首页|Studies from Southern University of Science and Technology (SUSTech) in the Area of Machine Learning Described (Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data)
Studies from Southern University of Science and Technology (SUSTech) in the Area of Machine Learning Described (Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data)
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Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “Predicting earthquakes through reasonable methods can significantly reduce the damage caused by secondary disasters such as tsunamis.” Funders for this research include National Natural Science Foundation of China; National Key R&D Program of China; Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology. Our news journalists obtained a quote from the research from Southern University of Science and Technology (SUSTech): “Recently, machine learning (ML) approaches have been employed to predict laboratory earthquakes using stick-slip dynamics data obtained from sheared granular fault experiments. Here, we adopt the combined finite-discrete element method (FDEM) to simulate a two-dimensional sheared granular fault system, from which abundant fault dynamics data (i.e., displacement and velocity) during stick-slip cycles are collected at 2203 “sensor” points densely placed along and inside the gouge. We use the simulated data to train LightGBM (Light Gradient Boosting Machine) models and predict the gougeplate friction coefficient (an indicator of stick-slips and the friction state of the fault). To optimize the data, we build the importance ranking of input features and select those with top feature importance for prediction. We then use the optimized data and their statistics for training and finally reach a LightGBM model with an acceptable prediction accuracy (R2 = 0.94). The SHAP (SHapley Additive exPlanations) values of input features are also calculated to quantify their contributions to the prediction.”
Southern University of Science and Technology (SUSTech)ShenzhenPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Mdpi
