CRS‐stack‐based seismic imaging considering top‐surface topography

Heilmann, Z.; Mann, J.; Koglin, Ingo

doi:10.1111/j.1365-2478.2005.00565.x

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Surface consistent residual static corrections are calculated by our software maximizing the stack‐power with a CRS‐stack‐based procedure (Koglin et al . ), which works similarly to the super‐trace cross‐correlation method of Ronen and Claerbout (). The algorithm is based on cross‐correlating traces from the stacked section with the moveout‐corrected prestack traces of the corresponding CRS supergather.…”

Section: Eiagrid: Crs‐based‐imaging Service On a Cloud Infrastructurementioning

confidence: 99%

Real‐time imaging and data analysis for shallow seismic data using a cloud‐computing portal

Heilmann

Deidda

Satta

et al. 2013

Near Surface Geophysics

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In-field real-time data processing is still today a crucial factor that could definitively boost the success\ud and spread of shallow seismic reflection and multichannel ground-penetrating radar (GPR)\ud methods in the near-surface geoscience community, as it allows efficient data acquisition and cost-effective\ud results, especially for modern surveys generating large volumes of data in a short time. To\ud help fulfil this need, we present a cloud-computing solution combining the powerful computational\ud capabilities of a cloud infrastructure with a subsurface imaging workflow based on a parallelized\ud grid version of the Common-Reflection-Surface (CRS) stack, a macro-velocity model independent\ud imaging method that is very suitable for real-time imaging, as its data-driven implementation avoids\ud time-consuming human interaction in prestack velocity analysis. Our portal is accessible from the\ud field by any mobile computer having a wireless data connection. The user-friendly web-browser\ud interface allows, already during acquisition, uploading of the recorded data to remote computing\ud facilities, where a quality control (QC) data analysis report is automatically produced. When the\ud number of uploaded shot records is large enough to produce a subsurface image, stacking, velocity\ud model determination and prestack time migration can be performed fast and highly automated,\ud optionally after applying some preprocessing (e.g., gain, trace balancing and filtering) using the\ud preprocessing toolbox of the portal. To demonstrate the use of the presented system, we simulate\ud in-field data processing for an already published shallow seismic data set and compare our results\ud with the original ones. The data set, consisting in seismic P-wave data, was collected to image\ud Palaeozoic bedrock at the Flumendosa River Delta, Sardinia (Italy), with a data acquisition set-up\ud driven by the expected bedrock depth, which, turning out wrong, prevented a detailed velocity\ud analysis and a good time migration. Using the QC data analysis reports to run the imaging routines\ud of our cloud portal we produced within less than an half an hour stacked and migrated sections very\ud close to those published in the original work. These results, if obtained in the field, would have\ud allowed an immediate update of the experimental set-up. Therefore, we are optimistic that the proposed\ud cloud-computing solution (or similar systems) can boost the spread of shallow seismic reflection\ud and multi-offset GPR surveys in near-surface investigations, similar to what happened in the\ud field of electrical and electromagnetic surveying after suitable real-time imaging and data analysis\ud systems emerged

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Section: Eiagrid: Crs‐based‐imaging Service On a Cloud Infrastructurementioning

confidence: 99%

Real‐time imaging and data analysis for shallow seismic data using a cloud‐computing portal

Heilmann

Deidda

Satta

et al. 2013

Near Surface Geophysics

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“…Two different extensions of the CRS stack to handle top‐surface topography have been introduced: Chira et al (2001) and Heilmann (2003) considered a smooth topography, whereas Zhang (2003) directly addressed arbitrary topography. Heilmann, Mann and Koglin (2006) showed a combination of these two approaches in a cascaded application within the CRS stack method.…”

Section: Residual Static Correction By Means Of Crs Attributesmentioning

confidence: 99%

CRS‐stack‐based residual static correction

Koglin

Mann

Heilmann

2006

Geophysical Prospecting

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A B S T R A C TIn the case of onshore data sets, the acquired reflection events can be strongly impaired due to rough top-surface topography and inhomogeneities in the uppermost lowvelocity layer, the so-called weathering layer. Without accounting for these influences, the poor data quality will make data processing very difficult.Usually, the correction for the top-surface topography is not perfect. The residuals from this correction and the influence of the weathering layers lead to small distortions along the reflection events. We integrated a residual static correction method into our data-driven common-reflection-surface-stack-based imaging workflow to further eliminate such distortions. The moveout-corrected traces and the stacked pilot trace are cross-correlated to determine a final estimate of the surface-consistent residual statics in an iterative manner.As the handling of top-surface topography within the common-reflection-surface stack is discussed in a separate paper in this special issue, the corresponding residual static correction will be explained in more detail. For this purpose, the results obtained with a data set from the Arabian Peninsula will be presented.

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“…A more pragmatic method is to use traveltime operators (kinematic operators) to describe the wave propagation in the near-surface area. Bagaini and Alkhalifa (2006) proposed operators with low-order parametrization that focus the data at a certain datum level and Heilmann, Mann and Koglin (2006) considered a so-called common reflection surface (CRS)-based imaging solution, where the two-way reflection times are described with less than 10 parameters in the 2D case with surface topography included. In order to accommodate more complex wave propagation effects Hindriks and Duijndam (1999), Kelamis et al (1999) and Al-Ali and Verschuur (2006), proposed more flexible traveltime operators dedicated to the near-surface area.…”

Section: Introductionmentioning

confidence: 99%

Solving the complex near‐surface problem using 3D data‐driven near‐surface layer replacement

Sun

Verschuur

Vrolijk³

2014

Geophysical Prospecting

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In many land seismic situations, the complex seismic wave propagation effects in the near‐surface area, due to its unconsolidated character, deteriorate the image quality. Although several methods have been proposed to address this problem, the negative impact of 3D complex near‐surface structures is still unsolved to a large extent. This paper presents a complete 3D data‐driven solution for the near‐surface problem based on 3D one‐way traveltime operators, which extends our previous attempts that were limited to a 2D situation. Our solution is composed of four steps: 1) seismic wave propagation from the surface to a suitable datum reflector is described by parametrized one‐way propagation operators, with all the parameters estimated by a new genetic algorithm, the self‐adjustable input genetic algorithm, in an automatic and purely data‐driven way; 2) surface‐consistent residual static corrections are estimated to accommodate the fast variations in the near‐surface area; 3) a replacement velocity model based on the traveltime operators in the good data area (without the near‐surface problem) is estimated; 4) data interpolation and surface layer replacement based on the estimated traveltime operators and the replacement velocity model are carried out in an interweaved manner in order to both remove the near‐surface imprints in the original data and keep the valuable geological information above the datum. Our method is demonstrated on a subset of a 3D field data set from the Middle East yielding encouraging results.

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CRS‐stack‐based seismic imaging considering top‐surface topography

Cited by 17 publications

References 24 publications

Real‐time imaging and data analysis for shallow seismic data using a cloud‐computing portal

Real‐time imaging and data analysis for shallow seismic data using a cloud‐computing portal

CRS‐stack‐based residual static correction

Solving the complex near‐surface problem using 3D data‐driven near‐surface layer replacement

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