吴思弘 Sihong Wu 🏄🏻‍♀

吴思弘 Sihong Wu 🏄🏻‍♀

Postdoctoral Research Fellow

Publications 📝

You can also find my articles on my Google Scholar profile.

Journal articles

  1. Wu, S., Sun, J. & Chen, J., 2025. Variational inference for geophysical inverse problems using normalization flows: an unsupervised approach to electromagnetic data inversion. Geophysical Journal International, 242(3), ggaf239. [Link]
  2. Wu, S., Thoram, S., Sun, J., Sager, W.W. & Chen, J., 2025. Characterizing Marine Magnetic Anomalies: A machine learning approach to advancing the understanding of oceanic crust formation. Journal of Geophysical Research: Solid Earth, 130(2), e2024JB030682. [Link]
  3. Wu, S., Huang, Q. & Zhao, L., 2024. Physics-informed deep learning-based inversion for airborne electromagnetic data. Geophysical Journal International, 238, 1774-1789. [Link]
  4. Wu, S., Huang, Q. & Zhao, L., 2023. Fast Bayesian inversion of airborne electromagnetic data based on the invertible neural network. IEEE Transactions on Geoscience and Remote Sensing, 61, 5907211. [Link]
  5. Wu, S., Huang, Q. & Zhao, L., 2023. A deep learning-based network for the simulation of airborne electromagnetic responses. Geophysical Journal International, 233, 253-263. [Link]
  6. Wu, S., Huang, Q. & Zhao, L., 2022. Instantaneous inversion of airborne electromagnetic data based on deep learning. Geophysical Research Letters, 49(10), e2021GL097165. [Link]
  7. Wu, S., Huang, Q. & Zhao, L., 2021. Convolutional neural network inversion of airborne electromagnetic data. Geophysical Prospecting, 69(8-9), 1761-1772. [Link]
  8. Wu, S., Huang, Q. & Zhao, L., 2021. De-noising of transient electromagnetic data based on the long short-term memory-autoencoder. Geophysical Journal International, 224(1), 669-681. [Link]
  9. Huang, Q., Wu, S. & Xue, J., 2025. Data science and machine learning in geo-electromagnetics: A review. Surveys in Geophysics, accepted. [Link]
  10. Su, Y., Wu, S., Sun, J., Wu, X., Huang, Y., Chen, J., Lu, L., Wei, X. & Christiansen, R., 2025. Natural hydrogen exploration by joint sparse inversion of geophysical measurements and integrated geological interpretation. International Journal of Hydrogen Energy, 173, 151040. [Link]
  11. Xue, J., Huang, Q., Wu, S., Zhao, L. & Ma, B., 2024. Real-time dual-parameter full-waveform inversion of GPR data based on robust deep learning. Geophysical Journal International, 238, 1755-1771. [Link]
  12. Xue, J., Huang, Q., Wu, S. & Zhao, L., 2024. Detection of ULF geomagnetic anomalies prior to the Tohoku-Oki Earthquake by the multi-reference station method. IEEE Transactions on Geoscience and Remote Sensing, 62, 5910009. [Link]
  13. Xue, J., Wu, S., Huang, Q., Zhao, L., Sarlis, N. V. & Varotsos, P. A., 2023. RASE: A real-time automatic search engine for anomalous seismic electric signals in geoelectric data. IEEE Transactions on Geoscience and Remote Sensing, 61, 5905911. [Link]
  14. Xue, J., Huang, Q., Wu, S. & Nagao, T., 2022. LSTM-autoencoder network for the detection of seismic electric signals. IEEE Transactions on Geoscience and Remote Sensing, 60, 5917012. [Link]
  15. Wang, K., Huang, Q. & Wu, S., 2020. Application of long short-term memory neural network in geoelectric field data processing. Chinese Journal of Geophysics (in Chinese), 63(8), 3015-3024. [Link]

