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


ACTA GEOPHYSICA, vol.62, no.6, pp.1310-1327, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62 Issue: 6
  • Publication Date: 2014
  • Doi Number: 10.2478/s11600-014-0207-8
  • Journal Name: ACTA GEOPHYSICA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1310-1327
  • Keywords: multi-layer perceptron neural networks, multi-channel analysis, dispersion curve, near-surface structure, MENDERES-MASSIF, WESTERN ANATOLIA, INVERSION, DISPERSION, TURKEY, DECONVOLUTION, EVOLUTION, VELOCITY, SOUTHERN, CAVITIES
  • Dokuz Eylül University Affiliated: Yes


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 AydA +/- n city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.