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.
%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
}