Bibliographic Details
| Title: |
Random noise attenuation via convolutional neural network in seismic datasets |
| Authors: |
Ruishan Du, Wenhao Liu, Xiaofei Fu, Lingdong Meng, Zhigang Liu |
| Source: |
Alexandria Engineering Journal, Vol 61, Iss 12, Pp 9901-9909 (2022) |
| Publisher Information: |
Elsevier, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Engineering (General). Civil engineering (General) |
| Subject Terms: |
Seismic data interpretation, Convolution neural network, Seismic fault, Random noise, Denoising, Engineering (General). Civil engineering (General), TA1-2040 |
| More Details: |
With the explosive growth in seismic data acquisition and the successful application of convolutional neural networks to various image processing tasks within multidisciplinary fields, many researchers have begun to research convolutional neural networks based seismic interpretation techniques. Seismic random noise attenuation is a key step in seismic data processing. In seismic data interpretation, faults are an important geological structure that has great significance for accumulation and migration of oil and gas reservoirs. Random noise within seismic data will seriously affect the accuracy of subsequent data processing and interpretation. Therefore, it is crucial to eliminate random noise in seismic data. This paper aimed to improve the Signal-to-Noise Ratio of seismic data, and proposed an intelligent convolutional neural network noise reduction framework. In this paper, the median filtering, the mean filtering, and the proposed algorithm is used to denoise seismic fault data. Experimental results show that the method not only yields a higher Signal-to-Noise Ratio, but also preserves more useful fault information. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1110-0168 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1110016822001752; https://doaj.org/toc/1110-0168 |
| DOI: |
10.1016/j.aej.2022.03.008 |
| Access URL: |
https://doaj.org/article/4ff3496527db44779f9c17eefe1be95a |
| Accession Number: |
edsdoj.4ff3496527db44779f9c17eefe1be95a |
| Database: |
Directory of Open Access Journals |
