Search Results (72)

Soil degradation and poor fertility severely constrain vegetation growth in urban ecosystems, particularly in compacted and nutrient-depleted tree pits. Mulching has emerged as an effective strategy to improve soil quality and regulate soil–microbe–plant interactions, yet the combined use of organic and inorganic mulching in urban landscapes remains underexplored. In this study, a one-year field experiment was conducted to evaluate the effects of four mulching treatments on soil bacterial community diversity and functional potential. Four treatments were applied green waste compost + wood chips (GW), green waste compost + wood chips + volcanic rocks (GWV), green waste compost + wood chips + pebbles (GWP), and a non-mulched control (CK). Organic mulching (GW) effectively reduced bulk density, enhanced cellulase and protease activities, increased bacterial community richness and balance, and enriched microbial genes associated with carbon and nitrogen metabolism, while organic–inorganic mulching further promoted soil nutrition and reshaped bacterial community structure. Soil pH, nitrogen content, and protease activity served as key drivers of bacterial community structure and function. These findings demonstrate that different mulching practices provide distinct ecological advantages, and together highlight the role of mulching in regulating soil–microbe–plant interactions and improving urban tree pit management. Full article

For investigating and modeling the swelling potential of anhydrite rocks, it is important to define a fast but accurate, reliable, and repeatable procedure for mineral identification and quantification of anhydrite mineral in rock samples. We propose a quantitative evaluation of the applicability of two different SEM-based approaches (namely, image analysis and the use of the O/S atomic ratio) for the identification and quantification of anhydrite in polished slices of rock. We compare the results obtained with the bulk densities of the samples and with the outcomes of thermogravimetric analyses, demonstrating high convergence between the different data. We eventually propose a critical comparison between the proposed approaches and the existing methods, overall providing a practical guide for the selection of the best analytical procedure for the quantification of anhydrite content in rocks and, consequently, for the correct estimation of swelling potential. Full article

Machine learning-guided predictive models are attractive in rock modeling for different scholars to obtain continuous profiles of rock compressive strength in rock engineering. The major objectives of the study are to assess the implications of machine learning (ML)-based connectionist models to obtain the unconfined compressive strength (UCS) of rock, to perform parametric sensitivity analysis on petrophysical parameters, and to develop an improved correlation for UCS prediction. The least-squares support vector machine (LSSVM) is applied to develop data-driven models for the prediction of UCS. Additionally, the random forest (RF) algorithm is applied to verify the effectiveness of predictive models. A database containing well-logging data is processed and utilized to construct connectionist models to obtain UCS. For the efficacy of predictive models, statistical performance indicators such as the coefficient of determination (CC), average percentage relative error, and maximum average percentage error are utilized in the study. It is revealed that the RF- and LSSVM-based models for predicting UCS perform excellently with high precision. Considering the parametric sensitivity analysis in the predictive models for UCS, the formation compressional wave velocity and formation gamma-ray are the most strongly contributing predictor variables rather than other input variables such as the modulus of elasticity, acoustic shear wave velocity, and rock bulk density. The improved correlation for predicting UCS shows high precision, achieving a CC of 96% and root mean squared error of 0.54 MPa. This systematic research workflow is significant and can be utilized for connectionist robust model development and variable selections in the petroleum and mining fields, such as predicting reservoir properties, the drilling rate of penetration, sanding potentiality of hydrocarbon reservoir rocks, and for the practical implications of boring and geotechnical engineering projects. Full article

The present study provides a detailed compilation and analysis of the bulk density and magnetic susceptibility of the rocks from the northwest Himalayas, Pakistan. The area is tectonically extremely complex and comprises sedimentary, metamorphic, and igneous rocks. These rocks range in age from Early Proterozoic to Recent. During the fieldwork, 476 rock samples were collected for density measurements and 410 for magnetic susceptibility measurements from the major rock units exposed in the study area. The measured physical parameters reveal a significant difference in the density and susceptibility of the rocks present in the investigated area. The sedimentary rock units belonging to the Indian Plate show the lowest mean values for bulk density, followed by metasedimentary rocks, Early Proterozoic rocks, igneous and metaigneous rock units of the Indian Plate, Indus Suture Melange Zone, and Kohistan Island Arc rocks, respectively. The magnetic susceptibility of sedimentary rock units of the Indian Plate has the lowest mean values, followed by metasedimentary rocks of the Indian Plate, igneous and metaigneous rock units of the Indian Plate, Early Proterozoic rocks of the Indian Plate, Kohistan Island Arc rocks, and Indus Suture Melange Zone. In brief, the sedimentary rocks of the Indian Plate have the lowest bulk density and magnetic susceptibility values, whereas the Kohistan Island Arc rocks have the highest values. Overall, the bulk density and magnetic susceptibility of rock units in the study area follow those predicted for different types of rocks. These measurements can be used to develop possible potential field models of the northwest Himalayas to better understand the tectonics of the ongoing continental-to-continental collision, as well as for many other geological analyses. Full article

