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Generating land gravity anomalies from satellite gravity observations using PIX2PIX GAN image translation

Generative Adversarial Networks (GANs), specifically the Pix2Pix GAN, are used to effectively map gravity anomalies from satellite to ground, and adapt the Pix2Pix GAN model for large-scale data transformation. The impact of varying patch sizes on model performance is investigated using key metrics to ensure improved accuracy in gravity anomaly mapping. The model used 2728 satellite, and 2728 ground Bouguer gravity anomaly images from northern and northeast part of Ethiopia. 5456 images were used for training and 552 for testing. The findings indicate that Intermediate patch sizes, particularly 70 x 70 pixels, significantly enhanced model accuracy by capturing global features and contextual information. Additionally, models incorporating L2 loss with LcGAN demonstrated superior performance across qualitative metrics compared to those with L1 loss. The study will contribute to improve geophysical exploration by providing an alternative method that generates more accurate gravity maps, thereby enhancing the precision of geological models and related applications.

Ketersediaan
218551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
9 hlm PDF, 3.426 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.24, December 2024
Subjek
Gravity anomaly
Pix2Pix GAN
GAN and image-to-image translation
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Generating land gravity anomalies from satellite gravity observations using PIX2PIX GAN image translation
    Generative Adversarial Networks (GANs), specifically the Pix2Pix GAN, are used to effectively map gravity anomalies from satellite to ground, and adapt the Pix2Pix GAN model for large-scale data transformation. The impact of varying patch sizes on model performance is investigated using key metrics to ensure improved accuracy in gravity anomaly mapping. The model used 2728 satellite, and 2728 ground Bouguer gravity anomaly images from northern and northeast part of Ethiopia. 5456 images were used for training and 552 for testing. The findings indicate that Intermediate patch sizes, particularly 70 x 70 pixels, significantly enhanced model accuracy by capturing global features and contextual information. Additionally, models incorporating L2 loss with LcGAN demonstrated superior performance across qualitative metrics compared to those with L1 loss. The study will contribute to improve geophysical exploration by providing an alternative method that generates more accurate gravity maps, thereby enhancing the precision of geological models and related applications.
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