انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Revisiting wintertime budget of local finite-amplitude wave activity in the Northern Hemisphere storm tracks Revisiting wintertime budget of local finite-amplitude wave activity in the Northern Hemisphere storm tracks 1 24 219260 10.30499/ijg.2025.494060.1655 FA Amin Fazl Kazemi Ph.D., Student of Meteorology, Institute of Geophysics, University of Tehran, Tehran, Iran Alireza Mohebalhojeh Professor, Institute of Geophysics, University of Tehran, Tehran, Iran 0000-0002-5906-8486 Mohammad Mirzaei Associate Professor, Institute of Geophysics, University of Tehran, Tehran, Iran 0000-0003-0813-3994 Farhang Ahmadi-Givi Professor, Institute of Geophysics, University of Tehran, Tehran, Iran 0000-0002-9487-4862 Journal Article 2024 12 15 The climatological distribution of the local finite-amplitude wave activity (LWA) and its three-dimensional flux components are studied for boreal winter (December to February) using the JRA-55 reanalysis dataset for the period 1979–2023. The methods for extracting the Lagrangian reference state of quasigeostrophic potential vorticity and obtaining density-weighted vertical average are modified to expand the domain of validity to include highland and low-latitude regions of the Northern Hemisphere. A novel summarization method is also proposed to extract spatial scale and identify the local dominant balances within the LWA budget terms. Additionally, variations in the LWA flux divergence components are examined for the layers representing the upper, middle and lower troposphere, as well as for the vertical profiles in the selected horizontal areas with the storm tracks. The differences observed in the distribution of LWA and its flux divergence, compared to previous studies, are qualitatively consistent with the available Eulerian diagnostics for small-amplitude disturbances. The finite-amplitude Lagrangian formulation makes it possible to provide more accurate estimates of the nonconservative source/sink processes. These estimates are revised using the diagnostics related to the geostrophic states, based on a formal quasigeostrophic inversion. The main dominant balance observed for the column-averaged budget is between the near-surface baroclinicity, which generates the vertical flux of LWA, and the nonconservative processes. The results show that, while the North Atlantic and North Pacific storm tracks exhibit similar patterns with regard to the source of LWA, they behave quite differently at the regional scale in terms of the balance between the LWA budget terms. The climatological distribution of the local finite-amplitude wave activity (LWA) and its three-dimensional flux components are studied for boreal winter (December to February) using the JRA-55 reanalysis dataset for the period 1979–2023. The methods for extracting the Lagrangian reference state of quasigeostrophic potential vorticity and obtaining density-weighted vertical average are modified to expand the domain of validity to include highland and low-latitude regions of the Northern Hemisphere. A novel summarization method is also proposed to extract spatial scale and identify the local dominant balances within the LWA budget terms. Additionally, variations in the LWA flux divergence components are examined for the layers representing the upper, middle and lower troposphere, as well as for the vertical profiles in the selected horizontal areas with the storm tracks. The differences observed in the distribution of LWA and its flux divergence, compared to previous studies, are qualitatively consistent with the available Eulerian diagnostics for small-amplitude disturbances. The finite-amplitude Lagrangian formulation makes it possible to provide more accurate estimates of the nonconservative source/sink processes. These estimates are revised using the diagnostics related to the geostrophic states, based on a formal quasigeostrophic inversion. The main dominant balance observed for the column-averaged budget is between the near-surface baroclinicity, which generates the vertical flux of LWA, and the nonconservative processes. The results show that, while the North Atlantic and North Pacific storm tracks exhibit similar patterns with regard to the source of LWA, they behave quite differently at the regional scale in terms of the balance between the LWA budget terms.

