I am now a joint Ph.D. candidate in the School of Electronic Information and Electrical Engineering at Shanghai Jiao Tong University, advised by Rui Zhao. I am also fortunate to be co-advised by Rui Su and Lei Bai at the Shanghai Artificial Intelligence Laboratory.

I received my B.S. degree in Geophysics from the School of Earth and Space Sciences at the University of Science and Technology of China, where I was advised by Prof. Junlun Li.

My research interests lie in developing AI-driven methods for geophysical data analysis, with a particular focus on seismic data processing, remote sensing interpretation, and geophysical inversion techniques, including Full Waveform Inversion and Surface Wave Inversion.

🔥 News

• 2025.08: 🔥 OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion now available on arXiv.
• 2025.08: 🎉 DispFormer: A Pretrained Transformer Incorporating Physical Constraints for Dispersion Curve Inversion accepted by JGR: Machine Learning & Computation.
• 2025.04: 🔥 Deep Reparameterization for Full Waveform Inversion: Architecture Benchmarking, Robust Inversion, and Multiphysics Extension now available on arXiv.
• 2025.03: 🎉 Automatic Differentiation-Based Full Waveform Inversion With Flexible Workflows published in JGR: Machine Learning & Computation.
• 2025.02: 🎉 Detailed documentation for ADFWI released. ADFWI — welcome to star and fork!

📝 Publications

JGR M&C

Automatic differentiation‐based full waveform inversion with flexible workflows
Liu, F., Li, H., Zou, G., & Li, J. (2025)

🔗Code Repository: github.com/liufeng2317/ADFWI

• ADFWI: A PyTorch-based full waveform inversion framework leveraging automatic differentiation.
• Multi-Physics: Supports acoustic, elastic, and anisotropic wave propagation.
• Multi-Workflow: Integrates diverse misfit functions, regularization methods, and optimizers for flexible inversion design.
• Multi-Platform: Runs on both CPU and GPU with parallel computing support.

Arxiv

Deep Reparameterization for Full Waveform Inversion: Architecture Benchmarking, Robust Inversion, and Multiphysics Extension (Version 2)
Liu, F., Li, Y., Su, R., Huang, J., & Bai, L. (2025)

🔗Code Repository: [github.com/liufeng2317/ADFWI]

• DRFWI: A deep reparameterization framework for full waveform inversion (FWI).
• Architecture Benchmarking: Evaluates CNNs, UNets, MLPs, and other architectures for model reparameterization.
• Robustness: Assesses inversion performance under varying noise levels and sparse acquisition.
• Multi-Physics Extension: Introduces a backbone-branch structure for multiparameter inversion, reducing cross-parameter interference.

JGR M&C

DispFormer: A Pretrained Transformer Incorporating Physical Constraints for Dispersion Curve Inversion
Liu, F., Deng, B., Su, R., Bai, L., & Ouyang, W. (2025)

🔗Code Repository: github.com/liufeng2317/DispFormer

• DispFormer: A pretrained transformer for flexible dispersion curve inversion, from global synthesis to regional applications.
• Zero-shot: Directly inverts dispersion curves of arbitrary lengths without target-specific training.
• Few-shot: Outperforms state-of-the-art global search methods with minimal training data.
• Multi-Physics Extension: Easily adaptable to tasks like 1D electrical inversion, receiver function inversion, and multi-modal surface wave inversion.

GJI

Multimodal surface wave inversion with automatic differentiation
Liu, F., Li, J., Fu, L., & Lu, L. (2024)

🔗Code Repository: github.com/liufeng2317/ADsurf

• ADsurf: A PyTorch-based framework for multimodal surface wave inversion using automatic differentiation.
• High-Performance: Optimized using GPU with parallel computing support (10x faster than CPU).
• Uncertainty Quantification: Provides uncertainty estimates for inversion results.

Arxiv

OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion
Liu, F., Zhao, S., Gu, X., Ling, F., Zhuang, P., Li, Y., Su, R., Fang, L., Zhou, L., Huang, J., & Bai, L. (2025)

🔗Code Repository: github.com/liufeng2317/OpenSWI

• Massive-Scale: 22M shallow and 1.26M deep synthetic samples, plus real-world observations.
• Comprehensive Coverage: From near-surface geology to deep Earth structures.
• Benchmark Standard: Unified dataset for developing and evaluating AI-based inversion methods.
• Open & Reproducible: Generated via the SWIDP pipeline and fully released with code and examples.

