Ditapis dengan
Toward earthquake early warning: A convolutional neural network for rapid ear…
Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude, i.e., large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category. However, the current standard information signal processing routi…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 5.714 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region r…
The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable insights. In this study, we present a fully automated approach for augmenting earthquake catalogue within the HKPR. Our …
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 15.177 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Blockly earthquake transformer: A deep learning platform for custom phase pic…
Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable performance of existing (i.e., pre-trained) deep-learning detector/picker models, there are still some cases where the direct applications of such models do not generalize well. In such cases, substant…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 3.595 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
PolarCAP – A deep learning approach for first motion polarity classificatio…
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 7 hlm PDF, 6.055 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
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 damage of individual buildings subjected to sequences of past earthquakes in Italy, we have generated ShakeMaps for all the events with magnitude greater than 5.0 that have contributed to these sequences. …
- Edisi
- -
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 16 hlm PDF, 9.708 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 publicly-available seismic data nowadays requires automated, fast, and reliable tools to carry out a multitude of tasks, such as the detection of small, local earthquakes in areas characterized by sparsity…
- 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
SeisAug: A data augmentation python toolkit
A common limitation in applying any deep learning and machine learning techniques is the limited labelled dataset which can be addressed through Data augmentation (DA). SeisAug is a DA python toolkit to address this challenge in seismological studies. DA. DA helps to balance the imbalanced classes of a dataset by creating more examples of under-represented classes. It significantly mitigates ov…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 6.236 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Developing ground motion prediction models for West Java: A machine learning …
Indonesia, one of the most earthquake-prone countries in the world, is currently developing an Earthquake Early Warning (EEW) system. A key component of this system, the Regional EEW, relies on Ground Motion Prediction models (GMPMs) to issue end-user alerts. However, in West Java, one of the pilot regions for this project, there is a lack of region-specific GMPMs essential for accurate early w…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 10,819 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Deep learning for real-time P-wave detection: A case study in Indonesia’s e…
Detecting seismic events in real-time for prompt alerts and responses is a challenging task that requires accurately capturing P-wave arrivals. This task becomes even more challenging in regions like Indonesia, where widely spaced seismic stations exist. The wide station spacing makes associating the seismic signals with specific even more difficult. This paper proposes a novel deep learning-ba…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 16 hlm PDF, 16.531 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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