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Different types of noise can be suppressed using targeted denoising methods. For outlier noise with singular amplitudes, many classical denoising methods suffer from signal leakage. To mitigate this issue, we developed a statistics-based mask method and incorporated it into the compressive sensing (CS) framework, in order to remove outlier noise. A statistical analysis for seismic data amplitudes was first used to identify the locations of traces containing outlier noise. Then, the outlier trace locations were compared with a mask matrix generated by jitter sampling, and we replaced the sampled traces of the jitter mask that had the outlier noise with their nearby unsampled traces. The optimized sampling matrix enabled us to effectively identify and remove outliers. This optimized mask strategy converts an outlier denoising problem into a data reconstruction problem. Finally, a sparsely constrained inverse problem was solved using a soft-threshold iteration solver to recover signals at the null locations. The feasibility and adaptability of the proposed method were demonstrated through numerical experiments for synthetic and field data. The results showed that the proposed method outperformed the conventional f-x deconvolution and median filter method, and could accurately suppress outlier noise and recover missed expected signals.<\/jats:p>","DOI":"10.3390\/rs15020447","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T03:11:02Z","timestamp":1673493062000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Outlier Denoising Using a Novel Statistics-Based Mask Strategy for Compressive Sensing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5509-0432","authenticated-orcid":false,"given":"Weiqi","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Deep Oil and Gas, School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6652-5366","authenticated-orcid":false,"given":"Jidong","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Deep Oil and Gas, School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Deep Oil and Gas, School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenchun","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Deep Oil and Gas, School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaomiao","family":"Sun","sequence":"additional","affiliation":[{"name":"Software & Service Outsourcing\/IT, Qingdao Vocational and Technical College, Qingdao 266555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"V179","DOI":"10.1190\/geo2016-0240.1","article-title":"Attenuation of noise and simultaneous source interference using wavelet denoising","volume":"82","author":"Yu","year":"2017","journal-title":"Geophysics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1093\/gji\/ggaa071","article-title":"Denoising of seismic data in desert environment based on a variational mode decomposition and a convolutional neural network","volume":"221","author":"Zhao","year":"2020","journal-title":"Geophys. 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