# Construction of a Geological Fault Corpus and Named Entity Recognition
###### Abstract  
The rapid and effective extraction of fault entities is a fundamental process in constructing a fault knowledge graph. As a key method for recording and preserving fault data, a fault investigation report holds significant potential for extracting valuable information. This paper proposes a fault knowledge annotation system that incorporates geographic information, fault attribute, fault structure, fault activity, fault geomorphology, and fault hazard. The system is developed based on a comprehensive analysis of the textual characteristics of fault investigation reports. Additionally, we establish a fine-grained corpus tailored for this task and apply a combination of BERT and BiLSTM-CRF for named entity recognition in the fault domain. We compare the performance of our model with a non-pre-training baseline model. The experimental results demonstrate that (1) the F1 value of entity recognition based on the faulty corpus exceeds 80%, which validates the efficacy of the faulty corpus; (2) the BERT model can effectively utilize available information. The corpus to adjust the subsequent tasks, thus improving the model output; (3) the proposed BERT-BiLSTM-CRF model and ALBERT-BiLSTM-CRF models have superior extraction performance in comparison to the no-pre-training model. This study not only provides a theoretical basis for the effectiveness of the BERT-BiLSTM-CRF model in fault entity identification, but also establishes a solid data foundation for the subsequent construction of the fault knowledge map. In addition, it offers reliable technical support for practical application areas such as geological surveys, disaster early warning, and urban planning, thereby promoting the advancement of data-driven research in the field of geology. k 2024 2025 15 2465 2  
mine valuable information from geological data and apply it to geoscience research [1]. In 2019, China launched the 'International Big Science Programme on Deep-Time Digital Earth' to establish the world's largest geoscientific database and build the'Deep-time Digital Earth' (DDE) with the support of big data related to Earth's evolution. DDE aims to advance the development of intelligent, information-driven Earth science research, which has garnered significant attention in the academic community [2; 3]. One of the key objectives of DDE is the creation of a shared geoscientific knowledge graph, a task that has been widely recognized as central to the project's goals [4; 5]. A knowledge graph is a branch of artificial intelligence that plays a crucial role in intelligent decision-making systems, recommender system, and question-answering systems. Entity extraction is an important technique for constructing large-scale knowledge graphs, as it enables the extraction of knowledge from diverse textual data sources, which is then integrated into the knowledge graph, providing foundational data for its development. Building a high-quality corpus can effectively improve the accuracy of named entity recognition [6]. In the field of geology, scholars have constructed various specialized corpora, including a stratigraphic corpus [7], a mineral corpus [8], a geological entity corpus [9], and a geological time entity corpus [10]. Existing studies primarily focus on coarse-grained geological entities such as minerals, stratigraphy, and geological time. These limited geological entity types are insufficient for addressing the complex and diverse natural language processing tasks that arise in geological research. Therefore, it is necessary to construct a corpus of fine-grained fault entities containing fault domain knowledge. Entity extraction, also known as named entity recognition (NER), refers to the process of extracting relevant entity information from text [11]. In the fault domain, the task of named entity recognition involves identifying key concepts from fault geological survey reports, such as fault name, physical properties, fault structure, and stratigraphic lithology, and classifying them into predefined categories.