In this paper fast methods are proposed for search the fracture zones in seismic databases on two types of data: seismic section (two-dimensional data) and seismic cube (three-dimensional data). These methods are an integral part of the mapping technology for filtering channels and large volumes of seismic data and useful for automating interpretation of heterogeneous seismic data. The proposed methods for searching the similarity of fracture zones were investigated using the Open Seismic Repository reference dataset, which contains information about geological rocks in the area of the North Sea and compared with other known methods for solving this problem, the results were discussed in the article.
In this article we discuss the approach to information extraction (IE) using neural language models. We provide a detailed overview of modern IE methods: both supervised and unsupervised. The proposed method allows to achieve a high quality solution to the problem of analyzing the relevant labor market requirements without the need for a time-consuming labelling procedure. In this experiment, professional standards act as a knowledge base of the labor domain. Comparing the descriptions of work actions and requirements from professional standards with the elements of job listings, we extract four entity types. The approach is based on the classification of vector representations of texts, generated using various neural language models: averaged word2vec, SIF-weighted averaged word2vec, TF-IDF-weighted averaged word2vec, paragraph2vec. Experimentally, the best quality was shown by the averaged word2vec (CBOW) model.
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