FUZZY LOGIC AND APLLICATIONS IN GEOPHYSICS: A SEISMOLOGY EXAMPLE

Year 2013, Volume: 15 Issue: 43, 15 - 29, 01.01.2013

İlknur Kaftan , Elif Balkan Müjgan Şalk

Abstract

With the effect of advancing technology, Fuzzy logic has become one of the most common methods
used in solving problems during the recent years. Solutions of the many ill defined/unidentified events
in nature/earth are made possible by means of fuzzy logic. Wide ranges of applications and obtaining
successful results are caused the increasing interest on this method.
Applications of Fuzzy logic on Geophysics are also increasing day by day. It is used on
particularly inversion of seismic, electromagnetic and resistivity data, prediction of some physical
parameters and estimation studies. The aim of this study is to compile the articles which are about
Fuzzy logic application on geophysics and to summarize its intended purpose. Analyzing of the
Earthquake data of Western Anatolia Using with Adaptive Neurofuzzy Inference System, is given an
example of this method as a seismological application

Keywords

Fuzzy logic, Membership function, Geophysics, Earthquake weekly frequency

BULANIK MANTIK (FUZZY LOGIC) VE JEOFİZİKTE KULLANIM ALANLARI: SİSMOLOJİ ÖRNEĞİ

Abstract

Bulanık mantık, teknolojinin de etkisiyle son yıllarda birçok problemin çözümünde yaygın olarak
kullanılan yöntemlerden biridir. Doğada kesin olarak tanımlanamayan birçok olayın bulanık mantık
yardımıyla çözümleri mümkün hale gelmiştir. Uygulama alanının geniş olması ve birçok problemin
çözümünde başarılı sonuçların elde edilmesi bu yönteme olan ilgiyi arttırmıştır.
Bulanık mantığın jeofizik alanındaki uygulamaları da giderek artmaktadır. Özellikle sismik,
elektromanyetik ve özdirenç gibi yöntemlerin ters çözümünde ayrıca parametre tayini ve ön kestirim
gibi uygulamalarda kullanılmaktadır. Bu çalışmada bulanık mantığın günümüze kadar olan jeofizik
uygulamaları derlenmiş ve yaygın olarak kullanım amaçları özetlenmeye çalışılmıştır. Batı Anadolu
deprem katalog verilerinin Uyarlanabilir Yapay Sinir-Bulanık Mantık Çıkarım Sistemi (Adaptive
Neurofuzzy Inference System) (UYBÇS) ile değerlendirilmesi üzerine örnek bir çalışmaya yer
verilmiştir

