Understanding geological faults is crucial for the oil and gas industry, as it afects the production performance of reservoirs. Nevertheless, the fragmented and ambiguous nature of geological fault information hinders eficient information retrieval. Formal geological ontologies ofer a solution by enabling domain-specific data integration. One challenge that persists in ontology development is defining a set of relevant terms with good coverage used in the domain community. Based on the TF-IDF method, we conduct a term frequency study of fault-related concepts in recent academic paper abstracts. We select papers from diverse journals and evaluate terms with geologists and ontologists. The results align with experts' knowledge and contribute to the construction of a vocabulary list for the geological fault knowledge model and pave the path for thorough ontological analyses of geological faults, which facilitates data retrieval and mitigates semantic ambiguity. Future work includes improving the quality of the generated vocabulary list, implementing the proposed corpus internally, and considering more in-house technical documents for a more comprehensive coverage.
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A comprehensive understanding of a geological fault is crucial for the oil and gas industry, as it
directly influences reservoir quality, potentially leading to leaks or maintaining a seal [
The term ‘fault’ can represent a spatial arrangement structure, an abstract 2D plane, or a 3D
deformed volume [
Building formal geological ontologies stands out as a promising solution for domain-specific
data integration and retrieval in the oil and gas industry. In semantic technologies, an ontology
is a formal (machine-readable), explicit specification of a conceptualization (abstract of the
world that we want to represent in the knowledge) that is shared and agreed upon by the
domain community [
Within the geological community, various ontologies have been proposed, ranging from
core-ontology for geology [
The development of an ontology requires the involvement of ontologists, domain experts
and users to define the purpose, scope, requirements, etc [
In this paper, to support the knowledge modeling of geological fault for the energy industry, we conduct a term frequency study (Sect. 2) of the ‘fault’ concept and its related terms from academic paper abstracts. Compared to the verbose full paper, the abstract contains the most important concepts of the research objectives. Cao et al. [22] compared topics extracted from academic papers abstracts and full text and found that the similarity between results is higher when more documents are analyzed. To balance the focus and extension of fault concept, the selected papers range from domain-specific, domain-related, and industrial-related to general domain journals. We listed the renowned geoscience journals with good impact factors and citation scores, and then specialists chose the most relevant for each domain. The extracted terms are subsequently presented to geologists and ontologists to assess their alignment with the domain’s understanding (Sect. 3). The evaluation shows promising results. The entire corpus for this study is publicly available.
In our pursuit of identifying terms relevant to the Geological Fault Domain, we adapted the methodology from Garcia et al. [18]. Our approach consisted of the following steps: (i) the selection of scientific journals within the domain of interest; (ii) the compilation of a comprehensive corpus comprising abstracts; (iii) the application of TF-IDF analysis to determine the primary keywords for the Geological Fault Domain; and (iv) a final evaluation of these components: Term Frequency ( ) and Inverse Document Frequency ( ). The term frequency, denoted as (, )
, is the sum of term occurrences in document . In contrast, the inverse document frequency is given by the formula: () = [
() • represents the total number of documents in the document set. • ()
is the number of documents in the document set that contain the term .
Ultimately, the TF-IDF score for a term in document is computed as: − (, ) = (, ) ∗ () = (, ) ∗ ( [
() ] + 1)
For our TF-IDF calculation, we utilized abstracts of academic papers as our documents. Abstracts ofer distinct advantages, as they condense essential information and vocabulary into concise sentences and are accessible for a wide range of papers, including those not available as open-access. Our initial step involved the selection of relevant scientific papers. The TF-IDF method compares the vocabulary of a specific set of texts against the broader corpus. To ensure a diverse vocabulary and to balance the focus and scope of fault-related terms, we initially chose scientific journals within the Geological Fault Domain. Gradually, we expanded our selection to encompass papers from broader knowledge areas. Figure 2 illustrates the distribution of papers across diferent knowledge areas and highlights our chosen journals.
Once we compiled the corpus containing all abstracts, we preprocessed it by removing stop words, applying stemming (a technique that reduces words to their root or base form), and converting all text to lowercase. Subsequently, we calculated the TF-IDF scores for all words and expressions across all documents, considering single words (1-gram) as well as two (2-gram) and three-word (3-gram) expressions. Summing the TF-IDF values across documents within our domain of interest resulted a list of the most significant keywords.
