In past decades, the reckless exploitation of rare earth mines devastated the ecological environment. Under strict regulation and governance, the exploitation has gradually gotten back on track in recent years. In this regard, timely and accurate assessment of the ecological environment quality of rare earth management areas is indispensable for regional mine development planning, ecological protection, and sustainable development. Being relatively objective and providing instant results, the Remote Sensing Ecological Index (RSEI) is widely used in evaluating ecological environment quality. This paper combined Landsat 8 OLI multispectral imagery with meteorological, land type, and other data to set the Net Primary Productivity (NPP). The NPP reflects detailed regional vegetation destruction and climate variation, the greenness index of RSEI. We also used kernel principal component analysis (KPCA) to obtain the improved ecological index K-RSEI_NPP while evaluating the ecological environment quality of rare earth mining areas in southern Jiangxi and compared this with the traditional RSEI results. The results indicate that: (1) PC1 accounts for 88.51% of the results obtained based on K-RSEI_NPP, and the average correlation coefficient with each index reaches 0.757, which integrates the characteristics of the four indicators; (2) Compared with other indexes, the K-RSEI_NPP proposed in this paper can better display the detailed information of the ecological environment in the rare earth mining areas to differentiate mining areas under various statuses and cities with different vegetation coverage, and its results were consistent with the actual verification. Therefore, our K-RSEI_NPP can provide an effective basis for monitoring and evaluating the ecological environment of the mining area.

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.

Scientific and rational monitoring of eco-environmental effects induced by mining activities is a prerequisite for optimizing mining planning and contributes to the advancement of ecological civilization. Remote sensing and multi-source data provide advanced methods for long-term dynamic evaluation of mining-induced eco-environmental effects. This study systematically constructs eco-environmental effect indicators tailored to mining characteristics and establishes quantitative extraction methods based on Landsat data and spectral indices. The Mine Eco-environmental Effect Index (MEEI) was developed using kernel principal component analysis (KPCA). The Heidaigou Open-pit Coal Mine in Jungar Banner was selected as the study area to validate the MEEI’s performance and analyze ecological dynamics across five key temporal phases. Results indicate the following: (1) the KPCA-based MEEI effectively integrates multi-indicator features, offering an objective representation of comprehensive eco-environmental impacts; (2) from 1990 to 2020, the ecological trajectory of the coal mine followed a pattern of “sharp deterioration → gradual slowdown → relative stabilization”, with post-mining restoration and management measures significantly mitigating negative impacts and improving regional ecological quality. This study provides a methodological framework for dynamic evaluation of mining-related eco-environmental effects, supporting sustainable mining practices and ecological governance.

Deep and ultra-deep hydrocarbon resources are of strategic importance for energy security. The pronounced ductility of deep reservoirs and faults makes hydraulic fracture propagation and induced seismicity mechanisms fundamentally different from those in conventional brittle reservoirs. This review systematically synthesizes recent theoretical, experimental, and numerical advances in hydraulic-fracturing-induced seismicity, covering triggering mechanisms, fault reactivation risk evaluation, and perspectives. A core distinction is identified: brittle faults exhibit instantaneous stick-slip rupture with high seismic frequency and significant magnitude, whereas ductile faults undergo stable aseismic creep and progressive slip, with long-term deformation prone to delayed large earthquakes—their nucleation shows unique mechanical responses including high stress drop, low rupture velocity, and low seismic radiation efficiency. Subsequently, four major challenges are distilled for risk evaluation systems: insufficient dynamic characterization of mechanical parameters in ductile reservoirs, lack of fracturing-control strategies adapted to ductile behavior, poor understanding of multi-scale slip transition mechanisms, and inadequacy of multi-field coupling models in capturing long-term delayed evolution. Traditional brittle-based risk frameworks cannot characterize the time-dependent slip and progressive reactivation of ductile faults, limiting their applicability to deep reservoirs. Future works are proposed, including refined characterization of mechanical parameters under high temperature and pressure, intelligent full-cycle hydrofracturing control, quantitative criteria for slip activation, and optimization of long-term multi-field coupling models. This study elucidates recent progress in hydrofracturing-induced seismicity mechanisms and quantitative risk assessment in ductile reservoirs, filling a gap in the conventional brittle-dominant research. It also provides theoretical support for seismic risk evaluation and early warning in deep fracturing operations, with significant implications for improving induced seismicity management and ensuring safe, efficient, and sustainable deep-resource development.