Results 41 to 50 of about 24,569,148 (220)

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Prediction method for rockburst hazard areas based on microseismic spatiotemporal flow map

Gong-kuang zidonghua
The spatiotemporal distribution characteristics of microseismic events are closely related to the intensity of rockbursts in coal mines. Prediction methods and visual representations of microseismic data based on machine learning are important technical ...
CAO Anye, LIU Yapeng, WANG Zikun, YANG Xu, NIU Qiang   +4 more
doaj   +1 more source

Advances in vital‐sign prediction and early‐warning models for underground coal mine workers integrating environmental factors

Deep Underground Science and Engineering, EarlyView.
This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu, Dawei Cui, Yingjie Liu, Yuntao Liang, Yue Zhang   +4 more
wiley   +1 more source

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

Deep 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, Xueqiu He, Zhenlei Li, Ping Wang, Dazhao Song, Fuxing Jiang, Shan Yin, Anliang Lü, Menghan Wei, Taoping Zhong   +9 more
wiley   +1 more source

Adaptive microseismic noise estimation and denoising

, 2016
Microseismic data recorded by surface arrays are often strongly contaminated by unwanted random noise. This background noise makes the detection of small magnitude events difficult.
Seyed Mostafa Mousavi, Langston, Charles A., Mousavi, S. Mostafa, Charles Langston   +3 more
core   +1 more source

An Effective Denoising Method Based on Cumulative Distribution Function Thresholding and its Application in the Microseismic Signal of a Metal Mine With High Sampling Rate (6 kHz)

Frontiers in Earth Science, 2022
Microseismic events can be used to analyze the risk of tunnel collapse, rock burst, and other mine hazards in space and time. In practice, the artificial activities and other signals at the mining site can seriously interfere with the microseismic ...
Da Zhang, Da Zhang, Da Zhang, Zhiyi Zeng, Zhiyi Zeng, Zhiyi Zeng, Yaqian Shi, Yaqian Shi, Yaqian Shi, Ying Chang, Ying Chang, Ying Chang, Rui Dai, Rui Dai, Rui Dai, Hu Ji, Hu Ji, Hu Ji, Peng Han, Peng Han, Peng Han   +20 more
doaj   +1 more source

Exponentially‐constrained Gaussian mixture model for quantifying vertical development of water‐conducting fracture zones in lithologically stratified mining roofs

Deep Underground Science and Engineering, EarlyView.
This study proposes an exponentially‐constrained Gaussian mixture model (EcGMM) to quantify vertical fracture heterogeneity in stratified roof strata. The model integrates a piecewise exponential decay term capturing global stress dissipation with Gaussian components representing localized fracture intensification at lithological interfaces.
Huiqing Yuan, Zhimin Xu, Weixiao Chen, Chenghang Zhang, Shijie Xiong, Yajun Sun   +5 more
wiley   +1 more source

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Applied Sciences
The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals.
Sihongren Shen, Bo Wang, Linfeng Zeng, Sheng Chen, Liujun Xie, Zilong She, Lanying Huang   +6 more
doaj   +1 more source

Reusing depleted oil and gas wells for geothermal energy: Feasibility, challenges, and a decision‐making framework for sustainable conversion

Deep Underground Science and Engineering, EarlyView.
The graphical abstract presents a structured decision‐support framework for assessing the feasibility of converting depleted oil and gas wells into geothermal energy systems. The left panel illustrates a comprehensive, multi‐stage decision‐making framework progressing from prefeasibility screening and technical–economic analysis to policy alignment ...
Karolina Bieryt, Yu Pan, Heinz Konietzky, Fei Wang   +3 more
wiley   +1 more source

Multicomponent microseismic data denoising by 3D shearlet transform

, 2018
The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent,
Mirko van der Baan, Chao Zhang
core   +1 more source