Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025787 |
Simulation of Fluvial Patterns with GANs Trained on a Data Set of Satellite Imagery | |
E. Nesvold; T. Mukerji | |
2021-03-17 | |
发表期刊 | Water Resources Research |
出版年 | 2021 |
英文摘要 | Models that can generate realistic Earth surface patterns are important both for geomorphological applications and as prior models for underdetermined inverse problems. Generative machine learning methods such as GANs and the increasing availability of large remote sensing data sets represents an exciting combination for this purpose. Several studies show promising results for GANs trained on artificial datasets in geostatistics, but it is necessary to further quantify how well such models reproduce and generalize real data. The conditioning ability of GANs is often evaluated based on output which originates from a trained generator. In reality, geophysical data necessarily arises from elsewhere. Here, we use more realistic training data than in previous studies and evaluate performance using an extensive set of metrics and real images outside the training dataset. The dataset consists of multispectral satellite imagery of 38 large river deltas, a type of Earth surface pattern which is limited in number. The channel network is used to create training images with four sedimentary facies, which are subsequently used to train a Wasserstein GAN of deltaic 2D patterns. GANs successfully reproduce all training data characteristics and produce manifold the number of combinations with respect to the training data. However, there does not seem to be an infinite number of discrete combinations of facies, and the posterior landscapes are not well‐shaped for efficient exploration in the presence of so‐called hard data. Thus, GANs should have many exciting applications in geosciences, but it will depend on the type of measurement data. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/319855 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | E. Nesvold,T. Mukerji. Simulation of Fluvial Patterns with GANs Trained on a Data Set of Satellite Imagery[J]. Water Resources Research,2021. |
APA | E. Nesvold,&T. Mukerji.(2021).Simulation of Fluvial Patterns with GANs Trained on a Data Set of Satellite Imagery.Water Resources Research. |
MLA | E. Nesvold,et al."Simulation of Fluvial Patterns with GANs Trained on a Data Set of Satellite Imagery".Water Resources Research (2021). |
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