Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition

Topic Information

Dear Colleagues,

There are many types of dynamic disasters, including water inrush, coal and gas outburst, rock burst, mine shock, etc. These various types of dynamic disasters occur in a variety of geological conditions. In addition, mining layout, mining methods, mining intensity, pressure relief control technology, etc., have an significant impact on the occurrence and prediction of dynamic disasters. The coupling of human mining activities and nature makes the mechanism of dynamic disasters complex, prediction accuracy low, and control difficult.

Although there are a number research articles on dynamic disasters, there is still considerable room for debate. Some of the frequently debated topics include:

• The influence of geological structure on the occurrence of dynamic disasters;
• Dynamic disaster control technology;
• Dynamic disaster occurrence mechanisms;
• Deep learning and intelligent identification of dynamic disaster occurrence laws based on big data;
• Common scientific problems of solid, liquid, and gas three-phase medium power disasters;
• Intelligent monitoring and early warning of dynamic disaster precursor characteristics.

Contributions covering any of these topics, including the perspective of the coordinated regulation of dynamic disasters and other disasters, are welcome, as are contributions using seismology knowledge to predict dynamic disasters.

We invite you to share your experience, field research results, and indicators obtained through the analysis of dynamic disaster occurrence mechanisms with your colleagues and contribute to strengthening our technological innovation in dynamic disaster occurrence mechanisms, early warning, regulation, and disaster reduction.

Prof. Dr. Feng Cui
Dr. Zhenlei Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
GeoHazards geohazards	2.3	2.6	2020	18.9 Days	CHF 1400	Submit
Minerals minerals	2.7	4.9	2011	17 Days	CHF 2400	Submit

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All Applied Sciences Energies GeoHazards Minerals

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17 pages, 8572 KB  

Open AccessArticle

Experimental Study on Pressure Wave Propagation in Mine Ventilation Disasters

by Shouguo Yang, Shuxin Mei, Xiaofei Zhang and Jun Liang

GeoHazards 2026, 7(2), 46; https://doi.org/10.3390/geohazards7020046 - 28 Apr 2026

Viewed by 536

Abstract

This study experimentally investigates the propagation characteristics of static pressure waves (S-waves) and dynamic pressure waves (D-waves) induced by coal and gas outbursts of varying intensities, utilizing a self-built 1:30 scaled laboratory mine ventilation model. Systematic measurements and quantitative [...] Read more.

This study experimentally investigates the propagation characteristics of static pressure waves (S-waves) and dynamic pressure waves (D-waves) induced by coal and gas outbursts of varying intensities, utilizing a self-built 1:30 scaled laboratory mine ventilation model. Systematic measurements and quantitative analyses were conducted to determine waveform morphology, propagation velocities, attenuation laws, and frequency distributions. The results demonstrate that outburst-induced D-waves exhibit a distinct full-sinusoidal waveform, whereas S-waves present a half-sinusoidal profile. Notably, the wavelength of both wave types remains highly stable regardless of initial outburst intensity and propagation distance. Conversely, the wave amplitude is positively correlated with the outburst intensity and attenuates progressively with distance. Furthermore, D-waves demonstrate a significantly higher sensitivity to propagation distance than S-waves. Spectral analysis confirms that the primary energy of both pressure waves is concentrated in the ultra-low-frequency range below 1.0 Hz. The average propagation velocities of S-waves and D-waves were measured at 395.67 m/s and 280.27 m/s, respectively, indicating that S-waves propagate considerably faster. It should be noted that since these findings were derived under scaled laboratory conditions, direct extrapolation to full-scale, long-distance field roadways requires further validation. Ultimately, this work elucidates the fundamental propagation mechanisms of attenuated pressure waves within mine ventilation networks, providing critical waveform signatures for the remote identification and localization of underground disaster sources. Full article

(This article belongs to the Topic Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition)

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16 pages, 2324 KB  

Open AccessArticle

The Study of Influence of Quarry Bench Elevation on the Prediction of Blasting Vibration Using Empirical Attenuation Equations and Artificial Neural Networks

by Chi-Han Wang, Yung-Chin Ding and Wei-Yuan Su

Appl. Sci. 2026, 16(7), 3556; https://doi.org/10.3390/app16073556 - 5 Apr 2026

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Abstract

Blasting operations in quarries are frequently carried out across benches with pronounced elevation variations, which affect the propagation of ground vibrations. This study examines vibration attenuation in a marble quarry in eastern Taiwan using both traditional empirical formulas and artificial neural networks (ANNs). [...] Read more.

Blasting operations in quarries are frequently carried out across benches with pronounced elevation variations, which affect the propagation of ground vibrations. This study examines vibration attenuation in a marble quarry in eastern Taiwan using both traditional empirical formulas and artificial neural networks (ANNs). Field measurements were collected from 54 production blasts, resulting in 322 vibration records at three distinct elevation levels. Several empirical equations—including an elevation correction factor—were applied and compared. Among these, the equation incorporating an adjusted elevation factor yielded higher R² values than the other empirical models. In parallel, a three-layer ANN trained in MATLAB, using inputs such as instantaneous charge, distance, elevation difference, and total charge per blast, achieved an R² of 0.951, highlighting total charge as a key parameter. Both the empirical and ANN methods proved effective for PPV prediction, but the ANN models demonstrated better accuracy when total charge was included. Full article

(This article belongs to the Topic Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition)

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23 pages, 4455 KB  

Open AccessArticle

Application of the CPO-CNN-BILSTM Hybrid Model for Evaluation of Water Abundance of the Roof Aquifer—A Case Study of WoBei Mine in Huaibei Coalfield, China

by Yuchu Liu, Qiqing Wang, Jingzhong Zhu, Dongding Li and Wenping Li

Appl. Sci. 2025, 15(21), 11816; https://doi.org/10.3390/app152111816 - 5 Nov 2025

Viewed by 796

Abstract

With the gradual increase in coal production capacity, the problem of water damage from the coal seam roof is becoming more and more prominent. Neogene loose strata overlie coal seams in eastern China, and pressurized aquifers commonly lie at the bottom of the [...] Read more.

With the gradual increase in coal production capacity, the problem of water damage from the coal seam roof is becoming more and more prominent. Neogene loose strata overlie coal seams in eastern China, and pressurized aquifers commonly lie at the bottom of the loose strata. The aquifers are mainly composed of unconsolidated sand, gravel, and weakly consolidated marl, which has strong permeability and an extremely unfavorable impact on safe production. Identifying the target area to prevent and control roof water damage can reduce the likelihood of water damage accidents in mines. This study takes the 8₅ mining district of Wobei mine as an engineering case. The discriminant indexes are selected for aquifer thickness, gradation coefficient, marlstone thickness, permeability, grouting quantity, and grouting termination pressure. A model integrating the newly proposed Crowned Porcupine Optimization (CPO, 2024), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) was constructed to predict unit water influx. A zonal map was generated based on the expected unit water influx of the fourth aquifer after grouting. In addition, the prediction results are compared with those from other models. Results indicate that the CPO-CNN-BiLSTM model achieves a higher accuracy and fewer errors in water abundance prediction, with an RMSE of 2.58 × 10⁻⁵ and an R² of 0.982 for the testing dataset. According to the prediction result, the fourth aquifer after grouting in the 8₅ mining district is divided into five water abundance zones. The strong and medium–strong water abundance zones are mainly distributed in the study area’s eastern region. A small portion of them is distributed in the northwestern and northern areas. This study provides a new insight for predicting the water abundance of thick loose aquifers and a theoretical basis for safe mining under thick loose aquifers. Full article

(This article belongs to the Topic Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition)

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