https://www.ijgeophysics.ir/article_219260_b16f297821fd18ee28fe0ee442395069.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Subsurface lithological interpretation of the landslide-prone Cipendawa area, Cianjur (Indonesia), using 2D and 3D inversion of aeromagnetic data Subsurface lithological interpretation of the landslide-prone Cipendawa area, Cianjur (Indonesia), using 2D and 3D inversion of aeromagnetic data 25 42 219840 10.30499/ijg.2025.493770.1654 FA Ahmad Ali Muckharom M.Sc., Physics Department, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia Agus Setyawan Professor, Physics Department, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia 0000-0002-6234-2765 Agustya Adi Martha Ph.D., National Research and Innovation Agency (BRIN RI), West Java, Indonesia 0009-0005-5288-0817 Bono Pranoto M.Sc., National Research and Innovation Agency (BRIN RI), West Java, Indonesia Tio Azhar Prakoso Setiadi Ph.D., Student, National Research and Innovation Agency (BRIN RI), West Java, Indonesia 0000-0002-2429-6147 Journal Article 2024 12 13 Cipendawa, Cianjur (Indonesia), has been declared unsuitable as a permanent housing relocation site for earthquake victims due to the slope of the land, soft soil conditions, and the potential for volcanic eruptions. This research aims to interpret the subsurface structures of the Cipendawa area using the aeromagnetic method, considering variations caused by heterogeneous subsurface lithology. Data acquisition was carried out using a drone-mounted Sensys R3 magnetometer at 135 measurement points, including measurements of total magnetic field. Data processing included IGRF (International Geomagnetic Reference Field) correction, reduction to the pole (RTP), and both two-dimensional and three-dimensional modeling. Qualitative interpretation results indicate three magnetic anomaly patterns. High magnetic anomalies (245 nT-441 nT), observed in the southeastern and northwestern parts of the area, are interpreted as volcanic rocks such as basalt and andesite. Medium magnetic anomalies (193 nT-238 nT), located in the southwestern and central areas, are thought to be sandstone. Low magnetic anomalies (-21 nT-185 nT), found in the northern parts, are interpreted as limestone and sandstone. The analysis of three cross-sections shows variations in rock susceptibility from -23×10-4SI to 54×10-4SI within the depth range of 0-40 meters. It indicates that the northern area consists of sedimentary rocks, such as sandstone, formed by river flow that carries magnetite-rich minerals, while the southern area is composed of volcanic rocks, such as andesite breccia, which align with the geological map due to magma intrusion from Mount Gede in the past. Furthermore, three-dimensional modeling of the Cipendawa area indicates that the landslide-prone sedimentary rock layer is located in the northern part, while the hard rock layer in the southeastern part is more stable. However, the southeastern region could still experience landslides in the event of tectonic activity. Cipendawa, Cianjur (Indonesia), has been declared unsuitable as a permanent housing relocation site for earthquake victims due to the slope of the land, soft soil conditions, and the potential for volcanic eruptions. This research aims to interpret the subsurface structures of the Cipendawa area using the aeromagnetic method, considering variations caused by heterogeneous subsurface lithology. Data acquisition was carried out using a drone-mounted Sensys R3 magnetometer at 135 measurement points, including measurements of total magnetic field. Data processing included IGRF (International Geomagnetic Reference Field) correction, reduction to the pole (RTP), and both two-dimensional and three-dimensional modeling. Qualitative interpretation results indicate three magnetic anomaly patterns. High magnetic anomalies (245 nT-441 nT), observed in the southeastern and northwestern parts of the area, are interpreted as volcanic rocks such as basalt and andesite. Medium magnetic anomalies (193 nT-238 nT), located in the southwestern and central areas, are thought to be sandstone. Low magnetic anomalies (-21 nT-185 nT), found in the northern parts, are interpreted as limestone and sandstone. The analysis of three cross-sections shows variations in rock susceptibility from -23×10-4SI to 54×10-4SI within the depth range of 0-40 meters. It indicates that the northern area consists of sedimentary rocks, such as sandstone, formed by river flow that carries magnetite-rich minerals, while the southern area is composed of volcanic rocks, such as andesite breccia, which align with the geological map due to magma intrusion from Mount Gede in the past. Furthermore, three-dimensional modeling of the Cipendawa area indicates that the landslide-prone sedimentary rock layer is located in the northern part, while the hard rock layer in the southeastern part is more stable. However, the southeastern region could still experience landslides in the event of tectonic activity.

