SUMMARY: One of the main constraints on the use of satellite radar data for monitoring natural hazards is the existence of atmospheric signals. In particular, volcanic deformation can be difficult to identify because atmospheric phase delays can mask or even mimic ground deformation signals. Eliminating atmospheric signals is particularly crucial for high-relief volcanoes such as Ağrı, Tendürek, Acigöl, Göllüdağ and Hasandağ in the Eastern and Central Anatolia. To overcome the atmospheric effects, we use high-resolution ECMWF weather models coupled with an empirical phase-elevation approach for correcting Sentinel-1 interferograms. We apply these methods to two areas of Turkey, the first of which covers three volcanoes in Central Anatolia (Acigöl, Göllüdağ, Hasandağ) between January 2016 and December 2018 and the second covers two volcanoes in Eastern Anatolia (Ağrı, Tendürek) between September 2016 and December 2018. The reduction in standard deviation (quality factor) is calculated for both ascending and descending tracks and the atmospheric corrections are found to perform better on descending interferograms in both cases. Then, we use a least-squares approach to produce a time-series. For Central Anatolia, we used 416 ascending and 415 descending interferograms to create 144 and 145 cumulative displacement maps, respectively, and for Eastern Anatolia, we used 390 ascending and 380 descending interferograms to produce 137 and 130 cumulative displacement maps, respectively. We find that the temporal standard deviation before atmospheric corrections ranges between 0.9 and 3.7 cm for the five volcanoes in the region and is consistently higher on ascending track data, which is acquired at the end of the day when solar heating is greatest. Atmospheric correction reduces the standard deviation to 0.5–2.5 cm. Residual signals might be due to the ice-cap at Ağrı and agriculture near Acigöl. We conclude that these volcanoes did not experience significant magmatic deformation during this time period, despite the apparent signals visible in individual uncorrected interferograms. We demonstrate that atmospheric corrections are vital when using InSAR for monitoring the deformation of high-relief volcanoes in arid continental climates such as Turkey.

Egypt’s mineral resources are an effective means of raising the country’s income. Consequently, searching for deep subsurface ore deposits is essential. In this study, we map the subsurface structure and the occurrence of related gold-ore mineralization deposits in ophiolitic-assemblage rocks around the Wadi El-Saqia area in the Central Eastern Desert of Egypt. Our approach combines airborne geophysical data (aeromagnetic mapping) and geochemical analyses. We enhanced the aeromagnetic data and interpreted them using edge-detection methods, such as the first-order vertical derivative (FVD), the analytic signal (AS), the total horizontal derivative (THD), the tilt derivative (TD), the tilt angle (TA), the theta map, both grid and porphyry analyses from the Centre for Exploration Targeting (CET), and Euler deconvolution (ED) techniques. Utilizing these methods, we located the main structural lineaments/contacts that control the distribution of hydrothermal alteration zones. In addition, our geochemical analyses use the mineral chemistry of pyroxene and plagioclase to describe their tectonic and magmatic evolution. The airborne geophysical results revealed that NW–SE, NE–SW, N–S, and E–W structural orientations are prevalent in the studied area, with depths ranging from less than 50 m to about 600 m. To validate the findings, we carried out geochemical sampling, which indicates that pyrite, galena, pyrrhotite, and electrum contain good percentages of gold (ranging from 0.01–0.09 wt%, 0.03–0.1 wt%, 0.12–0.14 wt%, and 53.55–55.01 wt%, respectively). In this study, we were thus able to find preferred locations for gold mineralization, which highlights the value of combining aeromagnetic and geochemical data for mineral exploration.

This article aims to aid in exploring and forecasting hydrothermal minerals and gold deposits in Central Saudi Arabia (SA), with a focus on structural contexts. Remote sensing (RS) and satellite-based Bouguer gravity (SBG) data were integrated in order to create a mineral prediction map for the researched location. Data from the Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) were transformed and enhanced using a variety of approaches. The delineation of hydrothermal alteration zones (HAZs) and highlighting of structural discontinuities in the OLI data were made possible using band ratios and oriented principal component analysis (PCA). Additionally, the underlying structural features were successfully exposed by processing the SBG using a variety of edge detection techniques, like the analytical signal (AS), total horizontal derivative (THD), tilt angle (TA), horizontal tilt angle (TDX), theta map (TM), horizontal derivative of the tilt derivative (HD_TDR), horizontal gradient of the tilt angle (HGTA), tilt angle of the analytical signal (TAAS), and soft sign function (SF). As a result, more prominent lineaments were found in the NW–SE, NNW–SSE, NE–SW, and NNE–SSW directions than in the N–S and E–W directions. The GIS incorporated surface/subsurface geological structure density maps with zones of hydrothermal alteration. It was found that the lineaments derived from the analysis of the RS and SBG data were more in line with the HAZs, which demonstrated the common connection between alteration zones and deep lineaments. The findings revealed a mineral prediction map with extremely low to extremely high probabilities. Overall, combining RS and SBG data effectively identified probable mineralization sites associated with hydrothermal processes and made it easier to create this study’s final predictive mineralization map.

This study was conducted to determine potential groundwater storage areas in the semi-arid Oltu Basin in northeastern Turkey. The groundwater potential of the basin was analyzed by evaluating eight geographical factors: lithology, linear density, soil depth, land use, precipitation, geomorphology, slope, and drainage density. These factors were classified and weighted using remote sensing, geographical information systems (GIS), and the analytic hierarchy process (AHP). The obtained data were modeled using ArcGIS software, and a potential groundwater storage map of the Oltu Basin was created. The results show that there is a high groundwater potential in areas of the basin close to the stream bed, while the groundwater potential is low in mountainous and steeply sloped regions. The study provides significant findings for sustainable water resource management in the region and future water resources planning.