A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.</jats:p

Ore Genesis of the Abu Ghalaga Ferro-Ilmenite Ore Associated with Neoproterozoic Massive-Type Gabbros, South-Eastern Desert of Egypt: Evidence from Texture and Mineral Chemistry

Massif-type mafic intrusions (gabbro and anorthosite) are known for their considerable resources of vanadium-bearing iron–titanium oxide ores. Massive-type gabbroic and anorthosite rocks are frequently associated with magmatic rocks that have significant quantities of iron, titanium, and vanadium. The most promising intrusions that host Fe-Ti oxide ores are the gabbroic rocks in the south-eastern desert. The ilmenite ore deposits are hosted in arc gabbroic and anorthosite rocks. They are classified into three types, namely black ore, red ore, and disseminated ore. The black ilmenite ore is located at the deeper level, while the oxidized red ore is mainly located at or near the surface. Petrographically, the gabbro and ilmenite ores indicate a crystallization sequence of plagioclase, titaniferous pyroxene, and ilmenite. This reveals that the ilmenite is a magmatic deposit formed by the liquid gravity concentration of ilmenite following the crystallization of feldspar and pyroxene. Meanwhile, quartz, tremolite, zoisite, and opaque minerals are accessory minerals. The Fe-Ti ores are composed of ilmenite hosting exsolved hematite lamellae of variable sizes and shapes, gangue silicate minerals, and some sulfides. The X-ray diffraction (XRD) data reveal the presence of two mineral phases: ilmenite and hematite formed by the unmixing of the ferroilmenite homogeneous phase upon cooling. As a result, the ore is mostly made up of hemo-ilmenite. Using an electron microscope (SEM), as well as by observing the textures seen by the ore microscope, ilmenite is the dominant Fe-Ti oxide and contains voluminous hematite exsolved crystals. Under the scanning electron microscope, ilmenite contained intergrowths of hematite as a thin sandwich and lens shape. The formation of hematite lamellae indicates an oxidation process. Mineral chemistry-based investigations reveal late/post-magmatic activity at high temperatures. The examined ilmenite plots on the ferro-ilmenite line were created by continuous solid solution over 800 °C, whereas the analyzed magnetite and Ti-magnetite plot near the magnetite line and were formed by continuous solid solution exceeding 600 °C

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

GEOLGICAL STRUCTURE MAPPING OF THE BENTONG-RAUB SUTURE ZONE, PENINSULAR MALAYSIA USING PALSAR REMOTE SENSING DATA

The Bentong-Raub Suture Zone (BRSZ) of peninsular Malaysia was selected as case study to evaluate the capability of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) satellite remote sensing data for structural geology mapping in tropical environments. The structural elements in the BRSZ were enhanced using multi-polarization configuration of PALSAR data at a regional scale. Adaptive local sigma and directional filters were applied to PALSAR data for detailed structural mapping. Numerous tectonic lineaments with consistent variation in trend, length and density were detected in the study area. Structural analysis of the BRSZ reveals that two distinct parts can be defined, a western part affected mainly by ductile fabrics in the Cameron Highlands and an eastern part affected mainly by brittle deformation in the BRSZ. Ductile deformation indicates several generation of folding in the Cameron Highlands. Several faults, joints and fractures represent brittle deformation events in the BRSZ. The results of this study demonstrate the usefulness of PALSAR satellite remote sensing data for mapping geological structures in tropical environments

Alteration zone Mapping in the Meiduk and Sar Cheshmeh Porphyry Copper Mining Districts of Iran using Advanced Land Imager (ALI) Satellite Data

This study evaluates the capability of Earth Observing-1 (EO1) Advanced Land Imager (ALI) data for hydrothermal alteration mapping in the Meiduk and Sar Cheshmeh porphyry copper mining districts, SE Iran. Feature-oriented principal components selection, 4/2, 8/9, 5/4 band ratioing were applied to ALI data for enhancing the hydrothermally altered rocks associated with porphyry copper mineralization, lithological units and vegetation. Mixture-tuned matched-filtering (MTMF) was tested to discriminate the hydrothermal alteration areas of porphyry copper mineralization from surrounding environment using the shortwave infrared bands of ALI. Results indicate that the tested methods are able to yield spectral information for identifying vegetation, iron oxide/hydroxide and clay minerals, lithological units and the discrimination of hydrothermally altered rocks from unaltered rocks using ALI data