https://www.ijgeophysics.ir/article_219840_1f37f2b594a7eb42f3d5b24e807cd278.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Exploration and resource evaluations of mineral using modeling and GPR applications: case study in Morocco Exploration and resource evaluations of mineral using modeling and GPR applications: case study in Morocco 43 63 220783 10.30499/ijg.2025.500189.1666 FA Mohammed Hamdaoui Ph.D., Mathematics and Information Systems Laboratory, Faculty of Polydisciplinary Studies of Nador, Mohammed First University, Oujda, Morocco Faize Ahmed Ph.D., Mathematics and Information Systems Laboratory, Faculty of Polydisciplinary Studies of Nador, Mohammed First University, Oujda, Morocco Sara Said Ph.D., Department Research Center, High Studies of Engineering School (EHEI), Oujda, Morocco Journal Article 2025 01 23 This paper presents a contemporary methodology for mineral research and exploration, with a particular focus on iron ores and geophysical mapping in the Nador region of eastern Morocco. The application of Ground Penetrating Radar (GPR) in the exploration of subsurface objects and minerals has demonstrated significant success and efficacy. GPR technology has emerged as a powerful, non-destructive tool for the exploration and resource assessment of minerals. One of the primary objectives of this study is to utilize geophysical mapping to identify the locations and quantities of mineral deposits for future exploitation and exploration. To interpret the data and results obtained from GPR, we employed modeling techniques to simulate GPR signals for the detection of various minerals using the GprMax2d software. The algorithm underpinning this program has been enhanced to improve its effectiveness and accuracy in detecting targets and determining their physical properties. This study leveraged the simulation and modeling to interpret data and compare the amplitude of reflected signals from the surfaces of different objects and minerals. This paper presents a contemporary methodology for mineral research and exploration, with a particular focus on iron ores and geophysical mapping in the Nador region of eastern Morocco. The application of Ground Penetrating Radar (GPR) in the exploration of subsurface objects and minerals has demonstrated significant success and efficacy. GPR technology has emerged as a powerful, non-destructive tool for the exploration and resource assessment of minerals. One of the primary objectives of this study is to utilize geophysical mapping to identify the locations and quantities of mineral deposits for future exploitation and exploration. To interpret the data and results obtained from GPR, we employed modeling techniques to simulate GPR signals for the detection of various minerals using the GprMax2d software. The algorithm underpinning this program has been enhanced to improve its effectiveness and accuracy in detecting targets and determining their physical properties. This study leveraged the simulation and modeling to interpret data and compare the amplitude of reflected signals from the surfaces of different objects and minerals.

https://www.ijgeophysics.ir/article_220783_9cc0f3fc9a414790ce157d50cc6d7ee3.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Assessing performances of pattern informatics method variants: a comparative analysis in Zagros, Iran Assessing performances of pattern informatics method variants: a comparative analysis in Zagros, Iran 65 80 222805 10.30499/ijg.2025.507911.1677 FA Raheleh Shiryazdi Ph.D, Student, Department of Seismology, IIEES, Tehran, Iran Majid Mahood Assistant Professor, Department of seismology, IIEES, Tehran, Iran Yaser Radan Assistant Professor, Faculty of Passive Defense, Malek Ashtar University of Technology, Tehran, Iran 0000-0003-3121-7184 Journal Article 2025 02 20 Iran is one of the most seismically active regions in the world. Considering the growing field of earthquake prediction, it seems to be one of the strategies for reducing earthquake damage and managing crises during earthquakes. Given its successful application in various parts of the world, we examined the performance of the Pattern Informatics (PI) method in forecasting earthquakes in Iran's Zagros region. The PI method has a suitable physical basis and clarity in computational procedures. Unlike many other methods, it does not require processing such as windowing or declustering of earthquake catalogs. It considers the seismic patterns of quiescence and anomalous activity without predefined conditions or patterns. The main criterion in this method is the number of events exceeding a specific threshold and counted in regional cells that have been networked using a particular algorithm. In the modified method, besides counting the earthquakes in the central cell, the effects of eight neighboring cells are also considered, influencing the probability of an earthquake event in the central cell. This study investigates the application of the original and modified global versions of this method to the selected seismic catalogs of Iran. To reduce the effect of varying seismic nature and to prevent errors arising from different averaging methods in the seismic regions of Iran with diverse tectonic characteristics, the Zagros tectonic province was chosen based on the division of Iran's provinces. In this tectonic province, retrospective predictions were conducted along with evaluations and comparisons of the results to validate the method. The results showed that with acceptable spatial accuracy, this method could be used to predict earthquakes larger than the catalog’s completeness threshold. For this purpose, the success rate and false alarm rate were calculated by changing the cell dimensions and plotting Molchan and ROC (receiver operating characteristic) evaluation diagrams, given that changes in spatial parameters have the most significant impact on the calculations in this method. The optimal method was determined between the original and modified versions with the most suitable cell dimensions. Based on the results, the original PI method with a ∆x = 0.3° grid showed the best evaluation results for the Zagros region. The catalog used in this study was compiled from the Iranian Seismological Center (ISC) and the International Seismological Center (ISCR) from 1980 to 2021 within the geographical range of 44° to 64° longitude east and 25° to 40° north latitude. Iran is one of the most seismically active regions in the world. Considering the growing field of earthquake prediction, it seems to be one of the strategies for reducing earthquake damage and managing crises during earthquakes. Given its successful application in various parts of the world, we examined the performance of the Pattern Informatics (PI) method in forecasting earthquakes in Iran's Zagros region. The PI method