🎙 Geophysical Inversion

• Online on arXiv: Liu, F., Zhao, S., Gu, X., Ling, F., Zhuang, P., Li, Y., Su, R., Fang, L., Zhou, L., Huang, J., & Bai, L. (2025). OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion
• Online on arXiv: Liu, F., Li, Y., Su, R., Huang, J., & Bai, L. (2025). Deep reparameterization for full waveform inversion: Architecture benchmarking, robust inversion, and multiphysics extension
• Accept by JGR: Machine Learning and Computation: Liu, F., Deng, B., Su, R., Bai, L., & Ouyang, W. (2025). DispFormer: A Pretrained Transformer Incorporating Physical Constraints for Dispersion Curve Inversion
• Published in JGR: Machine Learning and Computation: Liu, F., Li, H., Zou, G., & Li, J. (2025). Automatic differentiation‐based full waveform inversion with flexible workflows
• Published in Geophysical Journal International: Liu, F., Li, J., Fu, L., & Lu, L. (2024). Multimodal surface wave inversion with automatic differentiation

🎙 Seismic Monitoring

• Submitted to NeurIPS 2025: Wang, F., Chen, M., He, X., Zhang, Y.-F., Liu, F., et al. (2025). OmniEarth-bench: Towards holistic evaluation of Earth’s six spheres and cross-spheres interactions with multimodal observational earth data
  • Contribution: Manuscript revision and partial contribution to data collection and curation.
• Submitted to GRL: Wang, X., Liu, F., Su, R., Wang, Z., Bai, L., & Ouyang, W. (2025). SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model
  • Contribution: Responsible for manuscript revision and overall coordination.
• Online on arXiv: Zhang, T., Liu, F., Yuan, Y., Su, R., Ouyang, W., & Bai, L. (2024). Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning
  • Contribution: Designed and implemented the main framework and code.
• Published in Bulletin of the Seismological Society of America: Han, X., Li, Z., Liu, F., Li, J., & Yao, H. (2025). Real-time local shear-wave splitting measurement: Application to the vicinity of the baihetan hydropower plant
  • Contribution: Provided the real-time monitoring code.
• Published in Earthquake Research Advances: Li, J., Yao, H., Wang, B., Yang, Y., Hu, X., Zhang, L., Ye, B., Yang, J., Li, X., Liu, F., Chen, G., Guo, C., & Yang, W. (2022). A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi MS 6.4 aftershock monitoring in southwest China
  • Contribution: Designed and implemented the main framework and code.

🎙 Remote Sensing

• Submitted to NeurIPS 2025: Zhao, S., Liu, F., Zhang, X., Chen, H., Xiao, P., & Bai, L. (2025). Temporal-Spectral-Spatial Unified Remote Sensing Dense Prediction
  • Contribution: Methodology, Manuscript writting and revision.
• Submitted to AAAI 2025: Zhao, S., Liu, F., Zhang, X., Chen, H., Han, T., Gong, J., Tao, R., Xiao, P., Bai, L., & Ouyang, W. (2025). Transforming weather data from pixel to latent space (Version 2)
  • Contribution: Methodology, Manuscript writting and revision.

🎖 Honors and Awards

• 2024.06: Outstanding Graduates of University of Science and Technology of China (master)
• 2021.06: Outstanding Graduates of Jilin University (bachelor)
• 2021.05: President’s Scholarship of Jilin University
• 2021.05: Nomination Award for Top Ten Self-Reliant and Self-Improving College Students of Jilin University (10 candidates)
• 2020.11: National Scholarship (Undergraduate)(Top 1%)
• 2019.11: National Scholarship (Undergraduate)(Top 1%)
• 2018.11: National Scholarship (Undergraduate)(Top 1%)

📖 Educations

• 2024.09 - Now, PhD, Information and Communication Engineering, Shanghai Jiao Tong University
• 2021.09 - 2024.06, Master, Geophysics, University of Science and Technology of China
• 2017.09 - 2021.06, Bachelor, Exploration Technology and Engineering, Jilin University

💻 Open-source Projects and Datasets

📚 Projects

• ADFWI : An Automatic Differentiation-based Waveform Inversion Framework Implemented in PyTorch.
• ADsurf : An automatic differentiation based (AD-based) multimodal surface wave inversion tools
• DispFormer : A pretrained dispersion curve inversion network that can handle dispersion data of any length, offering flexibility and efficiency for a wide range of applications.
• OpenSWI : A massive-scale benchmark dataset for surface wave inversion.

📊 Datasets

• OpenSWI-dataset : A massive-scale benchmark dataset for surface wave inversion.

• DispFormer-dataset:: A benchmark dataset for surface wave dispersion curve inversion.