Salient features in seismic images

Geophysical Journal International

AlRegib

2018

Salt bodies are important subsurface structures with significant implications for hydrocarbon accumulation and sealing in offshore petroleum reservoirs. This study presents an unsupervised workflow for delineating the surface of salt bodies from 3-D seismic surveying based on a multi-attribute k-means cluster analysis. The workflow consists of four steps. First, a suite of seismic attributes are selected and computed from the volume of original seismic amplitude, each of which separates the target salt boundaries from the surrounding non-boundary seismic features in its unique way. Second, two sets of representative samples are manually picked in an interpreter-specified vertical section to help initialize the centres of the boundary and nonboundary clusters. Third, the k-means cluster analysis is performed on the seismic attributes to generate a clustering model for volumetric processing. Finally, applying the built k-means model to every sample in the seismic volume provides us with a probability volume in which the high values indicate the presence of salt-dome boundaries. The effectiveness of the proposed method is verified for interpreting the multiple salt bodies in the F3 seismic data set over the Netherlands North Sea, and the generated salt-boundary volume can serve as input for more advanced salt interpretation, such as salt surface/body extraction, to assist structural framework modelling in the subsalt zones of high geologic complexities and petroleum potentials. We conclude that the proposed workflow paves the way for computer-aided seismic interpretation in a more comprehensive manner by incorporating more advanced machine learning algorithms (e.g. artificial neural network) and targeting other important seismic structures (e.g. fault).

Section: Introductionmentioning

confidence: 99%

Multi-attribute k-means clustering for salt-boundary delineation from three-dimensional seismic data

Geophysical Journal International

AlRegib

2018

“…However, with the striking increase in the size of seismic data over the last few years, researchers in academia and industry have utilized semi-automated seismic interpretation software and tools to overcome the time-consuming and laborintensive manual interpretation. Researchers have proposed several methods to delineate salt domes which include edgebased detection methods by Aqrawi et al (2011), Zhou et al (2007), and Amin and Deriche (2015b), texture-based methods by Berthelot et al (2013), Shafiq et al (2017b), and Wang et al (2015), active-contour-based methods by Haukas et al (2013) and Shafiq et al (2015), Saliency-based methods by Drissi et al (2008) and Shafiq et al (2017a), machine-learning-based methods by Guillen et al (2015) and Amin and Deriche (2015a), and different image processing techniques by Halpert et al (2009), Lomask et al (2007), Felzenszwalb and Huttenlocher (2004), Larrazabal et al (2015), Wu (2016), and Qi et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Salt dome detection within migrated seismic volumes using phase congruency

SEG Technical Program Expanded Abstracts 2017

Alaudah

et al. 2017

In this paper, we propose a method based on phase congruency (PC) for computational seismic interpretation with an application to subsurface structures delineation within migrated seismic volumes. Phase congruency highlights 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. We show the effectiveness of the proposed method on the SEAM dataset, which models the complex salt structures found in the Gulf of Mexico. Experimental results show that the proposed method effectively delineates salt domes within migrated seismic volumes.

“…Similar study to develop a heuristic knowledge of the experts doing interpretation of seismic images is presented in [16]. The authors of [17,18] proposed algorithms for automated horizons picking by detecting salient features followed by computing pixels entropy and fragments connectivity, respectively. On the other hand, the authors in [19] and [20] proposed novel algorithms for the detection and delineation of salt domes based on visual saliency.…”

Section: Introductionmentioning

confidence: 99%

Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

2017 IEEE International Conference on Image Processing (ICIP)

Long

Alshawi

et al. 2017

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 stateof-the-art methods for saliency detection.