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Speaker: Yantao Luo, Technology Geoscientist, SLB

Filtering out unwanted coherent noise present in the data is one of the first steps in most seismic processing workflows. Coherent noise such as large-amplitude ground roll in land data can mask the smaller-amplitude reflection signals, therefore hampering the processing and imaging tasks. Traditionally, coherent noise filtering is performed using f-k or f-x domain dip filters. However, artifacts can be introduced when transforming the data to/from f-x and f-k domains before/after the filtering. Also, they face challenges when separating dips that are close to each other in the f-k domain. In this talk, we will show a machine learning velocity/dip filtering method to separate slow and fast events directly in the x-t domain and its applications including ground roll removal, wavefield separation, and demultiple.

Speaker Biography: 
I'm a Technology Geoscientist at SLB, working on a broad range of seismic data processing techniques for both marine and land surveys. I am also heavily involved in developing new technology solutions utilizing the surface-distributed acoustic sensing (S-DAS) data. My PhD in Geophysics focused on the imaging of crust and mantle structure beneath the Appalachians, which has taught me a lot on how to squeeze as much information as possible from a little amount of data. My work in the industry so far, on the other hand, has been mostly on filtering and distilling useful information from a huge amount of data. I enjoy all aspects of playing with and generating insights from any sorts of data.

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