Exploration
Kamran Mostafaei; Mohammad Nabi Kianpour; Mahyar Yousefi; Meisam Saleki
Abstract
Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting ...
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Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting optimal thresholds. This study proposes the Grey Wolf Optimizer (GWO) algorithm as a novel approach for identifying the optimal boundary between anomalies and background. Stream sediment geochemical data from a copper-mineralized area of the Sarduiyeh-Baft sheets in southeast Iran were utilized for analysis. The Geochemical Mineralization Probability Index (GMPI) was first calculated for Cu-Au, Mo-As, Pb-Zn, and porphyry distributions. Subsequently, fractal methods were used to identify anomalous populations within each GMPI. The GWO algorithm was then applied to these distributions to determine the optimal thresholds. Risk analysis, calculated as the ratio of covered copper occurrences to the covered area, revealed superior reliability for the GWO-derived limit compared to those obtained using fractal methods. For porphyry GMPI values, while the fractal reliability indices are 0.127, 0.44, and 0.5, the GWO limit achieved a value of 0.66. Risk analysis for Cu-Au distribution also caused more desired result for GWO limit rather that fractal ones. This demonstrates the enhanced performance and superior reliability of the GWO algorithm for optimizing anomaly detection thresholds in GMPI data.
Exploration
Bardiya Sadraeifar; Maysam Abedi; Seyed Hossein Hosseini
Abstract
The Shavaz iron deposit, located in the southwest Yazd province in Central Iranian Block, near The Bafq metallogenic belt, is a significant and economically valuable iron oxide-apatite resource. It features hematite and a minor content of magnetite, detectable through potential field geophysical ...
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The Shavaz iron deposit, located in the southwest Yazd province in Central Iranian Block, near The Bafq metallogenic belt, is a significant and economically valuable iron oxide-apatite resource. It features hematite and a minor content of magnetite, detectable through potential field geophysical surveys. This study aimed to target magnetite mineralization within the deposit using constrained susceptibility inversion. We began by simulating a multi-source synthetic model with three identical cubes at different depths to evaluate the sparse norm inversion approach. The method was then applied to the case study after the essential magnetic data corrections. To refine the interpretation of residual magnetic anomalies and gain insights into their source and depth, the analytic signal and upward continuation methods were employed. Inversion results across different cross-sections revealed two distinct, shallow, lens-shaped magnetite mineralizations with an average vertical extent of 60 meters. Notably, one magnetite body lies approximately 30 meters deeper due to the Dehshir-Baft fault influence. Low normalized mis-fit values confirmed the successful minimization of the objective function during inversion. Additionally, the reconstructed susceptibility models align well with the previous geological studies and borehole data, demonstrating the efficiency of the sparse norm inversion algorithm.
Exploration
mobin saremi; Abbas Maghsoudi; Reza Ghezelbash; mahyar yousefi; Ardeshir Hezarkhani
Abstract
Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were ...
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Mineral prospectivity mapping (MPM) is a multi-step and complex process designed to narrow down the target areas for exploratory activities in subsequent stages. To pinpoint promising zones of porphyry copper mineralization in the Varzaghan district, NW Iran, various exploration evidence layers were employed in alignment with the conceptual model of these deposits. These layers encompass fault density, proximity to intrusive rocks, multi-element geochemical anomalies, and distances to phyllic and argillic alterations. The geochemical anomaly maps, recognized as the most effective layers, were generated through staged factor analysis (SFA) and the geochemical mineralization probability index (GMPI). Other layers were weighted using a logistic function, and their values were transformed into 0 -1 interval. Ultimately, to integrate the weighted layers, the fuzzy gamma operator and the geometric average method were applied. The normalized density index and prediction-area (P-A) plot were employed to evaluate the MPM models. The findings indicate that the developed models possess considerable validity and can be effectively utilized for planning future exploration endeavors.
Exploration
reza Shahnavehsi; Farnusch Hajizadeh
Abstract
The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose ...
