SalSi: A new seismic attribute for salt dome detection
This work addresses the domain-specific problem of seismic interpretation for geologists and engineers, offering an incremental improvement by introducing a new attribute based on existing saliency theory.
The paper tackles the problem of detecting salt dome bodies in seismic volumes by proposing a new saliency-based attribute called SalSi, which models the human vision system to highlight areas of high attention; experimental results on a real dataset from the North Sea show its effectiveness, with validation using ROC curves and AUC metrics.
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.