پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,533 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,124,850 |
| تعداد دریافت فایل اصل مقاله | 97,233,416 |
برآورد عمق وشکل حفره های زیرزمینی با استفاده از دستگاه واسط عصبی فازی تطبیقی چندگانه با داده های گرانی سنجی | ||
| فیزیک زمین و فضا | ||
| مقاله 6، دوره 42، شماره 3، آذر 1395، صفحه 535-548 اصل مقاله (1.89 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2016.55631 | ||
| نویسندگان | ||
| علیرضا حاجیان* 1؛ حسین زمردیان2 | ||
| 1عضو هیئت علمی دانشکده علوم پایه دانشگاه آزاد واحد نجف آباد | ||
| 2عضو هیئت علمی دانشگاه آزاد اسلامی واحد علوم وتحقیقات | ||
| چکیده | ||
| در این مقاله به منظور اکتشاف حفرات زیرزمینی با شکلهای نزدیک به کره، استوانه افقی یا عمودی ودر راستای بالابردن دقت نتایج تفسیر بی هنجاریهای گرانی ،کمک به تجربیات مفسر و مقاومت بیشتر در برابر سطوح متفاوت نوفه ، از شبکه عصبی-فازی تطبیقی چند گانه MANFIS استفاده شده است. در این پژوهش با قرار گرفتن دو سیستم عصبی فازی تطبیق پذیر به صورت موازی با یکدیگر یک شبکه عصبی-فازی تطبیق پذیر چند گانه طراحی شد که خروجی یکی فاکتور شکل حفره زیرزمینی وخروجی دیگری عمق مربوط به حفره می باشد. به منظور امتحان دقت عملکرد شبکه عصبی فازی طراحی شده در حضور نویز، روش ارائه شده ابتدا برای داده های مصنوعی با 5 درصد و10 درصد نویز مورد آزمون قرار گرفت . در مجموع نتایج نشان داد استفاده توام از شبکه های عصبی ومنطق فازی علاوه بر آن که ابزاری مفید جهت کمک به مفسردر مرحله تفسیر عمق وشکل حفره های زیرزمینی از روی داده های گرانی است، بلکه صحت تفسیر بی هنجاری های گرانی را نیزافزایش می دهد . همچنین بر خلاف روشهای موجود با رهیافت عصبی محض در اینجا بدون پیش فرض شکل درباره چشمه گرانی امکان تخمین شکل چشمه علاوه بر تخمین عمق آن وجود دارد. پس از اطمینان از صحت عملکرد شبکه عصبی- فازی طراحی شده برای داده های مصنوعی، به منظور امتحان روی داده های واقعی برای داده های گرانی سایت بند باهاما تست ومورد ارزیابی قرار گرفت که با نتایج واقعی حاصل از گمانه زنی ها و حفاری های موجود تطابق خوبی دارد | ||
| کلیدواژه‌ها | ||
| تخمین عمق؛ شبکه عصبی-فازی؛ ANFIS؛ گرانی سنجی | ||
| عنوان مقاله [English] | ||
| Estimation of depth and shape of subsurface cavities via Multi Adaptive Neuro-Fuzzy Interference System using Gravity data | ||
| نویسندگان [English] | ||
| Alireza Hajian1؛ | ||
| 1Department of Physics Najafabad Branch,Islamic Azad University | ||
| چکیده [English] | ||
| In common classical methods of cavity depth estimation through microgravity data, usually when a pre-geometrical model is considered for the cavity shape, the simple geometrical models of sphere, vertical cylinder and horizontal cylinder are commonly used. It is obviously an important fact that in real conditions the shapes of the cavities are not exactly sphere, horizontal cylinder or vertical cylinder but are near or to some extent near to these simple models. The linguistic variables “near to” or “to some extent near to” are consisting of fuzzy concepts and can be described as “fuzzy” variables. The membership degree of each fuzzy variable shows how much the variable is near to the mentioned simple shapes. Using the fuzzy variables leads to better results with more accuracy because in real conditions the nature of the cavities shape is “fuzzy” so that their shape is not exactly but near to the mentioned simple shapes. Consequently, in this paper in order to help the gravity data interpreter to enhance the accuracy of his/her interpretation a neuro-fuzzy model namely Multi Adaptive Neuro-Fuzzy Interference System (MANFIS) is used. When the neural network alone is used the challenge is its black-box operation so that there is no possibility for sensitive analysis but neuro-fuzzy networks consist of the sensitive analysis via the if-then fuzzy rules achieved during the training process. In Multi Adaptive Neuro-Fuzzy Interference System, the network output before