Oct 22, 2016 · We focus on the robust detection of salt domes from seismic surveys. We discuss a novel feature-ranking classification model for saltdome detection for seismic images using an optimal set of texture attributes.

Salt Dome Interpretation Tool ver 1.0 . At CeGP, we are conducting research on the automation of seismic interpretation for post-migrated seismic volumes using advanced pattern recognition, machine learning, and human vision system models.

Apr 1, 2019 · The gradient of texture (GoT) method for salt dome detection, recently introduced by Shafiq et al. (2015b), Wang et al. (2015), and Shafiq et al. (2017) measures the texture dissimilarity between two neighboring cubes and windows, respectively, to …

In this study, we used a new textural seismic attribute known as the gradient of texture to characterize the change of seismic character between the salt body and its surrounding geology. It calculates the texture gradient in two adjacent windows around a sample in different directions.

Due to the limitations of manual picking, automatic segmentation algorithms are preferred to locate salt domes within the seismic images. In this work, we propose a robust salt dome detection technique based on combined edge and texture attributes.

Oct 18, 2015 · Accurate salt dome detection from 3D seismic data is crucial to different seismic data analysis applications. We present a new edge based approach for salt dome detection in migrated 3D seismic data.

In this paper, we apply a phase-based edge detection algorithm, namely phase congruency (PC), and a texture-based algorithm, namely gradient of texture (GoT), to localize a salt dome within SEAM dataset.

Aug 25, 2016 · Our experimental results show that the proposed codebook-based workflow can detect salt domes with good accuracy, superior to existing salt-dome detection techniques. In this letter, we present a novel supervised codebook-based learning model for salt-dome detection in seismic imaging using texture-based attributes.

In this paper, we investigate and study the performance of three texture feature extraction methods for detecting salt domes using Bayesian classifier. The Bayesian classifier is trained using one source of seismic data and evaluated using another seismic data source.

Detecting salt domes in seismic images is very important for the exploration of petroleum reservoirs. However, this task is characterized as being time-consuming when performed by human interpreters due to the structural complexity of …