Unsupervised Machine Learning Techniques for Improving Reservoir Interpretation Using Walkaway VSP and Sonic Log Data Article Swipe
Mateusz Zaręba
,
Tomasz Danek
,
M. Stefaniuk
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16010493
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/en16010493
In this paper, we present a detailed analysis of the possibility of using unsupervised machine learning techniques for reservoir interpretation based on the parameters obtained from geophysical measurements that are related to the elastic properties of rocks. Four different clustering algorithms were compared, including balanced iterative reducing and clustering using hierarchies, the Gaussian mixture model, k-means, and spectral clustering. Measurements with different vertical resolutions were used. The first set of input parameters was obtained from the walkaway VSP survey. The second one was acquired in the well using a full-wave sonic tool. Apart from the study of algorithms used for clustering, two data pre-processing paths were analyzed in the context of matching the vertical resolution of both methods. The validation of the final results was carried out using a lithological identification of the medium based on an analysis of the drill core. The measurements were performed in Silurian rocks (claystone, mudstone, marly claystone) lying under an overburdened Zechstein formation (salt and anhydrite). This formation is known for high attenuating seismic signal properties. The presented study shows results from the first and only multilevel walkaway VSP acquisition in Poland.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en16010493
- https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912
- OA Status
- gold
- Cited By
- 5
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313471984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313471984Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en16010493Digital Object Identifier
- Title
-
Unsupervised Machine Learning Techniques for Improving Reservoir Interpretation Using Walkaway VSP and Sonic Log DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-02Full publication date if available
- Authors
-
Mateusz Zaręba, Tomasz Danek, M. StefaniukList of authors in order
- Landing page
-
https://doi.org/10.3390/en16010493Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912Direct OA link when available
- Concepts
-
Cluster analysis, Context (archaeology), Geology, Drill, Artificial intelligence, Pattern recognition (psychology), Matching pursuit, Data set, Computer science, Algorithm, Engineering, Compressed sensing, Mechanical engineering, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4313471984 |
|---|---|
| doi | https://doi.org/10.3390/en16010493 |
| ids.doi | https://doi.org/10.3390/en16010493 |
| ids.openalex | https://openalex.org/W4313471984 |
| fwci | 1.69539633 |
| type | article |
| title | Unsupervised Machine Learning Techniques for Improving Reservoir Interpretation Using Walkaway VSP and Sonic Log Data |
| biblio.issue | 1 |
| biblio.volume | 16 |
| biblio.last_page | 493 |
| biblio.first_page | 493 |
| 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 | 0.9994999766349792 |
| 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 | 0.9976000189781189 |
| 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/T10399 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9965000152587891 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2211 |
| topics[2].subfield.display_name | Mechanics of Materials |
| topics[2].display_name | Hydrocarbon exploration and reservoir analysis |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C73555534 |
| concepts[0].level | 2 |
| concepts[0].score | 0.768817663192749 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[0].display_name | Cluster analysis |
| concepts[1].id | https://openalex.org/C2779343474 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6766735315322876 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[1].display_name | Context (archaeology) |
| concepts[2].id | https://openalex.org/C127313418 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5893563628196716 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[2].display_name | Geology |
| concepts[3].id | https://openalex.org/C173736775 |
| concepts[3].level | 2 |
| concepts[3].score | 0.445625901222229 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q58964 |
| concepts[3].display_name | Drill |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.43604224920272827 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4285372197628021 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C156872377 |
| concepts[6].level | 3 |
| concepts[6].score | 0.426928848028183 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6786281 |
| concepts[6].display_name | Matching pursuit |
| concepts[7].id | https://openalex.org/C58489278 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4214574098587036 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1172284 |
| concepts[7].display_name | Data set |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.37754181027412415 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.36307525634765625 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.25897830724716187 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C124851039 |
| concepts[11].level | 2 |
| concepts[11].score | 0.09850504994392395 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2665459 |
| concepts[11].display_name | Compressed sensing |
| concepts[12].id | https://openalex.org/C78519656 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[12].display_name | Mechanical engineering |
| concepts[13].id | https://openalex.org/C151730666 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[13].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/cluster-analysis |