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The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose a novel kind of clustering for data mining in the gravity field to reach the presenting connection among all elements in the same time. For this research work, 867 gravity surveying points were collected in the southern part of Iran (near diapir of Larestan) with a range of absolute gravity from 978579.672 to 978981.186. In this paper, clustering by self-organizing- maps, by utilizing scatter plot matrix is utilized for detecting the relation between the easting, northing, elevation, and absolute gravity simultaneously. In the proposed method, the relations between arrays, two by two, are defined, and like matrix, each raw and column has different i and j values, which represent elements of the studied area, instead of number; for example, array A23 is data division between i = 2 or raw two (in our case northing) and j = 3 or column, three (in our case elevation). In this algorithm, firstly, by using self-organizing maps, clustering is done, and this processing is generated to all arrays by scatter plot matrix, and in all arrays, three clusters are proposed; the result of this clustering shows that in arrays A12, A13, A14, A21, A23, A24, A31, A32, A41, A42, clustering is performed perfectly, and the relationship between the parameters of the studied area near Larestan salt, diaper, can be useful in notifying the properties of this salt diapir.
Exploration
Hamid Reza Baghzendani; Hamid Aghajani; Gholam Hossein Karami
Abstract
Karsts are important sources of groundwater, and it is crucial to determine their water volume and quality. The Ravansar Karst spring in the Kermanshah province is a significant water resource with a substantial water volume in the area. The source of this spring is the carbonate rock unit from the Cretaceous ...
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Karsts are important sources of groundwater, and it is crucial to determine their water volume and quality. The Ravansar Karst spring in the Kermanshah province is a significant water resource with a substantial water volume in the area. The source of this spring is the carbonate rock unit from the Cretaceous period and is affected by tectonic changes and faulting caused by movements related to the Zagros folding. In this work, geophysical methods of microgravity, electrical resistivity, and induced polarization have been utilized to identify the extent of karst development in the limestone units. The minimum residual gravity values are associated with karstification. The field dataset comprised two electrical profiles with the dipole- dipole and pole-dipole arrays. The resistivity and gravity data were inverted using a 2D algorithm based on the least square’s technique with a smoothing constraint. According to the processing and 3D modelling of gravity data; not only cavity-shaped voids and spacious cavity chambers were identified but also sub-structures and micro-karstification in carbonate rocks were detected. The most significant finding from the field survey is the detection of low gravimetric values, indicating relatively large holes and chambers that were previously unknown and inaccessible from ground level. These findings are consistent with known collapse and sediment infill features, as seen in surface sinkholes, cavities, and karstification systems. Geophysical surveys and field surveys show that the holes and karsts in the area are related to tectonic phenomena and faulting and are conduits for transporting water to the Ravansar spring.
Exploration
Zohre Hoseinzade; Mohammad Hassan Bazoobandi
Abstract
Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information ...
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Anomaly detection is the process of recognizing patterns in data that differ from the typical behavior. In geochemistry, this involves identifying hidden patterns and unusual components within the context of exploratory target identification. This issue is particularly significant when limited information is available about the area of interest. Therefore, employing methods that can aid in the exploration process under such conditions and with limited data is highly valuable. In this study, the Deep-Embedded Self-Organizing Map (DE-SOM), an unsupervised deep learning approach, was used to detect geochemical anomalies. The research focused on identifying multivariate geochemical anomalies in the Moalleman region. After detecting the region's geochemical anomalies, the effectiveness of the algorithm was assessed alongside two other types of SOM algorithms. For this purpose, the prediction area plot was utilized, with the intersection points for DE-SOM, Batch SOM, and SOM were determined to be 0.75, 0.67, and 0.65, respectively. The multivariate geochemical anomaly in the Moalleman area shows a good correlation with known mineral occurrences and the andesite and dacite units. Based on this, it can be stated that the DE-SOM method is a useful tool for identifying anomalies and patterns associated with mineralization.
Exploration
Hamid Geranian; Mohammad Amir Alimi
Abstract
This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided ...
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This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided into three and fifteen rock units. The classification accuracy of the resulting model is 90.97 and 84.85% for the three-class training and testing data, and 94.76 and 63.92% for the fifteen-class training and testing data, respectively. These models are then applied to the Sentinel-2 satellite images' data of the Birjand-IV region to prepare two preliminary geological maps. The Birjand-IV region's three-class geology map reveals that igneous rocks are present in the northern and southern regions, while sedimentary rocks occupy the middle section and metamorphic rocks are found within the region's igneous masses. Similarly, the fifteen-class geology map of Birjand-IV indicates that andesite, dacite, intermediate tuff rock units, and metamorphic rocks characterize the northern region. Conversely, the southern part of the region is mainly composed of ophiolite, flysch sediments, basaltic and ultra-basic volcanic rocks, and limestone and shale interlayers. Field studies in three areas confirm the accuracy of the preliminary geology maps.
