Research in Astronomy and Astrophysics

Abstract Shear zones play an essential role in controlling the distribution of ore resources, including Au and associated mineral deposits. Our study integrates aeromagnetic, structural, and remote sensing (RS) data to analyze the geodynamics, tectonics, and mineral potentiality of the Safaga-Semna shear belt (SSSB), Eastern Desert, Egypt. Improved logistic (IL) and Logistic function of the total horizontal gradient advanced edge techniques are applied to reduced-to-pole (RTP) data and upward continuation (UPWC) of RTP at depths of 1, 2, 3, 4, and 5 km, allowing the outlining of shallow and deep structural elements. The application of Landsat-8 imagery, Sentinel-2B color composites, and Sentinel-1A SAR data is essential for the processes of lithological and alteration mapping, as well as for the extraction of lineaments. This methodology reveals a significant correlation between the distribution of alteration zones, the density of lineaments, and the presence of gold deposits. The Safaga-Semna shear belt (SSSB) represents a multifaceted assemblage of rock units characterized by diverse deformation attributes. The most significantly deformed rocks consist of volcaniclastic metasediments and Hammamat sediments, whereas the least deformed group encompasses metavolcanic rocks, meta gabbro-diorite, and synorogenic granites. Notably, the central area of the belt displays intense deformation, with north–south shearing and folding predominantly affecting the volcaniclastic metasediments and metavolcanics. The SSSB has experienced four major deformation phases, designated as D1, D2, and D3. Shear zones striking northwest exert an influence on gold quartz veins located in Semna, Rodriun, and Massaghat, while north–south shear zones play a critical role in controlling gold mineralization across Gidami, Abu Marawat, Mirinda, and Abu Gaharish gold mines. Our multidisciplinary integrated approach highlights the tectonic evolution of SSSB and its implications for exploring mineralization. By integrating aeromagnetic, structural, and RS data, this study provides a detailed interpretation of the tectonic development and structural controls on mineralization. The results underscore the potential of integrating multiscale geological and geophysical datasets to improve our understanding of shear zones and associated mineralization processes.

Abstract Creep behaviour in rocks is a typical mechanical property that is directly linked to the stability of underground engineering. The deformations and rate of rocks creep are not only influenced by time but also by the loading and unloading history. To more accurately predict creep mechanical behaviour of salt rocks, the rocks hardening is described by introducing a state variable. A new three-dimensional creep constitutive model of salt rocks was established to describe the loading and unloading history effect of the rheological properties. In this paper, salt rocks creep tests under various loading and unloading histories were conducted to investigate how different loading routes affect the creep behaviour of salt rocks. The effects of the model state variables were analysed through different indicators. An example verification was carried out with the results of plastic deformation tests performed at different loading paths. The findings indicated the creep rate of stepped loading and the stepped unloading under the same stress level were significantly affected by the loading history. The proposed constitutive model can accurately fit the creep test curves of different loading paths, indicate that it can provided a prediction of the historical effect of the creep behaviour of salt rocks. Different parameters affect the different phases of the creep curve. The parameter k primarily affects the overall shape of the creep curves. Parameters m and c primarily influence the steady-state creep length and creep rates, excluding the initial cycle.

Abstract Permeability and porosity are critical parameters in CO2-ECBM and depend on the fine-scale fracture structure of coal. A self-developed high-pressure gas adsorption/desorption experimental system was used to conduct adsorption tests on coal at pressures ranging from 1 to 6 MPa. The CT scanning system was employed to scan coal samples before and after adsorption. This process established the three-dimensional fracture structure of the coal and simulated seepage behavior under different CO2 pressures. After CO2 adsorption, the fractures in the coal expanded significantly. The CO2 pressure had a notable impact on internal fracture development. As CO2 pressure increased, fracture structure parameters also increased, the fracture network became more complex and better connected, leading to an increase in the coal’s absolute permeability (K). The permeability was positively correlated with both the fractal dimension and porosity. The growth, expansion, intersection, and connectivity of microfractures caused by higher CO2 pressure were the main reasons for the increase in porosity and permeability. These findings provide theoretical guidance for CO2 geological sequestration and the development of coalbed methane resources.

Abstract Comparison is a crucial stage of site-level selection process. This study integrates the geographic information system (GIS) techniques and analyses the stability of predictions based on five machine learning models to identify key indices for site selection. The study results reveal that: (1) the relevant site selection index system was improved. The precision of predictions using the five machine learning models all reached 95%, with the deep neural networks (DNN) model achieving the highest precision at 96.4%, indicating its broader applicability for site selection. (2) A machine learning index optimisation process is proposed. Based on the results of index importance, indices are categorised as important, less important, and general. Using only the important indices yields satisfactory evaluation results. (3) A rapid assessment model was developed. In the DNN model, the results could be predicted more accurately by using approximately 25% of the data and 50% of the indices. This provides a reference for subsequent site selection for difficult-to-obtain data. This study aims to accumulate extensive data via future research to establish a model database. The database will help refine geological models for different types and stages of engineering projects and incorporate more site-specific models. The ultimate goal is to provide more convenient theoretical guidance and recommendations for subsequent site selection processes.