Signal preserving seismic interference noise attenuation on 3D marine seismic data

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It provides an image of the subsurface using the same principles as ultrasound medical imaging. As for any data acquired through hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors), the raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = under filtering, noise remaining in the data; optimal = good filtering; harsh = over filtering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km2) to QC the results of the swell noise attenuation process in marine seismic data.

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Using Data‐Regrouping Methods to Attenuate Shot‐to‐Shot Coherent Interference Noise in Marine Seismic Data

Earth and Space Science ◽

10.1029/2018ea000485 ◽

2019 ◽

Vol 6 (7) ◽

pp. 1098-1108

Author(s):

Honglei Shen ◽

Thomas Elboth ◽

Chunhui Tao ◽

Gang Tian ◽

Hanchuang Wang ◽

...

Keyword(s):

Seismic Data ◽

Interference Noise ◽

Using Data ◽

Marine Seismic

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Application of Machine Learning for the Automation of the Quality Control of Noise Filtering Processes in Seismic Data Imaging

10.20944/preprints202011.0541.v1 ◽

2020 ◽

Author(s):

mohamed mejri ◽

Maiza Bekara

Keyword(s):

Quality Control ◽

Seismic Data ◽

Early Stage ◽

Pressure Sensors ◽

Noise Attenuation ◽

Data Quality Control ◽

Statistical Measures ◽

Acceleration Sensors ◽

Marine Seismic ◽

The Cost

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. It~provides an image of the subsurface using the same principles as ultrasound medical imaging. As for any data acquired through hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors), the raw seismic data are heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Quality control (QC) is mandatory to give confidence in the denoising process and to ensure that a costly data re-acquisition is not needed. QC is done manually by humans and comprises a major portion of the cost of a typical seismic processing project. It is therefore advantageous to automate this process to improve cost and efficiency. Here, we propose a supervised learning approach to build an automatic QC system. The~QC system is an attribute-based classifier that is trained to classify three types of filtering (mild = under filtering, noise remaining in the data; optimal = good filtering; harsh = over filtering, the signal is distorted). The attributes are computed from the data and represent geophysical and statistical measures of the quality of the filtering. The system is tested on a full-scale survey (9000 km2) to QC the results of the swell noise attenuation process in marine seismic data.

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Formation geological depth image according to refraction and reflection marine seismic data

Geofizicheskiy Zhurnal ◽

10.24028/gzh.0203-3100.v39i6.2017.116375 ◽

2017 ◽

Vol 39 (6) ◽

pp. 106-121

Author(s):

A. O. Verpahovskaya ◽

V. N. Pilipenko ◽

Е. V. Pylypenko

Keyword(s):

Seismic Data ◽

Depth Image ◽

Marine Seismic

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PROCESSING AND IMAGING OF MARINE SEISMIC DATA FROM THE JEQUITINHONHA BASIN (BAHIA, BRAZIL)

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v33i3.743 ◽

2016 ◽

Vol 33 (3) ◽

Author(s):

Lourenildo W.B. Leite ◽

J. Mann ◽

Wildney W.S. Vieira

Keyword(s):

Seismic Data ◽

Sedimentary Basins ◽

Study Results ◽

Marine Seismic ◽

Present Case Study

ABSTRACT. The present case study results from a consistent processing and imaging of marine seismic data from a set collected over sedimentary basins of the East Brazilian Atlantic. Our general aim is... RESUMO. O presente artigo resulta de um processamento e imageamento consistentes de dados sísmicos marinhos de levantamento realizado em bacias sedimentares do Atlântico do Nordeste...

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