10.24416/UU01-ACSDR4
Amiri, Hamed
Hamed
Amiri
0000-0002-2981-1398
Original microstructural data of altered rocks and reconstructions using generative adversarial networks (GANs)
Utrecht University
2022
Research Data
Analytical and microscopy data
rock-fluid interaction
backscattered electron microscopy
microstructural quantification
image reconstruction
generative adversarial network (GAN)
EPOS
multi-scale laboratories
statistically-equivalent microstructures
acidic_igneous_rock
ultramafic_igneous_rock
Scanning Electrone Microscope
Phase Transition
Plümper, Oliver
0000-0001-7405-1490
Vasconcelos, Ivan
0000-0001-7405-1490
Jiao, Yang
0000-0001-6501-8787
Chen, Pei-En
0000-0001-5107-6281
2022-05-20T10:47:06.000000
2020-04-01/2022-02-25
en-us
Version 1.0
Open - freely retrievable
Creative Commons Attribution 4.0 International Public License
We image two altered rock samples consisting of a meta-igneous and a serpentinite showing an isolated porous and fracture network, respectively. The rock samples are collected during previous visits to Swartberget, Norway in 2009 and Tønsberg, Norway in 2012. The objective is to employ a deep-learning-based model called generative adversarial network (GAN) to reconstruct statistically-equivalent microstructures. To evaluate the reconstruction accuracy, different polytope functions are calculated and compared in both original and reconstructed images. Compared with a common stochastic reconstruction method, our analysis shows that GAN is able to reconstruct more realistic microstructures. The data are organized into 12 folders: one containing original segmented images of rock samples, one with python codes used, and the other 10 folder containing data and individual figures used to create figures in the main publication.
European Research Council