Exploration
Ahmadreza Erfan; Saeed Soltani Mohammad; Maliheh Abbaszadeh
Abstract
Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful ...
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Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods. Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful tools that robust techniques that integrate multiple predictive models to improve performance beyond that of any individual learner. This study proposes a novel ensemble method for estimating ore grades by localizing the base learner weights in ensemble method. Ordinary kriging, inverse distance weighting, k-nearest neighbors, support vector regression, and artificial neural networks have been used as the base learners of the algorithm. In ML base learners, coordinates (easting, northing and elevation) of samples have been defined as input nodes and grade has been defined as target. The proposed method has been validated for predicting the copper grade (Cu%) in Darehzar porphyry deposit. The performance of proposed method has been by individual base learners and famous ensemble methods. This comparison shows that performance of proposed method is better than other ones. The findings highlight the necessity of adapting ensemble methods to address spatial variability in geological data, thereby establishing a robust framework for ore grade estimation.
Exploration
Mojtaba Bazargani Golshan; Mehran Arian; Peyman Afzal; Lili Daneshvar Saein; Mohsen Aleali
Abstract
The purpose of this research is application of the Concentration-Number and Concentration-Area fractal models for determining the distribution pattern of REEs and lithium in mining area of the North Kochakali coal deposit. According to the Concentration-Area and Concentration-Number fractal graphs, four ...
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The purpose of this research is application of the Concentration-Number and Concentration-Area fractal models for determining the distribution pattern of REEs and lithium in mining area of the North Kochakali coal deposit. According to the Concentration-Area and Concentration-Number fractal graphs, four different geochemical groups were obtained for REEs and lithium in the mining area of North Kochakali coal deposit. The comparison of the threshold values and the models obtained based on the Concentration-Area and Concentration-Number fractal models indicate that the Concentration-Area Fractal model has performed better in determining different geochemical groups and separating anomalies from the background for REEs and lithium in North Kochakali coal deposit. Based on the fractal models in the mining area, the southeastern and western parts have the highest concentrations of REEs and the northeastern parts have the highest concentrations of lithium. These parts should be considered in mining operations due to their higher economic value. The locations of the REEs anomalies are consistent with the location of right-lateral faults with a normal component, since these faults are young and have operated after the formation of coal seams, so the mineralization of REEs in North Kochakali coal deposit is epigenetic.
Exploration
Shirin Jahanmirir; Ali Aalianvari; Hossein Ebrahimpour-Komleh
Abstract
This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous ...
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This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous geological conditions, and while machine learning approaches have offered improvements, they often require significant computational resources. The HMS algorithm, inspired by human cognitive processes, employs memory recall, adaptive clustering, and strategic selection to efficiently refine solutions. Our results demonstrate that HMS significantly outperforms established algorithms in predicting groundwater seepage, achieving an R² value of 0.9988, a Mean Squared Error (MSE) of 0.0002, and a Root Mean Squared Error (RMSE) of 0.0137. In comparison, the Whale Optimization Algorithm (WOA) achieved an R² of 0.9951 with much higher MSE and RMSE, and other methods, like Genetic Programming and ANN-PSO, show higher error values. The HMS algorithm also showed a Variance Accounted for (VAF) of 99.88% and a Mean Absolute Error (MAE) of 0.0041, further validating its high predictive accuracy. This study highlights the HMS algorithm’s superior performance and computational efficiency for optimizing groundwater seepage predictions, positioning it as a powerful tool for geotechnical engineering applications, including real-time monitoring.
Exploration
Shaghayegh Esmaeilzadeh; Ali Moradzadeh; omid Asghari; Reza Mohebian
Abstract
Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings ...
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Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings by incorporating self-updating local variogram models. Unlike the conventional approaches that rely on a single global variogram or fixed local variograms, the proposed method dynamically updates the spatial continuity models at each iteration using automatic variogram modeling and clustering of variogram parameters. The optimal number of clusters is determined using three cluster validity indices: Silhouette Index (SI), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). The method’s effectiveness was evaluated using a three-dimensional non-stationary synthetic dataset, demonstrating robust convergence when employing the SI and CH indices, with both achieving a high global correlation coefficient of 0.9 between the predicted and true seismic data. Among these, the CH index provided the best balance between the computational efficiency and inversion accuracy. The results highlight the method’s ability to effectively capture local spatial variability, while maintaining a reasonable computational cost, making it a promising approach for seismic inversion in complex sub-surface environments.
