۲-D Anticlinal Structure Modeling Using Feed-Forward Neural Network (FNN) Inversion of Profile Gravity Data: A Case Study from Iran
- سال انتشار: 1399
- محل انتشار: مجله فیزیک زمین و فضا، دوره: 46، شماره: 4
- کد COI اختصاصی: JR_JESPHYS-46-4_007
- زبان مقاله: انگلیسی
- تعداد مشاهده: 189
نویسندگان
Ph.D. Student, Department of Geology, Faculty of Sciences, University of Isfahan, Isfahan, Iran
Instructor, Department of civil Engineering, University College of Nabi Akram, Tabriz, Iran
M.Sc. Graduated, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
چکیده
The Anticlines are the main hydrocarbon traps on land or at sea. This structure is considered as the target of the many projects of gravity exploration all over the world. Artificial neural networks (ANNs) are used in order to solve prediction, estimation, and optimization problems. In this paper, the feed-forward neural network (FNN) is applied for modeling the anticlinal structure using gravity anomaly profile and the back propagation algorithm is used for artificial neural network training. Moreover, the scalene triangle model is employed to describe the geometry of anticlinal structure in analyzing gravity anomalies. In terms of neural network training, eight features among the synthetic gravity field variations curves along ۲۲۵۰۰ profiles are defined. These gravity profiles are computed based on different values of the scalene triangle parameters consisting of the top depth, bottom depth, limb angles and density contrast. The defined neural network contain three layers, eight neurons (the number of features) in the input layer, ۳۰ neurons in the hidden layer and six neurons (the number of scalene triangle parameters) in the output layer. In order to evaluate the performance of the trained neural network, the specified features related to a synthetic model, with and without random noise, are applied as the input data to train neural network. The parameters estimation error by FNN is negligible. The proposed method is illustrated with a real gravity data set from Korand region, Iran. The inferred anticlinal structures are compared with the interpreted map of the seismic data.کلیدواژه ها
Anticlinal structure, Feed-forward neural network (FNN), Gravity, Scalene triangle, Iranاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.