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Patitapaban Palo

2 accepted papers

2024

A Graph Neural Network Based Approach for Fault Delineation in Seismic Data using Graph Total Variation and Multigraph

ICASSP 2024accepted

Interpreting seismic data involves finding out subsurface geologic information. In seismic data interpretation, one of the crucial steps is to delineate seismic faults. Natural gas and oil reservoirs are more likely to be present where seismic faults exist. In this paper, we develop a graph neural n…

Cited by 0SourceScholar
2022

Seismic Fault Identification Using Graph High-Frequency Components as Input to Graph Convolutional Network

ICASSP 2022accepted

Many activities such as drilling and exploration in the oil and gas industries rely on identifying seismic faults. Using graph high-frequency components as inputs to a graph convolutional network, we propose a method for detecting faults in seismic data. In Graph Signal Processing (GSP), digital sig…

Cited by 0SourceScholar