Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition Article Swipe
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/2132732
Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this paper. The proposed method takes advantages both of variational mode decomposition and multivariate variational mode decomposition. This proposed method firstly segments the input data into a series of smaller ones with no overlapping and then applies multivariate variational mode decomposition to these smaller ones. High frequency-domain noise is filtered through sifting. Finally, the denoised smaller ones are concatenated to form components (or intrinsic mode functions) of the input signal. Synthetic and field data experiments validate the proposed method with different batch sizes and achieve higher signal-to-noise ratio than the variational mode decomposition method.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/2132732
- https://downloads.hindawi.com/journals/cin/2022/2132732.pdf
- OA Status
- hybrid
- Cited By
- 8
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214585209
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4214585209Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/2132732Digital Object Identifier
- Title
-
Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode DecompositionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-26Full publication date if available
- Authors
-
Guoning Wu, Guochang Liu, Junxian Wang, Pingping FanList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/2132732Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cin/2022/2132732.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://downloads.hindawi.com/journals/cin/2022/2132732.pdfDirect OA link when available
- Concepts
-
Hilbert–Huang transform, Multivariate statistics, Seismic trace, Wavelet, Noise (video), Mode (computer interface), Noise reduction, Mathematics, Decomposition, Computer science, Algorithm, White noise, Artificial intelligence, Statistics, Chemistry, Organic chemistry, Operating system, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4214585209 |
|---|---|
| doi | https://doi.org/10.1155/2022/2132732 |
| ids.doi | https://doi.org/10.1155/2022/2132732 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/35256873 |
| ids.openalex | https://openalex.org/W4214585209 |
| fwci | 1.47612131 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D012815 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Signal Processing, Computer-Assisted |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D059629 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Signal-To-Noise Ratio |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D058067 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Wavelet Analysis |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000465 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Algorithms |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D012815 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Signal Processing, Computer-Assisted |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D059629 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Signal-To-Noise Ratio |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D058067 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Wavelet Analysis |
| type | article |
| title | Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition |
| awards[0].id | https://openalex.org/G3810242484 |
| awards[0].funder_id | https://openalex.org/F4320326291 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2462020YXZZ006 |
| awards[0].funder_display_name | Science Foundation of China University of Petroleum, Beijing |
| biblio.issue | |
| biblio.volume | 2022 |
| biblio.last_page | 14 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T10271 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1908 |
| topics[0].subfield.display_name | Geophysics |
| topics[0].display_name | Seismic Imaging and Inversion Techniques |
| topics[1].id | https://openalex.org/T10892 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9976999759674072 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2212 |
| topics[1].subfield.display_name | Ocean Engineering |
| topics[1].display_name | Drilling and Well Engineering |
| topics[2].id | https://openalex.org/T10220 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.996999979019165 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2207 |
| topics[2].subfield.display_name | Control and Systems Engineering |
| topics[2].display_name | Machine Fault Diagnosis Techniques |
| funders[0].id | https://openalex.org/F4320326291 |
| funders[0].ror | |
| funders[0].display_name | Science Foundation of China University of Petroleum, Beijing |
| is_xpac | False |
| apc_list.value | 2100 |
| apc_list.currency | USD |
| apc_list.value_usd | 2100 |
| apc_paid.value | 2100 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2100 |
| concepts[0].id | https://openalex.org/C25570617 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7187482118606567 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1006462 |
| concepts[0].display_name | Hilbert–Huang transform |
| concepts[1].id | https://openalex.org/C161584116 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6862784624099731 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1952580 |
| concepts[1].display_name | Multivariate statistics |
| concepts[2].id | https://openalex.org/C25227454 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5914490222930908 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7446975 |
| concepts[2].display_name | Seismic trace |
| concepts[3].id | https://openalex.org/C47432892 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5725232362747192 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q831390 |
| concepts[3].display_name | Wavelet |
