Joint gravity and magnetic inversion using CNNs’ deep learning

Zhijing, B.; Wang, Y.; Wang, C.; Yu, C.; Lukyanenko, D.; Stepanova, I.; Yagola, A.

Статья опубликована в высокорейтинговом журнале

Информация о цитировании статьи получена из Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 15 мая 2024 г.

Авторы: Zhijing Bai, Wang Yanfei, Wang Chenzhang, Yu Caixia, Lukyanenko Dmitry, Stepanova Inna, Yagola Anatoly
Журнал: Remote Sensing
Том: 16
Номер: 7
Год издания: 2024
Издательство: MDPI
Местоположение издательства: Basel, Switzerland
Номер статьи: 1115
DOI: 10.3390/rs16071115
Аннотация: Enhancing the reliability of inversion results has always been a prominent issue in the field of geophysics. In recent years, data-driven inversion methods leveraging deep neural networks (DNNs) have gained prominence for their ability to address non-uniqueness issues and reducecomputational costs compared to traditional physically model-driven methods. In this study, we propose a GMNet machine learning method, i.e., a CNN-based inversion method for gravity and magnetic field data. This method relies more on data-driven training, and in the prediction phase after the model is trained, it does not heavily depend on a priori assumptions, unlike traditional methods. By forward modeling gravity and magnetic fields, we obtain a substantial dataset to train the CNN model, enabling the direct mapping from field data to subsurface property distribution. Applying this method to synthetic data and one-field data yields promising inversion results.
Добавил в систему: Ягола Анатолий Григорьевич

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Joint gravity and magnetic inversion using CNNs’ deep learningстатья

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