报告题目： Machine Learning in Subsurface Interpretation: Bridging seismic with geology

报告时间：

（一）2021年11月24日，星期三，上午8:30-10:30

（二）2021年12月08日，星期三，上午8:30-10:30

报告人：Haibin Di（邸海滨），Schlumberger（斯伦贝谢公司）

腾讯会议 ID：609 1164 8605

报告简介：

With the growing complexity of subsurface data particular 3D seismic in both size and resolution, efficient and accurate subsurface interpretation becomes more challenging and dependent on the development of powerful computational interpretation tools capable of mimicking an experienced interpreter’s knowledge, experience, and intelligence. There have been numerous recent efforts in implementing various machine learning algorithms into resolving the challenges in subsurface interpretation, including fault detection, facies identification, property estimation and so on. In the two talks, we will start with a set of simple examples on these ML applications, then share our attempt on further constraining the learning process by physics/geology, and finally conclude with an end-to-end workflow for integrated subsurface interpretation from seismic data to geologic models.

报告人简介：

Haibin Di (hdi@slb.com) is a Senior Data Scientist in the Subsurface Data Intelligence at Schlumberger. His research interest is in implementing machine learning algorithms particularly deep neural networks into multiple seismic applications, including stratigraphy interpretation, property estimation, denoising, seismic-well tie, and new energy site characterization. He has authored over 30 journal papers, 35 conference proceedings, and 10 abstracts, edited 3 journal special issue and 1 book, and holds 7 international patents on subsurface data analysis. Haibin received PhD degree in Geology from West Virginia University in 2016, worked as a postdoctoral researcher at Georgia Institute of Technology in 2016-2018, and joined Schlumberger in 2018. He is a member of SEG research committee.