Conference Papers

  1. Wu, S., Sun, J. & Chen, J. 2025. Australia’s shallow subsurface revealed: Fast continental-scale probabilistic inversion of AusAEM data via deep learning. In AGU (American Geophysical Union) Annual Meeting Abstracts.
  2. Chen, J., Wu, S., Wang, L., Chen, C. & Sun, J. 2025. Interpretable high-resolution mapping of terrestrial water storage dynamics on the Qinghai–Tibet Plateau based on deep learning. In AGU Annual Meeting Abstracts.
  3. Kietzmann, J.,Wu, S., Sun, J. & Niu, Q., 2025. Critical zone subsurface structure characterization of a mountainous catchment using seismic refraction and drone-based electromagnetics. In AGU Annual Meeting Abstracts.
  4. Zhou, C., Wu, S., Sun, J., Chen, J. & Huang, Y., 2025. Deep learning-based probabilistic imaging of Yellowstone’s hydrothermal system from AEM Data. In AGU Annual Meeting Abstracts.
  5. Wu, S., Sun, J. & Chen, J., 2025. An unsupervised deep learning approach to geophysical Bayesian inversion using normalizing flows. In the International Meeting for Applied Geoscience & Energy (IMAGE) Abstracts.
  6. Su, Y., Wu, S., Sun, J., Wu, X., Huang, Y., Chen, J., Lu, L., Wei, X. & Christiansen, R., 2025. Geophysical targeting of serpentinite for geologic hydrogen by joint analysis of gravity and magnetic data. In IMAGE Abstracts.
  7. Wu, S., Thoram, S., Sun, J., Sager, W. W. & Chen, J., 2024. Transforming the interpretation of marine magnetic anomalies through a machine learning-based framework. In AGU Annual Meeting Abstracts.
  8. Wu, S., Sun, J. & Chen, J., 2024. Fast model uncertainty evaluation of airborne frequency-domain electromagnetic data inversion based on deep learning. In AGU Annual Meeting Abstracts.
  9. Su, Y., Wu, S., Chen, J., Sun, J. & Lu, L., 2024. Identifying natural hydrogen reservoirs through integrated 3D aeromagnetic and gravity data inversion in Bartlett Springs fault zone in north California. In AGU Annual Meeting Abstracts.
  10. Sun, J., Wu, S., Chen, J. & Yin, Z., 2024. Bayesian inference of airborne electromagnetic data based on normalizing flows. In AGU Annual Meeting Abstracts.
  11. Huang, Q., Xue, J. & Wu, S., 2024. Data science and machine learning in geo-electromagnetics. In EM Induction Workshop Abstracts.
  12. Wu, S., Sun, J. & Chen, J., 2024. Stochastic inversion of frequency-domain airborne electromagnetic data based on deep learning. In IMAGE Abstracts.
  13. Kalu, D. V., Wu, S. & Sun, J., 2024. Empowering mineral exploration: Leveraging invertible neural networks for magnetotelluric data inversion and uncertainty quantification. In IMAGE Abstracts.
  14. Bittar, G., Su, Y., Wu, S., Sun, J., Wu, X., Huang, Y. & Chen, J., 2024. Fast inversion and uncertainty quantification of electromagnetic well logging data using invertible neural network. In IMAGE Abstracts.
  15. Wu, S., Sun, J. & Chen, J., 2024. Airborne electromagnetic data interpretation with deep learning-based stochastic inversion and posterior distribution clustering with application to salinization detection. In International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications (GEM) Abstracts.
  16. Wu, S., Huang, Q. & Zhao, L., 2023. Simultaneous resistivity imaging of airborne electromagnetic data based on deep learning. In JpGU (Japan Geoscience Union) Geoscience Union Meeting Abstracts.
  17. Wu, S., Huang, Q. & Zhao, L., 2023. Near real-time subsurface structure imaging using airborne electromagnetic data based on deep learning. In EGU (European Geosciences Union) General Assembly Abstracts.
  18. Wu, S., Huang, Q. & Zhao, L., 2022. Near real-time resistivity imaging from airborne electromagnetic data based on deep learning. In CGU (Chinese Geosciences Union) Annual Meeting Abstracts.
  19. Wu, S., Huang, Q. & Zhao, L., 2021. 1-D inversion of airborne transient electromagnetic data based on convolutional neural network. In CGU Annual Meeting Abstracts.
  20. Wu, S., Huang, Q. & Zhao, L., 2021. Convolutional neural network inversion of airborne transient electromagnetic data. In CIGEW (China International Geo-Electromagnetic Workshop) Abstracts.
  21. Wu, S., Huang, Q. & Zhao, L., 2020. De-noising of transient electromagnetic data based on the LSTM-autoencoder. In CGU Annual Meeting Abstracts.
  22. Wu, S. & Huang, Q., 2019. De-noising of transient electromagnetic data based on the LSTM- autoencoder. In CIGEW Abstracts.