In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and the strong parameter dependence in geophysical inversion. The model integrates porosity curves (compensated density, compensated neutron, and acoustic time difference), elastic modulus parameters (shear and bulk moduli), and nuclear magnetic porosity data for the construction of a multi-dimensional linear equation system, with calibration coefficients derived from core X-ray diffraction (XRD) data. The Adam algorithm dynamically optimizes the weights, solving the overdetermined equation system. We applied the method to the Asmari Formation in the M oilfield in the Middle East with 40 core samples for calibration, achieving a 0.91 fit with the XRD data. For eight additional uncalibrated samples from Well A, the fit reaches 0.87. With the introduction of the elastic modulus and nuclear magnetic porosity, the average relative error in mineral content decreases from 9.45% to 6.59%, and that in porosity estimation decreases from 8.1% to 7.1%. The approach is also scalable to elemental logging data, yielding inversion precision comparable to that of commercial software. Although the method requires a complete set of logging data and further validation of regional applicability for weight parameters, in future research, transfer learning and missing curve prediction could be incorporated to enhance its practical utility. Full article

Underground longwall mining conducted in the vicinity of the barrier pillars in the KWK ROW Ruch Marcel mine has led to volume changes in the rock mass. As the longwalls progressed, a gradual increase in stress occurred in the goaf overburden, as a result of which this part of the rock mass increased in density in relation to the surrounding strata. Seismic events occurring during mining as a result of elastic energy accumulation led to the relaxation of the medium and local decreases in its bulk density. The microgravimetric method is sensitive to variations in this physical parameter of rock. The most transparent effects of the differences in rock mass density can be observed by performing periodic local gravity field surveys and analysing their spatial and temporal variability. This paper analyses the relationship between ground deformations and the spatial and temporal gravity field distribution changes observed on the surface in the context of the safety of barrier pillars F1 and F2 in Marklowice (the GSB-GFO testing ground of project EPOS-PL+). Relative gravimetric surveys, referenced to the determined absolute values of g, were performed in 7 series over the period of 2021–2023. The collected data made it possible to chart differential maps of gravity field changes and anomalies with Bouguer reduction. The differential anomaly distributions between successive survey series and the reference series were analysed. This served as the basis for assessing the safety of the barrier pillars maintained by the mine and the possibility of ground deformation occurrence on the surface. Full article

The identification and prediction of effective reservoir rocks are important for evaluating the energy production potential of ultra-deep tight sandstone reservoirs. Taking the Keshen gas field, Tarim Basin, as an example, three distinct petrofacies are divided according to petrology, pores, and diagenesis. Petrofacies, well logs, and factor analysis are combined to predict effective reservoir rocks. We find that petrofacies A has a relatively coarse grain size, moderate mechanical compaction, diverse but low-abundance authigenic minerals, and well-developed primary and secondary pores. It is an effective reservoir rock. Petrofacies B and petrofacies C are tight sandstones with a poorly developed pore system and almost no dissolution. Petrofacies B features abundant compaction-susceptible ductile grains, intense mechanical compaction, and underdeveloped authigenic minerals, while petrofacies C features pervasive carbonate cementation with a poikilotopic texture. We combine well logging with gamma ray, acoustic, bulk density, neutron porosity, resistivity, and factor analyses to facilitate the development of petrofacies prediction models. The models reveal interbedded architecture where effective reservoir rocks are interbedded with tight sandstone, resulting in the restricted connectivity and pronounced reservoir heterogeneity. Classifying and combining well logs with a factor analysis to predict petrofacies provide an effective means for evaluating the energy potential of ultra-deep reservoirs. Full article