Exploration
V S S A Naidu Badireddi; Vije durga raju Mullagiri; mvs sekhar Bezawada; K S N Reddy; Ambili V
Abstract
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources ...
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The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources in a 39 square kilometers area, integrating geospatial analysis with field data from core sediment samples. The findings reveal a total of 2.681953 million tons of THM, including 2.434422 million tons of TEHM, with the highest concentration observed in the top 1-meter sea bed sediment layer (1.605286 million tons). Ilmenite, garnet, and sillimanite dominate the mineral assemblage, accompanied by smaller quantities of zircon, monazite, and rutile, offering an estimated revenue potential of $634 to $851 million USD. The application of ArcGIS methodologies, particularly inverse distance weighting (IDW) interpolation, enabled precise mapping of HM distribution, despite challenges such as wide sample spacing and shallow core penetration. While the study highlights the economic and industrial significance of the Bavanapadu sector, it also underscores environmental concerns, including habitat disruption and sediment degradation, associated with mining. Sustainable practices, such as advanced separation technologies, site rehabilitation, and comprehensive environmental impact assessments (EIAs), are essential to mitigate ecological impacts. This research demonstrates the efficacy of GIS-based techniques in resource estimation and sustainable mining, offering a replicable framework for coastal and offshore mineral resource management globally. The findings provide critical insights into balancing economic growth with environmental preservation, setting a benchmark for responsible heavy mineral extraction in dynamic coastal environments.
Exploration
Mohamed Y Amer; Adel M Salem; Mohammed S Farahat; Said Kamel Elsayed
Abstract
Sustainable production of sufficient energy to power the world’s economy with a minimum environmental footprint has been one of the most significant challenges for the decades. Geothermal energy has been considered as one of the promising options to meet the world’s future energy demand. ...
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Sustainable production of sufficient energy to power the world’s economy with a minimum environmental footprint has been one of the most significant challenges for the decades. Geothermal energy has been considered as one of the promising options to meet the world’s future energy demand. The cost of drilling geothermal wells is between 35% and 50% of the total investment cost for the new high-temperature geothermal plants. This “up front” cost makes the geothermal plants more expensive to build than the conventional plants, and because of this and the perceived risk, a lot of attention has been focused on reducing this cost.This paper attempts to minimize the cost of drilling deep wells such as AG-119X, in Egypt of 20060 ft. in depths; in this well, the actual cost was more than the proposed by about five million USD. The actual cost of the drilling operation has been analyzed and compared with the proposed; by observing the cost of each drilling item, it was found that the power drive tools in the bottom hole assembly such as the downhole motor with Rotary Steerable drilling system (RSS) or turbodrill hydraulic downhole motor is the most costly element of the drilling operation in 8.5 holes, which tack thirteen trips in every trip with a new bit, and it was found that the turbodrill hydraulic downhole motor was costly effected in drilling the shush section, in this, and can save around 1756999 USD; this paper is a road map for reducing the cost of drilling geothermal wells.
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Johnny Henrry Ccatamayo-Barrios; Joe Alexis Gonzalez-Vasquez; Teofilo Donaires-Flores; Maxgabriel Alexis Calla-Huayapa
Abstract
This work aimed to categorize mineral resources in a copper deposit in Peru, using a machine learning model, integrating the K-prototypes clustering algorithm for initial classification and Random Forest (RF) as a spatial smoother. A total of 318,443 blocks were classified using geostatistical and geometric ...
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This work aimed to categorize mineral resources in a copper deposit in Peru, using a machine learning model, integrating the K-prototypes clustering algorithm for initial classification and Random Forest (RF) as a spatial smoother. A total of 318,443 blocks were classified using geostatistical and geometric variables derived from Ordinary Kriging (OK) such as kriging variance, sample distance, number of drillholes, and geological confidence. The model was trained and validated using precision, recall, and F1-score metrics. The results indicated an overall accuracy of 97%, with the measured category achieving 98% precision and an F1-score of 0.98. The total estimated tonnage was 5,859.36 Mt, distributed as follows: 1,446.13 Mt (measured), 2,249.22 Mt (Indicated), and 2,164.01 Mt (Inferred), with average copper grades of 0.43%, 0.33%, and 0.31% Cu, respectively. Compared to the traditional geostatistical methods, this hybrid approach improves classification objectivity, spatial continuity, and reproducibility, minimizing abrupt transitions between categories. The RF model proved to be a robust tool, reducing classification inconsistencies and better capturing geological uncertainty. Future studies should explore hybrid models (K-means with RF, ANN with K-Prototypes, gradient boosting, and deep learning) and incorporate economic variables to optimize decision-making in resource estimation.