has a suitable physical basis and clarity in computational procedures. Unlike many other methods, it does not require processing such as windowing or declustering of earthquake catalogs. It considers the seismic patterns of quiescence and anomalous activity without predefined conditions or patterns. The main criterion in this method is the number of events exceeding a specific threshold and counted in regional cells that have been networked using a particular algorithm. In the modified method, besides counting the earthquakes in the central cell, the effects of eight neighboring cells are also considered, influencing the probability of an earthquake event in the central cell. This study investigates the application of the original and modified global versions of this method to the selected seismic catalogs of Iran. To reduce the effect of varying seismic nature and to prevent errors arising from different averaging methods in the seismic regions of Iran with diverse tectonic characteristics, the Zagros tectonic province was chosen based on the division of Iran's provinces. In this tectonic province, retrospective predictions were conducted along with evaluations and comparisons of the results to validate the method. The results showed that with acceptable spatial accuracy, this method could be used to predict earthquakes larger than the catalog’s completeness threshold. For this purpose, the success rate and false alarm rate were calculated by changing the cell dimensions and plotting Molchan and ROC (receiver operating characteristic) evaluation diagrams, given that changes in spatial parameters have the most significant impact on the calculations in this method. The optimal method was determined between the original and modified versions with the most suitable cell dimensions. Based on the results, the original PI method with a ∆x = 0.3° grid showed the best evaluation results for the Zagros region. The catalog used in this study was compiled from the Iranian Seismological Center (ISC) and the International Seismological Center (ISCR) from 1980 to 2021 within the geographical range of 44° to 64° longitude east and 25° to 40° north latitude.

https://www.ijgeophysics.ir/article_222805_7ded608d1203ddded09af96fc84a60ff.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 A Review of Constitutive Modeling of Unsaturated Soils A Review of Constitutive Modeling of Unsaturated Soils 81 128 223051 10.30499/ijg.2025.514578.1687 FA Milad Mirzahosseini Ph.D. Student, Civil Eng. Dept., School of Engineering, Razi University, Kermanshah, Iran 0000-0002-2306-1223 Mahnoosh Biglari Associate Professor, Civil Eng. Dept., School of Engineering, Razi University, Kermanshah, Iran 0000-0002-1245-7740 Journal Article 2025 03 30 The mechanics of unsaturated soils are integral to understanding and predicting the behavior of diverse geotechnical and geo-environmental systems, including natural slopes, engineered embankments, landfill covers, and agricultural fields. Unlike saturated soils, unsaturated soils exhibit a complex interplay among air, water, and solid phases, in which suction and partial saturation are pivotal in governing stress-strain responses, volume changes, and fluid flow processes. Over the past several decades, extensive theoretical, experimental, and computational efforts have culminated in sophisticated constitutive models, reflecting the need to include moisture content and suction as additional state variables.This article comprehensively reviews these modeling endeavors, tracing historical developments from empirical extensions of saturated soil models to contemporary elasto-plastic, multi-scale, and data-driven frameworks. Emphasis is placed on the evolution of stress state variables, the role of hydraulic hysteresis, and the bidirectional coupling between mechanical and water retention behavior. Special attention is given to recent models that incorporate bound water structure and dehydration mechanisms in expansive clays, highlighting their influence on retention properties, suction, and thermomechanical responses. The paper also explores advances in small-strain stiffness modeling (which is crucial for predicting seismic behavior of unsaturated soils), machine learning integration, and coupled thermo-hydro-mechanical-chemical (THMC) processes. Practical challenges, including parameter calibration, are examined. The paper also offers examples of prominent constitutive models, detailing their mathematical formulations and underlying assumptions. Current trends, including the integration of machine learning, are evaluated, and future research directions are proposed, underscoring the importance of interdisciplinary collaborations and long-term monitoring to refine and validate constitutive models for unsaturated soils. The mechanics of unsaturated soils are integral to understanding and predicting the behavior of diverse geotechnical and geo-environmental systems, including natural slopes, engineered embankments, landfill covers, and agricultural fields. Unlike saturated soils, unsaturated soils exhibit a complex interplay among air, water, and solid phases, in which suction and partial saturation are pivotal in governing stress-strain responses, volume changes, and fluid flow processes. Over the past several decades, extensive theoretical, experimental, and computational efforts have culminated in sophisticated constitutive models, reflecting the need to include moisture content and suction as additional state variables.This article comprehensively reviews these modeling endeavors, tracing historical developments from empirical extensions of saturated soil models to contemporary elasto-plastic, multi-scale, and data-driven frameworks. Emphasis is placed on the evolution of stress state variables, the role of hydraulic hysteresis, and the bidirectional coupling between mechanical and water retention behavior. Special attention is given to recent models that incorporate bound water structure and dehydration mechanisms in expansive clays, highlighting their influence on retention properties, suction, and thermomechanical responses. The paper also explores advances in small-strain stiffness modeling (which is crucial for predicting seismic behavior of unsaturated soils), machine learning integration, and coupled thermo-hydro-mechanical-chemical (THMC) processes. Practical challenges, including parameter calibration, are examined. The paper also offers examples of prominent constitutive models, detailing their mathematical formulations and underlying assumptions. Current trends, including the integration of machine learning, are evaluated, and future research directions are proposed, underscoring the importance of interdisciplinary collaborations and long-term monitoring to refine and validate constitutive models for unsaturated soils.

