Research on the normalisation method of logging curves: taking XJ Oilfield as an example

During the long-term exploration and development of the oilfield, it is difficult to ensure that all well logging curves are measured by the same type of instrument, the same calibration standard and the same operation mode, For different wells, there must be systematic errors caused by these reasons. Therefore, in addition to environmental correction, it is necessary to standardise logging curves. In XJ oilfield, three logging companies use wireline logging and logging while drilling to complete logging, in multi-well logging interpretation. To eliminate the systematic errors of different measuring tools, to maximise the geological information reflected by logging curves, and to make logging interpretation follow the same standard as much as possible, it is necessary to standardise the logging curve in the whole oilfield. This article takes the standardisation of well 106 in XJ oilfield as an example, the standardisation of different methods was compared, the method of combining frequency histogram and mean variance is better.

An improved discrete elliptic imaging algorithm based on guided waves for defect localisation in curved plates

Many effective image-based algorithms are available for detecting and locating defects in flat plate-like structures. However, there are few studies on image-based algorithms for curved plate structures. An improved method is proposed in this article, which is based on the discrete elliptic imaging algorithm and the improved signal normalisation method. To verify its effectiveness, experiments using a circular array are conducted on curved plates with different degrees of bending. The experimental results show that the improved method can accurately locate defects in curved plates and, compared with the original discrete elliptic imaging algorithm, its range of error in locating a single defect is reduced from 20.2-33.3 mm to 2.7-5.1 mm.

HYPHEN

Narrative clinical records and biomedical articles constitute rich sources of information about phenotypes, i.e., markers distinguishing individuals with specific medical conditions from the general population. Phenotypes help clinicians to provide personalised treatments. However, locating information about them within huge document repositories is difficult, since each phenotypic concept can be mentioned in many ways. Normalisation methods automatically map divergent phrases to unique concepts in domain-specific terminologies, to allow location and linking of all mentions of a concept of interest. We have developed a hybrid normalisation method (HYPHEN) to handle concept mentions with wide ranging characteristics, across different text types. HYPHEN integrates various normalisation techniques that handle surface-level variations (e.g., differences in word order, word forms or acronyms/abbreviations) and lexical-level variations (where terms have similar meanings, but potentially unrelated forms). HYPHEN achieves robust performance for both biomedical academic text and narrative clinical records, and has the ability to significantly outperform related methods.

Modernising historical Slovene words

We propose a language-independent word normalisation method and exemplify it on modernising historical Slovene words. Our method relies on character-level statistical machine translation (CSMT) and uses only shallow knowledge. We present relevant data on historical Slovene, consisting of two (partially) manually annotated corpora and the lexicons derived from these corpora, containing historical word–modern word pairs. The two lexicons are disjoint, with one serving as the training set containing 40,000 entries, and the other as a test set with 20,000 entries. The data spans the years 1750–1900, and the lexicons are split into fifty-year slices, with all the experiments carried out separately on the three time periods. We perform two sets of experiments. In the first one – a supervised setting – we build a CSMT system using the lexicon of word pairs as training data. In the second one – an unsupervised setting – we simulate a scenario in which word pairs are not available. We propose a two-step method where we first extract a noisy list of word pairs by matching historical words with cognate modern words, and then train a CSMT system on these pairs. In both sets of experiments, we also optionally make use of a lexicon of modern words to filter the modernisation hypotheses. While we show that both methods produce significantly better results than the baselines, their accuracy and which method works best strongly correlates with the age of the texts, meaning that the choice of the best method will depend on the properties of the historical language which is to be modernised. As an extrinsic evaluation, we also compare the quality of part-of-speech tagging and lemmatisation directly on historical text and on its modernised words. We show that, depending on the age of the text, annotation on modernised words also produces significantly better results than annotation on the original text.