Muhammad Amir Shafiq

A texture-based interpretation workflow with application to delineating salt domes

AbstractWe propose a texture-based interpretation workflow and apply it to delineate salt domes in 3D migrated seismic volumes. First, we compute an attribute map using a novel seismic attribute, 3D gradient of textures (3D-GoT), which measures the dissimilarity between neighboring cubes around each voxel in a seismic volume across the time or depth, crossline, and inline directions. To evaluate the texture dissimilarity, we introduce five 3D perceptual and nonperceptual dissimilarity functions. Second, we apply a global threshold on the 3D-GoT volume to yield a binary volume and demonstrate its effects on salt-dome delineation using objective evaluation measures such as receiver operating characteristic curves and the areas under the curves. Third, with an initial seed point selected inside the binary volume, we use a 3D region growing method to capture a salt body. For an automated 3D region growing, we adopt a tensor-based automatic seed point selection method. Finally, we apply morphological postproce...

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.

Automated salt-dome detection using an attribute ranking framework with a dictionary-based classifier

AbstractWe have developed a dictionary-based classification approach for salt-dome detection within migrated seismic volumes. The proposed workflow uses seismic attributes derived from the gray-level co-occurrence matrix, Gabor filter, and higher order singular-value decomposition to effectively learn and detect the salt bodies. We use an information theoretic framework to rank the seismic attributes as per their salt-dome classification performance. Based on this ranking, we select the top K attributes for dictionary training, testing, and classification. To improve the accuracy of the detected salt bodies and make the proposed workflow robust to different data sets, we introduce a refining step that uses edge strength and energy values to detect the shape of the salt-dome boundary within the classified patches. The optimal set of attributes and the refining step ensure that the proposed workflow yields good results for detecting salt-dome boundaries even in the presence of weak seismic reflections. We u...

The role of visual saliency in the automation of seismic interpretation

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modeling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using SalSi, we can not only highlight the neighboring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post-processing. The proposed delineation workflow detects the salt-dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt-dome delineation algorithms, we have used a reference salt-dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared to other salt-dome delineation algorithms and shows a promising potential in seismic interpretation.

Phase Congruency for image understanding with applications in computational seismic interpretation

Phase Congruency (PC) can highlight small discontinuities in images with varying illumination and contrast using the congruency of phase in Fourier components. PC can not only detect the subtle variations in the image intensity but can also highlight the anomalous values to develop a deeper understanding of the images content and context. In this paper, we propose a new method based on PC for computational seismic interpretation with an application to subsurface structures delineation within migrated seismic volumes. We show the effectiveness of the proposed method as compared to the edge- and texture-based methods for salt domes boundary detection. The subjective and objective evaluation of the experimental results on the real seismic dataset from the North Sea, F3 block show that the proposed method is not only computationally very efficient but also outperforms the state of the art methods for salt dome delineation.

Direct adaptive inverse control of nonlinear plants using neural networks

Neural networks are universal approximators that can estimate and control plant dynamics under severe process nonlinearities. In this paper, we propose a direct adaptive inverse control (DAIC) for nonlinear plants based on multilayered feed forward neural network (MFNN) using back propagation through model (BPTM) algorithm. Neural Network (NN) controller is designed directly in the feed forward loop using the estimated model of nonlinear plant, which is obtained using forward modeling. NN adaptive control is designed such the NN controller responds online to the changes in system dynamics. NN adaptive controller adapts to the error signal between desired output and plant output; back propagated through estimated nonlinear plant model, using BPTM algorithm. Our proposed system is applicable to stable or stabilized, minimum/non-minimum phase, linear and nonlinear plants. Simulation results show the effectiveness of the proposed algorithm.