INTEGRATION OF PALSAR AND ASTER SATELLITE DATA FOR GEOLOGICAL MAPPING IN TROPICS

This research investigates the integration of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data for geological mapping applications in tropical environments. The eastern part of the central belt of peninsular Malaysia has been investigated to identify structural features and mineral mapping using PALSAR and ASTER data. Adaptive local sigma and directional filters were applied to PALSAR data for detecting geological structure elements in the study area. The vegetation, mineralogic and lithologic indices for ASTER bands were tested in tropical climate. Lineaments (fault and fractures) and curvilinear (anticline or syncline) were detected using PALSAR fused image of directional filters (N-S, NE-SW, and NW-SE).Vegetation index image map show vegetation cover by fusing ASTER VNIR bands. High concentration of clay minerals zone was detected using fused image map derived from ASTER SWIR bands. Fusion of ASTER TIR bands produced image map of the lithological units. Results indicate that data integration and data fusion from PALSAR and ASTER sources enhanced information extraction for geological mapping in tropical environments

REGIONAL GEOLGICAL MAPPING IN TROPICAL ENVIRONMENTS USING LANDSAT TM AND SRTM REMOTE SENSING DATA

Landsat Thematic Mapper (TM) and Shuttle Radar Topography Mission (SRTM) data were used to produce geological maps in tropical environments. Lineament, lithology and landform maps were produced for all states in peninsular Malaysia in this study. Kedah, Perak and Terengganu states have been selected as case studies to demonstrate the results of the data and techniques used. Directional filtering technique was applied to Landsat TM bands 4, 5 and 3 for lineament mapping. The lithology map was produced using Landsat TM bands combination consist of bands 4, 3 and 2. Digital elevation model and landform map were produced using SRTM data in 3 Dimension (3D) and 2 Dimension (2D) perspective views, respectively. The produced geological maps and the remote sensing data and methods applied in this study are mostly appropriate for hazard risk mapping applications and mineral exploration projects in the peninsular Malaysia and tropical environments

APPLICATION OF PALSAR-2 REMOTE SENSING DATA FOR LANDSLIDE HAZARD MAPPING IN KELANTAN RIVER BASIN, PENINSULAR MALAYSIA

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping

CHROMITITE PROSPECTING USING LANDSAT TM AND ASTER REMOTE SENSING DATA

Studying the ophiolite complexes using multispectral remote sensing satellite data are interesting because of high diversity of minerals and the source of podiform chromitites. This research developed an approach to discriminate lithological units and detecting host rock of chromitite bodies within ophiolitic complexes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. Three main ophiolite complexes located in south of Iran have been selected for the study. Spectral transform techniques, including minimum noise fraction (MNF) and specialized band ratio were employed to detect different rock units and the identification of high-potential areas of chromite ore deposits within ophiolitic complexes. A specialized band ratio (4/1, 4/5, 4/7) of ASTER, MNF components and Spectral Angle Mapper (SAM) on ASTER and Landsat TM data were used to distinguish ophiolitic rock units. Results show that the specialized band ratio was able to identify different rock units and serpentinized dunite as host rock of chromitites within ophiolitic complexes, appropriately. MNF components of ASTER and Landsat TM data were suitable to distinguish ophiolitic rock complexes at a regional scale. The integration of SAM and Feature Level Fusion (FLF) used in this investigation discriminated the ophiolitic rock units and prepared detailed geological map for the study area. Accordingly, high potential areas (serpentinite dunite) were identified in the study area for chromite exploration targets.The approach used in this research offers the image processing techniques as a robust, reliable, fast and cost-effective method for detecting serpentinized dunite as host rock of chromitite bodies within vast ophiolite complexes using ASTER and Landsat TM satellite data