METHOD OF UPDATING A VELOCITY MODEL OF SEISMIC WAVES IN AN EARTH FORMATION

摘要

A method involving automated salt body boundary interpretation employs multiple sequential supervised machine learning models which have been trained using training data. The training data may consist of pairs of seismic data and labels as determined by human interpretation. The machine learning models are deep learning models, and each of the deep learning models is aimed to address a specific challenge in the salt body boundary detection. The proposed approach consists of application of an ensemble of deep learning models applied sequentially, wherein each model is trained to address a specific challenge. In one example an initial salt boundary inference as generated by a first trained first deep learning model is subject to a trained refinement deep learning model for false positives removal.