Identifying the most explainable fault ruptured of the 2018 Palu-Donggala earthquake in Indonesia using coulomb failure stress and geological field report

This study investigates the relation between the Coulomb failure stress of the mainshock with the aftershocks sequence following the 2018 Palu-Donggala earthquake in Indonesia.We calculate the Coulomb failure stress using the available coseismic fault models,which had varied moment magnitudes between M_W7.53~M_W7.62.Different interpretations of the fault sources were suggested by previous studies.While two fault models suggested that one inland fault segment ruptured during the earthquake,another fault model proposed that two fault segments ruptured inland of Central Sulawesi and along the coast of Palu bay.We further overlay the positive and negative values of Coulomb failure stress with the reported relocated aftershock.We find that only by conducting Coulomb failure stress analysis,we can not favour the preference of the coseismic fault which explains aftershock distribution.This investigation demonstrates that additional observational data from geological field surveys are required to identify the surface rupture in comparison with the coseismic fault model.

Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.

Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.