Machine learning for seismic data analysis and processing
Palavras-chave:
seismic exploration, velocities, neural networksResumo
Machine learning is setting the pace in the advancement of data analysis in many fields of science, technology, and industry. In this context, seismic data processing and inversion are approached by strategies that extract the relevant information from the data almost automatically. Dictionary learning and neural networks are two common examples of algorithms capable of capturing the complex structures and patterns embedded in data and inferring or predicting certain information of interest from them. We use a residual dictionary denoising technique to attenuate the acquisition footprint in 3D seismic data. Besides, we demonstrate some progress in using a deep neural network to invert the seismic moment tensor in well-monitoring scenarios. Machine learning also includes global optimization techniques, such as simulated annealing and differential evolution. We explore how these two algorithms can automate processes in seismic exploration such as velocity analysis and well-tying, which are conventionally done by hand and are thus susceptible to user subjectivity and experience.
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