Qingzhen Wang

Title: Innovations of Qingzhen Wang in Seismic Inversion Technology

Introduction

Qingzhen Wang is a prominent inventor based in Xi'an, China. He has made significant contributions to the field of seismic inversion technology, particularly through his innovative approaches that integrate deep learning with model-driven methods. His work is characterized by a commitment to enhancing the reliability and interpretability of seismic data analysis.

Latest Patents

Qingzhen Wang holds a patent for a "Model-driven deep learning-based seismic super-resolution inversion method." This method involves several key steps: first, mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network. Second, it learns proximal operators using a data-driven method to construct the deep network ADMM-SRINet. Third, it obtains label data to train the deep network. Finally, it inverts test data using the trained deep network. This innovative approach combines the strengths of model-driven optimization and data-driven deep learning, resulting in a more reliable inversion outcome.

Career Highlights

Qingzhen Wang is affiliated with Xi'an Jiaotong University, where he continues to advance research in seismic inversion techniques. His work has garnered attention for its practical applications in geophysical exploration and resource management.

Collaborations

He collaborates with notable colleagues such as Jinghuai Gao and Hongling Chen, contributing to a dynamic research environment that fosters innovation and knowledge sharing.

Conclusion

Qingzhen Wang's contributions to seismic inversion technology exemplify the intersection of deep learning and traditional methods, paving the way for more reliable data analysis in geophysical applications. His innovative patent reflects a significant advancement in the field, showcasing his expertise and commitment to research excellence.