Deep convolutional autoencoders as generic feature extractors in seismologica…
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this id…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 1.991 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Data-driven approaches for time series prediction of daily production in the …
The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has e…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.029 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Classification random forest with exact conditioning for spatial prediction o…
Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region. Even thoug…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 7.744 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Capsule network-based approach for estimating grassland coverage using time s…
The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Moni…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 3,657 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Arriving at estimates of a rate and state fault friction model parameter usin…
The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the crit…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 1.956 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Application of neural network to speed-up equilibrium calculations in composi…
Injecting carbon dioxide (CO2) into reservoirs is a widely recognized method for enhanced oil recovery (EOR) and carbon storage. This study introduces an innovative Artificial neural network (ANN)-…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 1.456 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
ShakeDaDO: A data collection combining earthquake building damage and ShakeMa…
In this article, we present a new data collection that combines information about earthquake damage with seismic shaking. Starting from the Da.D.O. database, which provides information on the damag…
- Edisi
- -
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 9.708 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Seismic labeled data expansion using variational autoencoders
Supervised machine learning algorithms have been widely used in seismic exploration processing, but the lack of labeled examples complicates its application. Therefore, we propose a seismic labeled…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 7 hlm PDF, 3.230 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Local earthquakes detection: A benchmark dataset of 3-component seismograms b…
Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publi…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 3.299 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Exact Conditioning of Regression Random Forest for Spatial Prediction
Regression random forest is becoming a widely-used machine learning technique for spatial prediction that shows competitive prediction performance in various geoscience fields. Like other popular m…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 6.201 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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