Výsledky vyhledávání - "Kaka, Sanlinn"

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Well log data generation and imputation using sequence-based generative adversarial networks

Autor: Al-Fakih, Abdulrahman, Koeshidayatullah, A., Mukerji, Tapan, Al-Azani, Sadam, Kaka, SanLinn I.

Well log analysis is crucial for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsur

Externí odkaz: http://arxiv.org/abs/2412.00718

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Enhanced anomaly detection in well log data through the application of ensemble GANs

Autor: Al-Fakih, Abdulrahman, Koeshidayatullah, A., Mukerji, Tapan, Kaka, SanLinn I.

Although generative adversarial networks (GANs) have shown significant success in modeling data distributions for image datasets, their application to structured or tabular data, such as well logs, remains relatively underexplored. This study extends

Externí odkaz: http://arxiv.org/abs/2411.19875

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Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

Autor: Dia, Ben Mansour, Fehler, Michael, Kaka, SanLinn I., Scarinci, Andrea, Waheed, Umair bin, Gu, Chen

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (station

Externí odkaz: http://arxiv.org/abs/2403.01403

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Iterative interferometry-based method for picking microseismic events

Autor: Iqbal, Naveed, Al-Shuhail, Abdullatif A., Kaka, SanLinn I., Liu, Entao, Raj, Anupama Govinda, McClellan, James H.

Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break

Externí odkaz: http://arxiv.org/abs/1703.03154

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Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering

Autor: Liu, Entao, Zhu, Lijun, Raj, Anupama Govinda, McClellan, James H., Al-Shuhail, Abdullatif, Kaka, SanLinn I., Iqbal, Naveed

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose a

Externí odkaz: http://arxiv.org/abs/1612.01884

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