Entity relationship extraction for the geological domain is the basis for building a geological knowledge graph, and is very important for text information extraction and knowledge base construction in the geological domain. In view of the complexity of entity relations in geological domain and the lack of a manually annotated corpus, a joint extraction model for entity relations in geological domain is proposed, focusing on the recognition of complex overlapping relations in multiple geological texts and avoiding cascading errors caused by entity recognition errors in the traditional pipeline model. In this paper, a high-quality corpus of entity relations in the geological domain is constructed, and a pre-trained language model based on BERT (Bidirectional Encoder Representations from Transformers) and BiGRU (Bidirectional Gated Recurrent Units) is proposed. Recurrent Units and Conditional Random Field (CRF) sequence annotation models to achieve joint extraction of entity relations. Experiments were conducted on the constructed dataset, and the results showed that the F1 value of the joint extraction model proposed in this paper reached 0.671 for entity relationship extraction, which verified the effectiveness of the model in this paper for geological entity relationship extraction.