فصلنامه فیزیک زمین و فضا
سال پنجاهم شماره 4 (Winter 2025)

The present study attempts to map the thermal structures of part of the Gongola Basin, NE Nigeria, from airborne magnetic and gamma-ray spectrometry data, with implications for geothermal resource exploration. The residual of the Total Magnetic Intensity (TMI) was subdivided into nine spectral overlapping blocks and subjected to spectral analysis to deduce the geothermal parameters, Curie point depth (CPD), geothermal gradient and heat flow (HF). Likewise, the varying concentrations of radioelements (K, eTh and eU) within the study area were used to estimate the radiogenic heat production (RHP). The results show that the CPD, geothermal gradient and HF range from 17.31–46.02 km, 12.60–33.51 oCkm-1, and 31.63–84.10 mWm-2 respectively. On the other hand, the radiogenic heat production (RHP) of K, eU, and eTh ranges from 0.00–0.01 µWm-3 < 0.3 – 4.2 µWm-3 < 0.4 –7.4 µWm-3. While the total RHP ranges from 0.7 to 7.5 µWm-3. The high RHP obtained in the northwest, west, and southwest flanks of the study area might be a result of the dominant crystalline rocks; while the high RHP observed in the southern part might be attributable to the Pindiga Formation, composed of shale, limestone, and sandstone, which is highly rich in uranium. The regions (central, northwest, southwest, and southeast) with HF > 80 mWm-2 and RHPs of 2.5 µWm-3 and these meet the recommended values for good geothermal resources and could be considered good indicators for geothermal energy exploration in the study area.

The Passive Image Interferometry (PII) approach, which incorporates a cross-correlation process to reconstruct the green function between two stations, is emerging as an effective tool for studying seismic velocity changes. These changes provide significant information about the earth's structural and mechanical properties between the stations. Despite its numerous benefits, monitoring fault zones with PII can be challenging due to various processes that can cause velocity changes in the crust. In this study, we investigated the usefulness of this method on the noise recorded in two seismic stations near the fault zone that caused the Sefid-Sang earthquake with a magnitude of Mw = 6.1. Our study covers a period of 15 months, including 12 months before and three months after the earthquake. We investigated velocity changes across different frequency ranges and examined the effect of stacking on the results. Our analysis revealed a 0.3% increase in seismic velocity two months before the earthquake.

Predicting reservoir performance in the future is closely related to the accurate identification of reservoir history in the past. In this study, based on a new approach, risk zonation in hydrocarbon reservoirs has been evaluated using different available data include well production, characteristics of fractures and faults, rock heterogeneity and seismic data, which can be used in the field of reservoir management. This study is carried out on Mishrif formation in Sirri oil fields. The well test results have been used in the period between 1977 and 1992, and permeability in the drainage area of all production wells have been calculated by applying an empirical relationship between wellhead pressure and permeability. By calculating the permeability in the production wells, the strain values, which represent the compaction parameter, are estimated based on the permeability-strain empirical relationships. Strain value is considered us an important parameter for predicting the future oil production rate. In this study, the effect of different parameters on strain distribution such as fault effects, characteristics of reservoir fractures, rock heterogeneity and rock density have also been investigated. Based on the obtained strain results, all existing wells have been classified into three different regions including region A (referred to high rate of volumetric strain), region B (referred to moderate rate of volumetric strain), and region C (referred to low level of volumetric strain), which can be used for the future performance of the wells and for making accurate decisions regarding better management of Mishrif formation reservoir in Sirri oil field.

This research introduces the Pasqale rock/debris avalanche, an occurrence of a prehistoric landslide in the upstream region of Darband Valley in northern Tehran metropolitan area that is exemplified as an instance of a high-risk geohazard encountered near a metropolitan area. The initiation of the avalanche as a slide from a steep scarp with an elevation of approximately 3000 m nestled within Eocene volcanic rocks and tuff, is noted. The local geology and geomorphology of the Pasqale landslide are described in this paper, relying on topographical data, satellite imagery, and field observations. Through various considerations and the utilization of high-resolution satellite data, the total volume of the landslide is estimated to be 800,000 m3. Remarkably, the occurrence of this landslide is found to be influenced by intense fracturing and hydrothermal alterations of the Eocene pyroclastic rocks. Moreover, the seismic aspect of the region is emphasized, with particular attention given to the likelihood of a large earthquake being the most probable triggering factor for the Pasqale avalanche, originating from the Mosha or North Tehran faults. The significance of the cascading hazards that may be brought about following a major earthquake event in the northern Tehran metropolitan area is highlighted in this paper.

