First practical application that I know of using the next thing after TPUs (Tensor Processing Units): ASICs or Application Specific Integrated Circuit. Ideal dedicated hardware for the massive seismic data and processing.
Machine Learning (ML) algorithms build a mathematical model based upon representative sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task. I limit my discussion here to supervised learning in the context of a potential application to seismic data image processing of a real marine seismic dataset, and then discuss how the computational scale of such exercises reinforce the need to develop computing technology that is customized for large ML problems.
Found this interesting article in LinkedIn:
WORKING WITH 3D SEISMIC DATA IN PYTHON USING SEGYIO AND NUMPY (MOSTLY) by Matteo Nicoli. It comes with code, Python notebook and repository.
Keywords: segyio, seismic, python, notebook
Seismic report from the 1.2 terabytes file.
(/files/ST0202 Volve 4C FFOR.pdf)
Volve dataset. Seismic report from the 2.6 terabytes file.
I was able to selectively download the report for the seismic acquisition in file
Volve_Seismic_ST10010.zip. Thanks Yogendra Narayan Pandey for the Azure Explorer tip.
Does anyone with seismic expertise notice information of relevance?