Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM

Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM

Noise interference in Magnetotelluric (MT) signals significantly undermines the accuracy of subsurface resistivity analysis, leading to potential errors in geophysical interpretation and challenges in resource exploration. To address this critical issue, this study develops denoising models based on...

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Bibliographic Details
Main Authors: Widya Utama, Maman Hermana, Dwa D. Warnana, Wien Lestari, Muhammad N. A. Zakariah, Sherly A. Garini, Rista F. Indriani, Dhea P. Novian Putra, M Ulin Nuha Abduh, Alif N. F. Insani, Dandi Syahtia Pratama, Khairul Arifin Mohd Noh, Abdul Halim Abdul Latiff
Format: Article
Language:English
Published: Ital Publication 2025-06-01
Series:Journal of Human, Earth, and Future
Subjects:
convolutional neural network (cnn)
denoising
noise
long short-term memory (lstm)
magnetotelluric (mt)
Online Access:https://hefjournal.org/index.php/HEF/article/view/442
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https://hefjournal.org/index.php/HEF/article/view/442

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