Lecture Topic: Big Data and Machine Learning Applications for Seismic Data Processing

Lecturer: Dr. Hou Song (Senior Researcher)

Organization of Lecturer: CGG (General Company of Geophysics) London Research Center

Lecture Time: Monday, March 18, 2019, 10:30 a.m.-12:00 p.m.

Lecture Location: Mingbian Building D404

Lecture Content:

In this talk, we explore machine learning technologies to assist geophysicists to analyze large seismic datasets in a more efficient way without compromising details. We show how ML allows us to go beyond conventional QC and analysis, giving us rapid, multi-scale inspection of large datasets, with the processor being able to identify the likelihood of noisy data, which can then be isolated for detailed investigation. Furthermore, we show how ML can be used to solve inverse problems and complement classical, model driven, inversion schemes. A key advantage of ML, again, is speed since after training, the ML approach provides additional solutions of the inverse problem more or less for free.

About the Lecturer:

Dr. Hou Song graduated from the University of Edinburgh with Ph.D. in 2014. He is a senior researcher at the London Research Centre of CGG (General Company of Geophysics, a French geophysics company), the lead of machine learning steering team, and a senior expert in multi-component seismic data processing. His main area of research includes the application of artificial intelligence in seismic exploration and the development of subsea seismic exploration technologies. During his tenure, he has developed over a dozen of core technologies and software modules, including machine learning and big data-based seismic data analysis software, deep neural network-based seismic data processing module, preprocessing of subsea seismic data, surface wave inversion, shallow gas detection, converted wave imaging and inversion, suppression of multiple waves, and time-lapse earthquake, etc.

All students and staff are welcome!

Host Organizations:

Sichuan Provincial Key Laboratory of Natural Gas Geology

School of Geoscience and Technology

SWPU Science and Technology Department