| concepts[4].id | https://openalex.org/C99498987 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5628340840339661 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2210247 |
| concepts[4].display_name | Noise (video) |
| concepts[5].id | https://openalex.org/C48677424 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5459985733032227 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q6888088 |
| concepts[5].display_name | Mode (computer interface) |
| concepts[6].id | https://openalex.org/C163294075 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5367293357849121 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q581861 |
| concepts[6].display_name | Noise reduction |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.45777225494384766 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C124681953 |
| concepts[8].level | 2 |
| concepts[8].score | 0.45636671781539917 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q339062 |
| concepts[8].display_name | Decomposition |
| concepts[9].id | https://openalex.org/C41008148 |
| concepts[9].level | 0 |
| concepts[9].score | 0.41532862186431885 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[9].display_name | Computer science |
| concepts[10].id | https://openalex.org/C11413529 |
| concepts[10].level | 1 |
| concepts[10].score | 0.407992959022522 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[10].display_name | Algorithm |
| concepts[11].id | https://openalex.org/C112633086 |
| concepts[11].level | 2 |
| concepts[11].score | 0.2460714876651764 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q381287 |
| concepts[11].display_name | White noise |
| concepts[12].id | https://openalex.org/C154945302 |
| concepts[12].level | 1 |
| concepts[12].score | 0.21727913618087769 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[12].display_name | Artificial intelligence |
| concepts[13].id | https://openalex.org/C105795698 |
| concepts[13].level | 1 |
| concepts[13].score | 0.21030092239379883 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[13].display_name | Statistics |
| concepts[14].id | https://openalex.org/C185592680 |
| concepts[14].level | 0 |
| concepts[14].score | 0.07768389582633972 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[14].display_name | Chemistry |
| concepts[15].id | https://openalex.org/C178790620 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11351 |
| concepts[15].display_name | Organic chemistry |
| concepts[16].id | https://openalex.org/C111919701 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[16].display_name | Operating system |
| concepts[17].id | https://openalex.org/C115961682 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[17].display_name | Image (mathematics) |
| keywords[0].id | https://openalex.org/keywords/hilbert–huang-transform |
| keywords[0].score | 0.7187482118606567 |
| keywords[0].display_name | Hilbert–Huang transform |
| keywords[1].id | https://openalex.org/keywords/multivariate-statistics |
| keywords[1].score | 0.6862784624099731 |
| keywords[1].display_name | Multivariate statistics |
| keywords[2].id | https://openalex.org/keywords/seismic-trace |
| keywords[2].score | 0.5914490222930908 |
| keywords[2].display_name | Seismic trace |
| keywords[3].id | https://openalex.org/keywords/wavelet |
| keywords[3].score | 0.5725232362747192 |
| keywords[3].display_name | Wavelet |
| keywords[4].id | https://openalex.org/keywords/noise |
| keywords[4].score | 0.5628340840339661 |
| keywords[4].display_name | Noise (video) |
| keywords[5].id | https://openalex.org/keywords/mode |
| keywords[5].score | 0.5459985733032227 |
| keywords[5].display_name | Mode (computer interface) |
| keywords[6].id | https://openalex.org/keywords/noise-reduction |
| keywords[6].score | 0.5367293357849121 |
| keywords[6].display_name | Noise reduction |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.45777225494384766 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/decomposition |
| keywords[8].score | 0.45636671781539917 |
| keywords[8].display_name | Decomposition |
| keywords[9].id | https://openalex.org/keywords/computer-science |
| keywords[9].score | 0.41532862186431885 |
| keywords[9].display_name | Computer science |
| keywords[10].id | https://openalex.org/keywords/algorithm |
| keywords[10].score | 0.407992959022522 |
| keywords[10].display_name | Algorithm |
| keywords[11].id | https://openalex.org/keywords/white-noise |
| keywords[11].score | 0.2460714876651764 |
| keywords[11].display_name | White noise |
| keywords[12].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[12].score | 0.21727913618087769 |
| keywords[12].display_name | Artificial intelligence |
| keywords[13].id | https://openalex.org/keywords/statistics |
| keywords[13].score | 0.21030092239379883 |
| keywords[13].display_name | Statistics |
| keywords[14].id | https://openalex.org/keywords/chemistry |
| keywords[14].score | 0.07768389582633972 |
| keywords[14].display_name | Chemistry |
| language | en |
| locations[0].id | doi:10.1155/2022/2132732 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S72372694 |
| locations[0].source.issn | 1687-5265, 1687-5273 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1687-5265 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Computational Intelligence and Neuroscience |
| locations[0].source.host_organization | https://openalex.org/P4310319869 |
| locations[0].source.host_organization_name | Hindawi Publishing Corporation |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319869 |
| locations[0].source.host_organization_lineage_names | Hindawi Publishing Corporation |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://downloads.hindawi.com/journals/cin/2022/2132732.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Computational Intelligence and Neuroscience |
| locations[0].landing_page_url | https://doi.org/10.1155/2022/2132732 |
| locations[1].id | pmid:35256873 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Computational intelligence and neuroscience |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/35256873 |
