首页|New Machine Learning Study Findings Recently Were Reported by Researchers at Tai yuan University of Technology (Recovering 3d Basin Basement Relief Using High-pr ecision Magnetic Data Through Random Forest Regression Algorithm: a Case Study o f ...)
New Machine Learning Study Findings Recently Were Reported by Researchers at Tai yuan University of Technology (Recovering 3d Basin Basement Relief Using High-pr ecision Magnetic Data Through Random Forest Regression Algorithm: a Case Study o f ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news originating from Taiyuan, People's Republic of China, by NewsRx correspondents, research stated, "Inversion of magnetic basement interfac es in basins is essential for interpreting potential field data and studying geo thermal resource distribution, as well as basin formation and evolution. This pa per introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processi ng and machine learning techniques." Financial support for this research came from Shanxi Institute of Geological Sur vey CO., LTD.. Our news journalists obtained a quote from the research from the Taiyuan Univers ity of Technology, "The method creates magnetic base interface models and corres ponding magnetic anomaly data through the random midpoint displacement method an d magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical cont inuation, spatial derivatives, and fractional transformations. Feature attribute s are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analys is of both idealized and noisy models. The proposed machine learning-based appro ach is more intelligent, efficient, and accurately represents the relief of magn etic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural inform ation, paving the way for further research in magnetic interface inversion."
TaiyuanPeople's Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningTaiyuan Univers ity of Technology
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