dc.contributor.author | Caylak, Cagri | |
dc.contributor.author | Kaftan, Ilknur | |
dc.date.accessioned | 2019-07-27T12:10:23Z | |
dc.date.accessioned | 2019-07-28T09:56:43Z | |
dc.date.available | 2019-07-27T12:10:23Z | |
dc.date.available | 2019-07-28T09:56:43Z | |
dc.date.issued | 2014 | |
dc.identifier.issn | 1895-6572 | |
dc.identifier.issn | 1895-7455 | |
dc.identifier.uri | https://dx.doi.org/10.2478/s11600-014-0207-8 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/8111 | |
dc.description | WOS: 000343129800006 | en_US |
dc.description.abstract | 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | DE GRUYTER OPEN LTD | en_US |
dc.relation.isversionof | 10.2478/s11600-014-0207-8 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | multi-layer perceptron neural networks | en_US |
dc.subject | multi-channel analysis | en_US |
dc.subject | dispersion curve | en_US |
dc.subject | near-surface structure | en_US |
dc.title | Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm | en_US |
dc.type | article | en_US |
dc.relation.journal | ACTA GEOPHYSICA | en_US |
dc.contributor.department | [Caylak, Cagri] Cumhuriyet Univ, Dept Geophys Engn, Fac Engn, Sivas, Turkey -- [Kaftan, Ilknur] Dokuz Eylul Univ, Dept Geophys Engn, Fac Engn, Buca Izmir, Turkey | en_US |
dc.identifier.volume | 62 | en_US |
dc.identifier.issue | 6 | en_US |
dc.identifier.endpage | 1327 | en_US |
dc.identifier.startpage | 1310 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |