Microgravity and Its Applications in Geosciences

Gravity is the most important force which determines the structure and evolution of stars like the Sun as well as the structure and evolution of galaxies. The law of universal gravitation is generally sufficient to describe the gravity of the Earth, the Moon, or the planets orbiting the Sun. With the recent development of sensitive gravimeters, the gravity survey has become one of the most used geophysical tools in applied geosciences for tasks including: exploring for oil and gas fields by studying geological structures and salt dome intrusion, monitoring groundwater and geothermal reservoirs by determining recharge and discharge masses, monitoring volcanic activity and hydrothermal activity beneath volcanoes, monitoring CO2 movement during and after sequestration, locating active faults responsible for big earthquakes, and also exploring mines and detecting local cavities. In this chapter, we present a brief introduction to gravity and Bouguer gravity, the different corrections applied to measured gravity and follow with cases of applied microgravity measurements in different fields of geosciences

Applications of Remote Sensing in Geoscience

Remote sensing is becoming an important and useful tool in mapping large, remote areas and has many applications in geosciences such as geologic and geo-structural mapping, mineral and water exploration, hydrocarbon exploration, natural hazards analysis, and geomorphology. The recent advances in remote-sensing imaging acquisition and availability of images can help geoscientists to explore and prepare maps quickly and evaluate the geo-potential of any specific area on the globe. Advances in remote-sensing data analysis techniques have improved the capacity to map the geological structures and regional characteristics and can serve in mineral exploration in complex and poorly understood regions. In this chapter, geophysical remotely sensed data (airborne geophysics) are integrated with other sources of remotely sensed data to analyze three separate areas, one each for geological structure, lineament presence and orientation, and geothermal potential. Three case studies are discussed in this chapter from three countries—Afghanistan, United Arab Emirates, and Algeria—to show the effectiveness of remote sensing in mapping and detecting geo-structural, geomorphological, and geothermal characteristics of ground surfaces

Geothermal resources in Algeria

The geothermal resources in Algeria are of low-enthalpy type. Most of these geothermal resources are located in the northeastern of the country. There are more than 240 thermal springs in Algeria. Three geothermal zones have been delineated according to some geological and thermal considerations: (1) The Tlemcenian dolomites in the northwestern part of Algeria, (2) carbonate formations in the northeastern part of Algeria and (3) the sandstone Albian reservoir in the Sahara (south of Algeria). The northeastern part of Algeria is geothermally very interesting. Two conceptual geothermal models are presented, concerning the northern and southern part of Algeria. Application of gas geothermometry to northeastern Algerian gases suggests that the reservoir temperature is around 198 °C. The quartz geothermometer when applied to thermal springs gave reservoir temperature estimates of about 120 °C. The thermal waters are currently used in balneology and in a few experimental direct uses (greenhouses and space heating). The total heat discharge from the main springs and existing wells is approximately 642 MW. The total installed capacity from producing wells and thermal springs is around 900 MW.Algeria Geothermal North Africa Hot spring Renewable energy

Spatio-temporal evolution of the physico-chemical water characteristics of the Sebaou river valley (Great Kabylia, Algeria)

The Sebaou river valley (SRV) is located in the northern Tell region of Algeria, and is a significant source of ground and surface waters because of its geographical context within the northern Algerian Tell region. The water resources from the study area are important for both local people and environmental scientists since the area is undergoing significant industrial development. Therefore a quantitative and qualitative study of the surface and groundwater can help us to establish the current physico-chemical variables of the water resources, allowing comparisons with future studies. In this study we present the most comprehensive survey of the hydrogeological properties of this region reported to date, including the spatio-temporal variation of physico-chemical variables as studied by principal component analysis. This method allowed us to confirm the principal chemical facies and to differentiate the waters of the middle and upper Sebaou river from the waters of river tributaries, specifically the Aissi and Bougdoura rivers. The quality of these water resources is declining as a result of local development and so this study is expected to provide a valuable resource for future water management strategies in the region

Vehicle Auto-Classification Using Machine Learning Algorithms Based on Seismic Fingerprinting

Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers’ privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (Bus/Truck, Car, and Motorcycle) and background noise. By using vertical component seismic data, this study compares the decoding abilities of Logistic Regression, Support Vector Machine, and Naïve Bayes (NB) approaches to determine the class of automobiles. The Multiclass classifiers were trained on 4185 samples and tested on 1395 randomly chosen from actual and synthetic data sets. Additionally, we used the convolutional neural network approach as a baseline to assess the effectiveness of machine learning (ML) methods. The NB method showed relatively high classification accuracy during training for the three multiclass classification situations. Overall, we investigate an ML-based decoding technique that can be used for security and traffic analysis across large geographic areas without endangering driver privacy and is more effective and economical than conventional methods

Mapping CAMP formations of the northern flank of Tindouf Basin by integrating remotely sensed data, geochemistry and field observations

International audienc

Magnetic and gravity modeling and subsurface structure of two geothermal fields in the UAE

Highlights a. Faults are conduit-like for geothermal fields. b. Flow paths of rising hot waters are mapped. c. Hot springs are structurally controlled