https://www.ijgeophysics.ir/article_223051_ac401c62a10a72a7519150eb2f81046b.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 3D modeling of electro-facies using a geostatistical algorithm 3D modeling of electro-facies using a geostatistical algorithm 129 151 225819 10.30499/ijg.2025.508999.1678 FA Reda Al Hasan M.Sc., Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan university, Semnan, Iran Mohammad Hossein Saberi Associate Professor, Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan University, Semnan, Iran 0000-0001-5248-3663 Mohammad Ali Riahi Professor, Institute of Geophysics, University of Tehran, Tehran, Iran 0000-0002-3827-4467 Journal Article 2025 03 01 Facies analysis is a critical component of reservoir characterization studies. This study employs several clustering methods to generate reservoir electro-facies (EFs) from well logs. The clustering results were then distributed using geostatistical algorithms to create a reservoir facies model within a geometrical structure interpreted from seismic data. Well logs were classified into facies using multi-resolution graph-based clustering (MRGC) and self-organizing map (SOM) methods. Once distributed spatially with geostatistical techniques, the log-derived facies supported the conditional distribution of petrophysical properties by facies. A geostatistical approach, specifically sequential indicator simulation (SIS), was used to integrate well logs and interpreted seismic data, resulting in an accurate 3D facies model. This model was generated for the depth interval spanning the Frontier (Second Wall Creek) to the Crow Mountain horizons in the Teapot Dome. The 3D facies model aids in developing the field plan and identifying potential new well locations for drilling. Facies analysis is a critical component of reservoir characterization studies. This study employs several clustering methods to generate reservoir electro-facies (EFs) from well logs. The clustering results were then distributed using geostatistical algorithms to create a reservoir facies model within a geometrical structure interpreted from seismic data. Well logs were classified into facies using multi-resolution graph-based clustering (MRGC) and self-organizing map (SOM) methods. Once distributed spatially with geostatistical techniques, the log-derived facies supported the conditional distribution of petrophysical properties by facies. A geostatistical approach, specifically sequential indicator simulation (SIS), was used to integrate well logs and interpreted seismic data, resulting in an accurate 3D facies model. This model was generated for the depth interval spanning the Frontier (Second Wall Creek) to the Crow Mountain horizons in the Teapot Dome. The 3D facies model aids in developing the field plan and identifying potential new well locations for drilling.

https://www.ijgeophysics.ir/article_225819_655d0a6e3c2ded98e723ec6bc2b8ea6e.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 A data-driven approach to reservoir characterization: machine learning and seismic Attribute integration in the penobscot field, Nova Scotia basin A data-driven approach to reservoir characterization: machine learning and seismic Attribute integration in the penobscot field, Nova Scotia basin 153 165 225947 10.30499/ijg.2025.524352.1697 FA Khlieeq Ul Zaman M.Sc Student, Department of Earth Sciences, University of Sargodha, Pakistan Rani Ummay Farwa M.Sc Student, Department of Earth Sciences, University of Sargodha, Pakistan Syed Haroon Ali Assistant Professor, Department of Earth Sciences, University of Sargodha, Pakistan 0000-0002-8619-7005 Amjad Ali Assistant Professor, College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China Fahad Ali Assistant Professor, Department of Geology, Bacha Khan University, Charsadda, Khyber Pakhtunkhwa, Pakistan Muhammad Iqbal Hajana Assistant Professor, Department of Earth and Environmental Sciences, Bahria University, Islamabad, Pakistan Journal Article 2025 05 18 Petrophysical analysis and advanced attributes of machine learning are used to evaluate exploratory wells, B-41 and L-30 of Penobscot Basin, Nova Scotia, Canada. The main objective of this paper is to evaluate the petroleum system and the prospects and leads. Well B-41 and L-30 reached their TD at 3483m and 4360m respectively, both wells were declared dry and plugged abandoned. The petrophysical studies include Bulk density and neutron porosity cross plots, in both wells, neutron and porosity cross plots show the almost linear trend of values, showing clay and sand lithology. Porosity values of B-41 and L-30 are 8-10% and 10-12%, respectively. Shale volume of B-41 is 37-44% and for L-30 is 23-32%; however, both wells show a fair porosity, but water saturation is high, so it is not a favorable condition for hydrocarbons to accumulate. For using attributes of machine learning 11 sets of 2D seismic lines and 1 set of 3D seismic surveys were used an advance technique of machine learning known as SOM (Self Organizing Map) is used, which is computational data analysis technique which enables mapping of nonlinear data to lower dimension and also at different frequencies, for this analysis the frequencies of 11, 18, 26 Hz are used. Machine learning enables efficient and accurate predictions even with limited data, providing a more practical and streamlined alternative to conventional reservoir simulation