Keywords

Bulanık mantık, Üyelik fonksiyonu, Jeofizik, Deprem haftalık frekans

References

• Acar M., Haberler W. M., Ayan T. (2008): “Bulanık Çıkarım Sistemler ile Heyelan Bloklarının Belirlenmesi: Gürpınar Örneği”, Jeodezi, Jeoinformasyon ve Arazi Yönetimi Dergisi, Cilt 1/98.
• Akgün A., Sezer E. A., Nefeslioğlu H. A., Gökçeoğlu C., Pradhan B. (2012): “An Easy-to- Use MATLAB Program (MamLand) for the Assessment of Landslide Susceptibility Using a Mamdani Fuzzy Algorithm”, Computers and Geosciences, Cilt 38, s.23–34.
• Aksoy B., Ercanoglu M. (2012): “Landslide İdentification and Classification by Object-Based Image Analysis and Fuzzy Logic: An Example From the Azdavay Region (Kastamonu,Turkey)”, Computers and Geosciences, Cilt 38, s.87-98.
• Ali M., Chawathe A. (2000): “Using Artificial Intelligence to Predict Permeability From Petrographic Data”, Computers and Geosciences, Cilt 26, s.915-925.
• Alvanitopoulos P. F., Andreadis I., Elenas A. (2010): “ Neuro-Fuzzy Techniques for the Classification of Earthquake Damages in Buildings”, Measurement, Cilt 43, s.797–809.
• Aminzadeh F. (2005): “Applications of AI and Soft Computing for Challenging Problems in the Oil Industry”, Journal of Petroleum Science and Engineering, Cilt 47, s.5–14.
• Anifowose F., Abdulraheem A. (2011): “Fuzzy Logic-Driven and SVM-Driven Hybrid Computational Intelligence Models Applied to Oil and Gas Reservoir Characterization”, Journal of Natural Gas Science and Engineering, Cilt 3, s.505-517.
• Arrell K. E., Fisher P. F., Tate N. J., Bastin L. (2007): “A Fuzzy C-Means Classification of Elevation Derivatives to Extract the Morphometric Classification of Landforms in Snowdonia, Wales”, Computers and Geosciences, Cilt 33, s.1366–1381.
• Ataei M., Khalokakaei R., Hossieni M. (2009): “Determination of Coal Mine Mechanization Using Fuzzy Logic”, Mining Science and Technology, Cilt 19, s.0149–0154.
• Batyrshin I., Sheremetov L., Markov M., Panova A. (2005): ”Hybrid Method for Porosity Classification in Carbonate Formations”, Journal of Petroleum Science and Engineering, Cilt 47, s.35–50.
• Baykal N., Beyan T. (2004): “ Bulanık Mantık Uzman Sistemler ve Denetleyiciler”, Bıçaklar Kitabevi, Yayın No:10.
• Bezdek J. C., Ehrlich R., Full W. (1984): “FCM: The Fuzzy C-Means Clustering Algorithm”, Computer and Geoscience Volume, Cilt 10, s.191–203.
• Bodur K., Gökalp H. (2011): “Deprem Konumlarının Belirlenmesinde Bulanık Mantık Yaklaşımı”, 1. Türkiye Deprem Mühendisliği ve Sismoloji Konferansı, 11-14 ekim 2011, Odtü, Ankara.
• Brown C. B., (1985): “The Use of Fuzzy Sets in Seismic Engineering in the USA”, In: Feng, D. Y., and Liu, X. H. (eds.), Fuzzy Mathematics in Earthquake Researches, Seismological Press, Beijing, s.2–7.
• Choi K., Cho W., Kim D., Lee I. (2005): “Active Control for Seismic Response Reduction Using Modal-Fuzzy Approach”, International Journal of Solids and Structures, Cilt 42, s.779–794.
• Demicco R. V., Klir G. (2001): “Stratigraphic Simulations Using Fuzzy Logic to Model Sediment Dispersal”, Journal of Petroleum Science and Engineering, Cilt 31, s.135–155.
• Duan Z., Pang Z., Wang X. (2011): “Sustainability Evaluation of Limestone Geothermal Reservoirs with Extended Production Histories in Beijing and Tianjin, China” Geothermics, Cilt 40, s.125–135.
• Dubois M. K., Bohling G. C., Chakrabarti S. (2007): “Comparison of Four Approaches to a Rock Facies Classification Problem”, Computers and Geosciences, Cilt 33, s.599–617.