Exploration
Mahdi Bajolvand; Ahmad Ramezanzadeh; Amin Hekmatnejad; Mohammad Mehrad; Shadfar Davoodi; Mohammad Teimuri
Abstract
Shear Wave Slowness Log (DTSM) is one of the most important petrophysical logs applicable for studying reservoirs, especially geomechanical studying of the oil and gas fields. However, lack of this parameter in wellbore logging can import great sources of uncertainty into geomechanical studies. This ...
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Shear Wave Slowness Log (DTSM) is one of the most important petrophysical logs applicable for studying reservoirs, especially geomechanical studying of the oil and gas fields. However, lack of this parameter in wellbore logging can import great sources of uncertainty into geomechanical studies. This study aims to provide solutions for decreasing the uncertainty of geomechanical models with estimation of the DTSM log using the high accurate deep machine learning models. The main idea is using data from offset fields for extending the range of training data and improving the estimation ability and generalizability of machine learning models. For this purpose, petrophysical data from 8 wells of 4 Iranian oil fields were collected. In the first stage, data preprocessing was performed for reducing the effects of wrong data, missing value, noises, and outliers. Then, machine learning (regression learning-based and deep neural network-based) and analytical models implemented for estimating DTSM. The results indicated that the Gated Recurrent Unit (GRU) model with the values of 1.9 and 2.14 for RMSE and 0.99 for R-square had the most exact answers, for training and test data, respectively. Meanwhile, evaluation of the accuracy of the models on the validation well data indicated that GRU model with the values of 2.43 and 0.93 had been the most accurate model for RMSE and R-square, respectively. Accordingly, using a multi-field comprehensive data bank and applying machine learning methods are strongly recommended to estimate the DTSM, for the cases where limited offset data is available.
Exploration
Seyyed Saeed Ghannadpour; Samaneh Esmaelzadeh Kalkhoran; Maedeh Behifar; Hadi Jalili
Abstract
In this study, with the aim of identifying alteration zones related to the porphyry copper system, satellite images are processed in study area (the Zafarghand exploration area) in the NE of Isfahan. For this purpose, one of the common methods of separating geochemical anomalies from the background, ...
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In this study, with the aim of identifying alteration zones related to the porphyry copper system, satellite images are processed in study area (the Zafarghand exploration area) in the NE of Isfahan. For this purpose, one of the common methods of separating geochemical anomalies from the background, i.e. fractal Concentration-Number (C-N) model, has been employed. The C-N fractal model will normally be implemented on geochemical samples. While in this study, the digital number values belonging to the pixels of the ASTER sensor image are considered as a systematic sample network and also as input for this model. The output of this processing has been prepared in the form of maps of promising areas of the Zafarghand region. The correspondence of the resulting maps with the alteration map of the region shows that applying the proposed method in determining the propylitic and phyllic alteration zones has had acceptable performance. Finally, with the help of the aforementioned proposed method, a map of the promising areas of the study area has been prepared, and based on that, new zones of alterations have been introduced in the region.
Exploration
Abdalmajed Milad Shlof; Mohd Hariri Arifin; Muhammad Taqiuddin Zakaria; Emmanuel O. Salufu
Abstract
More than sixty thermal springs have been detected across Peninsular Malaysia, with about 75% conveniently located in easily accessible areas. The potential for thermal energy growth has been recognized at four hot spring localities: Lojing, Dusun Tua, Ulu Slim, and Sungai Klah. This article analyses ...
