Proposing a new strategy in multi-seismic attribute combination for identification of buried channel

Khasraji-Nejad, Hassan; Kahoo, Amin Roshandel; Monfared, Mehrdad Soleimani; Radad, Mohammad; Khayer, Keyvan

doi:10.1007/s11001-021-09458-6

Cited by 13 publications

(5 citation statements)

References 45 publications

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“…Numerous techniques and methods for feature extraction available in the literature are provided for better interpretation and analysis of seismic images (Khayer et al, 2022b). Seismic attributes are utilizing a wide range of time frequencies on seismic data to precisely locate underground channels and structures (Khasraji-Nejad et al, 2021). Although using seismic attributes to extract useful geological features is not a novel technique but it is a challenging task to select and choose the most relevant attributes to extract the necessary data due to the large varieties of seismic attributes and their diverse range of uses (Bulat and Long, 2001;Khasraji-Nejad et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Seismic attributes are utilizing a wide range of time frequencies on seismic data to precisely locate underground channels and structures (Khasraji-Nejad et al, 2021). Although using seismic attributes to extract useful geological features is not a novel technique but it is a challenging task to select and choose the most relevant attributes to extract the necessary data due to the large varieties of seismic attributes and their diverse range of uses (Bulat and Long, 2001;Khasraji-Nejad et al, 2021). Seismic image analysis techniques were precisely used to detect subsurface geological features and target reflectors (bed boundaries) by the addition of automatic seismic imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

Tounkara

Ehsan²,

Iqbal³

et al. 2023

Front. Earth Sci.

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The purpose of this research is to delineate the structures of the Lower Goru Formation, investigate fluid properties, and clarify the hydrocarbon-prone areas through seismic attributes analysis. First, the acquired data was matched by the interpretation datum. Structural analysis was done by performing horizon interpretation, fault interpretation, and contour mapping on the C-Interval of the Lower Goru Formation. Hydrocarbon zones were marked with the help of attribute analysis on seismic sections and were justified by petrophysical analysis. An integrated approach such as seismic structural interpretation, seismic attribute, spectral decomposition, and petrophysical analyses was used in current research to better understand geological structure and features. This research showed that normal faults are present in the area showing negative flower structure, horst and graben, and faults oriented north-west to south-east. The contour map shows structural inclination and faults bound closure near well locations. Variance attribute and spectral decomposition attribute were used to verify horizon lineation and fault behavior. Instantaneous amplitude and instantaneous phase attributes justify hydrocarbon bearing zones, and bright spots are present on seismic sections at C–Interval of Lower Goru Formation. Petrophysical analysis of the available wells showed a number of significant hydrocarbon zones having more than 55% of hydrocarbon saturation at the C-Interval of the Lower Goru Formation. The four possible reservoir zones in Sawan-02 well, two zones in Sawan-07 well, and three zones in Sawan-09 well are identified based on well data interpretation. Based on these analyses, the area of interest has a very good reservoir potential, structural closure, and visible bright spots. The current finding of this research will be helpful for future exploration and development of the Sawan area.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

Tounkara

Ehsan²,

Iqbal³

et al. 2023

Front. Earth Sci.

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“…Seismic data reconstruction is important in seismic imaging. Many traditional intelligent methods have been proposed to solve various problems in seismic imaging, such as seismic resolution enhancement (Alaei et al, 2018;Soleimani, 2016;Soleimani, 2017;Soleimani et al, 2018;Mahdavi et al, 2021), complex geological structure identification (Soleimani, 2015;Farrokhnia et al, 2018;Kahoo et al, 2021;Khasraji et al, 2021;Hosseini-Fard et al, 2022;Khayer et al, 2022a;Khayer et al, 2022b), and noise attenuation (Gholtashi et al, 2015;Siahsar et al, 2017;Anvari et al, 2018;Anvari et al, 2020). In recent years, deep learning has achieved outstanding successes in a variety of domains, including computer vision (Ferdian et al, 2020;Manor and Geva, 2015) and medical image processing (Li et al, 2021;Tavoosi et al, 2021), with its powerful representing ability.…”

Section: Introductionmentioning

confidence: 99%

Depthwise separable convolution Unet for 3D seismic data interpolation

Jin

Yang

et al. 2023

Front. Earth Sci.

