A neuro-fuzzy approach for prediction of longitudinal wave velocity

Neural Computing and Applications

Volumn 22, Issue 7-8, 2013, Pages 1685-1693

  Verma, A K ^a   Singh, T N ^b  

^a THE ENERGY AND RESOURCES INSTITUTE   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

ANFIS; Density; Fracture roughness coefficient; Harness; Porosity

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS; COMPRESSIONAL WAVE VELOCITY; FRACTURE ROUGHNESS; GENERALIZATION CAPABILITY; HARNESS; LONGITUDINAL WAVE VELOCITY; PHYSICOMECHANICAL PROPERTIES;

ACOUSTIC WAVE VELOCITY; DENSITY (SPECIFIC GRAVITY); FORECASTING; FRACTURE; GEOPHYSICS; MECHANICAL PROPERTIES; POROSITY; SEISMIC WAVES;

WAVE PROPAGATION;

EID: 84878550068     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-0817-5     Document Type: Article

References (20)

1. Alvarez Grima M, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnel Underground Space Technol 15(3): 259-269.
2. Aveline M (1964) Experimental results on the relation between micro-fissuration and speed of propagation of ultrasounds in the granites of Sidbore. Sci Terre 9(4): 439-448.
3. Berezkin VM, Mikhaylov IN (1964) On the correlational relationship between the density of rocks and their velocities of elastic wave propagation for the central and eastern regions of Russian platform. Geofiz Razvedka 16: 83-91.
4. Boadu FK (1997) Fractured rock mass characterization parameters and seismic properties: analytical studies. J Appl Geophys 36: 1-19.
5. Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3): 267-278.
6. Goodman RE (1989) Introduction to rock mechanics. Wiley, New York, 2nd edn.
7. Hogstrom K (1994) A study on strength parameter for aggregate from south western Sweden rocks. Res Rep (Chamer Univ of Technology, Goteborg, Sweden), pp 123-134.
8. Hudson TA, Jones ETW, New BM (1980) P-wave velocity measurements in a machine bored chalk tunnels. Q J Eng Geol 13: 33-43.
9. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Autom Control IEEE Trans Syst 42: 1482-1484.
10. Karpuz C, Pasamehmetoglu AG (1997) Field characterization of weathered Ankara andesites. Eng Geol 46: 1-17.
11. Kern H (1990) Laboratory seismic measurements: an aid in the interpretation of seismic field data. Terra Nova 2: 203-617.
12. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Mining Sci 45(8): 1446-1453, ISSN 1365-1609, doi: 10. 1016/j. ijrmms. 2008. 02. 007.
13. Nauck D, Klawonn F, Kruse R (1997) Foundations of neuro-fuzzy systems. Wiley, New York.
14. Price DG, Malone AW, Knill TL (1970) The application of seismic methods in the design of rock bolt system. Proc 1st Int Congr Int Assoc Eng Geol 2: 740-752.
15. Singh TN, Kanchan R, Verma AK (2004b) Prediction of blast induced ground vibration and frequency using an artificial intelligence technique. Int J Noise Vib Worldwide 35(11): 7-14.
16. Singh TN, Kanchan R, Saigal K, Verma AK (2004) Prediction of P-wave velocity and anisotropic properties of rock using artificial neural networks technique. J Sci Ind Res 63(1): 32-38.
17. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern (SMC-15) 1: 116-132.
18. Verma AK, Singh TN (2009) A neuro-genetic approach for prediction of compressional wave velocity of rock and its sensitivity analysis. Int J Earth Sci and Eng 2(2): 81-94.
19. Young RP, Hill TT, Bryan IR, Middleton R (1985) Seismic spectroscopy in fracture characterization. Q J Eng Geol 18: 459-479.
20. Yudborovsky I, Vilenskaya SM (1962) Some results of study of the elastic properties of rocks of western central Asia. Akad Nauk Turkmen SSR Ser Fiz-Tekh Khim Geol Nauk 3: 26-31.