2 research outputs found
SalSi: A new seismic attribute for salt dome detection
In this paper, we propose a saliency-based attribute, SalSi, to detect salt
dome bodies within seismic volumes. SalSi is based on the saliency theory and
modeling of the human vision system (HVS). In this work, we aim to highlight
the parts of the seismic volume that receive highest attention from the human
interpreter, and based on the salient features of a seismic image, we detect
the salt domes. Experimental results show the effectiveness of SalSi on the
real seismic dataset acquired from the North Sea, F3 block. Subjectively, we
have used the ground truth and the output of different salt dome delineation
algorithms to validate the results of SalSi. For the objective evaluation of
results, we have used the receiver operating characteristics (ROC) curves and
area under the curves (AUC) to demonstrate SalSi is a promising and an
effective attribute for seismic interpretation.Comment: Proceedings of IEEE Intl. Conf. on Acoustics, Speech and Signal
Processing (ICASSP), Shanghai, China, Mar. 2016. arXiv admin note: text
overlap with arXiv:1812.1196
Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons
In this paper, we propose a novel approach for saliency detection for seismic
applications using 3D-FFT local spectra and multi-dimensional plane
projections. We develop a projection scheme by dividing a 3D-FFT local spectrum
of a data volume into three distinct components, each depicting changes along a
different dimension of the data. The saliency detection results obtained using
each projected component are then combined to yield a saliency map. To
accommodate the directional nature of seismic data, in this work, we modify the
center-surround model, proven to be biologically plausible for visual
attention, to incorporate directional comparisons around each voxel in a 3D
volume. Experimental results on real seismic dataset from the F3 block in
Netherlands offshore in the North Sea prove that the proposed algorithm is
effective, efficient, and scalable. Furthermore, a subjective comparison of the
results shows that it outperforms the state-of-the-art methods for saliency
detection