Application of artificial neural networks to fracture analysis at the Äspö HRL, Sweden: Fracture sets classification

International Journal of Rock Mechanics and Mining Sciences

Volumn 38, Issue 5, 2001, Pages 621-639

  Sirat, M ^a   Talbot, C J ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

Artificial neural networks; sp HRL; Data analysis; Fractures

Indexed keywords

BACKPROPAGATION; FRACTURE; LEARNING ALGORITHMS; ROCK MECHANICS; TRANSFER FUNCTIONS; VECTOR QUANTIZATION; VECTORS;

FRACTURE TESTS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; FRACTURE GEOMETRY; FRACTURE NETWORK; STRUCTURAL GEOLOGY;

ASPO; SWEDEN;

EID: 0035386660     PISSN: 13651609     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1365-1609(01)00030-2     Document Type: Article

References (30)

1. Use of neural network to do pattern recognition tasks in geoscience applications
2. An experiment in the use of trained neural networks for regional seismic event classification
3. A pattern recognition approach to geophysical inversion using neural nets
4. Using neural network to locate edges and linear features in satellite images
5. An introduction to computing with neural nets
6. Location of subsurface targets in geophysical data using neural networks
7. Application of neural network to the problem of mineral identification from well logs
8. Predicting permeability from porosity using artificial neural networks
9. Neural networks provide more accurate reservoir permeability
10. Applications of neural network computing to mining exploration in the Southern Abitibi Greenstone Belt
11. Application of artificial neural networks to paleoceanographic data
12. Technical note: Application of neural network models to rock mechanics and rock engineering
13. Identifying probable failure modes for underground openings using a neural network
14. Neural network analyses of stress-induced overpressures in the Pannonian Basin
15. Estimation of effective block conductivities based on discrete network analyses using data from the Äspö site
16. Development of a geological and a structural model of Äspö, Southeastern Sweden
17. Fracture analysis of the Äspö hard rock laboratory (HRL)
18. Learning representations by back-propagating errors
19. An empirical comparison of pattern recognition, neural nets, and machine learning classification methods
20. Neural networks offer an alternative to traditional regression