Huiyong Yin

To address the issues of reliance on static parameters and weak early warning in unmined areas in the risk prediction of water inrush from the bottom slab of deep ultra‐wide working faces, a dynamic mulit‐source monitoring‐driven water inrush risk prediction situation awareness system was constructed, taking the 400 m ultra‐wide working face of Shandong Binhu Coal Mine as the research object. A microseismic–electrical method coupling monitoring system was set up to collect data on microseismic depth, energy, and resistivity of the bottom rock strata, and training labels were created through spatio‐temporal alignment and information entropy superposition. Meanwhile, 10 control factors were selected to construct a dynamic and static fusion feature matrix. The whale optimization algorithm–convolutional neural network model was adopted to optimize the hyperparameters and establish the mapping relationship between features and risks. The results show that when the model advances 370, 550, and 820 m in the working face, the root mean square error gradually decreases to 0.0586, the prediction accuracy of the unmined area reaches 91.54%, and the performance is stable when the data volume fluctuates. This system studies the precise identification of high‐risk areas, guides on‐site measures to ensure safe mining, realizes full‐time and spatial dynamic inversion, and early warning of water inrush risks in deep and ultra‐wide working faces, and provides technical support for the prevention and control of water hazards in deep mining and underground engineering.

This study addresses the challenge of quantifying spatially differential vertical surface deformation (SDVSD). Traditional approaches using persistent scatterer interferometry (PSI) data often focus on bulk vertical surface deformation (VSD) but overlook directional variability and struggle with irregularly distributed persistent scatterer (PS) points, limiting comprehensive SDVSD analysis. This study proposes a regular triangle network (RTN)-based framework that tessellates the study area into uniform triangular units, enabling the systematic quantification of the SDVSD direction, magnitude and rate while mitigating spatial biases from uneven PS distributions. Applied to the Shixi area in China’s Northwest Xuzhou Coalfield, the RTN-based framework revealed that (1) the SDVSD directionality aligned with the coal strata dip and working face distribution, contrasting with VSD’s focus on the magnitude and rate alone; (2) SDVSD exhibited seasonal rate fluctuations suggesting environmental influences, and, unlike VSD, it has a non-additivity property in temporal evolution; (3) there was spatial divergence between SDVSD and VSD, i.e., high VSD rates did not necessarily correlate with high SDVSD rates, emphasizing the need for an independent spatial gradient analysis. This study demonstrates that the RTN-based framework effectively disentangles the directional and magnitude (rate) components of SDVSD, offering a robust tool for the identification of deformation hotspots and linking surface dynamics to subsurface processes. By formalizing the quantification of PSI-derived SDVSD, this study advances InSAR deformation monitoring, providing actionable insights for infrastructure risk mitigation and sustainable land management in mining regions and beyond.

Long-term coal mining activities have significantly disturbed the groundwater system, resulting in aquifer water characterized by high levels of Na⁺, SO₄²⁻, and total dissolved solid (TDS), posing environmental health risks. To investigate the disturbance effects of coal mining activities on the groundwater system and ascertain the goaf water (OGW) environmental impacts, this study focuses on the surface water (SW), major aquifers, and OGW of Jiaozhuang Coal Mine. Through ion analysis and self-organizing map (SOM) clustering, the study analyzes the hydrochemical characteristics of the aquifer water, summarizes the accumulation patterns of OGW, and evaluates water quality of irrigation and drinking using sodium adsorption ratio (SAR), sodium percentage (SSP), and comprehensive pollution index (F). The results show that the hydrochemical characteristics of the groundwater system are influenced by a combination of cation exchange, dissolution, and mixing processes, with deep aquifers exhibiting high Na⁺ and SO₄²⁻ levels. The OGW mainly originates from the coal roof sandstone aquifers water (RSW) and 3rd limestone aquifer water (3LW). Additionally, the groundwater shows high alkalinity and salinity hazards, with irrigation water quality assessments falling into general and unsuitable water quality area. Moreover, the groundwater quality is below Class III standards, with the worst being Class V, rendering it unsuitable as a drinking water source. Untreated discharge of OGW to the surface can easily threaten human drinking water health. The study results are helpful in identifying and controlling groundwater pollution caused by coal mining, ensuring the safety and sustainable utilization of water resources in mining areas and surrounding regions.

During the Holocene, the upper and middle reaches of the Yellow River valley in China were pivotal areas for agricultural development. Quantitatively reconstructing the spatiotemporal changes in prehistoric human land use is essential for understanding, from a long-term perspective, the interactions among evolutions of climate, agriculture, and human activities. Based on 327 archaeological sites and the PLUM (prehistoric land use model), we quantitatively reconstructed human land use changes from 6 to 3 ka BP (thousands of years before the present) in the Tao River valley, the second-largest tributary in the upper reach of the Yellow River valley. The results indicated that regional land use areas increased from 571 km² to 1468 km², with spatial expansion from the lower reach to the upper–middle reach of the Tao River valley. A comparison of the five areas distributed across the upper and middle reaches of the Yellow River valley revealed two different trends of increasing land use from 8 to 3 ka BP within these areas. The first group (the Wei River and Yiluo River valleys) exhibited rapid and substantial growth before 5 ka BP, while the second group (the Huangshui River and Tao River valleys, and the Yunlin district) showed a much slower and less significant increase before 5 ka BP, but a more obvious increase thereafter. The asynchronous increases in these areas indicate an expansion of land use from the southeastern part of the upper and middle reaches of the Yellow River Valley to across the entire region between 8 and 3 ka BP, which was primarily driven by agricultural development and cultural communication.