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In seismic exploration, dense and evenly spatial sampled seismic traces are crucial for successful implementation of most seismic data processing and interpretation algorithms. Recently, numerous seismic data reconstruction approaches based on deep learning have been presented. High dimension-based methods have the benefit of making full use of seismic signal at different perspectives. However, with the transformation of data dimension from low to high, the parameter capacity and computation cost of training deep neural network increase significantly. In this paper, we introduce depthwise separable convolution instead of standard convolution to reduce the operation cost of Unet for 3D seismic data missing trace interpolation. The structural similarity (SSIM), L1 hybrid loss function, and switchable normalization further improve the reconstruction performance of the network. The comparative experiments on the synthetic and field seismic data show that depthwise separable convolution can effectively reduce the number of network parameters and computation intensity with the interpolation results comparable to the standard convolution results.

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“…However, forward models, which rely on state-of-the-art approximations of the underlying physics, are computationally expensive, and inverse models often lead to non-unique solutions. Notwithstanding, noise reduction (e.g., Dong et al, 2006;Mafakheri et al, 2022) and data exploration (e.g., Khasraji-Nejad et al, 2021;Mahdavi et al, 2021;Mousavi et al, 2022) are key analyses that need to be carried out before any forward or inverse simulation. Particularly for studies of crustal deformation, an exhaustive inspection of observations can be a time-consuming task, considering the decades long records of hundreds or even thousands of GNSS stations located at some plate boundaries.…”

Section: Introductionmentioning

confidence: 99%

Mosaicking Andean morphostructure and seismic cycle crustal deformation patterns using GNSS velocities and machine learning

et al. 2023

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We use unsupervised machine learning techniques to analyze continental-scale crustal motions in areas affected by the seismic cycle of large subduction earthquakes along the Chilean Trench. Specifically, we use the agglomerative clustering algorithm as an exploratory tool to investigate spatial patterns in GNSS regional velocities without the complexity of modeling a physical source. We present a continental-scale velocity field including all available GNSS data for two-time windows (pre-2014, 2018–2021) that represents two periods with different deformation patterns of the seismic cycle. We test two different pre-processing methodologies for the design of machine learning features from the GNSS-derived velocities. The first method uses the direction and magnitude of the secular rates as input features to the clustering algorithm. These results show a clustering spatially related to seismic cycle deformation, separating latitudinal segments with different velocities in the fore-arc and back-arc, as well as regions affected by postseismic relaxation. Thus, highlighting the effectiveness of this method for mapping first-order patterns of active deformation in a subduction zone, that are particularly related to variations on interplate coupling and postseismic transient deformation. In a more sophisticated approach, we use surface strain and rotational rates from GNSS velocities as features in the second methodology. Here, we develop a novel methodology to estimate strain and rotation rates accounting for the spatial heterogeneity of the GNSS-network. We determine the spatial scale at which these features are estimated by least squares inversions, by using a Bayesian model class selection method. The distribution of stations allows to identify heterogeneities in strain and rotation rates at spatial scales larger than 50 km, being particularly notorious the main features of regional deformation at scales > 100 km. Interestingly, the results show a spatial correlation between seismic segmentation in the fore-arc and geologic and structural domains in the arc and back-arc. Our results demonstrate the ability of the combination of inverse and machine learning methods to efficiently identify active deformation patterns and their relationship to the subduction seismic cycle and regional-scale geological structures. Furthermore, our analysis suggests that Andean geological structures influence the observed deformation field.

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Proposing a new strategy in multi-seismic attribute combination for identification of buried channel

Cited by 13 publications

References 45 publications

Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

Analyzing the seismic attributes, structural and petrophysical analyses of the Lower Goru Formation: A case study from Middle Indus Basin Pakistan

Depthwise separable convolution Unet for 3D seismic data interpolation

Mosaicking Andean morphostructure and seismic cycle crustal deformation patterns using GNSS velocities and machine learning

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