Calculation of seismic attributes with the discrete wavelet transform

Rivera-Recillas, David Ernesto; Lozada-Zumaeta, M.; Campos-Enríquez, J. O.; Ronquillo-Jarillo, G.

doi:10.1190/1.1817729

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…One difference between DWT and CWT is their wavelets; DWT uses a discrete type transform. After trying some wavelets such as Haar, Moor, and Daubechies order 1-6, Daubechies order 4 for a four-time decomposition level was used (Mallat, 1989;Meyer, 1990;Daubechies, 1992;Rivera-Recillas et al, 2003).…”

Section: Multiresolution Filter Banksmentioning

confidence: 99%

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Differentiation and Recognition of Overlapped Layers in Seismic Data Using Discrete Wavelet Transform

Panah

Riahi

Zargar

2015

Petroleum Science and Technology

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Oil well jobs are highly dependent on earth layers position and their composition. So it is really important to interpret seismic data in such a good way that there are fewer problems. One major problem in seismic data interpretation is layers overlapping. Thin and close layers with similar properties may overlap cause of large signal length, thin layer thickness, and small distance between the layers. The goal was to separate overlapped layers and showing their real thickness and achieved by operating discrete wavelet transform (DWT) on the seismic data. DWT applied on 2D seismic section to overcome the problem. DWT uses high and low band-pass filters which separate signal in to high (details) and low (approximations) frequencies, and then by applying threshold makes some suspicious coefficients zero (which cause noise or overlapping). Inverse transform builds the final data in which the layers may not overlapped again. Final results provide proper locations for drilling jobs, and so determine pay zone thickness and usefulness in reservoir calculations and well completion. The results indicate the positive effect of discrete wavelet transform on seismic data on one southern Iranian field.

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Section: Multiresolution Filter Banksmentioning

confidence: 99%

“…Wavelet-based transformations offer new opportunities for improved processing algorithms and spectral interpretation methods. The continuous wavelet transform (CWT) performs a multiresolution analysis by contraction and dilatation of the wavelet functions (Mallat, 1989;Meyer, 1990;Rivera-Recillas et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Differentiation and Recognition of Overlapped Layers in Seismic Data Using Discrete Wavelet Transform

Panah

Riahi

Zargar

2015

Petroleum Science and Technology

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Diffraction separation method using shapeDTW and median-mean filter

Sheng,

Zhao,

Yang

et al. 2024

Preprint

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The subsurface small-scale geological structures are manifested as diffractions in seismic data. Diffraction imaging provides high-resolution details of discontinuities such as faults, collapse columns, and karst caves. However, this high-resolution information is often obfuscated by strong reflections, necessitating their removal prior to diffraction imaging. Here, we propose a novel diffraction separation method based on shape dynamic time warping (shapeDTW) and median-mean filter. The shapeDTW is an effective time series alignment method that utilizes the distance between temporal points within a neighborhood as the alignment criterion, which can accurately align strong energy events in seismic data. We implement shapeDTW to construct flattened reflection gathers, in which the reflections are aligned and therefore behaves as horizontal events with consistent strong amplitudes, while the diffractions appear as non-horizontal weak events. Leveraging this difference in shape and amplitude, the median-mean filter can effectively extract the reflections from flattened reflection gathers. Diffractions are separated from seismic data by subtracting the extracted reflections. The synthetic data experiment confirms the feasibility of the proposed method in eliminating strong reflected waves while preserving weak diffracted waves related to karst caves in seismic data with low signal-to-noise ratio. The field data application further illustrates its effectiveness in removing strong high-slope reflections, highlighting small-scale fracture-related detailed features.

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Multiresolution analysis applied to interpretation of seismic reflection data

Rivera-Recillas

Lozada-Zumaeta

Ronquillo-Jarillo

et al. 2005

GeofInt

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Hemos desarrollado el análisis multiresolutivo basado en la transformada ondicular discreta. En este trabajo ilustramos los usos potenciales en la exploración petrolera de la aplicación conjunta del análisis multiresolutivo y de los atributos sísmicos mediante un caso de estudio. Tres reflexiones mayores en la base de la ventana sísmica analizada están asociadas a formaciones geológicas con aceite (de edad Cretácico Inferior). Estas reflexiones mayores parecen continuarse hacia la izquierda por reflexiones menos claras y de menor amplitud. Basados únicamente en el análisis multiresolutivo de los datos sísmicos o en sus respectivos atributos convencionales (amplitud, fase y frecuencia instantáneas) no fue posible excluir la posible continuación. En efecto una continuación estratigráfica es sugerida por estos dos conjuntos de datos. Sin embargo, las resoluciones a diferentes escalas proporcionadas por el análisis multiresolutivo de los atributos sísmicos permitieron un análisis más fino. En efecto la continuidad sugerida en el análisis multiresolutivo de la sección sísmica y de los mismos atributos sísmicos, no fue apoyada completamente por el análisis de la fase. Es posible con la multiresolución de este atributo distinguir con confianza entre las reflexiones continuas de mayor amplitud y sus supuestas continuaciones. Las continuaciones están caracterizadas por reflexiones de baja amplitud, de media a alta frecuencia y continuas a tramos. Al estar aislada de las contribuciones de la información sísmica a otras escalas (o rangos de frecuencia) una característica sísmica dada es definida con más nitidez. Es este realzado adicional (es decir mayor calidad de la relación señal-ruido) lo que le permite a uno realizar un análisis discriminatorio más fino de mucha utilidad en la detección de hidrocarburos. El uso conjunto de la multriresolución y de los atributos sísmicos tiene aplicaciones potenciales en el campo de la sísmica de reflexión, tanto como indicador de hidrocarburos como en la caracterización de yacimientos carbonatados. El análisis multiresolutivo aquí desarrollado está basado en la transformada ondicular discreta, y representa una alternativa al análisis multiresolutivo basado en la transformada ondicular continua hoy disponible en la industria petrolera.

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Calculation of seismic attributes with the discrete wavelet transform

Cited by 3 publications

References 2 publications

Differentiation and Recognition of Overlapped Layers in Seismic Data Using Discrete Wavelet Transform

Differentiation and Recognition of Overlapped Layers in Seismic Data Using Discrete Wavelet Transform

Diffraction separation method using shapeDTW and median-mean filter

Multiresolution analysis applied to interpretation of seismic reflection data

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