📝 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.