Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling

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Geophysical modelling and data inversion are important tools for interpreting the physical properties of Earth's subsurface. Solving the inverse problem involves several computational steps and is generally a time consuming task. Artificial neural networks have the potential to speed up large computations. Such networks provide the means to model the relationship between the inputs and outputs without needing to know the physical model of the underlying problem. There are two main aspects that affect the performance of neural networks: optimization of network architecture and pre-processing of data. In this article, we investigate several traditional pre-processing techniques including the min-max scaling, z-score scaling, and the logarithmic transform scaling, and propose some novel data pre-processing approaches for the 1-D forward modelling of time-domain electromagnetic data based on signal characteristics. We evaluate the performance of the conventional and the proposed pre-processing methods against a 3% relative error metric, which corresponds to the typical data uncertainty, to show that forward data pre-processing has significant effect on the performance of neural networks. The proposed gate-wise min-max scaling achieves the best performance with 96% of gates within a 3% relative error, while the commonly used logarithmic transform results only in 75% of gates within a 3% relative error. We provide insights into how various pre-processing methods affect the performance of these networks and recommend optimal pre-processing strategies that may be used where similar data content is encountered to achieve superior performance. Finally, we show the effect of forward modelling accuracy in inverse modelling.

OriginalsprogEngelsk
TidsskriftIEEE Access
Vol/bind9
Sider (fra-til)34635-34646
Antal sider12
ISSN2169-3536
DOI
StatusUdgivet - feb. 2021

Bibliografisk note

Funding Information:
This work was supported in part by the Innovation Fund Denmark under the "MapField" project and in part by "TopSoil", an Interreg project supported by the North Sea Programme of the European Regional Development Fund of the European Union through the tTEM data from Soften and in part through the tTEM data from Gedved by "TREX", a project supported by a Gront Udviklings- og Demonstrations Program (GUDP) under ministry of food agriculture and sheries of Denmark (Grant No. 34009-18-1453).

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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