Mineral resources, particularly copper, are crucial for the sustained economic growth of developing countries like Kazakhstan. Over the past four decades, the diversity and importance of critical minerals for high technology and environmental applications have increased dramatically. Today, copper is a critical metal due to its importance in electrification. Porphyry deposits are important sources of copper and other critical metals. Conventional exploration methods for mapping alteration zones as indicators of high-potential zones in porphyry deposits are often associated with increased cost, time and environmental concerns. Remote sensing imagery is a cutting-edge technology for the exploration of minerals at low cost and in short timeframes and without environmental damage. Kazakhstan hosts several large porphyry copper deposits, such as Aktogay, Aidarly, Bozshakol and Koksai, and has great potential for the discovery of new resources. However, the potential of these porphyry deposits has not yet been fully discovered using remote sensing technology. In this study, a remote sensing-based mineral exploration approach was developed to delineate hydrothermal alteration zones associated with Aktogay porphyry copper mineralization in eastern Kazakhstan using Landsat-8 and ASTER satellite sensors. A comprehensive suite of image processing techniques was used to analyze the two remote sensing datasets, including specialized band ratios (BRs), principal component analysis (PCA) and the Crosta method. The remote sensing results were validated against field data, including the spatial distribution of geological lineaments and petrographic analysis of the collected rock samples of alteration zones and ore mineralization. The results show that the ASTER data, especially when analyzed with specialized BRs and the Crosta method, effectively identified the main hydrothermal alteration zones, including potassic, propylitic, argillic and iron oxide zones, as indicators of potential zones of ore mineralization. The spatial orientation of these alteration zones with high lineament density supports their association with underlying mineralized zones and the spatial location of high-potential zones. This study highlights the high applicability of the remote sensing-based mineral exploration approach compared to traditional techniques and provides a rapid, cost-effective tool for early-stage exploration of porphyry copper systems in Kazakhstan. The results provide a solid framework for future detailed geological, geochemical and geophysical studies aimed at resource development of the Aktogay porphyry copper mineralization in eastern Kazakhstan. The results of this study underpin the effectiveness of remote sensing data for mineral exploration in geologically complex regions where limited geological information is available and provide a scalable approach for other developing countries worldwide.

Effective and sustainable resource management is critical to the mining industry, which plays a significant role in the Kazakhstan economy. To achieve this goal, the use of remote sensing technologies has become necessary. These advanced tools are now integral to activities such as mineral mapping, research and environmental monitoring. Therefore, the purpose of this comprehensive review article is to scrutinize how remote sensing data is used specifically in the context of mining in Kazakhstan. The comprehensive review covers a wide range of aspects, including the use of remote sensing for mapping and mineral resource studies. Key findings highlight the growing importance of remote sensing technologies in Kazakhstan's mining industry as they facilitate the precise delineation of ore deposits, enable effective monitoring of environmental impacts and promote optimal use of natural resources. The article discusses the difficulties and limitations encountered in the application of remote sensing data. Particular attention is paid to the need to improve methods of data processing, correction of atmospheric influences and spectral analysis. The overview presented in this paper is of great importance to those involved in research, policy development and industrial sectors who are interested in using remote sensing data to enhance mining activities and promote environmental sustainability in Kazakhstan. By providing a comprehensive analysis of the subject, this review offers valuable information that can help researchers deepen their understanding of the topic and guide them toward more effective strategies.

Gravity anomalies play critical roles in geological analysis, geodynamic monitoring, and precise geoid modeling. Obtaining accurate gravity data is challenging, particularly in inaccessible or sparsely covered regions. This study evaluates machine learning (ML) methods—Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Ensemble of Trees—for predicting gravity anomalies in southeastern Kazakhstan and compares their effectiveness with traditional Kriging interpolation. A dataset, consisting of the simple Bouguer anomaly values, latitude, longitude, elevation, normal gravity, and terrain corrections derived from historical maps at a scale of 1:200,000, was utilized. Models were trained and validated using cross-validation techniques, with performance assessed by statistical metrics (RMSE, MAE, R²) and spatial error analysis. Results indicated that the Exponential GPR model demonstrated the highest predictive accuracy, outperforming other ML methods, with 72.9% of predictions having errors below 1 mGal. Kriging showed comparable accuracy and superior robustness against extreme errors. Most prediction errors from all methods were spatially associated with mountainous regions featuring significant elevation changes. While this study demonstrated the effectiveness of machine learning methods for gravity anomaly prediction, their accuracy decreases in complex terrain, indicating the need for further research to improve model performance in such environments.