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More than sixty thermal springs have been detected across Peninsular Malaysia, with about 75% conveniently located in easily accessible areas. The potential for thermal energy growth has been recognized at four hot spring localities: Lojing, Dusun Tua, Ulu Slim, and Sungai Klah. This article analyses Peninsular Malaysia's geothermal development's geological, geochemical, and geophysical research to assess its appropriateness and performance. The geological data provide insights into the structural characteristics and spatial distribution of thermal springs within the studied area. Geochemical studies measure reservoir temperatures, revealing the highest recorded temperature exceeds 189°C. The review shows that the hot springs are derived from a recharge region linked to high-altitude topography, with their source being meteoric water. Several geophysical techniques, such as transient electromagnet (TEM), gravity, land and satellite magnetic, ground penetration radar (GPR), seismic, resistivity, and induced polarization (IP), have been employed to examine the geothermal system in Malaysia. The sole magnetotelluric (MT) investigation at Ulu Slim deviates from this pattern. The source suggests uncertainty regarding accuracy related to station distance, highlighting these concerns. Most studies indicate that magma intrusion is the most likely heat source. To offer a comprehensive understanding of Peninsular Malaysia's geothermal potential, this study reviews previous research and presents a feasible model that incorporates all current facts.
Exploration
Mustafa Yasser Elgindy; Ahmed Zakaria Nooh; Ali Mostafa Wahba
Abstract
Kick monitoring, detection, and control are key elements to ensure safe drilling operations and avoid catastrophic blow-out incidents that can cause loss of life, equipment, and environmental damage. Conventional kick detection systems such as the pit volume totalizer and the flow in/out sensors identify ...
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Kick monitoring, detection, and control are key elements to ensure safe drilling operations and avoid catastrophic blow-out incidents that can cause loss of life, equipment, and environmental damage. Conventional kick detection systems such as the pit volume totalizer and the flow in/out sensors identify the kick after a large amount of influx has been recorded on the surface. So, we aim to recognize the kick before it enters the wellbore by detecting the abnormal formation pressure once the bit penetrates the rock. This paper proposes a new machine learning model as an alternative solution using field drilling parameters such as true vertical depth, porosity, bulk density, resistivity, rate of penetration, weight on bit, rotation per minute, torque, standpipe pressure, flow rate, flowline temperature, and gas level. The data-driven models were developed using three separate algorithms: K-Nearest Neighbor, Random Forest, and XG Boost. 6022 field data points were split for training, testing, and validation processes. On average, the model using the random forest algorithm showed the highest accuracy in forecasting the formation pressure, with root mean squared error values and coefficient of determination values of 12.868 and 0.9638, respectively. Streamlit Deployment tool was used to create a user interface program to facilitate the prediction process. The program was tested using 196 field data points and recorded a high accuracy of 95%.
Exploration
Parnian Javadi Sharif; Alireza Arab Amiri; Behzad Tokhmechi; Fereydoun Sharifi
Abstract
The technique referred to as Complex Resistivity (CR) or Spectral Induced Polarization (SIP) possesses the capability to distinguish between various kinds of minerals or the sources of induced polarization by utilizing the physical characteristics of minerals or polarizable inclusions. The Generalized ...
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The technique referred to as Complex Resistivity (CR) or Spectral Induced Polarization (SIP) possesses the capability to distinguish between various kinds of minerals or the sources of induced polarization by utilizing the physical characteristics of minerals or polarizable inclusions. The Generalized Effective Medium Theory of Induced Polarization (GEMTip) model is utilized to derive physical characteristics from SIP data. Different inversion methods are applied for this task, though they encounter difficulties such as computational costs, non-linearity, and the intricacy of the inverse issue. To tackle this, a new inversion approach based on Deep Learning (DL) via Convolutional Neural Network (CNN) is proposed for predicting the parameters of polarizable particles from SIP data. The CNN is trained on 20000 synthetic datasets produced using the GEMTip forward model. While DL networks address non-linearities, specific modifications are applied to synthetic datasets to evaluate the influence of non-linearity and correlation on the results. In the Kervian region southwest of Saqqez city, gold mineralization is linked to quartz and pyrite minerals, with two types of pyrite recognized - coarse-grained barren and fine-grained auriferous. The existence of sulfide mineral pyrite, along with variations in pyrite sizes, presents an attractive target for SIP exploration in the investigated area. The trained network is also validated on Gravian data and effectively retrieves parameters as evidenced by the data. The proposed methodology simplifies the inversion process by estimating parameters in one step, enabling a direct and efficient procedure.
Exploration
Mina Shafiabadi; Abolghasem Kamkar Rouhani
Abstract
Considering the effect of fractures in increasing hydrocarbon recovery, the study of reservoir rock fractures is of particular importance. Fractures are one of the most important fluid flow paths in carbonate reservoirs. Image logs provide the ability to detect fractures and other geological features ...