techniques. Petrophysical analysis and advanced attributes of machine learning are used to evaluate exploratory wells, B-41 and L-30 of Penobscot Basin, Nova Scotia, Canada. The main objective of this paper is to evaluate the petroleum system and the prospects and leads. Well B-41 and L-30 reached their TD at 3483m and 4360m respectively, both wells were declared dry and plugged abandoned. The petrophysical studies include Bulk density and neutron porosity cross plots, in both wells, neutron and porosity cross plots show the almost linear trend of values, showing clay and sand lithology. Porosity values of B-41 and L-30 are 8-10% and 10-12%, respectively. Shale volume of B-41 is 37-44% and for L-30 is 23-32%; however, both wells show a fair porosity, but water saturation is high, so it is not a favorable condition for hydrocarbons to accumulate. For using attributes of machine learning 11 sets of 2D seismic lines and 1 set of 3D seismic surveys were used an advance technique of machine learning known as SOM (Self Organizing Map) is used, which is computational data analysis technique which enables mapping of nonlinear data to lower dimension and also at different frequencies, for this analysis the frequencies of 11, 18, 26 Hz are used. Machine learning enables efficient and accurate predictions even with limited data, providing a more practical and streamlined alternative to conventional reservoir simulation techniques.

https://www.ijgeophysics.ir/article_225947_539caed5eb8be8890e921d62413a0d71.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Geoelectric investigation and physicochemical analysis of parts of Niger Delta, Nigeria, for groundwater quality assessment Geoelectric investigation and physicochemical analysis of parts of Niger Delta, Nigeria, for groundwater quality assessment 167 186 226465 10.30499/ijg.2025.530972.1709 FA Chimezie Charles Ofoha Ph.D., Department of Physics, Edwin Clark University, Kiagbodo, Delta State, Nigeria Ikechukwu Chukwuocha Ph.D. Student, Department of Physics, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria Chukwuemeka Ngozi Ehirim Professor, Department of Physics, University of Port Harcourt, Port Harcourt, Rivers State, Nigeria Journal Article 2025 06 22 Geoelectrical methods integrated with physicochemical analyses of borehole samples were employed to determine groundwater contamination levels in the area. Vertical electrical sounding (VES) and 2D resistivity techniques were conducted along the area's four transverses. The field data were processed using the RES2DINV and IPI2WIN software. Groundwater samples were collected from three boreholes in the area and one borehole outside the mechanic village area as a control. The results of the VES indicated that at depths beyond 18 m, resistivity greater than 200 Ωm suggested that fresh gasoline oil spills increased the electrical resistivity of the groundwater. Additionally, at depths beyond 2 m, with a resistivity range of less than 85 Ωm, biodegraded hydrocarbon spills in the auto mechanic village were evident, making the groundwater less electrically resistive. The 2D resistivity results showed two types of anomalies: high and low resistivity zones. The high resistivity zones indicate uncontaminated areas, while the low resistivity zones indicate contaminated areas. The physicochemical analyses and heavy metal groundwater samples revealed that gasoline oil infiltrated the subsoil down to the water table as a free-phase hydrocarbon. The depths of the water table and aquifer extended beyond 10 and 36 m, exhibiting high resistivity ranges greater than 287.7 and 548.6 Ωm, respectively. In conclusion, the gasoline oil spills saturate the unsaturated zone and subsequently leach into the groundwater aquifer at depths beyond 18 m, showing a high resistivity range greater than 350 Ωm. Geoelectrical methods integrated with physicochemical analyses of borehole samples were employed to determine groundwater contamination levels in the area. Vertical electrical sounding (VES) and 2D resistivity techniques were conducted along the area's four transverses. The field data were processed using the RES2DINV and IPI2WIN software. Groundwater samples were collected from three boreholes in the area and one borehole outside the mechanic village area as a control. The results of the VES indicated that at depths beyond 18 m, resistivity greater than 200 Ωm suggested that fresh gasoline oil spills increased the electrical resistivity of the groundwater. Additionally, at depths beyond 2 m, with a resistivity range of less than 85 Ωm, biodegraded hydrocarbon spills in the auto mechanic village were evident, making the groundwater less electrically resistive. The 2D resistivity results showed two types of anomalies: high and low resistivity zones. The high resistivity zones indicate uncontaminated areas, while the low resistivity zones indicate contaminated areas. The physicochemical analyses and heavy metal groundwater samples revealed that gasoline oil infiltrated the subsoil down to the water table as a free-phase hydrocarbon. The depths of the water table and aquifer extended beyond 10 and 36 m, exhibiting high resistivity ranges greater than 287.7 and 548.6 Ωm, respectively. In conclusion, the gasoline oil spills saturate the unsaturated zone and subsequently leach into the groundwater aquifer at depths beyond 18 m, showing a high resistivity range greater than 350 Ωm.

