Acoustic Emission Characteristics in Coal Failure from Chinese Coal

The present work studied the acoustic emission (AE) generated by uniaxial compression of coal from four major coal-producing regions in China-Guizhou, Shanxi, Inner Mongolia, and Xinjiang. Through AE signal monitoring and analysis, the study explored the relationship between AE signals, coal heterogeneity, and mechanical behavior. The findings affirmed that the AE energy distributions across all coal samples followed a power-law, which corroborates scale invariant failure dynamics similar to those of natural earthquakes. The more homogeneous samples, such as those from Guizhou and Shanxi, exhibited brittle behavior, and generated higher-energy AE events with a power-law coefficient of approximately 1.44. Conversely, the heterogeneous samples from Inner Mongolia and Xinjiang showed weaker mechanical behavior and lower energy emissions, with a power-law coefficient of 1.66. Additionally, the study compared the distribution of waiting times and aftershock sequences of the AE events, noting consistency with established geophysical models, such as Omori’s (1894) laws. Influenced by the internal structure and composition of heterogeneous coals, the heterogeneous coal samples in this study produced fewer shear cracks than the homogeneous coal samples. The findings yielded significant insights into the failure mechanisms of coal subjected to compressive stress and establish a basis for enhancing coal mine disaster prediction and preventive measures.

Authors

Institutions

Publication Details

Journal
Natural Resources Research
Published
2025-11-20
DOI
https://doi.org/10.1007/s11053-025-10574-y
Citations
2
Primary Topic
Coal Properties and Utilization
Type
article
Field-Weighted Citation Impact
2.29
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Acoustic Emission Characteristics in Coal Failure from Chinese Coal

Xiang Jiang, Zhaolong Ge, Saipeng Huang, Antoni Planes et al.
2 citations
Natural Resources Research
Coal Properties and Utilization
2.29
article

Acoustic Emission Characteristics in Coal Failure from Chinese Coal

Xiang Jiang, Zhaolong Ge, Saipeng Huang, Antoni Planes, Emma Valdés, Eduard Vives, Honglian Li
article en
2 citations

Abstract

The present work studied the acoustic emission (AE) generated by uniaxial compression of coal from four major coal-producing regions in China-Guizhou, Shanxi, Inner Mongolia, and Xinjiang. Through AE signal monitoring and analysis, the study explored the relationship between AE signals, coal heterogeneity, and mechanical behavior. The findings affirmed that the AE energy distributions across all coal samples followed a power-law, which corroborates scale invariant failure dynamics similar to those of natural earthquakes. The more homogeneous samples, such as those from Guizhou and Shanxi, exhibited brittle behavior, and generated higher-energy AE events with a power-law coefficient of approximately 1.44. Conversely, the heterogeneous samples from Inner Mongolia and Xinjiang showed weaker mechanical behavior and lower energy emissions, with a power-law coefficient of 1.66. Additionally, the study compared the distribution of waiting times and aftershock sequences of the AE events, noting consistency with established geophysical models, such as Omori’s (1894) laws. Influenced by the internal structure and composition of heterogeneous coals, the heterogeneous coal samples in this study produced fewer shear cracks than the homogeneous coal samples. The findings yielded significant insights into the failure mechanisms of coal subjected to compressive stress and establish a basis for enhancing coal mine disaster prediction and preventive measures.

Natural Resources ResearchVol. 35(1)
Chongqing University (CN), Chongqing Jiaotong University (CN), Universitat de Barcelona (ES)
Openalex Percentile: Top 10%
Coal Properties and Utilization
2.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.