NEURAL NETWORKS APPLICATIONS IN GEOLOGY - EXAMPLES FROM
LITERATURE
1
Instytut Górnictwa Politechniki Wrocławskiej
Corresponding author
Mining Science 2006;VIII(1):63-73
KEYWORDS
ABSTRACT
Artificial neural networks (ANN) are broadly used in earth sciences, especially in solving
problems that lack of data and understanding about processes that cause them. Authors showed a brief
review of applications of artificial neural networks in some scientific areas connected with geology.
ANN usage in surface subsidence, landslides prediction, large earthquakes prediction, lithofacies
indentification in petroleum mining, ore and groundwater resources estimation, drinking water
management and aggregate usage in road building was shown. Other areas where ANN are used in
geology were also shortly indicated.
REFERENCES (32)
1.
AMBROZIC T., TURK G., 2003. Prediction of subsidence due to underground mining by artificial neural networks. Computers & Geosciences 29, 627-637.
2.
ATTOH-OKINE N.O., FEKPE E.S.K., 1996. Strength characteristics modeling of lateric soils using adaptive neural networks. Construction and Building Materials 10, 577-582.
3.
BAK P., TANG C., 1989. Earthquakes as self-organized critical phenomena. Journal of Geophysical Research 94, 15635-15637.
4.
BALDWIN J.L., BATEMAN A.R.M., WHEATLEY C.L., 1990. Application of neural networks to the problem of mineral identification from well logs. The Log Analyst 31, 279-293.
5.
BODRI B., 2001. A neural-network model for earthquake occurrence. Journal of Geodynamics 33, 289-310.
6.
CAERS J., 2001. Geostatistical reservoir modelling using statistical pattern recognition. Journal of Petroleum Science and Engineering 29, 177-188.
7.
CARLSTEN S., LINDQVIST L., OLSSON O., 1989. Comparision between radar data and geophysical, geological and hydrogeological borehole parameters by multivariate analysis of data. SKB Technical Report 89-15. Swedish Geological Company, Sweden.
8.
CHANG H-C., KOPASKA-MERKEL D.C., CHEN H-C., DURRANS S.R., 2000. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system. Computers & Geosciences 26, 591-601.
9.
CHANG H-C., KOPASKA-MERKEL D.C., CHEN H-C., 2002. Identification of lithofacies using Kohonen self-organizing maps. Computers & Geosciences 28, 223-229.
10.
DOVETON J.H., 1994, Geologic Log Analysis Using Computer Methods. American Association of Petroleum Geologists, Tulsa, Oklahoma.
11.
FAUSETT L.V., 1994. Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Prentice Hall Inc., New Jersey.
12.
FENG X.T., WANG Y.J., YAO J.G., 1996. A neural network model for real-time roof pressure prediction in coal mines. International Journal of Rock Mechanics and Mining Science and Geomechanics Abstracts 33, 647-653.
13.
FINNIE G.J., 1999. Using neural networks to discriminate between genuine and spurious seismic events in mines. Pure and Applied Geophysics 154, 41-56.
14.
GHABOUSSI J., SIDARATA D.E., 1998. New Nested Adaptive Neural Networks (NANN) for constitutive modelling. Computers and Geotechnics 22, 29-52.
15.
HAFNER J., KOMAC M., 1998. Landsat TM lithological classification of Koper-Kozina area. Neural networks approach versus statistical clustering. W: Proceedings of the International onference on GIS for Earth Science Applications, Ljubljana, Słowenia, str. 41-55.
16.
HUANG Y., WANSTEDT S., 1998. The introduction of neural network system and its applications in rock engineering. Engineering Geology 49, 253-260.
17.
LEE S., RYU J-H., LEE M-J., WON J-S., 2003. Use of artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology 44, 820-833.
18.
LIBER A., LIBER-MADZIARZ E., 2003. Nowe badania wydajności ujęć eksploatowanych samoczynnie na przykładzie wód leczniczych w Sudetach, [W:] Współczesne Problemy Hydrogeologii Tom XI, cz. 1, Wydział Budownictwa Wodnego i Inżynierii Środowiska Politechniki Gdańskiej.
19.
LOGAR J., TURK G., 1997. Neural Network as a constitutive model of soil. Zeitschrift fur Angewandte Mathematik und Mechanik 77, 195-196.
20.
MUSIL M., PLESINGER A., 1996. Discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps. Bulletin of the Seismological Society of America 86, 1077-1090.
21.
NEUPANE K.M., ACHET S.H., 2004. Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Engineering Geology 74, 213-226.
22.
OSOWSKI S., 1996. Sieci neuronowe w ujęciu algorytmicznym. Wydawnictwa Naukowo-Techniczne, Warszawa.
23.
RUDAJEV V., CIZ R., 1999. Estimation of mining tremor occurence by using neural networks. Pure and Applied Geophysics 154, 57-72.
24.
SHI J., ORTIAGO J.A.R., BAI J., 1998. Modular neural networks for predicting settlements during tunnelling. J. Geotech, Geo-Env. Engg ASCE 124, 389-394.
25.
SZCZEPAŃSKA J, KMIECIK E., 2001. Wykorzystanie sieci neuronowych do oceny czasu oddziaływania składowiska odpadów górniczych na środowisko wodne, [W:] Współczesne Problemy Hydrogeologii, Tom X, cz. 2, Sudety Oficyna Wydawnicza Oddziału Wrocławskiego PTTK, Wrocław.
26.
TADEUSIEWICZ R., 1993. Sieci neuronowe. Akademicka Oficyna Wydawnicza RM, Warszawa.
27.
TURK G., LOGAR J., MAJES B., 2001. Modelling soil behaviour in uniaxial strain conditions by neural networks. Advances in Engineering Software 32, 57-64.
28.
WANSTEDT S., 1992. Geophysical borehole logging in Malmberget. Technical Report 1991:14T. Lulea University of Technology, Sweden.
29.
WONG P.M., SHIBLI S.A.R., 2001. Modelling a fluvial reservoir with multipoint statistics and proncipal components. Journal of Petroleum Science and Engineering 31, 157-163.
30.
WU X., ZHOU Y., 1993. Reserve estimation using neural networks techniques. Computers and Geosciences 19, 567-575.
31.
YANG Y., ZHANG Q., 1997. A hierarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering 30, 207-222.
32.
YANG Y, ZHANG Q., 1998. The Application of Neural Networks to Rock Engineering Systems (RES). International Journal of Rock Mechanics and Mineral Sciences 35, 727-745.
| eISSN: | 2353-5423 |
| ISSN: | 2300-9586 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
