# Construction of a Geological Fault Corpus and Named Entity Recognition
###### Abstract
Research on named entity recognition dates back several years, with early work primarily focused on rule-based and dictionary-based extraction methods [12]. The rule template-based approach generally involves manually writing extraction rules by domain experts or linguists. Although this method offers from low recall, limited scalability and poor portability of rules, and high cost [13]. In contrast, dictionary-based extraction methods use string matching to extract entities. While the accuracy of entity extraction is high, the large number of entities in the generic domain makes it challenging to construct a comprehensive entity dictionary, which limits its effectiveness for domain-specific entity extraction [14]. As extraction methods have advanced, some scholars have proposed statistical learning-based approaches that require pre-labelling a corpus and applying statistical methods to train probabilistic models. These models then predict whether individual segments of the text are entities, enabling the extraction of information from unlabeled data [15; 16]. With the widespread application of deep learning in natural language processing, deep neural networks have been successfully utilized in named entity recognition, yielding remarkable results [17; 18]. Compared with traditional models, deep learning-based methods directly use word vectors from text as inputs and achieve end-to-end named entity recognition through neural networks. This approach eliminates the reliance on manually defined features, marking it the dominant research direction today. Currently, neural networks used for named entity recognition mainly include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Hybrid Neural Network based on Transformer model. In the era of big data, deep learning offers new opportunities for information extraction tasks in the geological field. The BiGRU-CRF model constructed by [PERSON] et al. [19] targets geohazards for named entity recognition and circumvents the laborious process of manually constructing a corpus; however, the model is intricate to construct and requires a protracted period of time to train. The Att-BiLSTM-CRF model proposed by [PERSON] et al. [20] is able to extract geoscientific information from geoscientific reports; however, the model suffers from the problem of low accuracy in the recognition of long entities. In order to enhance the ability of deep learning models in feature extraction, [PERSON] et al. [21] proposed BERT, a pre-training model based on the Transformer mechanism. The purpose of the pre-training model is to improve the overall recognition effect of the model by learning a priori semantic knowledge from a large number of unlabelled corpora through a pre-training task. Despite the fact that BERT has been applied in the field of natural language processing, open-source BERT models are usually pre-trained based on general-purpose domain texts, which is difficult to be directly applicable to specific domains containing a large number of specialized terms and grammatical structures. To address the issues of the lack of fine-grained corpus in geological domain as well as the low specialization and recognition accuracy of existing models, this paper first analyzes the linguistic characteristics of six fault investigation reports, formulates a fault domain knowledge system, and constructs a fine-grained fault corpus. Secondly, building upon previous research, BERT is integrated into the BiLSTM-CRF model, resulting in the development of BERT-BiLSTM-CRF model for named entity recognition in tomographic survey texts. To evaluate the model's performance, a comparative analysis is conducted using the same corpus, comparing the Bert-based model with a non-pre-trained model. The results show that the fault corpus constructed by the authors and collaborators demonstrates better applicability, and the proposed model outperforms the baseline model in terms of recognition accuracy.