Missing and duplicate sample phenomenon in marine seismic data recording: Causes and quality-control procedures for its detection

2011 ◽  
Vol 30 (4) ◽  
pp. 452-457
Author(s):  
David Vidal ◽  
Pablo Alarcón ◽  
Diego Deiros
Keyword(s):  
Quality Control ◽  
Seismic Data ◽  
Data Recording ◽  
Control Procedures ◽  
Marine Seismic ◽  
Duplicate Sample

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

Geosciences ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 475
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.

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

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.

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

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

Studies and Application of Auger Monitoring System for Quality Control and Assurance of Al Bondpads

2016 ◽  
Author(s):  
Hua Younan
Keyword(s):  
Quality Control ◽  
Sample Preparation ◽  
Monitoring System ◽  
Failure Mechanisms ◽  
Wafer Fabrication ◽  
Contamination Level ◽  
Auger Analysis ◽  
Low Level ◽  
Monitoring Frequency ◽  
Control Procedures

Abstract In wafer fabrication (Fab), Fluorine (F) based gases are used for Al bondpad opening process. Thus, even on a regular Al bondpad, there exists a low level of F contamination. However, the F level has to be controlled at a lower level. If the F level is higher than the control/spec limits, it could cause F-induced corrosion and Al-F defects, resulting in pad discoloration and NSOP problems. In our previous studies [1-5], the theories, characteristics, chemical and physical failure mechanisms and the root causes of the F-induced corrosion and Al-F defects on Al bondpads have been studied. In this paper, we further study F-induced corrosion and propose to establish an Auger monitoring system so as to monitor the F contamination level on Al bondpads in wafer fabrication. Auger monitoring frequency, sample preparation, wafer life, Auger analysis points, control/spec limits and OOC/OOS quality control procedures are also discussed.

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

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...

SOUTH GEORGIA MICROCONTINENT: CURRENT TECTONIC SETTING FROM GPS AND MARINE SEISMIC DATA

2019 ◽  
Author(s):  
Ian W.D. Dalziel ◽  
◽  
Robert Smalley ◽  
Lawrence A. Lawver ◽  
Demian Gomez ◽  
...  
Keyword(s):  
Seismic Data ◽  
Tectonic Setting ◽  
South Georgia ◽  
Marine Seismic

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

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