An artificial neural network approach for the inversion of surface wave dispersion curves-Reference-Cited by-同舟云学术

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An artificial neural network approach for the inversion of surface wave dispersion curves

Published:2021-06-08 Issue:7 Volume:69 Page:1405-1432
ISSN:0016-8025
Container-title:Geophysical Prospecting
language:en
Short-container-title:Geophysical Prospecting

Author:

Yablokov Alexandr V.¹²³^ORCID,Serdyukov Alexandr S.¹²³^ORCID,Loginov Georgy N.¹^ORCID,Baranov Valery D.⁴

Affiliation:

1. Trofimuk Institute of Petroleum Geology and Geophysics of SB RAS Novosibirsk 630090 Russia

2. Chinakal Institute of Mining of SB RAS Novosibirsk 630091 Russia

3. Novosibirsk State University Novosibirsk 630090 Russia

4. Laboratory of Technologies of Inverse Problems Moscow 115035 Russia

Publisher

Wiley

Subject

Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.13107

Reference50 articles.

1. Elastic prestack seismic inversion through discrete cosine transform reparameterization and convolutional neural networks

2. Dispersion and the dissipative characteristics of surface waves in the generalized S-transform domain

3. Baeldung. (2020)Understanding forecast accuracy: MAPE WAPE WMAPE. Accessed April 28 2021.https://www.baeldung.com/cs/mape‐vs‐wape‐vs‐wmape.

4. Simulated annealing inversion of multimode Rayleigh wave dispersion curves for geological structure

5. Near-real-time near-surface 3D seismic velocity and uncertainty models by wavefield gradiometry and neural network inversion of ambient seismic noise

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1. Method of automated extracting dispersion curves based on time-frequency distribution of seismic data;Russian Journal of Geophysical Technologies;2024-02-21

2. Inversion of Radial Shear Velocity Profile for Acoustic Logging Using CNN-LSTM Network;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Seismic AVO Inversion Method for Viscoelastic Media Based on a Tandem Invertible Neural Network Model;IEEE Transactions on Geoscience and Remote Sensing;2024

4. Prediction of S‐wave velocity models from surface waves using deep learning;Near Surface Geophysics;2023-11-29

5. Determination of the contrast boundaries of the low velocity zone by analyzing the features of the dispersion curves of the Rayleigh wave velocities based on empirical dependencies;Russian Journal of Geophysical Technologies;2023-09-01

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