WebApr 14, 2024 · Coal-burst is a typical dynamic disaster that raises mining costs, diminishes mine productivity, and threatens workforce safety. To improve the accuracy of coal-burst risk prediction, deep learning is being applied as an emerging statistical method. Current research has focused mainly on the prediction of the intensity of risks, ignoring their … WebExisting decision-making tool for managing seismic risks, known as the traffic light system, is not robust enough. To meet the increasing needs for safe mining of energy at production sites, finding an advanced and efficient method to improve the traffic light system is …
Seismic structure interpretation based on machine learning: A …
WebJan 24, 2024 · Seismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by known geological laws and drilling and logging data. The principle of seismic inversion based on deep learning is to learn the mapping between seismic data and rock properties … Webmine Jianhua Hu, Tan Zhou *, Shaowei Ma, ... Alimoradi et al. learned Tunnel Seismic Prediction (TSP-203) data ... In general, the machine learning models, without combining optimisation standing seam forming machine
ConvLSTM for Predicting Short-Term Spatiotemporal ... - Springer
WebJan 24, 2024 · Seismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, … WebSep 1, 2024 · Predicting seismic events in coal mines based on underground sensor measurements September 2024 Authors: Andrzej Janusz Marek Grzegorowski University … WebFeb 28, 2024 · The researchers trained their neural network with inputs that they generated using the Marmousi model, a complex two-dimensional geophysical model that simulates the way seismic waves travel through geological structures … personal message to recommender example