https://www.ijgeophysics.ir/article_226465_cb972dbe36c53bba66e2df440f17e9b5.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Uncertainty estimation in earthquake magnitude determination using high-rate GPS data with Bootstrap method Uncertainty estimation in earthquake magnitude determination using high-rate GPS data with Bootstrap method 187 203 226759 10.30499/ijg.2025.502304.1675 FA Sara Bahrami Asl M.Sc., Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran 0009-0002-9993-0250 Khalil Bakhtiari Asl M.Sc., Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran 0009-0001-9539-5484 Khosro Moghtased- Azar Associate Professor, Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Journal Article 2025 02 16 Accurate analysis of earthquake magnitude and evaluation of its associated uncertainty are fundamental topics in seismology and disaster management. In this study, the earthquake magnitudes were estimated using high-rate GPS data for three major events: the 2011 Tohoku earthquake (Mw 9.1), the 2021 Chignik earthquake (Mw 8.2), and the 2018 Anchorage earthquake (Mw 7.1). The final estimated magnitudes using the proposed method were approximately Mw 9.25 for Tohoku, Mw 8.27 for Chignik, and Mw 7.08 for Anchorage, closely aligning with official reports. High-rate GPS data, due to their ability to precisely capture real-time crustal displacements, were utilized as the primary data source. These data provided valuable information on maximum displacement, dominant period, and epicentral distance, and were employed for estimating earthquake magnitude and analyzing uncertainty. To analyze the data, combinations of different GPS stations were used to minimize the impact of noisy data and achieve more stable results. The application of the Bootstrap statistical method reduced uncertainty values significantly from approximately 0.0039 to 0.0011 for Tohoku, 0.0031 to 0.0019 for Chignik, and 0.004 to 0.0015 for Anchorage. These results demonstrate the statistical robustness and effectiveness of the proposed approach. Compared to conventional seismic methods, which suffer from data clipping and saturation for large magnitudes, the high-rate GPS method provided stable and unbiased magnitude estimates without signal saturation issues. The findings underscore the importance of expanding GPS networks in tectonically active regions, integrating seismic and GPS data, and employing advanced algorithms for data processing. Furthermore, the research emphasizes the role of comprehensive data analysis in improving early earthquake warning systems. By incorporating high-rate GPS data with traditional seismic data, the precision and reliability of early warning systems can be significantly enhanced, ultimately reducing the potential for loss of life and property. Additionally, this study highlights the necessity of strengthening infrastructure resilience and investing in advanced monitoring technologies. In conclusion, this research successfully demonstrates that high-rate GPS data, combined with the Bootstrap method, can accurately estimate earthquake magnitudes, reduce uncertainty, and enhance the reliability of seismic assessments. These findings contribute to the development of more effective earthquake early warning systems and risk mitigation strategies. Accurate analysis of earthquake magnitude and evaluation of its associated uncertainty are fundamental topics in seismology and disaster management. In this study, the earthquake magnitudes were estimated using high-rate GPS data for three major events: the 2011 Tohoku earthquake (Mw 9.1), the 2021 Chignik earthquake (Mw 8.2), and the 2018 Anchorage earthquake (Mw 7.1). The final estimated magnitudes using the proposed method were approximately Mw 9.25 for Tohoku, Mw 8.27 for Chignik, and Mw 7.08 for Anchorage, closely aligning with official reports. High-rate GPS data, due to their ability to precisely capture real-time crustal displacements, were utilized as the primary data source. These data provided valuable information on maximum displacement, dominant period, and epicentral distance, and were employed for estimating earthquake magnitude and analyzing uncertainty. To analyze the data, combinations of different GPS stations were used to minimize the impact of noisy data and achieve more stable results. The application of the Bootstrap statistical method reduced uncertainty values significantly from approximately 0.0039 to 0.0011 for Tohoku, 0.0031 to 0.0019 for Chignik, and 0.004 to 0.0015 for Anchorage. These results demonstrate the statistical robustness and effectiveness of the proposed approach. Compared to conventional seismic methods, which suffer from data clipping and