Energy consumption worldwide has undergone a recent shift, with hydrocarbons, coal, and new energy sources taking center stage. However, fossil fuels face criticism due to their negative impacts on the environment through air pollution. Geothermal energy is a clean, renewable, and eco-friendly alternative that can be harnessed for power generation. The Lunayyir volcanic field is located in the western part of Saudi Arabia and has been identified as a potential geothermal resource. In this study, a comprehensive analysis of the gravity, magnetic, and land surface temperature data collected over the study area is conducted to investigate the geothermal potential. The gravity data are processed using standard techniques to correct for instrument drift, diurnal variations, and topographic effects and to filter out high-frequency noise. The magnetic data are processed using standard procedures. Analysis of the gravity data shows that the Lunayyir volcanic field is characterized by a series of gravity anomalies, which can be interpreted as indicative of the presence of subsurface geologic structures that are similar in geothermal areas, such as faults, fractures, and intrusions. The magnetic data, on the other hand, show that the volcanic field has a relatively low magnetic susceptibility, which is consistent with the presence of volcanic rocks implying shallow heat sources. Landsat satellite data are utilized to calculate the land surface temperature through the use of GIS software. Additionally, the normalized difference vegetation index (NDVI) is calculated using the near-infrared and red bands of the Landsat satellite. The land surface temperature of the year 2007 shows the relatively high temperature at the surface as compared to 2000 and 2021, which is an indication of volcanic activities in the subsurface. It is suggested that these structures may provide conduits for hot fluids, which could potentially be exploited for geothermal energy production. The obtained results emphasize the importance of combining different geophysical datasets to obtain a better understanding of underground geology and geothermal systems. In general, this study improves the assessment of the Lunayyir volcanic field in the western part of Saudi Arabia for geothermal energy.

The global demand for energy is increasing rapidly due to population growth, urbanization, and industrialization, as well as to meet the desire for a higher standard of living. However, environmental concerns, such as air pollution from fossil fuels, are becoming limiting factors for energy sources. Therefore, the appropriate and sustainable solution is to transition towards renewable energy sources to meet global energy demands by using environmentally friendly sources, such as geothermal. The Harrat Rahat volcanic field, located in the western region of the Kingdom of Saudi Arabia (KSA), gets more attention due to its geothermal potential as a viable site for geothermal energy exploration due to its high enthalpy. The prime objective of this study is to present up-to-date and comprehensive information on the utilization of borehole temperature and remote sensing data to identify the most prospective zones with significant geothermal activity favorable for exploration and drilling. A brief description of the selected wells and the methodology used to determine the petrophysical parameters relevant to the geothermal potential assessment are presented. Special emphasis is given to gamma-ray ray and temperature logs for calculating heat production and the geothermal gradient. The effectiveness of various machine learning techniques are assessed throughout this study for predicting the temperature-at-depth to evaluate the suitability of employing machine learning models for temperature prediction, and it is found that XG Boost provided excellent results. It can be observed that some linear anomalies can be traced in the NW, trending on the west side of the Harrat volcanic field based on magnetic data interpretation. The land surface temperature in 2021 exhibited higher temperatures compared to 2000, suggesting potential volcanic activity in the subsurface. It is concluded that the integration of remote sensing data with subsurface data provides the most reliable results.

The Saudi Arabia government announced the $500 billion mega project “NEOM City”, to build a cross-border mega city to connect Saudi Arabia, Egypt, and Jordon for attracting foreign investments to the region. NEOM city is situated on the eastern region of the Gulf of Aqaba with its western side in the Sinai Peninsula. The selected site for NEOM city is geographically remarkable; nevertheless, this site needs a detailed geological and geophysical investigation. Sinai Peninsula is a microplate between the Arabian and African plates. Its southern tip is located at a triple junction comprising the Gulf of Aqaba–Dead Sea Transform fault, the Gulf of Suez, and the Red Sea, leading to relatively higher seismic activities in the region. The current study aims to understand the thermal structure of the vicinity of NEOM city to address the potential geohazards and indicate geological attractions within and around the planned city. We use the magnetic data from which geothermal images can be obtained. The preliminary results indicate that there is geologic similarity between the southern part of Sinai Peninsula and the northern part of the Arabian shield. This is because the Gulf of Aqaba separates what was once a continues Neoproterozoic crust. In addition, the magnetic data showed the presence of prominent lineaments on either side of the Gulf of Aqaba. The notable lineaments might represent faults that could still be active. Hence, selecting a site of NEOM city to be east of the Gulf of Aqaba needs to be guided by the careful understanding of the potential hazards. In addition, shallow Curie depths near the Gulf of Aqaba are recognized as a source for renewable geothermal energy.

We invert gravity and magnetic anomalies for basement relief at the Harrat Rahat Volcanic Field (HRVF) for the purpose of evaluating its geothermal energy prospects. HRVF is dominated by basaltic scoria cones and other volcanic rocks overlying the Proterozoic basement. The area considered for this study is located within the northern HRVF and consists mainly of alkali basalts with lesser amounts of benmoreite, mugearite, hawaiite, and trachyte. Our approach adopts a global optimization technique using Particle Swarm Optimization with automated parameter selection, and a two-dimensional gravity-magnetic (GM) forward modeling procedure. The results of the PSO-based approach indicate a depth to the basement at 0.10–624 m, with greater depths within the central region of a solitary anomalous density body in the HRVF. The obtained basement geometry is corroborated by the depth estimates obtained from other potential field inversion methods. The regions with higher prospects are mapped for a targeted future geothermal energy exploration at the HRVF, based on our inversion results.

This study investigates the application of machine learning techniques for predicting volcanic vent locations based on aeromagnetic geophysical data. Magnetic data, known to reflect subsurface geological structures, presents a valuable source of information for understanding volcanic activity. Leveraging this data, we aim to develop and validate predictive models capable of discerning the presence of volcanic vents. Through a comprehensive data analysis, feature engineering, and model training, we explore the intricate relationships between magnetic variations and volcanic vent locations. Various machine learning algorithms were evaluated for their efficacy in binary classification, with a focus on identifying areas with a high likelihood of volcanic vent presence. The Random Forest model (RFM) was adopted given its high performance metrics, achieving a prediction accuracy of 92%. Our results demonstrate the successful prediction of volcanic vent locations, with a significant correlation of 86% between the actual and predicted vent locations and a high Degree of Certainty (DC) at 97%. This research contributes to the advancement of geospatial data analysis within the field of geoscience, showcasing the potential of machine learning in interpreting and utilizing magnetic data for volcanic hazard assessment and early warning systems. The findings represent a significant step towards enhancing our understanding of volcanic dynamics and improving the predictive tools available for volcanic hazard assessment.

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment.