{"title":"基于噪声估计和局部相似性的自适应分级去噪地震数据","authors":"Xueting Yang, Yong Li, Zhangquan Liao, Yingtian Liu, Junheng Peng","doi":"arxiv-2408.13578","DOIUrl":null,"url":null,"abstract":"Seismic data denoising is an important part of seismic data processing, which\ndirectly relate to the follow-up processing of seismic data. In terms of this\nissue, many authors proposed many methods based on rank reduction, sparse\ntransformation, domain transformation, and deep learning. However, when the\nseismic data is noisy, complex and uneven, these methods often lead to\nover-denoising or under-denoising. To solve this problems, we proposed a novel\nmethod called noise level estimation and similarity segmentation for graded\ndenoising. Specifically, we first assessed the average noise level of the\nentire seismic data and denoised it using block matching and three-dimensional\nfiltering (BM3D) methods. Then, the denoised data is contrasted with the\nresidual using local similarity, pinpointing regions where noise levels deviate\nsignificantly from the average. The remaining data is retained intact. These\nareas are then re-evaluated and denoised. Finally, we integrated the data\nretained after the first denoising with the re-denoising data to get a complete\nand cleaner data. This method is verified on theoretical model and actual\nseismic data. The experimental results show that this method has a good effect\non seismic data with uneven noise.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"15 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Graded Denoising of Seismic Data Based on Noise Estimation and Local Similarity\",\"authors\":\"Xueting Yang, Yong Li, Zhangquan Liao, Yingtian Liu, Junheng Peng\",\"doi\":\"arxiv-2408.13578\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Seismic data denoising is an important part of seismic data processing, which\\ndirectly relate to the follow-up processing of seismic data. In terms of this\\nissue, many authors proposed many methods based on rank reduction, sparse\\ntransformation, domain transformation, and deep learning. However, when the\\nseismic data is noisy, complex and uneven, these methods often lead to\\nover-denoising or under-denoising. To solve this problems, we proposed a novel\\nmethod called noise level estimation and similarity segmentation for graded\\ndenoising. Specifically, we first assessed the average noise level of the\\nentire seismic data and denoised it using block matching and three-dimensional\\nfiltering (BM3D) methods. Then, the denoised data is contrasted with the\\nresidual using local similarity, pinpointing regions where noise levels deviate\\nsignificantly from the average. The remaining data is retained intact. These\\nareas are then re-evaluated and denoised. Finally, we integrated the data\\nretained after the first denoising with the re-denoising data to get a complete\\nand cleaner data. This method is verified on theoretical model and actual\\nseismic data. The experimental results show that this method has a good effect\\non seismic data with uneven noise.\",\"PeriodicalId\":501270,\"journal\":{\"name\":\"arXiv - PHYS - Geophysics\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Geophysics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.13578\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Geophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.13578","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Graded Denoising of Seismic Data Based on Noise Estimation and Local Similarity
Seismic data denoising is an important part of seismic data processing, which
directly relate to the follow-up processing of seismic data. In terms of this
issue, many authors proposed many methods based on rank reduction, sparse
transformation, domain transformation, and deep learning. However, when the
seismic data is noisy, complex and uneven, these methods often lead to
over-denoising or under-denoising. To solve this problems, we proposed a novel
method called noise level estimation and similarity segmentation for graded
denoising. Specifically, we first assessed the average noise level of the
entire seismic data and denoised it using block matching and three-dimensional
filtering (BM3D) methods. Then, the denoised data is contrasted with the
residual using local similarity, pinpointing regions where noise levels deviate
significantly from the average. The remaining data is retained intact. These
areas are then re-evaluated and denoised. Finally, we integrated the data
retained after the first denoising with the re-denoising data to get a complete
and cleaner data. This method is verified on theoretical model and actual
seismic data. The experimental results show that this method has a good effect
on seismic data with uneven noise.