Neural Networks Application to Reduction of Train Caused Distortions in Magnetotelluric Measurement Data

• Autorzy: Bielecka M. , Danek T. , Wojdyła M. , Baran G.
• Języki publikacji: EN

Abstrakty

EN

Artificial intelligence methods for MT data processing are proposed. Distortions having a complex structure created by external artificial sources such as, for example, passing train were investigate. In the first part of this paper the time intervals with such type of distortions were found by using a special neuronal system. Next for time intervals found in the first stage the measure curve fragment is removed and then it is replied by the fragment created by a trained perceptron. The experiment showed that used method are effective.

Słowa kluczowe

EN

MT data; neural network

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2009
• Tom: Vol. 17/18
• Strony: 75--86
• Opis fizyczny: Bibliogr. 7 poz., rys.

Twórcy

autor

Bielecka M.

autor

Danek T.

autor

Wojdyła M.

autor

Baran G.

• Departament of Geoinformatics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland,

Bibliografia

• [1] Cagniard L.; Basic theory of the magnetotelluric method of geophysical prospecting, Geophysics, 18, 1953, pp. 605–637.
• [2] Tikhonov A.N.; The determination of the electrical properties of deep layers of the Earth’s crust, Dokl. Acad. Nauk. SSR, 73, 1950, pp. 295–297.
• [3]Manoj C., Nagarajan N.; The application of artificial neural networks to magnetotelluric time-series analysis, Geophysical Journal International, 153,2003, pp. 409–423.
• [4] Moorkamp M., Jones A.G., Rao C.K.; Processing magnetotelluric time series with adaptive filters and neural networks, 18th EM Induction Workshop, El Vendrell, Spain, 2006.
• [5] Popova I.V., Ogawa Y.; Application of a modified Hopfield neural network to noisy magnetotelluric data, Izvestiya Physics of the Solid Earth, Vol. 43,3, 2007, pp. 217–224.
• [6] Yee P.V., Haykin S.; Regularized Radial Basis Function Networks, Theory and Applications, 2001.
• [7] Tadeusiewicz R.; Sieci neuronowe, Akademicka Oficyna Wydawnicza, 1993.