Reviewerly/exHarmony huggingface dataset:
High-resolution velocity image beneath Hachijojima Island from dense seismic observation data

PreviousNext No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021High-resolution velocity image beneath Hachijojima Island from dense seismic observation dataAuthors: Shotaro KankeHiroyuki AzumaYoshiya OdaShotaro KankeTokyo Metropolitan UniversitySearch for more papers by this author, Hiroyuki AzumaTokyo Metropolitan UniversitySearch for more papers by this author, and Yoshiya OdaTokyo Metropolitan UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segj2021-079.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Hachijojima Island, one of the 50 active volcanoes that is continuously monitored its activity by Japan Meteorological Agency is located near the boundary between Pacific Plate and Philippine Sea Plate. There have been no eruptions since 1605, and volcanic activity has been relatively low in recent years. However, a volcanic earthquake swarm and a crustal movement were observed in 2002. In order to interpret volcanic activity correctly, it is essential to understand the subsurface structure beneath the volcano by means of geophysical method. In this study, therefore, we have installed 46 stations of seismograph on Hachijojima Island and Hachijo-kojima Island and conducted temporary seismic observation from September 2019 to March 2020 (182 days) to image the velocity structure beneath Hachijojima Island in higher resolution. We used the travel-time tomography program, SIMULPS12 (Evans et. al., 1994) to estimate the velocity image from high density seismic data. As the results, we obtained high-resolution three-dimensional velocity image up to the depth of 8km just beneath Mt. Nishiyama. There are two high Vp anomalies at the depth of 2-5km just beneath Mt. Nishiyama. These anomalies are continuous from deeper area and could represent the detailed structure of dikes penetrated in 2002. We compared our results with previous studies, confirmed the validity of the analysis results and discussed the subsurface structure of Hachijojima Island. Keywords: natural earthquake tomography, velocity structure, dense seismic observation, Hachijojima IslandPermalink: https://doi.org/10.1190/segj2021-079.1FiguresReferencesRelatedDetails Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 349 publication data© 2021 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Nov 2021 CITATION INFORMATION Shotaro Kanke, Hiroyuki Azuma, and Yoshiya Oda, (2021), "High-resolution velocity image beneath Hachijojima Island from dense seismic observation data," SEG Global Meeting Abstracts : 299-302. https://doi.org/10.1190/segj2021-079.1 Plain-Language Summary Keywordsnatural earthquake tomographyvelocity structuredense seismic observationHachijojima IslandPDF DownloadLoading ...
Discussion on the Integration Mode of Computer Network and Business English Teaching

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Adjectives are common in natural language, and their usage and semantics have been studied broadly. In recent years, with the rapid growth of knowledge bases (KBs), many knowledge-based question answering (KBQA) systems are developed to answer users’ natural language questions over KBs. A fundamental task of such systems is to transform natural language questions into structural queries, e.g., SPARQL queries. Thus, such systems require knowledge about how natural language expressions are represented in KBs, including adjectives. In this paper, we specifically address the problem of representing adjectives over KBs. We propose a novel approach, called Adj2SP, to represent adjectives as SPARQL query patterns. Adj2SP contains a statistic-based approach and a neural network-based approach, both of them can effectively reduce the search space for adjective representations and overcome the lexical gap between input adjectives and their target representations. Two adjective representation datasets are built for evaluation, with adjectives used in QALD and Yahoo! Answers, as well as their representations over DBpedia. Experimental results show that Adj2SP can generate representations of high quality and significantly outperform several alternative approaches in F1-score. Furthermore, we publish Lark, a lexicon for adjective representations over KBs. Current KBQA systems show an improvement of over 24% in F1-score by integrating Adj2SP.
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Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning

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Design and Implementation of Intelligent Identification Meter System Based on OpenCV

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Data Link Fault Location Model Based on Machine Learning

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On the Optimal Set of Features and the Robustness of Classifiers in Radar-based Silent Phoneme Recognition

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Automatic Image Alignment and Fusion in a Digital Photomontage

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Automatic generation of substation main wiring data of power grid fault analysis

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Media E-Learning Platform Podcast on Basic Competencies Apply Promotion For Online Marketing for Vocational High School Cullinary Art Students

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