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 morphologica l
attributes with limited calibration, which may provide a reliable alternative way for further
machine learning assisted seismic interpretation