AVO ANOMALY DETECTION BY ARTIFICIAL NEURAL NETWORK

摘要

Artificial neural networks (ANN) have recently attracted attention for their ability to "learn" and "estimate" the mapping relationships between the data (Liu et al., 1998). In order to detect amplitude variation with offset (AVO) anomalies due to presence of hydrocarbon, artificial neural network were trained to learn the relationship between a near-offset partially stacked trace and a far-offset partially stacked trace for non-hydrocarbon bearing rocks. Far traces can then be predicted by this learned relationship and the difference between observed and ANN predicted traces can potentially be used as a hydrocarbon indicator. An advantage of this method over conventional cross-plotting techniques is that it can be made insensitive to incorrect normal moveout corrections.