Results. In our investigation, we compiled lists of the most important terms and expressions for all subdomains of Figure 2; Tables 1 and 2 show respectively the keywords for ‘Geological fault domain’ and ‘Geological fault with tectonic domain’. We calculate the TF-IDF value for each word of the vocabulary (and for the 2-gram and 3-gram expressions) for every paper. Then, we summed and sorted all TF-IDF values of the documents of the same domain.
Besides the lists of important keywords for the geological fault domain, this paper presents two noteworthy outcomes: 1. We have assembled a corpus comprising 4,879 scientific papers that focus on various geoscience domains. 2-gram 3-gram 2. We have demonstrated a methodology for compiling documents and extracting important keywords from them.
Evaluations. In collaboration with geologists, we analysed the alignment between the highfrequency terms in our results and the domain knowledge. For the top-20 high-frequency terms in the Geological Fault Domain (table 1), 49 of 60 terms are closely related to the knowledge of fault; in the results of Geological Fault with tectonics Domain (table 2), only 40 of 60 terms are closely related to the knowledge of fault. It’s worth noting that geologists identified some terms as “noisy,” as they are used to describe faults or are related to specific study areas.
In addition to relevance checks, there are some interesting analysis results from the discussion between geologists and ontologists. In Tables 1 and 2, the terms shear zone and fault zone, in geologists’ view, are interchangeable in the context of brittle deformation. The terms damage zone and fault core are two components of fault zone. The term fault rock shares a certain level of similarity with fault core, but not necessarily the same. The pull apart basin is the result of normal fault, and thrust fault is a type of low angle fault. These distinctions, while clear to geologists in an academic context, highlight potential semantic ambiguities in everyday usage. Such distinctions prove invaluable when geologists seek specific data and information from databases. Additionally, we also noticed that our data sources are academically biased, which contributes to the presence of certain noisy terms. strike slip fault zone normal fault shear zone slip fault damag zone nw se fold thrust thrust belt ne sw fault slip fault core slip rate thrust fault tibetan plateau pre exist stress field fault segment strain rate fractur network
This paper proposes an approach with TF-IDF to quantify the term frequency of geological faults during the conceptualization phase of ontology development. The experiment has yielded interesting results for both geologists and ontologists. Our experiment contributes to developing basic terminology for creating knowledge models of geological faults, which will facilitate the retrieval of geological information and data from various sources. The experiment results also provide a basis with good knowledge coverage for ontological analysis to help geologists and ontologists deconstruct the semantically overloaded term ‘fault’ and its various hidden meanings. In future research, we plan to 1. refine the identified terms for improving the quality of the vocabulary list; 2. incorporate more industrial and technical documents for a more comprehensive analysis; 3. conduct ontological analyses of the terms and implement the proposed corpus to support the development of Petrobras’ in-house search engine. Acknowledgments This work was partially supported by the Norwegian Research Council via SIRIUS (237898), PeTWIN (294600) and Petrobras Researcher Center (CENPES). Code availability: https://github.com/fabiocorreacordeiro/GeoscienceCorpus [18] L. F. Garcia, F. H. Rodrigues, A. Lopes, R. d. S. A. Kuchle, M. Perrin, M. Abel, What geologists talk about: Towards a frequency-based ontological analysis of petroleum domain terms., in: ONTOBRAS, 2020, pp. 190–203. [19] L. Shi, C. Jianping, X. Jie, Prospecting information extraction by text mining based on convolutional neural networks–a case study of the lala copper deposit, china, IEEE access 6 (2018) 52286–52297. [20] E.-J. Holden, W. Liu, T. Horrocks, R. Wang, D. Wedge, P. Duuring, T. Beardsmore, Geodoca– fast analysis of geological content in mineral exploration reports: A text mining approach, Ore Geology Reviews 111 (2019) 102919. [21] Y. Ma, Z. Xie, G. Li, K. Ma, Z. Huang, Q. Qiu, H. Liu, Text visualization for geological hazard documents via text mining and natural language processing, Earth Science Informatics (2022) 1–16. [22] Q. Cao, X. Cheng, S. Liao, A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research 41 (????) 543–569. URL: https://www.emerald.com/insight/content/doi/10.1108/LHT-03-2022-0144/ full/html. doi:10.1108/LHT- 03- 2022- 0144.