Battery Earth: using the subsurface at Boulby underground laboratory to investigate energy storage technologies

Daniels, Katherine A.; Harrington, Jon F.; Wiseall, Andrew C.; Shoemark-Banks, Edward; Hough, Edward; Wallis, Humphrey C.; Paling, Sean M.

doi:10.3389/fphy.2023.1249458

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…These features make underground laboratories crucial locations for research across microphysics, astrophysics, cosmology, and geoscience [1]. Over the past decades, numerous international underground laboratories have been constructed and continuously developed, including SNO in Canada [2], Kamioka in Japan [3], Modane and LSBB in France [4,5], Boulby in the UK [6], Baksan in Russia [7,8], and SarGrav in Italy [9,10], among others. These underground laboratories range in volume from a few hundred to thousands of cubic meters, with vertical rock cover thicknesses from a few hundred meters to over two thousand meters.…”

Section: Introductionmentioning

confidence: 99%

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

Tian,

Xie,

Wang

2024

Atmosphere

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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.

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Section: Introductionmentioning

confidence: 99%

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

Tian,

Xie,

Wang

2024

Atmosphere

View full text Add to dashboard Cite

show abstract

“…These features make underground laboratories crucial locations for research across microphysics, astrophysics, cosmology, and geoscience [1]. Over the past decades, numerous international underground laboratories have been constructed and continuously developed, including SNO in Canada [2], Kamioka in Japan [3], Modane and LSBB in France [4,5], Boulby in the UK [6], Baksan in Russia [7,8], and SarGrav in Italy [9,10], among others. These underground laboratories have volumes ranging from a few hundred to thousands of cubic meters, with vertical rock cover thicknesses ranging from a few hundred meters to over 2,000 meters.…”

Section: Introductionmentioning

confidence: 99%

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

Tian,

Xie,

Wang

2024

Preprint

View full text Add to dashboard Cite

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. 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 surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and 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.

show abstract

Battery Earth: using the subsurface at Boulby underground laboratory to investigate energy storage technologies

Cited by 2 publications

References 27 publications

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

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

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

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