Open AccessJournal Article10.3390/app13137952

High-Performance Computing and Artificial Intelligence for Geosciences

- 07 Jul 2023

- Vol. 13, Iss: 13, pp 7952-7952

TL;DR: Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system as discussed by the authors , as well as its underlying mechanisms and patterns.

Abstract: Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system [...]

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1. How does high-performance computing aid geoscience research?

High-performance computing provides powerful computational capabilities for geoscience research. It enables scientists to process large-scale datasets and conduct more precise simulations and predictions. Through high-performance computing, scientists can enhance their understanding of the dynamical processes within the Earth system, resulting in improved accuracy and timeliness of weather forecasting, climate simulations, earthquake early warning systems, and more. The development of high-performance computing technologies has enabled scientists to leverage tools such as parallel computing and supercomputers to meet the demands of complex Earth systems. This advancement in computational power has significantly contributed to the progress of geoscience research.

Citations

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Different Stress Paths Lead to Different Failure Envelopes: Impact on Rock Characterisation and Design

Andre Vervoort

- 14 Oct 2023

- Applied Sciences

TL;DR: Different stress paths lead to different failure envelopes, influencing rock characterisation and design. Micro-fracturing processes differ between loading and unloading rock, resulting in different failure envelopes.

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Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit

Shunan Tao, +4 more

- 12 Jul 2024

- Applied Sciences

TL;DR: A large-scale heterogeneous parallel algorithm for the lattice Boltzmann method is implemented using OpenMP, MPI, Pthread, and OpenCL parallel technologies on the “Dongfang” supercomputer system and exhibited improved parallel efficiency and scalability.

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References

•Journal Article•10.3390/app12168153

Automatic Identification of Landslides Based on Deep Learning

Shuang Yang, +8 more

- 15 Aug 2022

- Applied Sciences

TL;DR: This paper combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentations task (mean IoU, recall, and precision) to measure the performance of the model.

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•Journal Article•10.3390/app12147055

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

Junyu Zhang, +3 more

- 13 Jul 2022

- Applied Sciences

TL;DR: This paper proposes a new algorithm combining histogram equalization (HE) and the Laplace algorithm, and uses this algorithm to process the luminance of the identified samples, and finally uses the YOLOv5 model to identify the samples.

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•Journal Article•10.3390/app12157708

Chinese Named Entity Recognition of Geological News Based on BERT Model

Chao-Chin Huang, +5 more

- 31 Jul 2022

- Applied Sciences

TL;DR: Zhang et al. as mentioned in this paper proposed a geological news named entity recognition (GNNER) method based on the bidirectional encoder representations from transformers (BERT) pre-trained language model.

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•Journal Article•10.3390/app122010544

Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models

Xing Du, +4 more

- 19 Oct 2022

- Applied Sciences

TL;DR: In this article , three unsupervised machine learning models (k-means, spectral clustering, and hierarchical clustering) were compared in modeling the susceptibility of the submarine landslide.

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•Journal Article•10.3390/app13031908

Dual-Encoder Transformer for Short-Term Photovoltaic Power Prediction Using Satellite Remote-Sensing Data

Hai-feng Cao, +6 more

- 01 Feb 2023

- Applied Sciences

TL;DR: Wang et al. as mentioned in this paper proposed a dual-encoder transformer (DualET) for short-term PV power prediction, which contains wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively.

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High-Performance Computing and Artificial Intelligence for Geosciences

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Most frequently asked questions

1. How does high-performance computing aid geoscience research?

Citations

Different Stress Paths Lead to Different Failure Envelopes: Impact on Rock Characterisation and Design

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit

References

Automatic Identification of Landslides Based on Deep Learning

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

Chinese Named Entity Recognition of Geological News Based on BERT Model

Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models

Dual-Encoder Transformer for Short-Term Photovoltaic Power Prediction Using Satellite Remote-Sensing Data

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