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Considering the effect of fractures in increasing hydrocarbon recovery, the study of reservoir rock fractures is of particular importance. Fractures are one of the most important fluid flow paths in carbonate reservoirs. Image logs provide the ability to detect fractures and other geological features and reservoir layers. In this study, two approaches were used to detect fractures using FMI image log in two wells A and B located in one of oilfields in southwest of Iran. In the first stage, the correction and processing of the FMI raw data were carried out to identify the number and position of fractures, as well as the dip, extension, classification, and density of fractures. In the second step, by considering that the fractures possess the edges in the FMI images, various edge detection filters such as Prewitt, Canny, Roberts, LOG, Zero-cross and Sobel were applied on the image data, and then, their performances for identification of fractures were compared. Finally, the automatic identification of fractures was done by applying the Hough transform algorithm and the results showed that Canny algorithm was the best option to perform Hough transformation. The comparison of the efficiency of the above-mentioned edge detection filters for identification of fractures, and more importantly, the automatic identification of fractures using the Hough transform algorithm can be considered as the novelty of this research work.
Exploration
Rashed Pourmirzaee; Hadi Jamshid Moghaddam
Abstract
In recent years, hyperspectral data have been widely used in earth sciences because these data provide accurate spectral information of the earth's surface. This research aims to apply match filtering (MF) on Hyperion hyperspectral imagery for mapping alteration mineral in the Astarghan area, NW Iran. ...
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In recent years, hyperspectral data have been widely used in earth sciences because these data provide accurate spectral information of the earth's surface. This research aims to apply match filtering (MF) on Hyperion hyperspectral imagery for mapping alteration mineral in the Astarghan area, NW Iran. Astarghan is located in the northwest of Iran where deposits of low-sulfide gold-bearing ore rocks occur as veins and stockworks. Therefore, at first, the Astarghan Hyperion scene was topographically and atmospherically corrected. Then, the data quality was surveyed to recognize bad bands and improve the accuracy of the subsequent processing steps. In MF analysis, it is a challenge to separate MF abundance images to target and background pixels. Therefore, to cope with this challenge, a moving threshold technique is proposed. The results indicated three indicative minerals including kaolinite, opal and jarosite. Then, the results were statistically verified by virtual verification and geological data. The verification was performed virtually using United States Geological Survey (USGS) spectral library data, which showed an agreement of 78.06%. Moreover, a comparison of the MF analysis results showed a good agreement with field investigations and overlaying with a detailed geological map of the study area. Finally, in this study the X-ray diffraction (XRD) of three indicative mineral samples was used to check the efficiency of the applied method.
Exploration
Babak Sohrabian; Abdullah Erhan Tercan
Abstract
Mineral Resources have commonly been estimated through the kriging method that assigns weights to the samples based on variogram distance to the estimation point without considering their values. More robust estimators such as spatial copulas are promising tools because they consider both distance ...
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Mineral Resources have commonly been estimated through the kriging method that assigns weights to the samples based on variogram distance to the estimation point without considering their values. More robust estimators such as spatial copulas are promising tools because they consider both distance and sample values in determining weights. The purpose of this study is to demonstrate the effectiveness of the Gaussian copulas (GC) by estimating the copper grade values in the Sungun porphyry copper deposit located in Iran. Performance of the method was compared to ordinary kriging (OK) and indicator kriging (IK) by running the Jackknife test of cross-validation. The metrics used in measuring performance of the methods are global accuracy and precision of the distribution of the estimates, error statistics, and variability for globally accurate and precise estimates. The case study shows advantages of GC over OK and IK by producing globally accurate and precise estimates with acceptable error statistics and variability.
Exploration
Eid R. Abo-Ezz; El Sayed I Selim; Hatem Aboelkhair; Haytham Sehsah
Abstract
The bimodal hypsometry of the Arabian-Nubian Shield in the Neoproterozoic triggered the formation of post-amalgamation marine bains in the low-stand terranes of the Arabian shield (AS). The carbonate successions in the extraordinary marine basins in the AS are intruded by granite plutons of different ...
