2022
DOI: 10.1007/s12594-022-2210-z
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Seismic Image Enhancement from Principal Component Analysis: A Case Study from KG Basin

Abstract: In exploration seismology, it is extremely important to enhance the image of the sub-surface in terms of geological features by suppressing the noise. The weighted stacking methods are found to be superior to the conventional stacking and result in good quality seismic images. The weighted stacking process based on principal component analysis (PCA) is employed in the present study to enhance the seismic image from the Krishna-Godavari (KG) basin, India. In present study, the weights are calculated by threshol… Show more

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Cited by 2 publications
(2 citation statements)
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“…PCA transforms the values of a set of linearly uncorrelated variables, known as PCs, into the observations of a set of potentially correlated variables via an orthogonal transformation. In this research, weights were calculated using a weighted superposition approach based on PCA by thresholding how similar each data trace was to a reference trace created using PCA [26]. Since there are as many principal components in the data as there are variables, the principal components are constructed in such a way that the first component occupies the maximum possible variance in the set.…”
Section: Circuit Theory and Mcr Modelmentioning
confidence: 99%
“…PCA transforms the values of a set of linearly uncorrelated variables, known as PCs, into the observations of a set of potentially correlated variables via an orthogonal transformation. In this research, weights were calculated using a weighted superposition approach based on PCA by thresholding how similar each data trace was to a reference trace created using PCA [26]. Since there are as many principal components in the data as there are variables, the principal components are constructed in such a way that the first component occupies the maximum possible variance in the set.…”
Section: Circuit Theory and Mcr Modelmentioning
confidence: 99%
“…(1) Based on the n-dimensional observation sample matrix X with p characteristic variables, the method and steps for calculating the principal component are as follows (Akbar et al, 2022;Manz, 2022;Ramesh and Satyavani, 2022):…”
Section: Geological Settingmentioning
confidence: 99%