kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-2-435-450kaz29-2924Research ArticleНЕФТЕГАЗОВАЯ ИНЖЕНЕРИЯ И ГЕОЛОГИЯOIL AND GAS ENGINEERING, GEOLOGYВЫДЕЛЕНИЕ ЗОН ПЕРСПЕКТИВНОСТИ СТРАТИФОРМНОЙ МЕДНО-КОБАЛЬТОВОЙ МИНЕРАЛИЗАЦИИ НА ОСНОВЕ МЕТОДОВ ОБУЧЕНИЯ БЕЗ УЧИТЕЛЯ НА ЮЖНОЙ ОКРАИНЕ МЕДНОГО ПОЯСА (ЗАМБИЯ)UNSUPERVISED DELINEATION OF PROSPECTIVITY ZONES FOR STRATIFORM CU-CO IN THE SOUTHERN COPPERBELT MARGIN (ZAMBIA)https://orcid.org/0000-0003-1501-7772СадуовА.SaduovA.

ст. преподаватель.

Алматы

Senior Lecturer.

Almaty

a.saduov@satbayev.universitySatbayev UniversityКазахстанSatbayev UniversityKazakhstan202627062026232435450Copyright © Садуов А., 20262026Садуов А.Saduov A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2924

В статье представлен подход к выделению перспективных зон стратиформной медно-кобальтовой минерализации на южной окраине Центрально-Африканского Медного пояса (Замбия) на основе исключительно аэрогеофизических данных, включающих гравиметрические, магнитные, радиометрические и топографические параметры. Для анализа были использованы три алгоритма кластеризации – K-Means, Fuzzy C-Means (FCM) и самоорганизующиеся карты Кохонена (Self-Organizing Maps, SOM), после чего результаты были объединены в рамках консенсусной кластеризации для выделения устойчивых целевых зон. По каждому кластеру были рассчитаны и сопоставлены ключевые геофизические производные, включая производную наклона, полную горизонтальную производную и аналитический сигнал, а также радиометрические отношения U/Th и U/K. Наиболее перспективный кластер был валидирован по литературно обоснованным геофизическим пороговым значениям и показал соответствие известным поисковым критериям и региональным структурным закономерностям. Итоговая вероятностная карта перспективности была построена по значениям принадлежности FCM и дополнительно разделена на три уровня уверенности. Несмотря на отсутствие буровых данных, не позволяющее выполнить прямую количественную оценку точности, полученные результаты показывают, что методы обучения без учителя, примененные к многопараметрическим аэрогеофизическим данным, могут эффективно использоваться для ранних стадий поисково-разведочных работ в условиях дефицита геологической информации. Предложенный подход может рассматриваться как воспроизводимая основа для ИИ-ориентированного прогнозирования перспективности в слабо изученных территориях.

This study presents an unsupervised workflow for delineating prospectivity zones for stratiform copper-cobalt mineralization in the southern margin of the Central African Copperbelt, Zambia, using only airborne geophysical data (gravity, magnetics, radiometry, terrain). Three clustering algorithms-K-Means, Fuzzy C-Means (FCM), and Self-Organizing Maps (SOM)-were applied, followed by consensus clustering to isolate robust target zones. Key geophysical filters (tilt derivative, total horizontal derivative, analytic signal) and radiometric ratios (U/Th, U/K) were computed and compared across clusters. The most prospective cluster was validated against literature-calibrated geophysical thresholds and was found to match known exploration criteria and regional structural trends. A final probabilistic prospectivity map was generated from FCM membership, classifying targets into three confidence levels. While lack of drill-hole data prevents quantitative accuracy assessment, this approach demonstrates that fully unlabeled machine learning on airborne data can effectively guide early-stage exploration in data-sparse regions. The proposed workflow offers a reproducible framework for AI-driven prospectivity mapping in frontier terrains.

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The authors declare that there are no conflicts of interest present.