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The bimodal hypsometry of the Arabian-Nubian Shield in the Neoproterozoic triggered the formation of post-amalgamation marine bains in the low-stand terranes of the Arabian shield (AS). The carbonate successions in the extraordinary marine basins in the AS are intruded by granite plutons of different causative types, with major shear zones pathways. Therefore, the conditions for the formation of skarn deposits are mature at the contact of the carbonate succession with the causative granite plutons. Multidisciplinary approaches including ASTER data, Magnetic data, and geochemical data have been applied to the Murdama basin to locate the promising areas for skarn deposits. The Murdama basin has contrasting magnetic anomalies of different intensity at the contact between the Murdama limestone and the post-Murdama causative batholiths; significant magnetic anomalies exist at the contact with the Idah causative magmas. Lineaments related to the Najd fault system (NFS) exist eastward, where calc-silicate alteration-related minerals were evolved, with no clues for penetrative effect for such alteration activity along pathways related to the fracture system or at contact with the Abanat suite. Different spectral mapping techniques, including Spectral Information Divergence (SID), Spectral Angle Mapper (SAM), and Constrained Energy Minimization (CEM) confirm that the Idah suite is the predominant causative magma in the study area with highly evolved calc-silicate alteration-related minerals, such as wollastonite, garnet, and pyroxene. Meanwhile, The Idah suite has been identified as the main causative magma for other reduced skarn localities that have been recorded from the Murdama basin, i.e. the Qitan and An Nimriyah South. Alteration related mineral zones of kaolinite, chlorite, muscovite, and hematite are evolved alongside with calc-silicate minerals at the contact bewteen Idah suite, and the Murdama carbonate member. The geochemical data suggests reducing effect for the Idah suite at the contact between the Murdama carbonate succession and Idah plutons. These preliminary results of this study need detailed field investigations and geochemical explorations for the proposed skarn deposits in the Neoproterozoic molasse basins of the AS.
Exploration
Devraj Dhakal; Kanwarpreet Singh
Abstract
Landslides pose significant risks to human life, infrastructure, and the environment, particularly in geologically unstable regions like the Himalayas. This study aims to develop and validate landslide susceptibility maps using Frequency Ratio (FR) and Information Value (IV) models within a GIS framework. ...
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Landslides pose significant risks to human life, infrastructure, and the environment, particularly in geologically unstable regions like the Himalayas. This study aims to develop and validate landslide susceptibility maps using Frequency Ratio (FR) and Information Value (IV) models within a GIS framework. Employing high-resolution geospatial data, including geomorphological, topographical, and hydrological factors derived from high-resolution digital elevation models (DEMs) and other geospatial datasets. The susceptibility maps were classified into five categories: Low, Moderate, High, Very High, and Extremely High. The models were trained and validated using a landslide inventory of 1313 landslide events, with a 70:30 split for training and testing datasets. The predictive performance of the models was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve, yielding AUC values of 84.1 for the FR model and 83.9 for the IV model. The Landslide Density Index (LDI) further confirmed the models' reliability, indicating higher landslide densities in the predicted high-susceptibility zones. The study demonstrates that both FR and IV models are effective tools for landslide susceptibility mapping and its validation. The findings highlight the FR model's superior predictive accuracy in this specific area. Future research should leverage advanced machine learning techniques, such as XGBoost, Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN), to enhance the reliability and precision of landslide susceptibility models.
Exploration
Moslem Jahantigh; Hamid Reza Ramazi
Abstract
Fuzzy c-means (FCM) is an unsupervised machine learning algorithm. This method assists in integrating airborne geophysics data and extracting automatic geological map. This paper tries to combine airborne geophysics data consisting of aeromagnetic, potassium, and thorium layers to classify the lithological ...
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Fuzzy c-means (FCM) is an unsupervised machine learning algorithm. This method assists in integrating airborne geophysics data and extracting automatic geological map. This paper tries to combine airborne geophysics data consisting of aeromagnetic, potassium, and thorium layers to classify the lithological map of the Shahr-e-Babak area, a world-class porphyry area in the south of Iran. The resulting clusters with FCM show appropriate coincidence with the geological map of the study area. The clusters are adapted with high magnetic anomalies corresponding to the mafic volcanic rocks and the clusters with high radiometric signature associated with igneous rocks. The cluster is associated with low magnetic anomaly and low radioelements concentration representing sedimentary rocks. some clusters are associated with two or more lithological formations due to similar signatures of geophysics properties. The fuzzy score membership in all clusters is above 0.71 indicating a high correlation between geological signatures and multigeophysical data. This study shows geophysical signatures analyzed with the machine learning method can reveal geological units.