Rock dust (RD) is a by-product of the precious metal mining industry. Some mining operations produce close to 2,000,000 Mg of RD/year, posing disposal issues. This study evaluated the physicochemical and microbial properties of RD from gold mining and its potential use in RD-based growing media. Ten media formulations were tested: Promix (Control), 100% (RD), 100% topsoil (TS), 50% RD + 50% topsoil (RDT), 25% RD + 75% topsoil (RT), 50% RD + 50% Promix (RP), 50% RD + 25% biochar + 25% Promix (RBP), 50% RD + 25% compost + 25% Promix (RCP), 50% RD + 50% biochar (RB), and Huplaso (negative control). RD particle size ranged from 0.1 to 2 mm with a bulk density of 1.5 g cm⁻³, while RD-based media ranged from 0.8 to 1.1 g cm⁻³ showing increased porosity. Nutrient content was analyzed using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), and the active microbial community assessed using PLFA biomarkers via GC-MS/FID, n = 4 and p = 0.05. Microbial analysis identified five classes (protozoa, eukaryotes, Gram-positive (G+), Gram-negative (G−), and fungi (F)), with a significant increase in G−, G+, and F in RD-based amendment RBP (28%) compared to control P (9%). G+, G−, and F showed a strong negative correlation (r = −0.98) with pH, while calcium correlated positively (r = 0.85) with eukaryotes and a strong positive correlation (r = 0.95) of cation exchange capacity with G+. This study suggests blending RD with organic amendments improves physicochemical quality and microbial activity, supporting its use in crop production over disposal. Full article

In the context of advancements in deep resource development and underground space utilisation, deep underground engineering faces the challenge of investigating the mechanical behaviour of rocks under high-stress conditions. The present study is based on a gold mine, and the bulk ore taken from the mine perimeter rock was processed into two sets of specimens containing semicircular arched roadways with half and full penetrations. The tests were carried out using a true triaxial rock test system. The results indicate that the true triaxial stress–strain curve included stages such as compression density, linear elasticity, yielding, and destructive destabilisation following the peak; the yield point was more pronounced than that in uniaxial and conventional triaxial tests; and the peak stress and strain of the semi-excavation were higher than those of the full excavation. Furthermore, full excavation led to greater deformation along the σ₃ direction. The acoustic emission energy showed a sudden increase during the unloading stage, then fluctuated and increased with increasing stress until significant destabilisation occurred. Additionally, increased burial stress in the half-excavation decreased the proportion of tension cracks and shear cracks. Conversely, in semi-excavation, the proportion of tensile cracks decreased, while that of shear cracks increased. However, the opposite was observed in full excavation. In terms of fractal dimension, semi-excavation fragmentation due to stress concentration followed a power distribution, while the mass fragmentation in full excavation followed a random distribution due to uniform stress release. Furthermore, the specimen strength was positively correlated with fragmentation degree, and primary defects also influenced this degree. This study provides a crucial foundation for predicting and preventing rock explosions in deep underground engineering. Full article

Particles of selected materials, namely granulite quarry rock and itabirite iron ore, have been characterized regarding their shapes using reconstruction from 2D images and 3D laser scanning. Different levels of simplifications of particle geometry were initially proposed, with optimal fit-for-purpose shapes represented by polyhedral meta-particles containing 41 to 90 faces. From the distribution of aspect ratios, a total of 16 groups of shapes have been created. Preliminary validation of the shapes modeled was carried out by comparing bulk density measurements from simulations and experiments for granulite, resulting in very good agreement between the two. Further validation was then carried out by comparison of experiments for a gneiss rock and another itabirite sample to simulations, with good agreement between both. This database provides suitable representation of ore/rock shapes for DEM simulations in the software Rocky. Full article

The accurate prediction of the spatial distribution of soil organic carbon (SOC) and the identification of the mechanisms underlying its spatial differentiation are of paramount significance for the conservation and utilization of land and regional sustainable development. A total of 512 soil samples were collected from Wuchang and Shuangcheng County in Harbin City, Heilongjiang Province, China, which served as the study area. Six machine learning models, including Random Forest (RF), AdaBoost, Support Vector Regression (SVR), weighted average, Stacking, and Blending, were utilized to predict the spatial distribution of SOC and analyze its spatial differentiation. The result reveals that 12 environmental variables, including soil type, bulk density, pH, average annual precipitation, average annual temperature, net primary productivity (NPP), land use type, normalized difference vegetation index (NDVI), slope, elevation, soil parent material, and distance to rivers, are effective influencing factors on SOC in the study area. It turns out that the Stacking model, with an R² of 0.4327, performed the best in this study, followed by the weighted average, Blending, RF, AdaBoost, and SVR models; a heterogeneous integrated learning model may be more robust than an individual learner. The predicted SOC content is generally lower in the northwestern arable land and higher in the southeastern forest land. In addition, SOC differentiation shows that forest land and grass land with dark brown soil or swamp soil, soil covering igneous and metamorphic rocks with various minerals, higher elevation and slope, and suitable water-thermal and soil intrinsic conditions for aerobic microbial activity benefit the enrichment of SOC in the study area. The enrichment and depletion of SOC are jointly influenced by pedogenesis, microbial activity, and biodiversity. Full article

