Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm

• Autorzy: Caylak C. , Kaftan I.

Identyfikatory

DOI

10.2478/s11600-014-0207-8

• Języki publikacji: EN

Abstrakty

EN

This study proposes the use of multi-layer perceptron neural networks (MLPNN) to invert dispersion curves obtained via multi-channel analysis of surface waves (MASW) for shear S-wave velocity profile. The dispersion curve used in inversion includes the fundamental-mode dispersion data. In order to investigate the applicability and performance of the proposed MLPNN algorithm, test studies were performed using both synthetic and field examples. Gaussian random noise with a standard deviation of 4 and 8% was added to the noise-free test data to make the synthetic test more realistic. The model parameters, such as S-wave velocities and thicknesses of the synthetic layered-earth model, were obtained for different S/N ratios and noise-free data. The field survey was performed over the natural gas pipeline, located in the Germencik district of Aydın city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.

Słowa kluczowe

EN

multi-layer perceptron neural networks; multi-channel analysis; dispersion curve; near-surface structure

PL

wielowarstwowe perceptronowe sieci neuronowe; analiza wielokanałowa; krzywa dyspersji; warstwa przypowierzchniowa

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2014
• Tom: Vol. 62, no. 6
• Strony: 1310--1327
• Opis fizyczny: Bibliogr. 55 poz.

Twórcy

autor

Caylak C.

• Cumhuriyet University, Faculty of Engineering, Department of Geophysical Engineering, Sivas, Turkey

autor

Kaftan I.

• Dokuz Eylül University, Faculty of Engineering, Department of Geophysical Engineering, Buca-Izmir, Turkey

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