Neural network modeling of Herglotz—Wiechert inversion of multiparametric travel-time curves of seismic waves
Using artificial neural networks to solve a problem of plotting travel-time curves of seismic waves can create nonlinear travel-time model of P and S phases of seismic waves arrangement as a function of several arguments: source depth, magnitude, back azimuth and epicenter distance. Construction of...
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| Date: | 2017 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Інститут геофізики ім. С.I. Субботіна НАН України
2017
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| Series: | Геофизический журнал |
| Online Access: | http://dspace.nbuv.gov.ua/handle/123456789/125274 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Neural network modeling of Herglotz—Wiechert inversion of multiparametric travel-time curves of seismic waves / M. Lazarenko, O. Herasymenko // Геофизический журнал. — 2017. — Т. 39, № 4. — С. 3-14. — Бібліогр.: 8 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| Summary: | Using artificial neural networks to solve a problem of plotting travel-time curves of seismic waves can create nonlinear travel-time model of P and S phases of seismic waves arrangement as a function of several arguments: source depth, magnitude, back azimuth and epicenter distance. Construction of three-dimensional travel-time relationships and their use for modeling of hadographs and their inversion are considered on examples of seismic records Ukrainian seismic stations. Examples of inversion locus within the model Herglotz—Wiechert and features of application of the model in a real environment for single seismic stations, and generalization for arbitrary coordinate of the source and the point of signal registration in the Black Sea region are given. |
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