A method for classifying pre‐stack seismic data based on amplitude–frequency attributes and self‐organizing maps

Molino-Minero-Re, Erik; Rubio-Acosta, Ernesto; Benítez-Pérez, Héctor; Brandi-Purata, Juan Marcos; Pérez-Quezadas, Nora Isabel; García-Nocetti, Demetrio Fabián

doi:10.1111/1365-2478.12607

Cited by 4 publications

(2 citation statements)

References 23 publications

(34 reference statements)

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“…; Molino‐Minero‐Re et al . ). Facies types identified by the clustering analysis can be mapped to show their distribution across the geological targets.…”

Section: Introductionmentioning

confidence: 97%

“…The most common way to combine multi-attribute information is clustering analysis using statistical methods or neural network-based techniques (e.g. Coléou, Poupon and Azbel 2003;de Matos, Osorio and Johann 2007;Pussak et al 2014;Molino-Minero-Re et al 2018). Facies types identified by the clustering analysis can be mapped to show their distribution across the geological targets.…”

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confidence: 99%

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Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Bauer

Norden

Ivanova

et al. 2019

Geophysical Prospecting

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At the geothermal test site near Groß Schönebeck (NE German Basin), a new 3D seismic reflection survey was conducted to study geothermal target layers at around 4 km depth and 150°C. We present a workflow for seismic facies classification and modelling which is applied to a prospective sandstone horizon within the Rotliegend formation. Signal attributes are calculated along the horizon using the continuous Morlet wavelet transform. We use a short mother wavelet to allow for the temporal resolution of the relatively short reflection signals to be analysed. Time‐frequency domain data patterns form the input of a neural network clustering using self‐organizing maps. Neural model patterns are adopted during iterative learning to simulate the information inherent in the input data. After training we determine a gradient function across the self‐organizing maps and apply an image processing technique called watershed segmentation. The result is a pattern clustering based on similarities in wavelet transform characteristics. Three different types of wavelet transform patterns were found for the sandstone horizon. We apply seismic waveform modelling to improve the understanding of the classification results. The modelling tests indicate that thickness variations have a much stronger influence on the wavelet transform response of the sandstone horizon compared with reasonable variations of seismic attenuation. In our interpretation, the assumed thickness variations could be a result of variable paleo‐topography during deposition of predominantly fluvial sediments. A distinct seismic facies distribution is interpreted as a system of thicker paleo‐channels deposited within a deepened landscape. The results provide constraints for the ongoing development of the geothermal test site.

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“…; Molino‐Minero‐Re et al . ). Facies types identified by the clustering analysis can be mapped to show their distribution across the geological targets.…”

Section: Introductionmentioning

confidence: 97%

mentioning

confidence: 99%

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Bauer

Norden

Ivanova

et al. 2019

Geophysical Prospecting

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Tensor discriminant dictionary classification method for prestack seismic reflection patterns

Cai

Peng²,

Yang

2022

Appl. Geophys.

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Robust prestack seismic facies analysis using shearlet transform-based deep learning

Liu

Yue

et al. 2022

Journal of Geophysics and Engineering

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One of the primary purposes of seismic stratigraphy is to evaluate the components of seismic layer relationships within a depositional chronology. Prestack seismic images contain a wealth of information, such as variations in the offset and azimuth of a seismic event, and naturally produce higher-resolution seismic facies analysis results than poststack data. However, prestack data usually suffer from potential unreliability issues due to low signal-to-noise ratios. As this is often overlooked, the present facies analysis methods sometimes fail to extract accurate features from prestack images, which inevitably influences the facies analysis results. To address this issue, this article provides a robust data-driven technique for extracting offset-temporal features via shearlet transform-based deep convolution autoencoders (STCAEs). Unlike the present facies analysis in the time domain, STCAE can optimally represent prestack images at multiple scales and directions through the two-dimensional shearlet transform, which preserves fine edges while suppressing noise in prestack images. Subsequently, robust features are extracted from prestack images in a data-driven manner through a contractive convolutional autoencoder network. We compare our method with other advanced methods and demonstrate the advantages of the proposed approach in classifying seismic layers in prestack data.

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A method for classifying pre‐stack seismic data based on amplitude–frequency attributes and self‐organizing maps

Cited by 4 publications

References 23 publications

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

Tensor discriminant dictionary classification method for prestack seismic reflection patterns

Robust prestack seismic facies analysis using shearlet transform-based deep learning

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