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Al-Hassa city, located in Eastern Saudi Arabia, boasts the world’s largest oasis and the most expansive naturally irrigated lands. Historically, a total of 280 natural springs facilitated significant groundwater discharge and irrigation of agricultural land. Furthermore, the water in certain springs formerly had a high temperature. The spatial variability of the water quality was evident. At the same time, Al-Hassa Oasis is situated on the northeastern side of the Ghawar field, which is the largest conventional onshore oil field in the world in terms of both reserves and daily output (approximately 3.8 mmb/d). The aforementioned traits suggest an intricate subsurface that has not yet been publicly and thoroughly characterized. Due to the presence of significant cultural noise caused by agricultural and nearby industrial activities, a robust, easy-to-use, and accurate geophysical method (gravity) was used to cover an area of 350 km2, producing the 3D subsurface model of the study area. A total of 571 gravity stations were collected, covering the whole Al-Hassa Oasis and parts of the nearby semi-urban areas. The gravity data were corrected and processed, and a 3D inversion was applied. The resulting 3D geophysical subsurface modeling unveiled an intricate subterranean configuration and revealed lateral variations in density, indicating the presence of a potential salt dome structure, as well as fracture zones that serve as conduits or obstacles to the flow of the subsurface fluids. This comprehensive modeling approach offers valuable insights into the subsurface dynamics of the broader study area, enhancing our understanding of its qualitative tectonic and hydraulic features and their impacts on the area’s natural resources, such as groundwater and hydrocarbons.

The main geological structures in the Dammam Dome are defined by integrating geophysical measurements and applying new methodological approaches. Dammam Dome is characterized by a well-developed fracture/joints system; thus, high complexity of the subsurface is expected. Direct Current Resistivity (DCR) and Seismic Refraction (SR) geophysical survey aimed to map the Dammam Dome’s near-surface features. The geophysical data were acquired along two profiles in the northern part of Dammam Dome. To maximize the results from conducting DCR and SR measurements over a complex area, a combined local and global optimization algorithm was used to obtain high-resolution near-surface images in resistivity and velocity models. The local optimization technique involves individual and joint inversion of the DCR and SR data incorporating appropriate regularization parameters, while the global optimization uses single and multi-objective genetic algorithms in model parameter estimation. The combined algorithm uses the output from the local optimization method to define a search space for the global optimization algorithm. The results show that the local optimization produces satisfactory inverted models, and that the global optimization algorithm improves the local optimization results. The joint inversion and processing of the acquired data identified two major faults and a deformed zone with an almost N–S direction that corresponds with an outcrop were mapped in profile one, while profile two shows similar anomalies in both the resistivity and velocity models with the main E–W direction. This study not only demonstrates the capability of using the combined local and global optimization multi-objectives techniques to estimate model parameters of large datasets (i.e., 2D DCR and SR data), but also provides high-resolution subsurface images that can be used to study structural features of the Dammam Dome.

Most geophysical inversions face the problem of non-uniqueness, which poses a challenge in the mapping and delineation of the subsurface anomalies. To tackle this challenge, a combined local and global optimization approach is considered for jointly inverting two-dimensional direct current resistivity (DCR) and seismic refraction (SR) data that aim to estimate the corresponding physical model parameters. In this combined approach, the output of the local optimization method is used to determine the search space and tuning parameters for the global optimization algorithm. The multi-objective genetic algorithm (non-dominated sorting genetic algorithm) was utilized to jointly optimize the objective functions of two different methods. Because the genetic algorithm is a population-based optimization method, it requires numerous forward calculations. To deal with the expected high computational cost associated with this approach, parallel computing was utilized for the forward function evaluations to reduce the run time of the entire process. The proposed approach was tested using synthetic two-dimensional resistivity and velocity models that had three different types of anomalies (dyke, positive, and combined positive and negative). The results showed an improvement in the anomaly delineation in the output of the combined local and global optimization method compared with the local optimization method. Additionally, similar synthetic models were tested using only the single objective global optimization algorithm (conventional global optimization), which showed promising anomaly delineation.

We conducted an initial high-power CSEM (controlled-source electromagnetic method) survey in a coastal salt-flat area in the broader area of Half Moon Bay, in the southern part of Dammam Peninsula in the eastern province of Saudi Arabia. The primary purpose of this work was to verify the technology, but we were also able to detect and characterize potential economic brines in the study area. For a high-quality data acquisition, several transmitter–receiver configurations, different acquisition parameters, and passive and active EM data were collected, evaluated, processed, and interpreted to characterize the subsurface. The long-offset EM (LOTEM) and the focused-source EM (FSEM) were the optimum configurations due to the high-quality of the collected data. This is a starting point for using the CSEM method towards the O&G, geothermal, CO2 sequestration, groundwater, lithium brine, and other natural resources’ exploration and exploitation in the Gulf countries.