saturation for large magnitudes, the high-rate GPS method provided stable and unbiased magnitude estimates without signal saturation issues. The findings underscore the importance of expanding GPS networks in tectonically active regions, integrating seismic and GPS data, and employing advanced algorithms for data processing. Furthermore, the research emphasizes the role of comprehensive data analysis in improving early earthquake warning systems. By incorporating high-rate GPS data with traditional seismic data, the precision and reliability of early warning systems can be significantly enhanced, ultimately reducing the potential for loss of life and property. Additionally, this study highlights the necessity of strengthening infrastructure resilience and investing in advanced monitoring technologies. In conclusion, this research successfully demonstrates that high-rate GPS data, combined with the Bootstrap method, can accurately estimate earthquake magnitudes, reduce uncertainty, and enhance the reliability of seismic assessments. These findings contribute to the development of more effective earthquake early warning systems and risk mitigation strategies.

https://www.ijgeophysics.ir/article_226759_413ea635e1aa741dc412b440265935bb.pdf

انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 Regionalization, temporal-spatial characteristics, and trend of changes in precipitation and drought in Iran Regionalization, temporal-spatial characteristics, and trend of changes in precipitation and drought in Iran 205 240 227424 10.30499/ijg.2025.499798.1663 FA Peyman Mahmoudi Associate Professor, Department of Physical Geography, Faculty of Geography and Evironmental Planning, University of Sistan and Baluchestan, Zahedan, Iran 0000-0003-2138-0973 Abdolraoof Shahozei M.Sc., Department of Physical Geography, Faculty of Geography and Evironmental Planning, University of Sistan and Baluchestan, Zahedan, Iran Journal Article 2025 01 13 This study regionalizes Iran's precipitation and analyzes the temporal-spatial behavior of precipitation and drought in Iran. It used two sets of precipitation data. The first data set is the monthly precipitation data of 63 synoptic stations and the second data set is the Gridded precipitation data of three databases: Global Precipitation Climatology Center (GPCC), Climatic Research Unit (CRU) of the University of East Anglia (UEA) of England and University of Delaware (UDel) of the United States. As the results showed, the GPCC Gridded precipitation data set with a very high correlation with Iranian station data was recognized as the most suitable Gridded precipitation data set for Iran. Cluster analysis on this precipitation dataset showed Iran can fall under seven different precipitation regions. Various characteristics of droughts, such as intensity, duration, and frequency of each precipitation region, were extracted and analyzed through four standardized precipitation indices (SPI), percentage of normal precipitation (PNPI), precipitation variability (RVI), and deciles (DI). The results disclosed that the three precipitation regions of Southwest (SW), Central Iran (CI), and Northwest-Northeast (NW-NE), among the seven precipitation regions of Iran, are the most vulnerable precipitation regions to drought. This study regionalizes Iran's precipitation and analyzes the temporal-spatial behavior of precipitation and drought in Iran. It used two sets of precipitation data. The first data set is the monthly precipitation data of 63 synoptic stations and the second data set is the Gridded precipitation data of three databases: Global Precipitation Climatology Center (GPCC), Climatic Research Unit (CRU) of the University of East Anglia (UEA) of England and University of Delaware (UDel) of the United States. As the results showed, the GPCC Gridded precipitation data set with a very high correlation with Iranian station data was recognized as the most suitable Gridded precipitation data set for Iran. Cluster analysis on this precipitation dataset showed Iran can fall under seven different precipitation regions. Various characteristics of droughts, such as intensity, duration, and frequency of each precipitation region, were extracted and analyzed through four standardized precipitation indices (SPI), percentage of normal precipitation (PNPI), precipitation variability (RVI), and deciles (DI). The results disclosed that the three precipitation regions of Southwest (SW), Central Iran (CI), and Northwest-Northeast (NW-NE), among the seven precipitation regions of Iran, are the most vulnerable precipitation regions to drought.

https://www.ijgeophysics.ir/article_227424_0786b040a26e4266e2f3bfb1826655c7.pdf