A Glitch Detection and Removal Method for Three‐Component Seismic Data From Mars Based On Deep Learning

References

1. Brinkman, N., D.Sollberger, C.Schmelzbach, S. C.Stähler, and J.Robertsson. 2023. “Applications of Time‐Frequency Domain Polarization Filtering to InSight Seismic Data.” Earth and Space Science10: e2023EA003169.
   [Google Scholar]
2. Ceylan, S., J. F.Clinton, D.Giardini, et al. 2021. “Companion Guide to the Marsquake Catalog From InSight, Sols 0–478: Data Content and Non‐Seismic Events.” Physics of the Earth and Planetary Interiors310: 106597.
   [Google Scholar]
3. Iwan, W. D., M. A.Moser, and C.‐Y.Peng. 1985. “Some Observations on Strong‐Motion Earthquake Measurement Using a Digital Accelerograph.” Bulletin of the Seismological Society of America75: 1225–1246.
   [Google Scholar]
4. Li, S., J.Zhang, Q.Cheng, F.Zhu, and LLiu. 2023. “Seismic Profile Denoising Based on Common‐Reflection‐Point Gathers Using Convolution Neural Networks.” Journal of Geophysics and Engineering20, no. 2: 240–254.
   [Google Scholar]
5. Liu, D., W.Wang, X.Wang, C.Wang, J.Pei, and WChen. 2020. “Poststack Seismic Data Denoising Based on 3‐D Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing58, no. 3: 1598–1629.
   [Google Scholar]
6. Lognonné, P., W. B.Banerdt, D.Giardini, et al. 2019. “SEIS: Insight's Seismic Experiment for Internal Structure of Mars.” Space Science Reviews215: 12.
   [Google Scholar]
7. Lu, K., Z.Liu, S.Hanafy, and G.Schuster. 2020. “Noise Reduction With Reflection Supervirtual Interferometry.” Geophysics85, no. 3: V249–V256.
   [Google Scholar]
8. Luo, Y., and N.Mesgarani. 2019. “Conv‐TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation.” IEEE/ACM Transactions on Audio, Speech, and Language Processing27: 1256–1266.
   [Google Scholar]
9. Naghizadeh, M.2012. “Seismic Data Interpolation and Denoising in the Frequency‐Wavenumber Domain.” Geophysics77, no. 2: V71–V80.
   [Google Scholar]
10. Nealson, K. H.1997. “The Limits of Life on Earth and Searching for Life on Mars.” Journal of Geophysical Research: Planets102: 23675–23686.
    [Google Scholar]
11. Olyaei, M. H., H.Jalali, A.Noori, et al. 2018. “Fault Detection and Identification on UAV System With CITFA Algorithm Based on Deep Learning.” In Electrical Engineering (ICEE), Iranian Conference on, 988–993.
    [Google Scholar]
12. Pham, N., and SFomel. 2024 “Seismic Data Augmentation for Automatic Fault Picking Using Deep Learning.” Geophysical Prospecting72: 125–141.
    [Google Scholar]
13. Scholz, J.‐R., R.Widmer‐Schnidrig, P.Davis, et al. 2020. “Detection, Analysis, and Removal of Glitches from InSight's Seismic Data From Mars.” Earth and Space Science7: e2020EA001317.
    [Google Scholar]
14. Shao, J., Y.Wang, Y.Yao, S.Wu, Q.Xue, and X.Chang. 2019, “Simultaneous Denoising of Multicomponent Microseismic Data by Joint Sparse Representation With Dictionary Learning.” Geophysics84, no. 5: KS155–KS172.
    [Google Scholar]
15. Vackář, J., J.Burjánek, and J.Zahradník. 2015. “Automated Detection of Long‐Period Disturbances in Seismic Records; MouseTrap Code.” Seismological Research Letters86: 442–450.
    [Google Scholar]
16. Van Den Ende, M. P. A., and J. P.Ampuero. 2020. “Automated Seismic Source Characterization Using Deep Graph Neural Networks.” Geophysical Research Letters47: e2020GL088690.
    [Google Scholar]
17. Wang, J., Z.Xiao, C.Liu, D.Zhao, and ZYao. 2019. “Deep Learning for Picking Seismic Arrival Times.” Journal of Geophysical Research: Solid Earth124, no. 7: 6612–6624.
    [Google Scholar]
18. Wu, S., Y.Wang, Z.Di, and X.Chang. 2018, “Random Noise Attenuation by 3D Multi‐Directional Vector Median Filter.” Journal of Applied Geophysics159: 277–284.
    [Google Scholar]
19. Wu, X., S.Yan, J.Qi, and H.Zeng. 2020. “Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images.” Journal of Geophysical Research: Solid Earth125: e2020JB019685.
    [Google Scholar]
20. Xu, W., Q.Zhu, and L.Zhao. 2022. “GlitchNet: A Glitch Detection and Removal System for SEIS Records Based on Deep Learning.” Seismological Research Letters93: 2804–2817.
    [Google Scholar]
21. Zahradnik, J.2005. “Long‐Period Pulses in Broadband Records of Near Earthquakes.” Bulletin of the Seismological Society of America95: 1928–1939.
    [Google Scholar]
22. Zhao, Y. X., Y.Li, and N.Wu. 2022, “Unsupervised Dual Learning for Seismic Data Denoising in the Absence of Labelled Data.” Geophysical Prospecting70: 262–279.
    [Google Scholar]
23. Zhou, R., X.Yao, G.Hu, and F.Yu. 2021. “Learning From Unlabelled Real Seismic Data: Fault Detection Based on Transfer Learning.” Geophysical Prospecting69: 1218–1234.
    [Google Scholar]
24. Zhu, W., S. M.Mousavi, and G. C.Beroza. 2019. “Seismic Signal Denoising and Decomposition Using Deep Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing57: 9476–9488.
    [Google Scholar]