This paper presents an underground silver mining operation outside Gomez Palacio, Durango, Mexico, terminated around the 1930s, of which previous knowledge of its operations was poor. Durango’s current silver exploration campaigns are likely to overlook historic silver mining sites due to interest in specific prospect regions. A two-dimensional (2D) Electrical Resistivity Imaging (ERI) survey coupled with reconnaissance of the area was performed at this historic silver mining site. The exploration campaign aimed to find the abandoned mineshaft, map its subsurface extent, and explore the occurrence of mineralization zones (silver ore). The ERI survey comprised five profiles measured with the extended dipole-dipole array with a consistent electrode spacing of 5 m. The smooth, robust, and damped least-squares inversion methods were used to invert the 2D data. Our field observations and ERI survey results collectively reveal the following findings: (a) reconnaissance reveals mining infrastructure consistent with historical mining activity; the infrastructure includes a complex of habitational rooms, an ore-processing pit near a concrete slab next to a dirt road, and two limestone-wall structures interpreted as the entrance of abandoned backfilled mineshafts named Mesquite and Lechuguilla; (b) high-resistivity anomalies suggest vestiges of shallow, underground mine workings including backfilled mineshafts that connect a mine gallery complex, and (c) various low-resistivity anomalies, juxtaposed against mine galleries, suggestive of unmined shallow vein-type and manto-type mineralization of hydrothermal origin. The imaging depth is estimated at ~65 m. Underground silver mining moved southwards and was limited to ~40 m depth.

Sustainable agriculture management typically requires detailed characterization of physical, chemical, and biological aspects of soil properties. These properties are essential for agriculture and should be determined before any decision for crop type selection and cultivation practices. Moreover, the implementation of soil characterization at the beginning could avoid unsustainable soil management that might lead to gradual soil degradation. This is the only way to develop appropriate agricultural practices that will ensure the necessary soil treatment in an accurate and targeted way. Remote sensing and geophysical surveys have great opportunities to characterize agronomic soil attributes non-invasively and efficiently from point to field scale. Remote sensing can provide information about the soil surface (or even a few centimeters below), while near-surface geophysics can characterize the subsoil. Results from the methods mentioned above can be used as an input model for soil and/or soil/water interaction modeling. The soil modeling can offer a better explanation of complex physicochemical processes in the vadose zone. Considering their potential to support sustainable agriculture in the future, this paper aims to explore different methods and approaches, such as the applications of remote sensing, geophysics, and modeling in soil studies.

Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.

A detailed gravity survey with 235 measurements was carried out at the King Fahd University of Petroleum and Minerals campus, which is located at the crest of the oil-bearing Dammam Dome (Eastern Province of Saudi Arabia). This survey allows us to better understand the geometry of the underlying Dammam Dome and its tectonic regime. The acquired data were processed using conventional gravity data reduction techniques. The effectiveness of terrain correction was evaluated using several recently developed algorithms. Afterward, processed data were subject to geophysical filters for edge detection (terracing transformation and horizontal gradient) and depth estimation (tilt derivative and 3D inversion). 3D Bouguer maps were generated and compared to the proposed geological models for the Dammam Dome. The results show the existence of ENE-WSW striking tectonic lines, where two nearly vertical, km long tectonic lines were predominant. The orientation of these tectonic lines defines an NNW-SSE trend for the least principal stress axis (σ3) and an ENE-WSW trend for the σ1σ2 stress plane of the driving stress regime, fitting well with the transtension stress regime recently suggested for the area. More importantly, the results of this study demonstrate that the Dammam Dome was affected by the intraplate stresses transferred from the convergence between the Arabian and Eurasian plates along the Zagros orogeny.

This work demonstrates the potential of Fe-modified biochar for the treatment of arsenic (As) simulated wastewater and the monitoring of adsorption in real-time. Specifically, we propose the utilization of date-palm leaves for the production of biochar, further modified with Fe in order to improve its adsorption function against inorganic pollutants, such as As. Both the original biochar and the Fe-modified biochar were used for adsorption of As in laboratory batch and column experiments. The monitoring of the biochar(s) performance and As treatment was also enhanced by using the spectral induced polarization (SIP) method, offering real-time monitoring, in addition to standard chemical monitoring. Both the original and the Fe-modified biochar achieved high removal rates with Fe-modified biochar achieving up to 98% removal of As compared to the 17% by sand only (control). In addition, a correlation was found between post-adsorption measurements and SIP measurements.

This study develops a comprehensive seismic risk model for the city of Chania, in Greece, which is located ina highly seismic-prone region due to the occurrenceof moderate to large earthquakes because of the nearby major subduction zone between African and Eurasian tectonic plates. The main aim is to reduce the seismic risk for the study area by incorporating the spatial distribution of the near-surface shear wave velocity model and the soil classification, along with all possible seismic sources, taking into account historical events. The study incorporates and correlates various ground motion scenarios and geological fault zones as well as information on existing buildings to develop a seismic risk model using QuakeIST software, and then the seismic hazard and a realistic prediction of resulting future adverse effects are assessed. The developed model can assist the municipal authorities of Chania to be prepared for potential seismic events, as well as city planners and decisionmakers, who can use the model as an effective decision-making tool to identify the seismic vulnerability of the city buildings and infrastructure. Thus, this study enables the implementation of an appropriate and viable earthquake-related hazards strategy to mitigate damage and losses in future earthquakes.