Ditapis dengan
Irregularly sampled seismic data interpolation via wavelet-based convolutiona…
Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine learning and deep learning, convolutional neural networks (CNNs) are applied for interpolating irregularly sampled seismic data. CNN based approaches can address the apparent defects of traditional inter…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 5.141 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Attenuation of seismic migration smile artifacts with deep learning
Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in the kinematics of reflection and migration smiles. Several ‘conventional’ filtering methods exist and recently deep learning based workflows have been proposed. A deep learning workflow can be a …
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 12.044 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
A study on small magnitude seismic phase identification using 1D deep residua…
Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more ro…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 2.201 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
A convolutional recurrent neural network for strong convective rainfall nowca…
Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being difficult to predict by standard meteorological numerical models. This work proposes the M5Images method for performing the very short-term prediction (nowcasting) of heavy convective rainfall usin…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.368 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Wavefield solutions from machine learned functions constrained by the Helmhol…
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation provides wavefield solutions that are dimensionally reduced, per frequency, compared to the time domain, which is useful for many applications, like full waveform inversion. However, our ability to attain such wavefield…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 3.131 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. Monitoring grasslands by using big data has emerged as a popular area of research in recent years. As grassland degradation is a slow and gradual process, the accurate identification of grassland cover is…
- 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
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 data expansion method based on deep variational Autoencoders (VAE), which are made of neural networks and contains two parts-Encoder and Decoder. Lack of training samples leads to overfitting of the …
- 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
Automatic fault instance segmentation based on mask propagation neural networ…
Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil. Recently, significant advances have been made towards fault semantic segmentation using deep learning. However, few studies employ deep learning in fault instance segmentation. We introduce mask propagation neural network for fault instance segmentatio…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 5 hlm PDF, 1.456 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
X-ray Micro-CT based characterization of rock cuttings with deep learning
Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process and can also be prone to subjectivity leading to errors. In this paper, a framework for the classification of multiple types of rock structures…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 6.037 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Semantic segmentation framework for atoll satellite imagery: An in-depth expl…
This paper presents a framework for semantic segmentation of satellite imagery aimed at studying atoll morphometrics. Recent advances in deep neural networks for automated segmentation have been valuable across a variety of satellite and aerial imagery applications, such as land cover classification, mineral characterization, and disaster impact assessment. However, identifying an appropriate s…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 16 hlm PDF, 3.046 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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