I am now a 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 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 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

• 2026.05: 🎉 Transforming Weather Data from Pixel to Latent Space was accepted by ICML 2026 as a spotlight paper.
• 2026.04: 🎉 OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion was published in Earth System Science Data.
• 2026.03: 🔥 TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology is now available on arXiv.
• 2026.03: 🔥 OpenEarth-agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation is now available on arXiv.
• 2025.12: 🎉 Deep Reparameterization for Full Waveform Inversion: Architecture Benchmarking, Robust Inversion, and Multiphysics Extension was published in Petroleum Science.

📝 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.

Petroleum Science

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

🔗Code Repository: github.com/liufeng2317/ADFWI

• DRFWI: A deep reparameterization framework for full waveform inversion.
• Architecture Benchmarking: Evaluates CNNs, U-Nets, MLPs, and related 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 adapts to tasks such as 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 on GPU with parallel computing support.
• Uncertainty Quantification: Provides uncertainty estimates for inversion results.

ESSD

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. (2026)

🔗Code Repository: github.com/liufeng2317/OpenSWI

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

🎙 Geophysical Inversion

• Online on arXiv: Dong, S., Gao, J., Li, Y., Gao, Z., Wu, B., & Liu, F. (2026). A Multi-Physics Alternating Coupled Inversion Using Gravity and Full Waveform Data in Salt Dome
• Online on arXiv: Zou, G., Li, J., Liu, F., Zheng, X., Xie, J., & Chen, G. (2025). Self-Reinforced Deep Priors for Reparameterized Full Waveform Inversion
• Published in IEEE Transactions on Geoscience and Remote Sensing: Lin, J., Zhao, Y., Liu, F., Chen, J., Li, J., & Zhang, Y. (2026). Efficient Transient Electromagnetic Inversion With Automatic Differentiation
• Published in Earth System Science Data: Liu, F., Zhao, S., Gu, X., Ling, F., Zhuang, P., et al. (2026). OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion
• Published in Petroleum Science: Liu, F., Li, Y.-X., Su, R., Huang, J.-P., & Bai, L. (2025). Deep Reparameterization for Full Waveform Inversion: Architecture Benchmarking, Robust Inversion, and Multiphysics Extension
• Published in Journal of Geophysical Research: 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 Journal of Geophysical Research: 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

• Online on OpenReview: 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
• Published in Geophysical Research Letters: Wang, X., Liu, F., Su, R., Wang, Z., Fang, L., Zhou, L., Bai, L., & Ouyang, W. (2026). SeisMoLLM: Advancing Seismic Monitoring via Cross-Modal Transfer With Pretrained Large Language Model
• 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
• 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
• 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

🛰 Remote Sensing

• Published in ICML: Zhao, S., Liu, F., Zhang, X., Chen, H., Han, T., Gong, J., Tao, R., Xiao, P., Bai, L., & Ouyang, W. (2026). Transforming Weather Data from Pixel to Latent Space
• Online on arXiv: Zhao, S., Liu, F., Zhang, X., Chen, H., Xiao, P., & Bai, L. (2025). Temporal-Spectral-Spatial Unified Remote Sensing Dense Prediction

🤖 Scientific Agents & AI for Science

• Online on arXiv: Liu, F., Xu, J., Cui, X., Wang, X., Guo, Z., et al. (2026). TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
• Online on arXiv: Zhao, S., Liu, F., Zhang, X., Chen, H., Gu, X., et al. (2026). OpenEarth-agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation
• Online on arXiv: Jiang, Y., Lou, W., Wang, L., Tang, Z., Feng, S., et al. (2025). SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
• Online on arXiv: Xu, W., Zhou, Y., Zhou, Y., Cao, Q., Li, S., et al. (2025). Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows

* Co-first authors.

🎖 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%)

📖 Education

• 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.