• Duru N., Kurtulmuş C., Canbay M. (2008): “Gürültü Etkilerinin Bulanık Mantık Temelli Bir Yöntemle Analizi”, Uygulamalı Yerbilimleri, Cilt 2, (Ekim- Kasım 2008).
• El-Sebakhy E. A. (2009): “Data Mining in Forecasting PVT Correlations of Crude Oil Systems Based on Type1 Fuzzy Logic Inference Systems”, Computers and Geosciences, Cilt 35, s.1817–1826.
• Farifteh, J.,, Farshad, T, A., George, R.J., (2006): “ Assessing salt-affected soils using remote sensing, solute modelling and geophysics”, Geoderma, Cilt130, s. 191–206.
• Feng D. Y., Lou S. B., Lin M. Z., Gu J. P., Zhong T. J., Chen H. C. (1982): “Application of Fuzzy Mathematics in Evaluating Earthquake İntensity”, Earthquake Engineering and Engineering Vibration, Cilt 2, Sayı 3, s.16–28.
• Finol J., Guo Y. K., Jing X. D. (2001): “A Rule Based Fuzzy Model for the Prediction of Petrophysical Rock Parameters”, Journal of Petroleum Science and Engineering, Cilt 29, s.97-113.
• Foody G. M. (2000): “Estimation of Sub-Pixel Land Cover Composition in the Presence of Untrained Classes”, Computers and Geosciences, Cilt 26, s.469-478.
• Frances A. P., Lubczynski M. V. (2011): “Topsoil Thickness Prediction at the Catchment Scale by Integration of Invasive Sampling, Surface Geophysics, Remote Sensing And Statistical Modeling”, Journal of Hydrology, Cilt 405, s.31–47.
• Ghayoumian J., Saravi M. M., Feiznia S., Nouri B., Malekian A. (2006): “Application of GIS Techniques to Determine Areas Most Suitable for Artificial Groundwater Recharge in a Coastal Aquifer in Southern Iran”, Journal of Asian Earth Sciences Cilt 30, s.364–374.
• Gholami V., Mohaghegh S. D. (2011): “Fuzzy Upscaling in Reservoir Simulation: An Improved Alternative to Conventional Techniques”, Journal of Natural Gas Science and Engineering, Cilt 3, s.706-715.
• Gökçeoğlu C. (2002): “A Fuzzy Triangular Chart to Predict the Uniaxial Compressive Strength of the Ankara Agglomerates from Their Petrographic Composition”, Engineering Geology, Cilt 66, s.39–51.
• Grandjean G., Hibert C., Mathieu F., Emilie G., Malet J. P. (2009): “Monitoring Water Flow in a Clay-Shale Hillslope from Geophysical Data Fusion Based on a Fuzzy Logic Approach”, C. R. Geoscience, Cilt 341, s.937–948.
• Helmy T., Fatai A., Faisal K. (2010): “Hybrid Computational Models for the Characterization of Oil and Gas Reservoirs”, Expert Systems with Applications, Cilt 37, s.5353–5363.
• Henley S. (2006): “The Problem of Missing Data in Geoscience Databases”, Computers and Geosciences, Cilt 32, s.1368–1377.
• Hibert C., Grandjean G., Bitri A., Travelletti J., Malet J. P. (2012): “ Characterizing Landslides Through Geophysical Data Fusion: Example of the La Valette Landslide (France)”, Engineering Geology, Cilt 128, s.23–29.
• Hsieh B., Lewis C., Lin Z. (2005): “Lithology Identification of Aquifers from Geophysical Well Logs and fuzzy Logic Analysis: Shui-Lin Area, Taiwan”, Computers and Geosciences, Cilt 31, s.263–275.
• Jim J. (2005): “Reservoir Properties Determination Using Fuzzy Logic and Neural Networks from Well Data in Offshore Korea”, Journal of Petroleum Science and Engineering, Cilt 49, s.182–192.
• Khoukhi A. (2012): “Hybrid Soft Computing Systems for Reservoir PVT Properties Prediction”, Computers and Geosciences, Cilt 44, s.109–119.
• Klose C. D. (2002): “Fuzzy Rule-Based Expert System for Short-Range Seismic Prediction”, Computers and Geosciences, Cilt 28, s.377–386.
• Li Y., Anderson S. R. (2006): “Facies Identification from Well Logs: A Comparison of Discriminant Analysis and Naïve Bayes Classifier”, Journal of Petroleum Science and Engineering, Cilt 53, s 149–157.
