Ditapis dengan
Robust high frequency seismic bandwidth extension with a deep neural network …
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various deterministic methods based on Wiener filtering exist to increase the temporal frequency bandwidth and compress the seismic wavelet in a process called spectral shaping. Auto-encoder neural networks with convo…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 23.132 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Near-surface velocity estimation using shear-waves and deep-learning with a U…
Estimation of good velocity models under complex near-surface conditions remains a topic of ongoing research. We propose to predict near-surface velocity profiles from surface-waves transformed to phase velocity-frequency panels in a data-driven manner using deep neural networks. This is a different approach from many recent works that attempt to estimate velocity from directly reflected body w…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 12.932 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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
Single image multi-scale enhancement for rock Micro-CT super-resolution using…
Micro-CT, also known as X-ray micro-computed tomography, has emerged as the primary instrument for pore-scale properties study in geological materials. Several studies have used deep learning to achieve super-resolution reconstruction in order to balance the trade-off between resolution of CT images and field of view. Nevertheless, most existing methods only work with single-scale CT scans, ign…
- Edisi
- Vol.22, June 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 5.431 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Where are the outcrops? Automatic delineation of bedrock from sediments using…
The delineating of bedrock from sediment is one of the most important phases in the fundamental process of regional bedrock identification and mapping, and it is usually manually performed using high-resolution optical remote-sensing images or Light Detection and Ranging (LiDAR) data. This task, although straightforward, is time consuming and requires extensive and specialized labor. We contrib…
- Edisi
- Vol.18 June 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 10.364 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Deep learning approach for Sentinel-1 surface water mapping leveraging
Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ‘data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid development in cloud comput…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 13 hlm PDF, 4.033 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Mapping sugarcane in Thailand using transfer learning, a lightweight convolut…
Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of sugarcane plantations would help in formulating effective strategies to reduce burning. High resolution satellite imagery combined with deep-learning technologies can be effective to map sugarcane with high precision. However, land cover mapping using high resolution data and…
- Edisi
- Vol.1, October 2021
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 8 hlm. PDF, 2.575 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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