scholarly journals Muting the noise cone in near‐surface reflection data: An example from southeastern Kansas

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1332-1338 ◽  
Author(s):  
Gregory S. Baker ◽  
Don W. Steeples ◽  
Matt Drake
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Hydrocarbon Exploration ◽  
Seismic Reflection Profile ◽  
Seismic Data Processing ◽  
Near Surface ◽  
Surface Reflection ◽  
Reflection Data ◽  
Data Volume ◽  
Total Data

A 300-m near‐surface seismic reflection profile was collected in southeastern Kansas to locate a fault(s) associated with a recognized stratigraphic offset on either side of a region of unexposed bedrock. A substantial increase in the S/N ratio of the final stacked section was achieved by muting all data arriving in time after the airwave. Methods of applying traditional seismic data processing techniques to near‐surface data (200 ms of data or less) often differ notably from hydrocarbon exploration‐scale processing (3–4 s of data or more). The example of noise cone muting used is contrary to normal exploration‐scale seismic data processing philosophy, which is to include all data containing signal. The noise cone mute applied to the data removed more than one‐third of the total data volume, some of which contains signal. In this case, however, the severe muting resulted in a higher S/N ratio in the final stacked section, even though some signal could be identified within the muted data. This example supports the suggestion that nontraditional techniques sometimes need to be considered when processing near‐surface seismic data.

Velocity-independent redatuming: A new approach to the near-surface problem in land seismic data processing

2002 ◽  
Vol 21 (8) ◽  
pp. 730-735 ◽  
Author(s):  
Panos G. Kelamis ◽  
Kevin E. Erickson ◽  
Dirk J. Verschuur ◽  
A. J. Berkhout
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Seismic Data Processing ◽  
Near Surface ◽  
New Approach

EXPLORATION HORIZONS FROM NEW SEISMIC CONCEPTS OF CDP AND DIGITAL PROCESSING

Geophysics ◽  
1967 ◽  
Vol 32 (2) ◽  
pp. 207-224 ◽  
Author(s):  
John D. Marr ◽  
Edward F. Zagst
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Synthetic Data ◽  
Digital Processing ◽  
Seismic Data Processing ◽  
Multiple Reflections ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recent Developments

The more recent developments in common‐depth‐point techniques to attenuate multiple reflections have resulted in an exploration capability comparable to the development of the seismic reflection method. The combination of new concepts in digital seismic data processing with CDP techniques is creating unforeseen exploration horizons with vastly improved seismic data. Major improvements in multiple reflection and reverberation attenuation are now attainable with appropriate CDP geometry and special CDP stacking procedures. Further major improvements are clearly evident in the very near future with the use of multichannel digital filtering‐stacking techniques and the application of deconvolution as the first step in seismic data processing. CDP techniques are briefly reviewed and evaluated with real and experimental data. Synthetic data are used to illustrate that all seismic reflection data should be deconvolved as the first processing step.

Borehole-Driven 3D Surface Seismic Data Processing Using DAS-VSP Data

2021 ◽  
Author(s):  
Gang Yu ◽  
Junjun Wu ◽  
Yuanzhong Chen ◽  
Ximing Wang
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Seismic Data ◽  
Field Data ◽  
Seismic Data Processing ◽  
3D Surface ◽  
Data Volume ◽  
Vsp Data ◽  
Seismic Data Acquisition ◽  
Anisotropy Parameters

Abstract A 3D surface seismic data acquisition project was conducted simultaneously with 3D DAS-VSP data acquisition in one well in Jilin Oilfield of Northen China. The 3D surface seismic data acquisition project covered an area of 75 km2, and one borehole (DS32-3) and an armoured optical cable with high temperature single mode fiber were used to acquire the 3D DAS-VSP data simultaneously when the crew was acquiring the 3D surface seismic data. The simultaneously acquired 3D DAS-VSP data were used to extract formation velocity, deconvolution operator, absorption, attenuation (Q value), anisotropy parameters (η, δ, ε) as wel as enhanced the surface seismic data processing including velocity model calibration and modification, static correction, deconvolution, demultiple processing, high frequency restoration, anisotropic migration, and Q-compensation or Q-migration. In this project, anisotropic migration, Q-migration was conducted with the anisotropy parameters (η, δ, ε) data volume and enhanced Q-field data volume obtained from the joint inversion of both the near surface 3D Q-field data volume from uphole data and the mid-deep layer Q-field data volume from all available VSP data in the 3D surface seismic surveey area. The anosotropic migration and Q-migration results show much sharper and focussed faults and and clearer subsutface structure.

A hybrid learning-based framework for seismic denoising

2019 ◽  
Vol 38 (7) ◽  
pp. 542-549 ◽  
Author(s):  
Chengbo Li ◽  
Yu Zhang ◽  
Charles C. Mosher
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Hybrid Learning ◽  
Environmental Noise ◽  
Great Promise ◽  
Seismic Data Processing ◽  
Weak Signals ◽  
Coherent Noise ◽  
Near Surface ◽  
Conventional Methods

Noise attenuation has been a long-standing problem in seismic data processing. It presents unique challenges on land due to a complex near surface coupled with unavoidable environmental noise sources. In many cases, weak signals are embedded in much stronger noise, which makes conventional methods less effective at extracting those signals. In addition, conventional methods may lack adaptability to various noise types and patterns. Machine learning has shown great promise in solving geophysical problems including seismic data processing and interpretation. Here, we propose a novel method that is applicable to attenuating both incoherent noise, such as environmental noise, and coherent noise, such as ground roll and scattered noise, under a unified learning-based framework. This framework takes advantage of conventional methods to build the initial models and then employs dictionary learning and sparse inversion to invert both signal and noise simultaneously. The proposed method augments conventional methods by leveraging learning to recover residual weak signals from strong noise. We have applied this hybrid learning-based method successfully to some of the most difficult data areas where conventional denoising methods underperformed. Synthetic and real data examples demonstrate the effectiveness of the method for various noise types.

Reverse-time demigration using the extended-imaging condition

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. WA97-WA105 ◽  
Author(s):  
Wiktor Waldemar Weibull ◽  
Børge Arntsen
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Seismic Data Processing ◽  
Reverse Time ◽  
Image Domain ◽  
Seismic Reflection Data ◽  
Imaging Condition ◽  
Reflection Data ◽  
Time Migration

The forward and inverse process of seismic migration and demigration or remodeling has many useful applications in seismic data processing. We evaluated a method to reobtain the seismic reflection data after migration, by inverting the common image point gathers produced by reverse-time migration (RTM) with an extended-imaging condition. This provided a transformation of the results of seismic data processing in the image domain back to the data domain. To be able to reconstruct the data with high fidelity, we set up demigration as a least-squares inverse problem and we solved it iteratively using a steepest-descent method. Because we used an extended-imaging condition, the method is not dependent on an accurate estimate of the migration-velocity field, and it is able to accurately reconstruct both primaries and multiples. At the same time, because the method is based on RTM, it can accurately handle seismic reflection data acquired over complex geologic media. Numerical results showed the feasibility of the method and highlighted some of its applications on 2D synthetic and field data sets.

Sign in / Sign up

Export Citation Format

Share Document