Randy Harsuko

• Master of Engineering
• PhD Student at King Abdullah University of Science and Technology

Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can be stored accurately and efficiently in a generative model. These stored velocity model distribution...

Seismic data often face challenges in their utilization due to noise contamination, incomplete acquisition , and limited low-frequency information, which hinder accurate subsurface imaging and interpretation. Traditional processing methods rely heavily on task-specific designs to address these challenges and fail to account for the variability of d...

Seismic data often face challenges in their utilization due to noise contamination, incomplete acquisition, and limited low-frequency information, which hinder accurate subsurface imaging and interpretation. Traditional processing methods rely heavily on task-specific designs to address these challenges and fail to account for the variability of da...

Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can be stored accurately and efficiently in a generative model. These stored velocity model distribution...

The performance of full‐wave inversion (FWI) depends highly on how we compare the simulated data to observed ones. The simplified assumptions used to generate the simulated data make such comparison even harder. To address this challenge, we introduce SiameseFWI, a novel approach to FWI that plays a critical role in the comparative analysis of simu...

Full waveform inversion (FWI) is capable of generating high‐resolution subsurface parameter models, but it is susceptible to cycle‐skipping when the data lack low‐frequency components. Unfortunately, such components (<5.0 Hz) are often tainted by noise in real seismic exploration, which hinders the application of FWI. To address this issue, we deve...

Machine learning-based seismic processing models are typically trained separately to perform seismic processing tasks (SPTs) and, as a result, require plenty of high-quality training data. However, preparing training data sets is not trivial, especially for supervised learning (SL). Despite the variability in seismic data across different types and...

StorSeismic is a recently introduced model based on the Transformer network to adapt to various seismic processing tasks through its pretraining and fine-tuning strategy. In the original implementation, StorSeismic utilized a sinusoidal positional encoding and a conventional self-attention mechanism, both borrowed from natural language processing (...

Full waveform inversion (FWI) is capable of generating high-resolution subsurface parameter models, but it is susceptible to cycle-skipping when the data lack low-frequency. Unfortunately, the low-frequency components (< 5.0 Hz) are often tainted by noise in real seismic exploration, which hinders the application of FWI. To address this issue, we d...

StorSeismic is a recently introduced model based on the Transformer to adapt to various seismic processing tasks through its pretraining and fine-tuning training strategy. In the original implementation, StorSeismic utilized a sinusoidal positional encoding and a conventional self-attention mechanism, both borrowed from the natural language process...

Machine learning-based seismic processing tasks (SPTs) are typically trained separately and require plenty of training data. However, acquiring training data sets is not trivial, especially for supervised learning (SL). Nevertheless, seismic data of different types and from different regions share generally common features, such as their sinusoidal...

Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeismic, as a framework for seismic data processing, which consists of neural network pre-training and fine-tuning procedures. We, specifically, utilize a neural network as a...

Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeismic, as a dataset centric framework for seismic data processing, which consists of neural network pre-training and fine-tuning procedures. We, specifically, utilize a neur...

In 2018, Lombok Island was hit by a series of destructive earthquakes. According to Indonesian Meteorological, Climatological, and Geophysical Agency data, about 1,973 felt earthquakes (M > 3) which shaken Lombok were recorded during August 2018 with three earthquakes with the largest magnitude of 6.9 Mw, 6.8 Mw, and 6.2 Mw. National Board for Disa...

Because of its robustness and practicality, the Horizontal-to-Vertical Spectral Ratio (HVSR) method has been widely used to obtain subsurface structure, mainly the sediment thickness that resides over bedrock. The method uses Fourier Transform to obtain frequency spectrum and calculate the H/V ratio. However, the conventional Fourier Transform meth...

ABSTRAK Pemodelan tiga dimensi (3-D) mampu memberikan informasi yang jelas pada target survei. Suatu pemodelan dapat dikatakan baik apabila parameter input dan proses dalam pemodelan tersebut dapat diatur sesuai dengan kebutuhan. Tujuan dari pemodelan tiga dimensi (3-D) yang dilakukan adalah untuk menghitung nilai respon anomali gravitasi di permuk...