A Stratigraphic Prediction Method Based on Machine Learning

A Stratigraphic Prediction Method Based on Machine Learning

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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 Covid-19 Döneminde E-Şikâyet Yönetimi Perspektifinden Müşterilerin Çevrimiçi Alışverişte Karşılaştıkları Sorunlar 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 Możliwości zastosowania metody Mystery Shopping w ocenie jakości usług turystycznych. Studium przypadku – Termy w Białce Tatrzańskiej 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.