The presence of cracks, joints, faults, fractures and lineaments and their interconnectivity influence the water movement in hard rock areas. The groundwater level in Jodhpur city, western Rajasthan, has been rising significantly over the past few years, and this has attracted attention due to the region’s unfavourable impacts. In many parts of the city, particularly around popular markets, water levels have touched the ground. The rise in groundwater levels has caused water seepage in the basements of underground shops and houses throughout the city. Traditional field survey techniques require a lot of money and effort to map water seepage zones. In this study, subsurface signatures of linear features, faults and fractures were quickly and inexpensively mapped using a remote sensing methodology. Fracture zones that are prone to water seepage were identified using an electrical resistivity method. Based on meticulous analysis of satellite imagery, a number of minor and large lineament sets have been mapped, the majority of which cross over Kailana Lake-Takht Sagar and enter the city. According to the study of all available data, fractured zones exist, are linked by Kailana Lake-Takht Sagar, and act as conduits for water seepage. The presence of obvious lineaments also facilitates water seepage.

Identification of gas reservoirs as a main natural resource due to their economic importance has always been one of the most important issues in research studies in the oil and gas fields. Accurate localization of a gas reservoir through seismic data has been broadly studied. The final destination of all seismic attributes is to distinguish a specific feature. Accordingly, many seismic attributes have been developed, among which short-time Fourier transform (STFT)-based methods play an important role. The location of gas reservoirs can be detected, taking advantage of its particular criteria in seismic data. Generally, seismic signals are non-stationary as their frequency responses vary with time. Thus we propose an attribute that utilizes mixed components of STFT (MC-STFT). The novelty about this method is that without altering STFT method or adding any complexity, MC-STFT is able to detect gas reservoirs at high resolution. Simplicity and time efficiency can make a method superior. In fact, this method takes advantage of extracting three frequency components obtained by STFT. In the next step, we can do the second iteration of the procedure, this will represent the degree of sharpness of reduction in amplitude and again do the same jobs as before and it leads to this, making it more specific. We apply this method to three data sets, first, Marmousi model and then two other real seismic zero-offset sections. To evaluate the proposed method compared with the Synchrosqueezing STFT (SSTFT). The results confirm the good performance of MC-STFT in high-resolution gas reservoir detection.

In this study, utilizing the relationships between the energy of atomic/nuclear particles released from underground piezoelectric rocks and the elastic energy stored in these rocks, we introduced some methods to estimate the time/energy of incoming earthquakes in aseismic regions by measuring the energy of radiated particles. Since piezoelectric granite rocks make up approximately 60% of the Earth's crust, the increase in the energy of the detected particles in a certain period of time can be considered as an important precursor for the impending shallow earthquake. This analysis holds significant promise for enhancing the earthquake time and energy estimation methodologies. The detection of radiated particles from piezoelectric rocks can be achieved by utilizing detectors placed either on the surface or inside deep wells that are drilled near active faults. However, it is essential to note that the presented methodologies are approximations, as they rely on constant parameters for the piezoelectric material and presuppose that earthquakes occur within a piezoelectric block.

Accurate prediction of porosity holds significant importance across various domains within the oil and gas sector, encompassing activities such as reservoir delineation, well design, and production enhancement. However, conventional methodologies often encounter difficulties in capturing the intricate relationships among diverse data streams and porosity metrics. This study introduces a novel hybrid model framework aimed at refining the precision and resilience of porosity forecasts by integrating multiple machine learning methodologies and exploiting complementary data modalities. This hybrid architecture enables flexible and intricate integration of diverse models and data sources, potentially leading to enhanced overall porosity prediction accuracy. Notably, the proposed model incorporates several innovative elements, including the amalgamation of ensemble techniques and deep learning models tailored for sequential data, as well as the utilization of complementary data sources, such as well log and core data, to facilitate automatic feature learning and representation, thereby bolstering robustness and generalization capabilities. Experimental outcomes underscore the hybrid model's potential to achieve notable prediction accuracies, with R-squared values surpassing 0.93 on log data and 0.88 on core data sets, outperforming individual models. The model also exhibits commendable robustness and training efficiency, leveraging advanced methodologies such as ensemble techniques. In conclusion, this study underscores the promise of hybrid machine learning models as dependable tools for porosity prediction from core data. The insights gleaned from this research hold the potential to advance the understanding and optimization of porosity forecasting, thereby facilitating the formulation of more efficient reservoir management strategies.

