مدل‌سازی تصویری اکتشاف پتانسیل‌های معدنی با استفاده از ماشین‌ بردار پشتیبان

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکترای مهندسی معدن-اکتشاف مواد معدنی، گروه معدن، دانشگاه آزاد اسلامی واحد محلات

2 استادیار، گروه معدن، دانشگاه آزاد اسلامی واحد محلات.

3 استادیار، گروه معدن، دانشگاه آزاد اسلامی واحد محلات

چکیده

با ظهور داده‌های بزرگ در علوم زمین، مطالعات اکتشافی وارد ابعاد جدیدی شده ‌است. منظور از داده‌های بزرگ اطلاعات تصویری با وضوح بالا است. از آنجا ‌که این داده‌ها در علوم زمین حجم و تنوع بسیار زیادی دارند، استفاده از رویکردهای تجزیه و تحلیل داده‌های بزرگ در این حوزه ضروری است. در این مطالعه کاربرد ماشین بردار پشتیبان در بینایی ماشین در حوزه اکتشاف پتانسیل‌های معدنی مورد بررسی قرار می‌گیرد. در سال‌های اخیر طبقه‌بندی تصاویر توجه زیادی را در بینایی ماشین به‌خود معطوف کرده است که فرآیند آن شامل پیش‌پردازش و قطعه‌بندی، استخراج ویژگی و شناسایی کلاس مربوط است. در این مطالعه برای مدل‌سازی اکتشاف پتانسیل‌های معدنی از نقشه‌های زمین‌شناسی و تصاویر دورسنجی و از معماری الکس‌نت برای استخراج خودکار ویژگی‌ها استفاده شده و برای یادگیری الگوریتم، اطلاعات میدانی به‌کار گرفته می‌شود. در گام بعد برای مدل‌سازی به‌منظور شناسایی عوامل ساختاری در احتمال وقوع پتانسیل‌های معدنی، از ماشین بردار پشتیبان استفاده می‌شود. الگوریتم‌ها و شاخص‌های ارزیابی در هر مرحله در محیط متلب برنامه‌نویسی می‌شود. میزان دقت بدست آمده با استفاده از این روش، روی داده‌های آزمایشی 71 است. با توجه به مطالعه قبلی انجام شده توسط نویسندگان در شناسایی ساختارهای کانی‌زایی، متوسط دقت طبقه‌بندی داده‌های تصویری با استفاده از الگوریتم‌های شبکه عصبی کانولوشن 65 درصد، روش نقشه‌بردار زاویه طیفی در شناسایی زون‌های آلتراسیون 70 درصد و اعمال فیلترها در شناسایی گسل‌ها 28 درصد است. روش مورد استفاده در این تحقیق دقت بالایی دارد و از مزایای آن می‌توان به کاهش هزینه‌ها و افزایش سرعت در فرآیند تصمیم‌گیری اشاره کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Visual modeling of mineral potential exploration using support vector machine

نویسندگان [English]

  • Mandana Tahmooresi 1
  • Behnam Babaei 2
  • Saeed Dehghan 3
1 PhD student in Mining Engineering-Mineral exploration, Mining department, Mahallat Branch, Islamic Azad University, Mahallat, Iran.
2 Assistant Professor, Mining department, Mahallat Branch, Islamic Azad University, Mahallat, Iran.
3 Assistant Professor, Mining department, Mahallat Branch, Islamic Azad University, Mahallat, Iran.
چکیده [English]

With the advent of big data in geosciences, exploration studies have entered new dimensions. Big data means high resolution image information. Since these data in geosciences have a very large volume and variety, it is necessary to use big data analysis approaches in this field. In this study, the application of support vector machine in machine vision in the field of mineral potential exploration is investigated. In recent years, image classification has attracted a lot of attention in machine vision, whose processes include pre-processing and segmentation, feature extraction and related class identification. In this study, geological maps and remote sensing images are used to model the exploration of minerals potentials, and Alexnet architecture is used to automatically extract features, and field information is used to learn the algorithm. In the next step, support vector machine is used for modeling in order to identify structure factors in the occurrence probability of minerals potentials. Algorithms and evaluation indicators are programmed in MATLAB environment at each stage. The accuracy obtained using this method is 71% on the test data. According to the previous study conducted by the authors in identifying mineralization structures, the average accuracy of image data classification using convolutional neural network algorithms is 65%, the spectral angle mapper method in identifying alteration zones is 70% and applying filters in identifying faults is 28%. As can be seen, the method used in this research is highly accurate. Its advantages include reducing costs and speeding up the decision-making processes.

کلیدواژه‌ها [English]

  • Visual modeling
  • Mineral potential exploration
  • Support vector machine
  • Gonabad
  • Iran
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