Version 1
: Received: 1 October 2020 / Approved: 2 October 2020 / Online: 2 October 2020 (15:32:31 CEST)
How to cite:
Mejri, M.; Mejri, A.; Bekara, M. Application of Machine Learning for Quality Control of Noise Attenuation Processes in Seismic Data Imaging. Preprints2020, 2020100048. https://doi.org/10.20944/preprints202010.0048.v1
Mejri, M.; Mejri, A.; Bekara, M. Application of Machine Learning for Quality Control of Noise Attenuation Processes in Seismic Data Imaging. Preprints 2020, 2020100048. https://doi.org/10.20944/preprints202010.0048.v1
Mejri, M.; Mejri, A.; Bekara, M. Application of Machine Learning for Quality Control of Noise Attenuation Processes in Seismic Data Imaging. Preprints2020, 2020100048. https://doi.org/10.20944/preprints202010.0048.v1
APA Style
Mejri, M., Mejri, A., & Bekara, M. (2020). Application of Machine Learning for Quality Control of Noise Attenuation Processes in Seismic Data Imaging. Preprints. https://doi.org/10.20944/preprints202010.0048.v1
Chicago/Turabian Style
Mejri, M., Aymen Mejri and Maiza Bekara. 2020 "Application of Machine Learning for Quality Control of Noise Attenuation Processes in Seismic Data Imaging" Preprints. https://doi.org/10.20944/preprints202010.0048.v1
Abstract
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. It is based on emitting a sound (pressure) wave through the subsurface and recording the reflected echoes using hydrophones (pressure sensors) and/or geophones (velocity/acceleration sensors). Contrary to medical imaging, which is done in real time, subsurface seismic imaging is an offline process that involves a huge volume of data and needs considerable computing power. 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 = underfiltering, noise remaining in the data; optimal = good filtering; harsh = overfiltering, 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. The results are encouraging and helped identify localized issues that were difficult for a human to spot.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
