Results 111 to 120 of about 6,116 (263)

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

open access: yesDeep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Rock mechanical properties under reconstructed deep in situ thermo–hydro–mechanical conditions: Implications for CO₂‐related injection scenarios

open access: yesDeep Underground Science and Engineering, EarlyView.
The graphical abstract illustrates a reconstructed in situ thermo‐hydro‐mechanical (THM) framework in which porosity serves as the central variable linking stress, pore pressure, and temperature to evolving mechanical properties of rocks. Under burial conditions, in situ stress, pore pressure, and temperature jointly govern volumetric strain and ...
Mingyuan Lu   +5 more
wiley   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

open access: yesDeep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang   +3 more
wiley   +1 more source

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

open access: yesDeep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang   +6 more
wiley   +1 more source

Investigation on the failure evolution characteristics of surrounding rock in deep cross‐fault roadway: Based on physical model test and numerical simulation

open access: yesDeep Underground Science and Engineering, EarlyView.
This study reveals the failure evolution characteristics of deep cross‐fault roadway surrounding rock under excavation support and periodic weighting. Periodic weighting readily induces fault activation, with the spatial distribution of failed rock masses being controlled by the fault strike and dip.
Tiezhu Li   +4 more
wiley   +1 more source

Multi‐factor coupling effects in hydraulic fracturing of laminated shale: Experimental insights and physics‐informed neural network‐driven optimization

open access: yesDeep Underground Science and Engineering, EarlyView.
This study establishes a multi‐factor coupling framework for predicting breakdown pressure in laminated shale by integrating experimental hydraulic fracturing tests, physics‐informed neural networks (PINNs), and Sobol sensitivity analysis. It reveals how differential stress, the bedding dip angle, and the injection rate interact to influence fracture ...
Tao Wang   +6 more
wiley   +1 more source

Integrated numerical simulation and source‐sink matching methodology for Basin‐Wide CO2 storage assessment in depleted reservoirs: A Cambay Basin case study

open access: yesDeep Underground Science and Engineering, EarlyView.
This study presents an integrated workflow to evaluate secure and large‐scale CO2 storage in depleted oil reservoirs of the Cambay Basin, India. Industrial CO2 sources are systematically matched with mature oil fields using a quantitative source–sink framework to minimize transport distance and cost.
Bhaskarjyoti Khanikar
wiley   +1 more source

Mechanism of “seesaw‐type” rock burst in coal seam mining beneath mountainous areas

open access: yesDeep Underground Science and Engineering, EarlyView.
This study reveals the mechanism of “seesaw‐type” rock bursts during coal mining beneath mountainous areas. The advancing working face induces nonuniform fracturing of the overburden. The detached mountain mass then undergoes a seesaw‐type rotational movement around a shifting pivot, driving the primary fracture through a characteristic “open‐close ...
Chao Zhou   +9 more
wiley   +1 more source

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