Routine estimates of the seismic moment (M0) by the Global Centroid Moment Tensor Project (GCMT) for large and moderate earthquakes, provide a valuable resource for tectonic analysis. In this research, the source type of 614 earthquakes that occurred in the Iranian plateau between 1676 and 2023, whose moment tensor solutions are available in the GCMT catalogue, was investigated. Here, earthquakes are grouped according to dip angle values of the T, P, and B axes which were taken from GCMT catalogue. The mechanism is considered as strike-slip or normal faulting when the dip angle of the B or P axes exceeds 60°, respectively. When the dip angle of T axis exceeds 50°, the mechanism is proposed as thrust faulting. The focal mechanism study of 614 earthquakes showed that about 55.4, 25.4 and 2.9 percent of them have reverse, strike-slip and normal mechanisms, respectively. The average value equal to 0.12 for  values of 614 earthquakes shows that the non-double-couple component in the deviatoric tensors is very small. Also, comparison of seismic moment estimated for 182 earthquakes by IRSC and GCMT, shows that there is an almost systematic bias in the seismic moment estimated by IRSC (Iranian Seismological Center). Seismic moment values reported by IRSC are lower than those determined by GCMT.

An analysis was conducted on the 2014 Murmuri earthquake sequence in the Zagros Mountains of Iran, aiming to determine the main fault plane. The sequence comprised of an initial Mw 6.2 earthquake, followed by five aftershocks with magnitudes exceeding 5.4. Events were relocated to enhance understanding of the hypocenter uncertainties. The primary earthquake, registering a magnitude of Mw 6.2, was followed by a sequence of events with Mw>5 within 24 hours of the main shock. To identify the earthquake’s source parameters, three components—local waveforms reported by the broadband networks of the Iranian Seismological Center (IRSC), the International Institute of Earthquake Engineering and Seismology (IIEES), and the Iraqi Seismological Network (ISN) were utilized. The analysis was conducted using the ISOLA software, employing a multiple-point source representation and the iterative deconvolution method. The events were relocated using the HYPOINVERSE code to ensure highly accurate results. The stations provided comprehensive coverage, contributing to the high reliability of the results. The method employed in the paper is the H-C method. This simple and readily applicable technique proves highly effective when precise information on the event location and its Centroid Moment Tensor (CMT) solution is available. The findings indicate that the Mountain Front Fault (MFF) can be identified as the causative fault plane of the event.

An Electrical Resistivity Survey was conducted in the Irewolede Estate, located within the Ilorin Metropolis, with the objective of evaluating the susceptibility of the weathered geological layer to contamination. A total of 16 Vertical Electrical Sounding data points were collected and analyzed to delineate the geo-electric stratigraphy, assess hydrogeological implications, and evaluate aquifer vulnerability. The investigation identified five distinct geo-electric layers, including a shallow aquiferous layer exhibiting resistivity values ranging from 28.2 to 876 Ωm and thickness measurements ranging from 4.94 to 71.5 m. The spatial distribution of the Aquifer Vulnerability Index (AVI) indicated that the majority of the region exhibited moderate vulnerability, succeeded by areas of low vulnerability. Regions characterized by high and extremely low vulnerability were noted as small, isolated patches. In order to avert aquifer contamination, the study advocates for thorough geophysical and geotechnical investigations prior to the establishment of future landfills in the region. The findings of this research provide significant insights into the subsurface geological conditions and aquifer vulnerability, which can guide decisions regarding the appropriate placement of groundwater resources and the siting of landfills. By implementing proactive strategies, the potential risk of aquifer contamination can be effectively mitigated, thereby ensuring a sustainable and secure water supply for the community.

