94: The case to adjust quicker to machine learning for geophysics

In this episode, host Andrew Geary speaks with Mehdi Aharchaou on machine learning and AI, the featured special section in October's The Leading Edge. Mehdi shares his expertise in machine learning as we explore the potential and limitations of this cutting edge research. Mehdi highlights how these papers can improve the industry and why he views explainable AI and machine learning as the key to the future. He also explains why he believes the industry needs to do more to adopt and adjust to the digital revolution. This conversation covers important ground essential for all geophysicists and businesses - do not miss it! Visit https://seg.org/podcast for the complete show notes and links to this month's special section on smart city geophysics. BIOGRAPHY Mehdi Aharchaou is a research geophysicist at ExxonMobil. During his 7 years with the company, he has worked on various cutting-edge signal processing and inversion technologies, on topics such as deghosting, noise suppression, Q compensation, source designature, trace interpolation, sparse Radon and multiple attenuation; and he has presented on some of these topics at the SEG. Prior to joining the oil and gas industry, he graduated from Georgia Tech, Rice University and INP-ENSEEIHT (Toulouse, France) with three Masters degrees in Geophysics and Electrical Engineering, and he worked on multiple real-world problems in oceanography, borehole geophysics, in addition to earthquake and exploration seismology. His interests lie in the processing, imaging, and inversion of geophysical data. Recently, his interests evolved towards the integration of machine learning and artificial intelligence to automate the geophysical exploration workflow. CREDITS Original music by Zach Bridges. This episode was hosted, edited, and produced by Andrew Geary at 51 features, LLC. Thank you to the SEG podcast team: Ted Bakamjian, Jennifer Crockett, Ally McGinnis, and Mick Swiney.