Detecting surface mineral indicators with high spatial precision remains a significant challenge in mineral exploration, particularly in remote or geologically complex regions such as Eastern Kazakhstan. This study addresses this challenge by integrating high-resolution multispectral imagery from Unmanned Aerial Vehicles (UAVs) to map iron oxide distributions, key indicators of ore mineralization such as copper porphyry at the Kyzylkiya mining site in Eastern Kazakhstan. The novelty of this study is the development of a statistical fuzzy logic model that integrates UAV-derived spectral indices, including the Normalized Difference Vegetation Index (NDVI) and targeted band ratios, to generate probabilistic maps of iron oxide presence at a fine spatial resolution of 5.29 cm. This approach enhances prediction accuracy by incorporating uncertainty and variability in spectral responses. The model’s output was validated through a multi-stage process involving independent multispectral datasets and ground-truth sampling, achieving an overall accuracy of 80%. The results reveal concentrated iron oxide anomalies in the northeast and northwest of the study area, underscoring the method’s effectiveness. This integrated UAV-fuzzy logic framework demonstrates a scalable and cost-effective solution for early-stage mineral exploration and can be adapted to similar geological settings globally.

Мақалада Ақтоғай мыс-порфирлі кен орнын мысалға ала отырып, гидротермалды өзгерген аймақтарды жіктеу үшін машиналық оқыту алгоритмдерін қолдану нәтижелері қарастырылады. Зерттеу ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) спутнигінің мультиспектралды деректері, сондай-ақ далалық және зертханалық зерттеу нәтижелері негізінде жүргізілді. Жіктеу нәтижелері бойынша максималды ықтималдық әдісі ең жоғары тиімділікті көрсетті: жалпы дәлдік – 84,12%, Каппа коэффициенті – 0,79. Ал минималды қашықтық әдісінің дәлдігі 54%-ды, Каппа коэффициенті 0,39-ды құрады. Бұл нәтижелер гидротермалды өзгерген аймақтарды автоматты түрде карталауда машиналық оқыту алгоритмдерінің әлеуетін растайды This article presents the results of applying machine learning algorithms to classify hydrothermally altered zones, using the Aktogay porphyry copper deposit as a case study. The research is based on multispectral data from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite, as well as field and laboratory investigations. According to the classification results, the maximum likelihood algorithm demonstrated the highest effectiveness, with an overall accuracy of 84.12% and a Kappa coefficient of 0.79. The minimum distance algorithm showed an accuracy of 54%, with a Kappa coefficient of 0.39. These findings confirm the potential of machine learning algorithms for the automated mapping of hydrothermally altered zones В статье рассматриваются результаты применения алгоритмов машинного обучения для классификации гидротермально измененных зон на примере медно-порфирового месторождения Актогай. Исследование выполнено на основе мультиспектральных данных спутника ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), а также данных полевых и лабораторных исследований. По результатам классификации наивысшую эффективность показал метод максимального правдоподобия: общая точность составила 84,12%, коэффициент Каппа – 0,79. Метод минимального расстояния продемонстрировал точность 54%, при коэффициенте Каппа – 0,39. Полученные результаты подтверждают потенциал алгоритмов машинного обучения для автоматизированного картографирования гидротермально измененных зо

Создание актуального геоида и эталонного гравиметрического полигона – приоритетные задачи государства. Значения силы тяжести, определяемые современными технологиями, являются частью необходимых данных для решения этих задач. Получение точных значений силы тяжести на различных участках страны невозможно без применения высокоточных современных гравиметров. Применение актуальных относительных гравиметров в исследованиях силы тяжести Земли связано с надежностью данных, простотой и эффективностью использования инструментов, точностью выполняемых измерений и экономической целесообразностью. В данной статье изучены этапы совершенствования гравиметров, конструктивные особенности и принципы работы различных моделей Мемлекет үшін өзекті геоид және эталондық гравиметриялық полигон құру – басым міндеттер. Бүгінгі таңдағы технологиялар арқылы анықталатын ауырлық күшінің мәндері бұл міндеттерді шешу үшін қажетті деректердің бір бөлігі болып табылады. Елдің әртүрлі бөліктерінде ауырлық күшінің дәл мәндерін алу жоғары дәлдікті ұстаушы заманауи гравиметрлерді қолданбастан мүмкін емес. Заманауи салыстырмалы гравиметрлерді ауырлық күшін зерттеуде қолданудың себебі – деректердің сенімділігі, құралдардың пайдалануының ыңғайлылығы мен тиімділігі, өлшеулердің дәлдігі және экономикалық тиімділік. Бұл мақалада гравиметрлерді жетілдіру кезеңдері, конструкциялық ерекшеліктері және әр түрлі модельдердің жұмыс принциптері зерттелген The creation of an up–to-date geoid and a reference gravimetric polygon are priority tasks of the state. Gravity values determined by modern technologies are part of the necessary data to solve these problems. Obtaining accurate gravity values in various parts of the country is impossible without the use of high-precision modern gravimeters. The use of current relative gravimeters in studies of the Earth’s gravity is associated with the reliability of data, the simplicity and efficiency of using tools, the accuracy of measurements performed and economic feasibility. This article examines the stages of improving gravimeters, design features and principles of operation of various models