• Luchetta A., Manetti S. (2003): “ A Real Time Hydrological Forecasting System Using a Fuzzy Clustering Approach”, Computers and Geosciences, Cilt 29, s.1111–1117.
• Luo X., Dimitrakopoulos R. (2003): “ Data-Driven Fuzzy Analysis in Quantitative Mineral Resource Assessment”, Computers and Geosciences, Cilt 29, s.3-13.
• Marano G. C., Morrone E., Sgobba S., Chakraborty S. (2010): “ A fuzzy Random Approach of Stochastic Seismic Response Spectrum Analysis”, Engineering Structures, Cilt 32, s.3879–3887.
• McBratney A. B., Mendonça S. M. L., Minasny B. (2003): “On Digital Soil Mapping”, Geoderma, Cilt 117, s.3–52.
• Metternicht G. I., Zinck J. A. (2003): “Remote Sensing of Soil Salinity: Potentials and Constraints”, Remote Sensing of Environment, Cilt 85, s.1 –20.
• Miles S. B., Keefer D. K. (2009): “Evaluation of CAMEL-Comprehensive Areal Model of Earthquake-Induced Landslides”, Engineering Geology, Cilt 104, s.1-15.
• Nayak P. C., Sudheer K. P., Rangan D. M., Ramasastri K. S. (2004): “A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series”, Journal of Hydrology, Cilt 291, s.52–66.
• Nikravesh M., Aminzadeh F. (2001): “Mining and Fusion of Petroleum Data with Fuzzy Logic and Neural Network Agents”, Journal of Petroleum Science and Engineering, Cilt 29, s.221 238.
• Nordlund U. (1999): “FUZZIM: Forward Stratigraphic Modeling Made Simple”, Computers and Geosciences, Cilt 25, s.449-456.
• Oh J., Pradhan B. (2011): “Application of a Neuro-Fuzzy Model to Landslide-Susceptibility Mapping for Shallow Landslides in a Tropical Hilly Area”, Computers and Geosciences, Cilt 37, s.264–1276.
• Olatunji S. O., Selamat A, Abdulraheem A. (2011): “Modeling the Permeability of Carbonate Reservoir Using Type-2 Fuzzy Logic Systems”, Computers in Industry, Cilt 62, s.147– 163.
• Ouenes A. (2000): “Practical Application of Fuzzy Logic and Neural Networks to Fractured Reservoir Characterization”, Computers and Geosciences, Cilt 26, s. 953-962.
• Park I., Choi J., Lee M. J., Lee. S. (2012): “Application of An Adaptive Neuro-Fuzzy Inference System to Ground Subsidence Hazard Mapping”, Computers and Geosciences, Cilt 48, s.228-238
• Piotrowsky J. A. (1997): “Subglacial Hydrology in North-Western Germany During the Last Glaciation: Groundwater Flow, Tunnel Valleys and Hydrologıcal Cycles”, Quaternary Science Reviews, Cilt. 16, s.169-185.
• Rahman M. S., Zahaby K. M. E. (1997): “Probabilistic Liquefaction Risk Analysis Including Fuzzy Variables”, Soil Dynamics and Earthquake Engineering, Cilt 16, s.63- 79.
• Rajabi M., Bohloli B., Ahangar E. G. (2010): “Intelligent Approaches for Prediction of Compressional, Shear and Stoneley Wave Velocities from Conventional Well Log Data: A Case Study from the Sarvak Carbonate Reservoir in the Abadan Plain (Southwestern Iran)”, Computers and Geosciences, Cilt 36, s.647–664.
• Rezaee M. R., Kadkhodaie I. A., Barabadi A. (2007): “Prediction of Shear Wave Velocity from Petrophysical Data Utilizing Intelligent Systems: An Example from a Sandstone Reservoir of Carnarvon Basin, Australia”, Journal of Petroleum Science and Engineering, Cilt 55, s. 201–212.
• Regmi N. R., Giardino J .R., Vitek J. D. (2010): “Assessing Susceptibility to Landslides: Using Models to Understand Observed Changes in Slopes”, Geomorphology, Cilt 122, s.25–38.
• Robinson V. R. (1990): “Interactive Machine Acquisition of a Fuzzy Spatial Relation”, Computer and Geoscience, Cilt 16, s.857-872.
