Labeled DAS Data Synthesis with CycleGANs

References

1. Binder, G. and Chakraborty, D. [2019]. Detecting microseismic events in downhole distributed acoustic sensing data using convolutional neural networks. In SEG Technical Program Expanded Abstracts 2019, 4864–4868.
   [Google Scholar]
2. Boitz, N. and Shapiro, S. [2024]. Detection of microseismic events in continuous DAS data using convolutional neural networks. The Leading Edge, 43(1):16–22.
   [Google Scholar]
3. Chavarria, J. A., Ugueto, G., Oukaci, Y., and Laflame, L. [2022]. Assessing single and multiwell fiber optic data for microseismic monitoring: An example from hfts2. SPE/AAPG/SEG Unconventional Resources Technology Conference, page D011S006R001.
   [Google Scholar]
4. Glubokovskikh, S., Shashkin, P., Shapiro, S., Gurevich, B., & Pevzner, R. [2023]. Multiwell fiber optic sensing reveals effects of CO 2 flow on triggered seismicity. Seismological Research Letters, 94(5), 2215–2230.
   [Google Scholar]
5. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. [2014]. Generative adversarial nets. Advances in neural information processing systems, 27.
   [Google Scholar]
6. Huot, F., Lellouch, A., Given, P., Luo, B., Clapp, R. G., Nemeth, T., Nihei, K. T., and Biondi, B. L. [2022]. Detection and characterization of microseismic events from fiber-optic DAS data using deep learning. Seismological Society of America, 93(5):2543–2553.
   [Google Scholar]
7. Porras, J., Pecci, D., Bocchini, G. M., Gaviano, S., De Solda, M., Tuinstra, K., Lanza, F., Tognarelli, A., Stucchi, E., and Grigoli, F. [2024]. A semblance-based microseismic event detector for DAS data. Geophysical Journal International, 236(3), 1716–1727. https://doi.org/10.1093/gji/ggae016
   [Google Scholar]
8. Rashid, A., Tackie-Otoo, B. N., Abdul Latiff, A. H., Otchere, D. A., Jamaludin, S. N. F., and Asfha, D. T. [2025]. Research advances on distributed acoustic sensing technology for seismology. Photonics, 12(3).
   [Google Scholar]
9. Roeser, A., Boitz, N., and Shapiro, S. [2018]. Ray tracing with adaptive step size control in inhomogeneous anisotropic media. In 80th EAGE Conference and Exhibition 2018.
   [Google Scholar]
10. Stork, A. L., Baird, A. F., Horne, S. A., Naldrett, G., Lapins, S., Kendall, J.-M., Wookey, J., Verdon, J. P., Clarke, A., and Williams, A. [2020]. Application of machine learning to microseismic event detection in distributed acoustic sensing data. Geophysics, 85(5).
    [Google Scholar]
11. Tegtow, W., Boitz, N., & Shapiro, S. [2023]. Detection of microearthquakes triggered by hydraulic fracturing using time-and frequency-domain DAS-trace stacking techniques. In XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG).
    [Google Scholar]
12. Tegtow, W. [2025]. ElasticFDSG.jl: Simulating elastic wave propagation in 2D and 3D anisotropic media. https://github.com/wtegtow/ElasticFDSG.jl.
    [Google Scholar]
13. Tegtow, W., Boitz, N., Shapiro, S. A., and Chavarria, J. A. [2024]. Detection of microseismic events in continuous DAS recordings using semantic image segmentation. The Leading Edge, 43(11):740–746.
    [Google Scholar]
14. Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., and Trujillo, J. [1998]. A comparison of select trigger algorithms for automated global seismic phase and event detection. Bulletin of the Seismological Society of America, 88(1), 95–106. https://doi.org/10.1785/BSSA0880010095
    [Google Scholar]
15. Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. [2017]. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, 2223–2232.
    [Google Scholar]
16. Zhu, W. and Beroza, G. C. [2019]. Phasenet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1):261–273.
    [Google Scholar]
17. Zhu, W., Biondi, E., Li, J., Yin, J., Ross, Z. E., and Zhan, Z. [2023]. Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning. Nature Communications, 14(1):8192.
    [Google Scholar]