A SCiForest based semi-supervised learning method for the seismic interpretation of channel sand-body

Abstract

Machine learning technology has been widely applied in the field of seismic interpretation. In most cases, machine learning assisted seismic interpretation is calibrated or constrained by wells. However, due to the limitation of drilling cost, sometimes there are only a few samples can be obtained from the well-points for a specific layer, which is insufficient to guarantee the generalization ability of supervised learning. In this article, we propose a novelty semi-supervised method by combining the unsupervised isolation forest with split-selection criterion (SCiForest) algorithm and the supervised feature selection process together. The key of the proposed method is to be able to make full use of both the self-contained distribution information of multiple seismic attributes and the calibration information of limited well-points at the same time. To highlight the advantages of the proposed method referred to conventional supervised and unsupervised methods, we take the channel identification practice in the western Bohai Sea as a case study for comparison. Further discussion confirms that the proposed method can improve the visibility of channel effectively by fusing the relevant information in amplitude, frequency, and morphological attributes with limited calibration, which may provide a reliable alternative way for further machine learning assisted seismic interpretation.