| keywords[0].score | 0.768817663192749 |
| keywords[0].display_name | Cluster analysis |
| keywords[1].id | https://openalex.org/keywords/context |
| keywords[1].score | 0.6766735315322876 |
| keywords[1].display_name | Context (archaeology) |
| keywords[2].id | https://openalex.org/keywords/geology |
| keywords[2].score | 0.5893563628196716 |
| keywords[2].display_name | Geology |
| keywords[3].id | https://openalex.org/keywords/drill |
| keywords[3].score | 0.445625901222229 |
| keywords[3].display_name | Drill |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.43604224920272827 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.4285372197628021 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/matching-pursuit |
| keywords[6].score | 0.426928848028183 |
| keywords[6].display_name | Matching pursuit |
| keywords[7].id | https://openalex.org/keywords/data-set |
| keywords[7].score | 0.4214574098587036 |
| keywords[7].display_name | Data set |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.37754181027412415 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.36307525634765625 |
| keywords[9].display_name | Algorithm |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.25897830724716187 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/compressed-sensing |
| keywords[11].score | 0.09850504994392395 |
| keywords[11].display_name | Compressed sensing |
| language | en |
| locations[0].id | doi:10.3390/en16010493 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S198098182 |
| locations[0].source.issn | 1996-1073 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1996-1073 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Energies |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912 |
| 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 | Energies |
| locations[0].landing_page_url | https://doi.org/10.3390/en16010493 |
| locations[1].id | pmh:oai:doaj.org/article:489016917bfe4890873cdef770ab9e8c |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Energies, Vol 16, Iss 1, p 493 (2023) |
| locations[1].landing_page_url | https://doaj.org/article/489016917bfe4890873cdef770ab9e8c |
| locations[2].id | pmh:oai:mdpi.com:/1996-1073/16/1/493/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Energies; Volume 16; Issue 1; Pages: 493 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/en16010493 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5031743013 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9663-6593 |
| authorships[0].author.display_name | Mateusz Zaręba |
| authorships[0].countries | PL |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I686019 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland |
| authorships[0].institutions[0].id | https://openalex.org/I686019 |
| authorships[0].institutions[0].ror | https://ror.org/00bas1c41 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I686019 |
| authorships[0].institutions[0].country_code | PL |
| authorships[0].institutions[0].display_name | AGH University of Krakow |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mateusz Zareba |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland |
| authorships[1].author.id | https://openalex.org/A5090717950 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8101-7469 |
| authorships[1].author.display_name | Tomasz Danek |
| authorships[1].countries | PL |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I686019 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland |
| authorships[1].institutions[0].id | https://openalex.org/I686019 |
| authorships[1].institutions[0].ror | https://ror.org/00bas1c41 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I686019 |
| authorships[1].institutions[0].country_code | PL |
| authorships[1].institutions[0].display_name | AGH University of Krakow |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Tomasz Danek |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland |
| authorships[2].author.id | https://openalex.org/A5080300125 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-4442-9290 |
| authorships[2].author.display_name | M. Stefaniuk |
| authorships[2].countries | PL |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I686019 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Fossil Fuels, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland |
| authorships[2].institutions[0].id | https://openalex.org/I686019 |
| authorships[2].institutions[0].ror | https://ror.org/00bas1c41 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I686019 |
| authorships[2].institutions[0].country_code | PL |
| authorships[2].institutions[0].display_name | AGH University of Krakow |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Michal Stefaniuk |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Fossil Fuels, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-01-06T00:00:00 |
| display_name | Unsupervised Machine Learning Techniques for Improving Reservoir Interpretation Using Walkaway VSP and Sonic Log Data |
| 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 | 0.9994999766349792 |
| 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/W2042644197, https://openalex.org/W2073241848, https://openalex.org/W2785927776, https://openalex.org/W2020549994, https://openalex.org/W4220674950, https://openalex.org/W2381915087, https://openalex.org/W3130682819, https://openalex.org/W1980565639, https://openalex.org/W3162841105, https://openalex.org/W2103001330 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/en16010493 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S198098182 |
| best_oa_location.source.issn | 1996-1073 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1996-1073 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Energies |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912 |
