77c9d40f}, intrahash = {5f61ae20b326a5b8eb535123d068af9d}, journal = {Applied Sciences}, keywords = {68t07-artificial-neural-networks-and-deep-learning 86a15-seismology-earthquakes 86a32-geostatistics}, month = aug, number = 17, pages = 3553, publisher = {MDPI AG}, timestamp = {2022-07-04T01:45:32.000+0200}, title = {A Stratigraphic Prediction Method Based on Machine Learning}, url = {https://www.mdpi.com/2076-3417/9/17/3553}, volume = 9, year = 2019 }