کاربرد روش رگرسیون بردار پشتیبان در تخمین و مدل‌سازی پارامترهای سیال درگیر در کانسار مس پورفیری سونگون

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار/گروه مهندسی معدن /دانشکده مهندسی/دانشگاه کاشان

2 استاد/گروه معدن/ دانشکده مهندسی معدن و متالورژی/ دانشگاه صنعتی امیرکبیر

3 دانشیار/گروه مهندسی معدن /دانشکده مهندسی/دانشگاه کاشان

چکیده

مطالعه سیالات درگیر اغلب به صورت آزمایشگاهی و با هدف ارتقا صحت و دقت تجزیه‌های صورت گرفته انجام می‌شود. از آنجا که استفاده کاربردی از داده‌های حاصل از این مطالعات آزمایشگاهی می‌تواند در فرآیند اکتشاف کانسارها و یا دستیابی به اطلاعات اکتشافی تکمیلی از کانسارهای کشف شده سودمند باشد، در این مطالعه تخمین و مدل‌سازی پارامترهای ترمودینامیکی سیال درگیر (دمای همگنی، دمای یوتکتیک و شوری) در کانسار مس پورفیری سونگون انجام و در گام نخست، با استفاده از تخمین‌گر رگرسیون بردار پشتیبان، مدل سه‌بعدی این پارامترها تهیه شده است. دقت مدل‌سازی صورت گرفته جهت تخمین داده‌های سیالات درگیر شامل دمای همگنی، دمای یوتکتیک و شوری سیال درگیر به ترتیب برابر 76، 71 و 93 درصد می‌باشد. سپس براساس شرایط ترمودینامکی مساعد برای نهشت کالکوپیریت (بازه دمایی 300 تا 400 درجه سانتی‌گراد و شوری متوسط تا بالا)، از این مدل سه بعدی برای تهیه مدل پیش‌گویانه کانی‌زایی استفاده شده است. مقایسه مدل پیش‌گویانه با مدل بلوکی زمین‌شناسی عیار مس در محدوده کانسار نشان داد که تطابق مطلوبی بین این دو مدل وجود دارد. در نتیجه می‌توان 1) از مدل تهیه شده در ادامه فرآیند اکتشاف و با هدف اکتشافات تکمیلی بهره‌مند شد و 2) از این روش، برای شناسایی مناطق پرپتانسیل کانسارهایی که هنوز در مراحل اکتشافات مقدماتی هستند استفاده کرد.

کلیدواژه‌ها

عنوان مقاله [English]

Application of support vector regression method in estimating and modeling of fluid inclusion parameters in Sungun porphyry copper deposit

نویسنده [English]

  • Maliheh Abbaszadeh 1
1 University of Kashan
2
3
چکیده [English]

Extended abstract
Introduction
The background of 3D modeling of fluid inclusion data goes back to use of inverse distance weighting (IDW) method in the Caixiashan Pb and Zn deposit (Sun et al., 2011). This method in spite of having some advantages such as simplicity in basis is associated with disadvantages such as uncertainty in selection of weighting function and ignoring data distribution. Today, new methods have been proposed for estimation including the support vector machine method (Dutta et al., 2010). One of this method’s capabilities is in dealing with small data sets (Dutta, 2006; Zhang et al., 1998). In this study, fluid inclusion thermodynamic parameters have been estimated using support vector regression method. Predictive model of mineralization has been provided acording to 3D models resulted for fluid inclusion data and also assumption of proper thermodynamic conditions for chalcopyrite deposition in the Sungun porphyry copper deposit.
Material and Methods
In this study, a total of 173 data sets of fluid inclusions were obtained from 59 locations. This dataset using genetic algorithm method divided into training and testing sets (80% and 20%, respectively). Modeling of fluid inclusion thermodynamic parameters has been done by support vector regression method. The SVR is based on the statistical learning theory and the structural risk minimization.
Results and discussion
After preparing and determination of training and test datasets, radial basis kernel function (RBF) was selected in order to estimate and model the fluid inclusion thermodynamic parameters using the support vector regression method. Better functionality was the main reason of using this kernel. In the next step, parameters were needed to be carefully determined to obtain a model with high generalization ability. In this regard, the grid search method with cross validation was used to determine optimal values for the model parameters. Model was then trained using the training dataset and finally evaluated on the test dataset. Then fluid inclusion thermodynamic parameters for each block of deposit were estimated using support vector regression method. According to mineralogical and fluid inclusion studies in the Sungun porphyry copper deposit, it has been determined that chalcopyrite deposition is related to fluids with moderate to high salinity and temperatures of 300-400 °C. The predictive model was prepared based on these conditions and estimated thermodynamic Parameters in block model. In this model, each arbitrary block has been labeled on a scale of 1 to 4 (based on the favorable conditions for chalcopyrite deposition). These labels are possibility index for copper deposition. According to possibility index, proper zones have been determined in 3D model. In order to performance evaluation of support vector regression method, the predictive model was compared with 3D model of copper grade. The results of this comparison showed that prepared predictive 3D model has high consistent with copper grade block model.
Conclusion
In this study, 3D modeling of fluid inclusion data was performed to estimate the thermodynamic parameters affecting mineralization (homogenization and eutectic temperatures and salinity) using support vector regression method to determine potential mineralization points in the area. Using the 3D models, we found the homogenization and eutectic temperatures and fluids salinity (in different ranges of these factors) in the Sungun porphyry copper deposit. To evaluate the 3D modeling efficiency in advancing the exploration process of the porphyry deposits, the conformity between mineralization and thermodynamic variations of the fluid inclusions was investigated and, based on it; a tool called “Predictive Model” was presented for the evaluation of the occurrence of mineralization in different parts of the region. A comparison of the SVR-based predictive model and the copper grade block model shows acceptable conformity in low, medium, and high-grade regions.

کلیدواژه‌ها [English]

  • Machine learning algorithm
  • Support vector regression
  • Fluid inclusion
  • Sungun porphyry copper deposit
  • Predictive model

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پژوهشهای دانش زمین
دوره 12، شماره 3 - شماره پیاپی 47
پژوهشی
آذر 1400
صفحه 22-39

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