This paper presents the first low-frequency (LF) radio sounding in Iran for earthquake prediction and ionospheric remote sensing purposes. Two LF signals transmitted from Türkiye (162 kHz) and Tajikistan (252 kHz) are recorded in Tehran. The recorded data in 2019 is studied in detail. The diurnal variation of the LF signal is averaged over a one-month period to remove temporal variations as a result of background ionospheric irregularities as well as pre-seismic anomalies. The capability of the International Reference Ionosphere (IRI) model in explanation of the time evolution of the received signal is examined. The morning and evening termination time manifested from the amplitude of the received LF signal is compared against the variation of electron density obtained along the transmitter-receiver great circle path. It has been shown that simple comparison of the averaged electron density along the signal propagation path from the transmitter to the receiver coold be used to estimate the electron density during sunset and sunrise. A comprehensive method to advance the IRI estimation of the current state of the ionosphere is proposed. The LF signal anomalies associated with four earthquakes (EQ) near the propagated LF signals at 162 kHz from Türkiye to Tehran are investigated. The anomalous behavior of the LF signal within +/-15 days of the EQ is studied. Daytime variation as well as sunrise and sunset offset in days approaching each event is explored as a possible indicator of pre-seismic activity. The characterization of the LF radio signal and the possibility of earthquake prediction within the Iran plateau are discussed.

Population growth and climate change are worsening pressure on water supplies, altering rainfall-runoff patterns, and posing significant challenges for water management. Climate change profoundly affects society, particularly water reserves, through temperature shifts, precipitation changes, and disruptions of river flows, ultimately impacting water scarcity and ecosystems. The objective of this study is to project the possible effects of climate change on water yield in a cold-climate watershed located in Ardabil province. The GR4J conceptual model is used to simulate the hydrologic watershed response to changes in climatic factors. The HadCM3 model was used to examine meteorological parameters under the A1B climate scenario through implementing LARS-WG. The GR4J has been calibrated using a trial-and-error method to maximize the NS coefficient. The results were evaluated using NS and RE. The results showed a significant variation in water yield values across different periods. The biggest yearly water yield is in 2030, dropping to 49.79 million cubic meters in 2050, representing a 13.6 million cubic meters decrease. Based on the results, the highest positive change occurred in February, where the percentage increased from 93% in 2030 to 138% in 2060, representing a 45% increase. Additionally, the biggest negative change is projected in October, when the percentage decreased from 27% in 2030 to -11% in 2060, representing a decrease of -38%. The results suggest that flooding and extreme flow events will increase, while low flow events will decrease significantly under climate change conditions, and the simulated flow values also show more fluctuations in the projected periods.

A high-power electromagnetic (EM) wave can decay into an ion acoustic wave and a scattered EM wave in a plasma through a process called Stimulated Brillouin Scattering (SBS). A one-dimensional fully electromagnetic Finite-Difference Time-Domain (FDTD) method is used in a magnetized plasma with an increasing density ramp to simulate the propagation of a linearly polarized high-frequency (HF) radio wave traveling through the plasma along magnetic field lines. The study shows that the plasma splits the linearly polarized EM wave into two separate counter-rotating circularly polarized waves: the X-mode and the O-mode waves. The specific cutoff points for each of these circularly polarized waves are illustrated, with the X-mode reflecting at lower frequencies compared to the O-mode. As the radio wave approaches the cutoff frequency, it decays into a scattered high-frequency EM wave and a low-frequency wave. By analyzing the frequency spectrum of the scattered wave and the excited electrostatic low-frequency wave, the electrostatic wave is identified as an ion-acoustic (IA) mode, thus confirming the process as SBS. The growth rate of the excited longitudinal electrostatic wave is studied by calculating the excited longitudinal wave energy. The evolution of energy transfer and conversion from the HF wave to IA wave, as well as electron and ion kinetic energy, is investigated. The results indicate that electron and ion density perturbations experience similar fluctuations.

The significant variations in the solar and magnetic parameters during the peak solar activity periods necessitate a detailed analysis to understand the interactions between the solar wind and the magnetosphere. This research investigates the impact of various solar wind parameters on the Polar Cap (PC) magnetic activity index. The primary objective of this research is to identify and analyze the relationships between the solar wind speed (VSW), the solar wind dynamic pressure (PSW), and the interplanetary electric activity index (AE) with the PC index. A multilayer perceptron (MLP) artificial neural network model was used to explore these relationships. Identifying and predicting complex nonlinear relationships between the input variables and the PC index is the distinctive feature of the model. The dataset used in this research was obtained from the Defense Meteorological Satellite Program (DMSP) satellites and includes VSW, PSW, and AE parameters during periods of peak solar activity in 2002 and 2014. These data were used to analyze the temporal and seasonal variations of the PC index.The results indicate that artificial neural network models can effectively predict the PC index, and a strong correlation between the PC index and the input parameters, particularly in the first half of the years under research, has been observed. The results show the high potential of machine learning models to analyze and predict geomagnetic phenomena, which can improve the forecasting and management of geomagnetic disturbances, and serve as a suitable alternative to classical models.