the de-fuzzification stage, shows the interpreter how much the cavity shape is near to sphere, horizontal cylinder or vertical cylinder. In this research, two Adaptive Neuro-Fuzzy Interference System (ANFIS) models were paralleled to configure a Multi Adaptive Neuro-Fuzzy Interference System (MANFIS) so that one output of the designed MANFIS is the shape factor and the other is the depth of the cavity. The inputs of the MANFIS are some of the important features selected from the gravity signal along the selected principle profiles of the residual gravity map. In order to evaluate the designed MANFIS in the presence of noise in gravity data, the method was tested for synthetic data with 5% and 10% level of noise. The results showed that the joint neural networks and fuzzy logic makes it a suitable tool to help the interpreter to improve the accuracy of shape and depth estimation of cavities. Furthermore, the method is more robust to noise which were tested for two different noise levels one with low level of noise and other with medium level of noise added to the synthetic gravity data. Despite the available classical methods or net neural methods, here without any pre-assumption about the shape of the cavity, both the shape factor and depth are estimated. In is necessary to mention that the value of the estimated shape factor implies that which of the geometrical models among sphere, vertical cylinder or horizontal cylinder are better fitted to the real shape of the subsurface cavity. After checking and confirming the accuracy of the designed MANFIS for synthetic data, the method was tested for real data through micro-gravity data over a gravity site located in Great Bahama Free Port, west of North America. The results are very near to the available borehole and extracted data. | ||
| کلیدواژه‌ها [English] | ||
| Gravity, Adaptive Neuro-fuzzy Interference System, depth estimation, Shape factor | ||
| مراجع | ||
|
Abdelrahman, E. M., El-Araby, T. M. and Abo-Ezz, E. R., 2001, Three least-squares minimization approach to depth, shape, and amplitude coefficient determination from gravity data, Geophysics, 66, 1105-1109.
Arzi, A. A., 1975, Microgravity for engineering applications, Geophysical Prospecting, 23, 408-425.
Albora, A. M., Özmen, A. and Uçan, O. N., 2001, Residual separation of magnetic fields using a cellular neural network approach, Pure and Applied Geophysics, 158(9-10), 1797-1818, doi: 10.1007/PL00001244.
Boulanger, O. and Chouteau, M., 2001, Constraints in 3D gravity inversion, Geophysical Prospecting 49, 265-280.
Butler, D. K., 1980, Microgravimetric techniques for geotechnical applications, Miscellaneous Paper GL-80-13, U.S. Army Engineer, Water-ways Experiment station, Vicksburg, Miss.
Bescoby, D. J., Cawley, G. C. and Chroston, P. N., 2004, Enhanced interpretation of magnetic survey data using artificial neural networks: a case study from Butrint, southern Albania, Archaeological Prospection, 11(4), 189-199.
Colley, G. C., 1963, The detection of caves by gravity measurements, Geophysical Prospecting, 11, 1-9.
Debeglia, N. and Dupont, F., 2002, Some critical factors for engineering and environmental microgravity investigations, Journal of Applied Geophysics, 50, 435-454.
Elawadi, E., Salem, A. and Ushijima, K., 2001, Detection of cavities and tunnels from gravity data using a neural network, Exploration Geophysics, 32, 75-79.
Fajklewicz, Z., 1986, Origin of the anomalies of gravity and its vertical gradient over cavities in brittle rock, Geophysical Prospecting, 4(8), 1233-1254.
Grêt, A., Klingelé, E. E. and Kahle, H. G., 2000, Application of artificial neural networks for gravity interpretation in two dimensions: a test study, Bollettino Geofisica Teorica ed Applicata, 41(1), 1-20.