| locations[2].id | pmh:oai:doaj.org/article:67c424de80aa4b66a6e266db336c7c3f |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Computational Intelligence and Neuroscience, Vol 2022 (2022) |
| locations[2].landing_page_url | https://doaj.org/article/67c424de80aa4b66a6e266db336c7c3f |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:8898116 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Comput Intell Neurosci |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8898116 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5049684563 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2155-9738 |
| authorships[0].author.display_name | Guoning Wu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I204553293 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Science, China University of Petroleum (Beijing), Beijing, China |
| authorships[0].institutions[0].id | https://openalex.org/I204553293 |
| authorships[0].institutions[0].ror | https://ror.org/041qf4r12 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I204553293 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | China University of Petroleum, Beijing |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Guoning Wu |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | College of Science, China University of Petroleum (Beijing), Beijing, China |
| authorships[1].author.id | https://openalex.org/A5102027906 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3859-9042 |
| authorships[1].author.display_name | Guochang Liu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I204553293 |
| authorships[1].affiliations[0].raw_affiliation_string | State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China |
| authorships[1].institutions[0].id | https://openalex.org/I204553293 |
| authorships[1].institutions[0].ror | https://ror.org/041qf4r12 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I204553293 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | China University of Petroleum, Beijing |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Guochang Liu |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China |
| authorships[2].author.id | https://openalex.org/A5101484738 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7505-6960 |
| authorships[2].author.display_name | Junxian Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I204553293 |
| authorships[2].affiliations[0].raw_affiliation_string | College of Science, China University of Petroleum (Beijing), Beijing, China |
| authorships[2].institutions[0].id | https://openalex.org/I204553293 |
| authorships[2].institutions[0].ror | https://ror.org/041qf4r12 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I204553293 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | China University of Petroleum, Beijing |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Junxian Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | College of Science, China University of Petroleum (Beijing), Beijing, China |
| authorships[3].author.id | https://openalex.org/A5111840745 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6695-8946 |
| authorships[3].author.display_name | Pingping Fan |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I204553293 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Science, China University of Petroleum (Beijing), Beijing, China |
| authorships[3].institutions[0].id | https://openalex.org/I204553293 |
| authorships[3].institutions[0].ror | https://ror.org/041qf4r12 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I204553293 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | China University of Petroleum, Beijing |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Pingping Fan |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | College of Science, China University of Petroleum (Beijing), Beijing, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://downloads.hindawi.com/journals/cin/2022/2132732.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10271 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1908 |
| primary_topic.subfield.display_name | Geophysics |
| primary_topic.display_name | Seismic Imaging and Inversion Techniques |
| related_works | https://openalex.org/W4391848769, https://openalex.org/W2049717220, https://openalex.org/W2382026961, https://openalex.org/W3035364224, https://openalex.org/W2945726323, https://openalex.org/W2387307148, https://openalex.org/W2964599635, https://openalex.org/W2294522120, https://openalex.org/W3090108020, https://openalex.org/W2595761700 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1155/2022/2132732 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S72372694 |
| best_oa_location.source.issn | 1687-5265, 1687-5273 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1687-5265 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Computational Intelligence and Neuroscience |
| best_oa_location.source.host_organization | https://openalex.org/P4310319869 |
| best_oa_location.source.host_organization_name | Hindawi Publishing Corporation |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319869 |
| best_oa_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://downloads.hindawi.com/journals/cin/2022/2132732.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Computational Intelligence and Neuroscience |
| best_oa_location.landing_page_url | https://doi.org/10.1155/2022/2132732 |
| primary_location.id | doi:10.1155/2022/2132732 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S72372694 |
| primary_location.source.issn | 1687-5265, 1687-5273 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1687-5265 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Computational Intelligence and Neuroscience |
| primary_location.source.host_organization | https://openalex.org/P4310319869 |
