Kaixing Huang

Title: Kaixing Huang: Innovator in Seismic Prediction Methods

Introduction

Kaixing Huang is a prominent inventor based in Chengdu, China. He has made significant contributions to the field of seismic prediction, particularly in the analysis of total organic carbon (TOC) in shale formations. His innovative approach combines advanced neural network techniques with seismic data to enhance the accuracy of geological assessments.

Latest Patents

Kaixing Huang holds a patent for a "Seismic quantitative prediction method for shale TOC based on sensitive parameter volumes." This method involves determining a target stratum for TOC measurement, analyzing logging curves that correlate with TOC contents, and identifying sensitive parameters. The process includes constructing sample data, training a radial basis function (RBF) neural network, and utilizing seismic data to predict TOC content effectively.

Career Highlights

Huang is affiliated with Chengdu University of Technology, where he continues to advance research in seismic prediction methods. His work has garnered attention for its innovative use of neural networks in geological applications. With a focus on enhancing the accuracy of TOC predictions, Huang's contributions are paving the way for improved resource exploration techniques.

Collaborations

Kaixing Huang collaborates with notable colleagues, including Chaorong Wu and Cheng Liu. Their combined expertise fosters a dynamic research environment that encourages innovation and the development of new methodologies in seismic analysis.

Conclusion

Kaixing Huang's work exemplifies the intersection of technology and geology, showcasing how innovative methods can lead to significant advancements in resource exploration. His contributions to seismic prediction are invaluable, and his ongoing research promises to further enhance the field.