Soil nutrients are essential for plant survival, especially in karst regions where soil erosion is a significant threat, leading to ecosystem degradation. Rocks exposed in these areas contribute to fragmented soil coverage and the complex spatial distribution of soil nutrients, hindering vegetation recovery. In this study, we collected 60 soil samples (0–30 cm deep) from a typical rocky desertification slope. Classical statistics and geostatistics were used to assess the spatial variability of the following key soil properties: soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK). The study mapped a continuous surface of soil nutrients using the ordinary kriging method to analyze the spatial variability of the karst slope. The results showed that, except for the bulk density and porosity, which showed little variation, the other soil characteristics had moderate to high levels of variability. The SOC, TN, and TP levels decreased with soil depth, while the TK content increased with soil depth. Each soil layer has strong spatial autocorrelation in its SOC. The variability of TP and TK decreases with soil depth, indicating strong spatial autocorrelation. In the 0–10 cm soil layer, the SOC displays the highest level of continuity, with the TN exhibiting a higher level of variability compared to the other nutrients. Within the 10–20 cm soil layer, the SOC, TN, TP, and TK all exhibit strong spatial autocorrelation. Moving to the 20–30 cm soil layer, the structural variability of SOC is the most pronounced. The correlation between soil nutrients and other soil properties was not strong, with only a cumulative explanatory power of 11.81% in the first two axes of a redundancy analysis (RDA). Among them, the bulk density and silt content had a significant impact on soil nutrients. Studying the spatial variability of soil nutrients in rocky desertification areas is crucial for improving soil quality and promoting vegetation restoration. Full article

This study investigated young and mature coconut fibers as an asbestos replacement in fiber–cement flat sheets. The ratio of fiber content ranged from 5% to 9.5% in increments of 0.5% by weight of binder. Crushed rock dust (CRD) was also utilized in this study at a ratio of 50% as sand replacement. The results showed that the addition of young coconut fiber (YCF) and mature coconut fiber (MCF) in flat sheets increased with decreasing bulk density and thermal conductivity. The optimum fiber content was 6.5%–7% by weight of binder for two types of fiber with the highest modulus of rupture of 12–13 MPa. The modulus of rupture and density of fiber–cement flat sheets using YCF were higher than that of fiber–cement flat sheets using MCF, which was clarified by SEM results due to the denser structure of MCF. Moreover, the modulus of rupture was directly proportional to the modulus of elasticity in fiber–cement flat sheets. Full article

Climate warming may enhance the upslope migration of tree species at high elevations. In this context, few studies have analyzed the requirements of tree seedlings with respect to safe sites and soil conditions, particularly in the Alborz Mountains (Iran). We collected regeneration and environmental data focusing on small seedlings and young saplings at the upper limit of tree growth in the Alborz Mountains. For each life stage, we assessed the association of seedlings with safe site conditions (rocks, stones, deadwood, and canopy shade), microhabitat substrates (vegetation, litter, bare soil, and stones), and soil variables along elevational gradients. Our findings revealed strong associations between seedlings and facilitative elements (tree shade, deadwood, rocks, and stones). Fewer associations were observed with microhabitat substrates. These associations differed between the two establishment stages. In addition, seedlings of both life stages exhibited distinct associations with soil variables (total nitrogen, sand, and bulk density). The contrasting relationships between small seedlings and young saplings with safe site conditions, microhabitat substrates, and soil variables suggest that early life stages may have different site requirements compared to later stages. Our results suggest that the potential for upslope migration of tree species may depend on safe site conditions, particularly those provided by tree shade, deadwood, rocks, and stones. Full article

In this article, one of the main scientific directions was the search for ways of recycling coal mining waste to produce expanded clay granules. There are a number of scientific studies devoted to the use of various industrial wastes in the production of thermal insulation and fireproof expanded clay granules. The authors consider the production of granular porous aggregates based on pulverized fractions of igneous rocks—basalt, granite, and synertite, as well as man-made materials of various origins, to be promising. According to the results of the conducted studies, it was found that the optimal interval of the amount of waste in expanded clay was 4.0–6.0%, and the optimal firing temperature was 1150 °C with the production of samples with a bulk density of 0.337–0.348 t/m³ and with a compressive strength of 1.37–1.51 MPa under these conditions. Full article