Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification-Reference-Cited by-同舟云学术

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Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification

Published:2022-11 Issue:11 Volume:9 Page:
ISSN:2333-5084
Container-title:Earth and Space Science
language:en
Short-container-title:Earth and Space Science

Author:

Feng Bin¹²^ORCID,Chen Lirong³⁴,Xu Yongyang¹^ORCID,Zhang Yu²

Affiliation:

1. School of Geography and Information Engineering China University of Geosciences Wuhan China

2. Institute of Geophysical and Geochemical Exploration China Academy of Geological Science Langfang China

3. China Geological Survey Development and Research Center Beijing China

4. China University of Geosciences Beijing China

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022EA002626

Reference44 articles.

1. Pearson Correlation Coefficient

2. Intelligent Recognition of Ore‐Forming Anomalies Based on Multisource Data Fusion: A Case Study of the Daqiao Mining Area, Gansu Province, China

3. Page segmentation of historical document images with convolutional autoencoders

4. A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

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3. Identification of multi-mineral-species geochemical anomalies using Bayesian maximum entropy and the spectrum separable module-constrained convolutional autoencoder;Geochemistry: Exploration, Environment, Analysis;2024-02-26

4. Recognition of multivariate geochemical anomalies using a geologically-constrained variational autoencoder network with spectrum separable module – A case study in Shangluo District, China;Applied Geochemistry;2023-09

5. Image Identification and Land Use Management of Anomalous Minerals from Geophysical Information and Deep Convolutional Networks;The Journal of Geology;2023-07-01

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