On the use of geostatistical filtering techniques in seismic processing

Hoeber, H.; Coléou, T.; Meur, D. Le; Angerer, Erika; Lanfranchi, Pierre; Lecerf, D.

doi:10.1190/1.1817728

Cited by 9 publications

(4 citation statements)

References 7 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After picking of residual curvature on dense CIP pre-stack gathers we use geostatistical filtering techniques (Hoeber et al, 2003) to clean the pick volume and remove outliers. Figure 1 shows a set of CIP gathers with the initial RC picks (A) and after pick filtering (B).…”

Section: Dense Full Volume Rc Picking Volumetric 3d Geostatistical Fimentioning

confidence: 99%

Automatic, dense and volumetric picking for high‐resolution 3D tomographic model building

Dirks

Wang

Epili

et al. 2005

SEG Technical Program Expanded Abstracts 2005

View full text Add to dashboard Cite

As survey sizes are steadily increasing, 3D tomography as the key component of today's velocity model building workflows has to be performed on large data volumes within acceptable turnaround times. To provide highresolution velocity models, dense volumetric picking is required. This leads to even larger data volumes. It is therefore paramount to automate as many processing steps as possible to free the time of the geophysicist for the necessary QC. In this paper we present a modified workflow for high-resolution 3D tomography addressing the need for process automation. We are putting the emphasis of this paper on data preparation and the quality control of the inversion phase. The presented workflow allows performance of high-resolution tomography work on large data volumes over a significantly reduced cycle time.

show abstract

Section: Dense Full Volume Rc Picking Volumetric 3d Geostatistical Fimentioning

confidence: 99%

Automatic, dense and volumetric picking for high‐resolution 3D tomographic model building

Dirks

Wang

Epili

et al. 2005

SEG Technical Program Expanded Abstracts 2005

View full text Add to dashboard Cite

show abstract

“…Geostatistical filtering is a method used in many applications dealing with spatial data affected by spatially structured noises (Goovaerts andJacquez, 2004, Bourennane et al, 2012), among which seismic processing (Hoeber et al, 2003, Piazza et al, 2015. It relies on the assumption that the noisy signal at hand results from a complex phenomenon that can be seen as a superposition of independent simpler phenomena.…”

Section: Introductionmentioning

confidence: 99%

A matrix-free approach to geostatistical filtering

Pereira¹,

Desassis²,

Magneron³

et al. 2020

Preprint

View full text Add to dashboard Cite

In this paper, we present a novel approach to geostatistical filtering which tackles two challenges encountered when applying this method to complex spatial datasets: modeling the non-stationarity of the data while still being able to work with large datasets. The approach is based on a finite element approximation of Gaussian random fields expressed as an expansion of the eigenfunctions of a Laplace-Beltrami operator defined to account for local anisotropies. The numerical approximation of the resulting random fields using a finite element approach is then leveraged to solve the scalability issue through a matrix-free approach. Finally, two cases of application of this approach, on simulated and real seismic data are presented.

show abstract

“…It is obvious that nowadays, detailed seismic processing requires algorithms and techniques that are created in an innovative way using a combination of well-known seismic procedures and newly adapted techniques from different branches of science and other industries-like image processing (Zareba and Danek 2018a, b;Krasnov et al 2018;Li et al 2009), big data analysis (Gupta et al 2014;Rawat 2014), deep learning neural networks (Lewis and Vigh 2017), and the stock market (Bedi and Toshniwal 2018;Kyoung-jae 2003). There are plenty of established filters for seismic data pre-stack or post-stack filtering, enhancing, and analyzing based on a statistical approach (Wang et al 2016;Hoeber et al 2003). While these filters are useful, we noticed that in regions of complicated geology like the Carpathians (please note we will only refer to the Polish Carpathians including Central Carpathians, Outer Carpathians, and Carpathian Foredeep region in general), problematic residual noise occurs when techniques based solely on statistical approach are used, especially when they are directly applied to the parts of the seismic dataset where high-value dips or complicated fault systems are present.…”

Section: Introductionmentioning

confidence: 99%

The use of S-guided CREP methodology for advanced seismic structure enhancing processing

Zaręba¹,

Laskownicka²,

Zając³

2019

Acta Geophys.

View full text Add to dashboard Cite

To acquire refined seismic targets, the use of more accurate and more precise methods of seismic signal processing is crucial. Novel seismic processing approach is even more important in case of the complex geology of mountainous regions (e.g., Carpathians, Carpathian Foredeep) where sub-vertical layering of varying rock complex can be found, but also in case of old seismic data reprocessing (especially when the signal-to-noise ratio and general quality are low). Regardless of many research papers and significant progress in seismic processing techniques, a properly processed and interpreted seismic image is still hardly affordable for this region. Obtaining a new value in seismic data processing is a core function of the presented research. The use of dip-guided filtering combined with beam partitioned analysis and Z-domain dip analysis for reprocessing of 3D and 2D seismic data from the Carpathians region has been studied.

show abstract

On the use of geostatistical filtering techniques in seismic processing

Abstract: We present an overview of new and recent applications of multivariate geostatistical filtering techniques applied to 3D and 4D processing.

Cited by 9 publications

References 7 publications

Automatic, dense and volumetric picking for high‐resolution 3D tomographic model building

Automatic, dense and volumetric picking for high‐resolution 3D tomographic model building

A matrix-free approach to geostatistical filtering

The use of S-guided CREP methodology for advanced seismic structure enhancing processing

Contact Info

Product

Resources

About