A Signal-Noise-Ratio Enhancement Method for DAS-VSP Based on Heterogeneous Transfer Learning

Q. Feng; Y. Li; N. Wu; Y. Tian; Y. Zhao

doi:10.3997/2214-4609.202376014

oa A Signal-Noise-Ratio Enhancement Method for DAS-VSP Based on Heterogeneous Transfer Learning
Authors Q. Feng¹, Y. Li¹, N. Wu¹, Y. Tian¹ and Y. Zhao¹
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¹ Jilin University
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, 3rd EAGE Workshop on Fiber Optic Sensing for Energy Applications, Nov 2023, Volume 2023, p.1 - 3
DOI: https://doi.org/10.3997/2214-4609.202376014

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2023-11-15

2025-03-23

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References

Gorshkov, B. G., Simikin, D. E., Alekseev, A. E., Taranov, M. A., Zhukov, K. M., & Potapov, V. T. (2023). Brillouin-Scattering Induced Noise in DAS: A Case Study.Sensors, 23(12), 5402.
[Google Scholar]
Feng, Q., & Li, Y. (2021). Denoising deep learning network based on singular spectrum analysis—DAS seismic data denoising with multichannel SVDDCNN.IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11.
[Google Scholar]

/content/papers/10.3997/2214-4609.202376014

A Signal-Noise-Ratio Enhancement Method for DAS-VSP Based on Heterogeneous Transfer Learning

Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202376014

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oa A Signal-Noise-Ratio Enhancement Method for DAS-VSP Based on Heterogeneous Transfer Learning

Abstract

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The natural combination of full and image‐based waveform inversion

Poststack diffraction imaging using reverse‐time migration

Characterizing the effect of elastic interactions on the effective elastic properties of porous, cracked rocks

Fracture detection by Gaussian beam imaging of seismic data and image spectrum analysis

Laboratory measurements of guided‐wave propagation within a fluid‐saturated fracture