With the rapid development of artificial intelligence technology, deep learning techniques have begun to be applied in geological researchs. Compared to traditional lithofacies identification methods, the application of deep learning not only resulted in the implementation of efficient automatic lithofacies identification but also resulted in the accuracy improvement and uncertainty reduction of interpretation. Although excellent achievements in the field of single-well log identification have been obtained by using the powerful learning capability of deep learning, it is found that the feature representation of a single modality can hardly encompass the complete information of relevant geological bodies. In order to overcome the limitations of feature representation in a single modality and to optimally utilize the values contained in multiple modalities, some scholars have proposed the application of multimodal fusion method to enhance the model learning performance. Based on the previous research status, in this paper, aiming to elaborate on the application and development prospects of deep learning-related technologies in the field of lithofacies identification, we have introduced representative traditional identification techniques and those techniques integrated with the machine learning and have further highlighted the necessity and huge potential of machine learning applied in the lithofacies identification. Additionally, we proposed a fusion strategy for the combination of multimodal fusion technology with deep learning in order to provide prior knowledge and methods for related researchers. Finally, we have briefly summarized the current fusion strategies and have elaborated the existing challenges in relevant geological researches.
YANG et al. (Thu,) studied this question.