What is it about?
The new algorithm for inverting seismic surface waves data using an artificial neural network is developed. An approach to the selection of hyperparameters and an architecture of a neural network is described in detail. Examples of the inversion of a large number of synthetic dispersion curves of surface waves (1,250,000 curves) for media with a different number of layers are given, as well as examples of the inversion of synthetic dispersion curves for various geological media using an artificial neuron network, the Monte Carlo method and GWO. Finally, an example of field data validation is provided for a field in western Siberia.
Photo by Moritz Kindler on Unsplash
Why is it important?
The use of artificial neural networks makes it possible to obtain accurate inversion results in real-time. It expands the limits of applicability of the MASW method. But there is still no work on creating a complete inversion algorithm, which would include: determining the number of layers, the ranges of possible parameters of the velocity model from the observed dispersion curve, an approach to calculating a representative set of training data, a way to configure the architecture of an artificial neural network.
Read the Original
This page is a summary of: An artificial neural network approach for the inversion of surface wave dispersion curves, Geophysical Prospecting, June 2021, Wiley,
You can read the full text:
The following have contributed to this page