| primary_location.source.host_organization_name | Hindawi Publishing Corporation |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319869 |
| primary_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://downloads.hindawi.com/journals/cin/2022/2132732.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Computational Intelligence and Neuroscience |
| primary_location.landing_page_url | https://doi.org/10.1155/2022/2132732 |
| publication_date | 2022-02-26 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2021734622, https://openalex.org/W2177432006, https://openalex.org/W2294362001, https://openalex.org/W2282024151, https://openalex.org/W2954539448, https://openalex.org/W2784540332, https://openalex.org/W3090716486, https://openalex.org/W3121984162, https://openalex.org/W2020997493, https://openalex.org/W2099081746, https://openalex.org/W2081689217, https://openalex.org/W2020205028, https://openalex.org/W3131297087, https://openalex.org/W3033557345, https://openalex.org/W3111925087, https://openalex.org/W2603052873, https://openalex.org/W2007221293, https://openalex.org/W2809994916, https://openalex.org/W2125056386, https://openalex.org/W2090218979, https://openalex.org/W2151223736, https://openalex.org/W2971546421, https://openalex.org/W2000982976, https://openalex.org/W2318528603, https://openalex.org/W2488314661, https://openalex.org/W4232430671, https://openalex.org/W2123563890, https://openalex.org/W2052172657, https://openalex.org/W2958872067, https://openalex.org/W1604564446, https://openalex.org/W2582131869, https://openalex.org/W2728209061 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 85 |
| abstract_inverted_index.an | 4 |
| abstract_inverted_index.by | 25 |
| abstract_inverted_index.in | 7, 58 |
| abstract_inverted_index.is | 56, 107 |
| abstract_inverted_index.no | 91 |
| abstract_inverted_index.of | 67, 87, 125 |
| abstract_inverted_index.to | 47, 100, 118 |
| abstract_inverted_index.(or | 121 |
| abstract_inverted_index.Due | 46 |
| abstract_inverted_index.The | 10, 61 |
| abstract_inverted_index.and | 35, 71, 93, 130, 142 |
| abstract_inverted_index.are | 21, 116 |
| abstract_inverted_index.for | 42 |
| abstract_inverted_index.the | 81, 112, 126, 135, 148 |
| abstract_inverted_index.High | 104 |
| abstract_inverted_index.This | 76 |
| abstract_inverted_index.both | 66 |
| abstract_inverted_index.data | 83, 132 |
| abstract_inverted_index.form | 119 |
| abstract_inverted_index.into | 84 |
| abstract_inverted_index.mode | 12, 18, 29, 38, 54, 69, 74, 98, 123, 150 |
| abstract_inverted_index.ones | 89, 115 |
| abstract_inverted_index.role | 6 |
| abstract_inverted_index.than | 147 |
| abstract_inverted_index.then | 94 |
| abstract_inverted_index.this | 59 |
| abstract_inverted_index.were | 40 |
| abstract_inverted_index.with | 90, 138 |
| abstract_inverted_index.batch | 140 |
| abstract_inverted_index.data, | 50 |
| abstract_inverted_index.etc., | 20 |
| abstract_inverted_index.field | 131 |
| abstract_inverted_index.input | 49, 82, 127 |
| abstract_inverted_index.large | 48 |
| abstract_inverted_index.noise | 1, 106 |
| abstract_inverted_index.often | 22 |
| abstract_inverted_index.ones. | 103 |
| abstract_inverted_index.plays | 3 |
| abstract_inverted_index.ratio | 146 |
| abstract_inverted_index.sizes | 141 |
| abstract_inverted_index.takes | 64 |
| abstract_inverted_index.these | 101 |
| abstract_inverted_index.trace | 24 |
| abstract_inverted_index.higher | 144 |
| abstract_inverted_index.method | 63, 78, 137 |
| abstract_inverted_index.paper. | 60 |
| abstract_inverted_index.series | 86 |
| abstract_inverted_index.trace. | 26 |
| abstract_inverted_index.Seismic | 0 |
| abstract_inverted_index.achieve | 143 |
| abstract_inverted_index.applied | 23 |
| abstract_inverted_index.applies | 95 |
| abstract_inverted_index.firstly | 79 |
| abstract_inverted_index.lateral | 43 |
| abstract_inverted_index.method. | 152 |
| abstract_inverted_index.seismic | 8 |
| abstract_inverted_index.signal. | 128 |
| abstract_inverted_index.smaller | 88, 102, 114 |
| abstract_inverted_index.through | 109 |
| abstract_inverted_index.wavelet | 15, 33 |
| abstract_inverted_index.Finally, | 111 |
| abstract_inverted_index.denoised | 113 |
| abstract_inverted_index.filtered | 108 |
| abstract_inverted_index.proposed | 41, 57, 62, 77, 136 |
| abstract_inverted_index.segments | 80 |
| abstract_inverted_index.sifting. | 110 |
| abstract_inverted_index.validate | 134 |
| abstract_inverted_index.Synthetic | 129 |
| abstract_inverted_index.different | 139 |
| abstract_inverted_index.empirical | 11, 28 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.intrinsic | 122 |
| abstract_inverted_index.advantages | 65 |
| abstract_inverted_index.components | 120 |
| abstract_inverted_index.continuity | 44 |
| abstract_inverted_index.functions) | 124 |
| abstract_inverted_index.mini-batch | 51 |
| abstract_inverted_index.transform, | 16, 34 |
| abstract_inverted_index.attenuation | 2 |
| abstract_inverted_index.experiments | 133 |
| abstract_inverted_index.overlapping | 92 |
| abstract_inverted_index.variational | 17, 37, 53, 68, 73, 97, 149 |
| abstract_inverted_index.Multivariate | 27 |
| abstract_inverted_index.concatenated | 117 |
| abstract_inverted_index.multivariate | 31, 36, 52, 72, 96 |
| abstract_inverted_index.decomposition | 39, 55, 70, 99, 151 |
| abstract_inverted_index.consideration. | 45 |
| abstract_inverted_index.decomposition, | 13, 19, 30 |
| abstract_inverted_index.decomposition. | 75 |
| abstract_inverted_index.interpretation. | 9 |
| abstract_inverted_index.signal-to-noise | 145 |
| abstract_inverted_index.frequency-domain | 105 |
| abstract_inverted_index.synchrosqueezing | 14, 32 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5049684563, https://openalex.org/A5102027906 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I204553293 |
| citation_normalized_percentile.value | 0.78339314 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |