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Usage of Machine Learning Algorithms for Structural Boundaries Reconstruction Using The Non-Seismic Methods Data with Feature Selectionстатья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 8 апреля 2022 г.
- Авторы: Зайцев С.В., Ахметсафин Р.Д., Буденный С.А., Журавлев С.Д., Кисилев К.В., Орлов Р.В., Смелов А.С., Григорьев Г.С., Гулин В.Д., Ананьев В.В.
- Сборник: Data Science in Oil and Gas
- Серия: 2021
- Год издания: 2021
- Издательство: European Association of Geoscientists and Engineers
- Местоположение издательства: Netherlands
- Первая страница: 1
- Последняя страница: 6
- DOI: 10.3997/2214-4609.202156005
- Аннотация: Non-seismic methods (NSM) in geophysics are a crucial addition to classical seismic information. It helps to make decisions at early stages of geological exploration in case of limited information value conditions and provide a new knowledge about geological structure. While seismic exploration remains as the most spreading technique in field geophysics, non-seismic methods predominantly play a role of auxiliary methods, more often particular cases advocate self-sufficiency of NSM in application to exploration geophysical problems. The restoration of structural boundaries is especially important to restore structural boundaries in the space between seismic survey profiles. A simple solution in the form of interpolation does not provide the necessary prediction accuracy, and requires the creation of a complex, often nonlinear model, which is possible using machine learning (ML) methods. There is a large number of features at one measurement point – the values of the geophysical fields and their transformations (derivatives, filters in a window of different widths). The analysis of the importunateness of each feature before training the ML algorithm allows you to increase the accuracy of the constructed model.
- Добавил в систему: Зайцев Сергей Владимирович