• Roisenberg M., Schoeninger C., Rodrigues S. R. (2009): "A Hybrid Fuzzy-Probabilistic System for Risk Analysis in Petroleum Exploration Prospects”, Expert Systems with Applications, Cilt 36, s.6282–6294.
• Quenes A. (2000): “ Practical Application of Fuzzy Logic and Neural Networks to Fractured Reservoir Characterization”, Computer and Geoscience, Cilt 26, s.953-962.
• Sahimi M. (2000): “Fractal-Wavelet Neural-Network Approach to Characterization and Upscaling of Fractured Reservoirs”, Computers and Geosciences, Cilt 26, s.877-905
• Schmutz M., Guerin R., Andrieux P., Maquaire O. (2009): “Determination of the 3D Structure of An Earthflow by Geophysical Methods: The case of Super Sauze, in the French southern Alps”, Journal of Applied Geophysics, Cilt 68, s.500–507.
• Shyllon E. A., Olaleye J. B., Olunloyo V. O. S. (2001): “Litho-Seismic Data Handling for hydrocarbon Reservoir Estimate: Fuzzy System Modelling Approach”, Journal of Petroleum Science and Engineering, Cilt 31, s.165–173.
• Singha T. N., Sinha S., Singh V. K. (2007): “Prediction of Thermal Conductivity of Rock Through Physico-Mechanical Properties”, Building and Environment, Cilt 42, s.146–155.
• Singh U. K. (2011): “Fuzzy Inference System for Identification of Geological Stratigraphy off Prydz Bay, East Antarctica”, Journal of Applied Geophysics, Cilt 75, s.687–698.
• Sinha M., Gopinath N. S., Malik N. K. (2010): “Lunar Gravity Field Modeling Critical Analysis and Challenges”, Advances in Space Research, Cilt 45, s.322–349.
• Tahmasebi P., Hezarkhani A. (2012): “A fast and Independent Architecture of Artificial Neural Network for Permeability Prediction”, Journal of Petroleum Science and Engineering, Cilt 86–87, s.118–126.
• Tahmasebi P., Hezarkhani A. (2012): “A Hybrid Neural Networks-Fuzzy Logic-Genetic Algorithm for Grade Estimation”, Computers and Geosciences, Cilt 42, s.18-27.
• Tounsi M. (2005): “ An Approximate Reasoning Based Technique for Oil Assessment”, Expert Systems with Applications, Cilt 29, s.485–491.
• Tütmez B. (2007): “An Uncertainty Oriented Fuzzy Methodology for Grade Estimation”, Computers and Geosciences, Cilt 33, s.280–288.
• Tütmez B., Hatipoglu Z., Kaymak U. (2006): “Modelling Electrical Conductivity of Groundwater Using An Adaptive Neuro-Fuzzy Inference System”, Computers and Geosciences, Cilt 32, s.421-433.
• Tütmez B., Hatipoğlu Z. (2007): “Spatial Estimation Model of Porosity”, Computers and Geosciences, Cilt 33, s.465–475.
• Vahidnia M., Alesheikh A. A., Alimohammadi A., Hosseinali F. (2010): “A GIS-Based Neuro-Fuzzy Procedure for Integrating Knowledge and Data in Landslide Susceptibility Mapping”, Computers and Geosciences, Cilt 36, s.1101–1114.
• Wakefield M. I., Cook R. J., Jackson H., Thompson P. (2003): “Interpreting Biostratigraphical Data Using Fuzzy Logic: the Identification of Regional Mudstones within the Fleming Field, UK North Sea”, Developments in Petroleum Science, Cilt 51, Bölğm 24.
• Wong P., Boerner S. (2004): “Ranking Geological Drivers in Reservoir Problems: A Comparison Study”, Computers and Geosciences, Cilt 30, s.91–100.
• Yılmaz M., Arslan E. (2005): “Bulanık Mantığın Jeodezik Problemlerin Çözümünde Kullanılması”, Harita ve Kadastro Mühendisleri Odası, Mühendislik Ölçmeleri STB Komisyonu, 2. Mühendislik Ölçmeleri Sempozyumu, 23-25 Kasım 2005, İTÜ-İstanbul.
• Zellou A. M., Ouenes A. (2003): “Integrated Fractured Reservoir Characterization Using Neural Networks and Fuzzy Logic: Three Case Studies”, Developments in Petroleum Science, Cilt 51, Bölüm 26.