Gupta, O. P., 1983, A least-squares approach to depth determination from gravity data, Geophysics, 48, 357-360
Hajian, A., Ardestani, V. E., Lucas, C. and Hajian, M., 2006a, Detection of Hazardous Downlifting Regions by neural network through microgravity data, 1st Conference on GIS Technology and Natural Hazard Management, Tehran, May, 8-10.
Hajian, A., Ardestani, V. E. and Lucas, C., 2006b, Depth Estimation of Subsurface Cavities via multi-layer perceptron neural network from microgravity data, 6th International conference: Problems of Geocosmos, Saint Petersburg, Russia, May, 23-28.
Hajian, A., 2008, Depth estimation of gravity anomalies by Hopfield network, Proceeding of 5th Annual Meeting, AOGS: Asia Oceania Geosciences Society, Busan, Korea, 16-20, Jun, 424-438.
Hajian, A., 2010a, Intelligent interpretation of gravity data via a fuzzy approach for detecting subsurface cavities, proceeding of 7th Annual Meeting, AOGS: Asia Oceania Geosciences Society, Hyderabad, International Convention Center, India, 5-9, July.
Hajian, A., 2010b, Detection of subsurface Qanats using gravity data via multi-layer perceptrons, Journal of Advances in Geosciences, Solid Earth, 20, 247-256.
Hajian, A., Styles, P. and Zomorrodian, H., 2011, Depth estimation of cavities from microgravity data through multi adaptive neuro fuzzy interference System, 17th European Meeting of Environmental and Engineering Geophysics, Leicester, UK, 12-14 September.
Hajian, A., Zomorrodian, H., Styles, P., Greco, F. and Lucas, C., 2012, Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT), Near Surface Geophysics, 10, 221-234, doi:10.3997/1873-0604.2011039.
Li, Y. and Oldenburg, D.W., 1998, 3-D inversion of gravity data, Geophysics, 63,109-119.
Loganathan, C. and Girijia, K.V., 2013, Hybrid learning for adaptive neuro fuzzy interference system, International Journal of Engineering and Science, 2(11), 6-13.
Mohan, N. L., Anandadabu, L. and Roa, S., 1986, Gravity interpretation using Mellin transform, Geophysics, 52, 114-122.
Neumann, R., 1967, Lav gravimetrie de haute précision, application aux recherches de cavités, Geophysical Prospecting, 15, 116-134.
Odegard, M. E. and Berg, J. W., 1965, Gravity interpretation using the Fourier integral, Geophysics, 30, 424-438.
Osman, O., Albora, A. M. and. Ucan, O. N., 2006, A new approach for residual gravity anomaly profile interpretations: Forced Neural Network (FNN), Annals of Geophysics, 9, 65-78.
Osman, O., Albora, A. M. and Ucan, O. N., 2007, Forward mmodeling with Forced Neural Networks for gravity anomaly profile, Mathematical Geology, 39, 593-605, doi: 10.1007/s11004-007-9114-8.
Reid, A. B., Allsop, J. M., Granser, H., Millet, A. J. and Somerton, I. W., 1990, Magnetic interpretation in three dimensions using Euler Deconvolution, Geophysics, 55, 80-91.
Sharma, B. and Geldrat, L. P., 1968, Analysis of gravity anomalies of two-dimensional faults using Fourier transforms, Geophysical Prospecting, 16, 77-93.
Shaw, R. K. and Agarwal, P., 1990, The application of Walsh transforms to interpret gravity anomalies due to some simple geometrical shaped causative sources: a feasibility study, Geophysics, 55,843-850.
Smith, R. A., 1959, Some depth formulate for local magnetic and gravity anomalies, Geophysical Prospecting, 7, 55-63. Styles, P., McGrath, R., Thomas, E. and Cassidy, N. J., 2005, The use of microgravity for cavity characterization in Karstic terrains, Quarterly Journal of Engineering and Hydrogeology, 38,155-169.
Styles, P., Miller, S., Thomas, E. and Toon, S. M., 1999, Microgravity survey freeport container terminal phase II Grand Bahama, Report No.98073, Microsearch UK.
Thompson, D. T., 1982, EULDPH-A new technique for making computer-assisted depth estimations from magnetic data, Geophysics, 47, 31-37. | ||
|
آمار تعداد مشاهده مقاله: 2,549 تعداد دریافت فایل اصل مقاله: 980 |
||