Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A90263%2F24%3A00381467" target="_blank" >RIV/68407700:90263/24:00381467 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/atmos15060734" target="_blank" >https://doi.org/10.3390/atmos15060734</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/atmos15060734" target="_blank" >10.3390/atmos15060734</a>

Result language

angličtina

Original language name

Long Short-Term Memory Recurrent Network Architectures for Electromagnetic Field Reconstruction Based on Underground Observations

Original language description

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefiting from a low background noise level. Enhancing strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio, is both valuable and feasible. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at the surface, to achieve noise suppression through signal reconstructions. Both the theoretical analysis and the practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

—

Continuities

—

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Atmosphere

ISSN

2073-4433

e-ISSN

2073-4433

Volume of the periodical

15

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

18

Pages from-to

1-18

UT code for WoS article

001254452200001

EID of the result in the Scopus database

2-s2.0-85197128714