Aerosols affect cloud microphysical processes and lightning activity by acting as cloud condensation nuclei. To investigate this, we analyzed lightning density data from the Lightning Imaging Sensor (LIS), alongside cloud fraction, cloud-top height, ice cloud optical thickness, and Aerosol Optical Depth (AOD) data from MODIS, as well as Convective Available Potential Energy (CAPE) from ERA5 data for the period 2000-2014. The study focused on two distinct environmental areas (R1 and R2) in Iran: R1, located between 32.5°N-34°N and 46°E-48°E in the mountainous west of Iran, experiencing three distinct climates–Mediterranean, cold mountainous, and warm semi-desert. In contrast, R2, situated between 27.5°N-29°N and 50°E-52°E, is characterized by plains with a warm and dry climate in the north and a humid, warm climate in the south. Monthly variation analysis revealed that lightning activity and AOD correlate well in spring and autumn but diverge in winter, with a negative correlation in summer due to suppressed convective storms at high AOD levels. Annual variation analysis indicates higher electrical activity in R1, which frequently experiences sand and dust storms. The results showed a moderate positive correlation between AOD and lightning activity in both regions, attributed to various AOD sources such as black carbon, dust, sea salt, and sulphate. Cloud fraction, ice cloud optical thickness, and cloud-top height showed positive correlations with lightning density in both R1 and R2 However, the correlation between CAPE and lightning density was lower in R2, likely due to higher atmospheric humidity stabilizing the environment and reducing the frequency and intensity of thunderstorms.

Confident progress in developing the Russian Federation’s Arctic zone requires minimizing the negative impacts of space weather on electric power systems within the auroral oval. Some scientific studies propose methods for remote diagnostics of geoinduced currents (GIC) levels. However, despite the high accuracy of these methods, their applicability remains uncertain, and they cannot be implemented in regions lacking a dense coverage of reliable geomagnetic data sources, such as the Taimyr and Gydan Peninsulas and northern Yakutia.This paper discusses an approach to the non-hardware-based assessment of GIC levels in high-latitude electric power systems. The proposed method is based on GIC observation data from the Kola-Karelian transit area, which includes power transmission lines and substations forming a single chain over 1,100 km in length. Its distinctive feature is the use of auroras as natural indicators of the space weather conditions for problem-oriented interpretation.Using the example of the Vykhodnoy substation in the Northern Transit main power grid, it has been shown that the most probable (averaged over 30 minutes) GIC levels are 0.08 A, 0.23 A, and 0.68 A when auroras are observed to the north, at the zenith, and to the south, respectively. The probability of the average half-hour GIC level exceeding 2 A (when auroras are observed to the north, at the zenith, and to the south) is approximately 6%, 10%, and 15%, respectively. Finally, promising modernization methods and the applicability limits of the proposed approach are discussed.

Coronal Mass Ejections (CMEs), large-scale eruptions of plasma and magnetic field from the solar corona, have been detected as for a cause of significant space weather effects. Fundamental research on solar events complexity variations from the solar corona to 1 AU and beyond is critical to our physical understanding of the evolution and interactions of transients in the inner heliosphere. In the nonhomogeneous background solar wind flow, a three-dimensional, time-dependent numerical magnetohydrodynamic (MHD) model is considered to study the propagation of CMEs and their interaction with the background solar wind structures. A comprehensive analysis of the period from 2 to 8 June 2023, considering the complex structure, is investigated. This study addresses the need to explore the interplanetary evolution of CMEs and especially their complexity in the inner heliosphere. To analyze the accurate impact of the solar event on Earth, the Disturbance Storm Index (Dst) calculated by the numerical EUHFORIA code, is shown and verifies a calm phase followed by a mild disturbance from 2 to 8 June 2023. In summary, it is found that CMEs that occurred between 2 and 8 of June 2023, which were not significant and lacked considerable height time development, did not experience any increase during the propagation in the interplanetary space. Overall, it is found that EUHFORIA demonstrates the potential to investigate and even predict geomagnetic storms. This enables us to protect our technologies from the enormous financial damage of solar storms.