| 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 | Energies |
| best_oa_location.landing_page_url | https://doi.org/10.3390/en16010493 |
| primary_location.id | doi:10.3390/en16010493 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S198098182 |
| primary_location.source.issn | 1996-1073 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1996-1073 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Energies |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1996-1073/16/1/493/pdf?version=1672909912 |
| 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 | Energies |
| primary_location.landing_page_url | https://doi.org/10.3390/en16010493 |
| publication_date | 2023-01-02 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2151392485, https://openalex.org/W2902392494, https://openalex.org/W2889573908, https://openalex.org/W2801526137, https://openalex.org/W3006204437, https://openalex.org/W2950920477, https://openalex.org/W2506443482, https://openalex.org/W3153797272, https://openalex.org/W2161294688, https://openalex.org/W2098274171, https://openalex.org/W2085148951, https://openalex.org/W6675354045, https://openalex.org/W6762095435, https://openalex.org/W3176721104, https://openalex.org/W2131074607, https://openalex.org/W143945936, https://openalex.org/W2112617148, https://openalex.org/W2041300997, https://openalex.org/W1489991816, https://openalex.org/W4231029117, https://openalex.org/W3208978091, https://openalex.org/W1482639564, https://openalex.org/W2488678869, https://openalex.org/W2502655619, https://openalex.org/W4293775970, https://openalex.org/W2498094064, https://openalex.org/W4952878, https://openalex.org/W2150593711, https://openalex.org/W3048804154, https://openalex.org/W1993137812, https://openalex.org/W2886661658, https://openalex.org/W2060484766, https://openalex.org/W2412254497, https://openalex.org/W2129066856, https://openalex.org/W1930892594, https://openalex.org/W2051224630, https://openalex.org/W2404352102, https://openalex.org/W2952558372, https://openalex.org/W4255816075, https://openalex.org/W3204683737, https://openalex.org/W3020845342, https://openalex.org/W6788357715, https://openalex.org/W2339363073, https://openalex.org/W2991623456, https://openalex.org/W2093730316, https://openalex.org/W4242401062, https://openalex.org/W2101234009, https://openalex.org/W3122888391, https://openalex.org/W2939158045 |
| referenced_works_count | 49 |
| abstract_inverted_index.a | 5, 88, 128 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.an | 136, 155 |
| abstract_inverted_index.in | 84, 107, 146, 186 |
| abstract_inverted_index.is | 164 |
| abstract_inverted_index.of | 8, 11, 35, 69, 96, 110, 115, 120, 131, 138 |
| abstract_inverted_index.on | 21, 135 |
| abstract_inverted_index.to | 31 |
| abstract_inverted_index.we | 3 |
| abstract_inverted_index.The | 66, 79, 118, 142, 172 |
| abstract_inverted_index.VSP | 77, 184 |
| abstract_inverted_index.and | 47, 56, 160, 180 |
| abstract_inverted_index.are | 29 |
| abstract_inverted_index.for | 17, 99, 166 |
| abstract_inverted_index.one | 81 |
| abstract_inverted_index.out | 126 |
| abstract_inverted_index.set | 68 |
| abstract_inverted_index.the | 9, 22, 32, 51, 75, 85, 94, 108, 112, 121, 132, 139, 178 |
| abstract_inverted_index.two | 101 |
| abstract_inverted_index.was | 72, 82, 124 |
| abstract_inverted_index.Four | 37 |
| abstract_inverted_index.This | 162 |
| abstract_inverted_index.both | 116 |
| abstract_inverted_index.data | 102 |
| abstract_inverted_index.from | 25, 74, 93, 177 |
| abstract_inverted_index.high | 167 |
| abstract_inverted_index.only | 181 |
| abstract_inverted_index.that | 28 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.used | 98 |
| abstract_inverted_index.well | 86 |
| abstract_inverted_index.were | 41, 64, 105, 144 |
| abstract_inverted_index.with | 60 |
| abstract_inverted_index.(salt | 159 |
| abstract_inverted_index.Apart | 92 |
| abstract_inverted_index.based | 20, 134 |
| abstract_inverted_index.core. | 141 |
| abstract_inverted_index.drill | 140 |
| abstract_inverted_index.final | 122 |
| abstract_inverted_index.first | 67, 179 |
| abstract_inverted_index.input | 70 |
| abstract_inverted_index.known | 165 |
| abstract_inverted_index.lying | 153 |
| abstract_inverted_index.marly | 151 |
| abstract_inverted_index.paths | 104 |
| abstract_inverted_index.rocks | 148 |
| abstract_inverted_index.shows | 175 |
| abstract_inverted_index.sonic | 90 |
| abstract_inverted_index.study | 95, 174 |
| abstract_inverted_index.tool. | 91 |
| abstract_inverted_index.under | 154 |
| abstract_inverted_index.used. | 65 |
| abstract_inverted_index.using | 12, 49, 87, 127 |
| abstract_inverted_index.medium | 133 |
| abstract_inverted_index.model, | 54 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.rocks. | 36 |
| abstract_inverted_index.second | 80 |
| abstract_inverted_index.signal | 170 |
| abstract_inverted_index.Poland. | 187 |
| abstract_inverted_index.carried | 125 |
| abstract_inverted_index.context | 109 |
| abstract_inverted_index.elastic | 33 |
| abstract_inverted_index.machine | 14 |
| abstract_inverted_index.mixture | 53 |
| abstract_inverted_index.present | 4 |
| abstract_inverted_index.related | 30 |
| abstract_inverted_index.results | 123, 176 |
| abstract_inverted_index.seismic | 169 |
| abstract_inverted_index.survey. | 78 |
| abstract_inverted_index.Gaussian | 52 |
| abstract_inverted_index.Silurian | 147 |
| abstract_inverted_index.acquired | 83 |
| abstract_inverted_index.analysis | 7, 137 |
| abstract_inverted_index.analyzed | 106 |
| abstract_inverted_index.balanced | 44 |
| abstract_inverted_index.detailed | 6 |
| abstract_inverted_index.k-means, | 55 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.matching | 111 |
| abstract_inverted_index.methods. | 117 |
| abstract_inverted_index.obtained | 24, 73 |
| abstract_inverted_index.reducing | 46 |
| abstract_inverted_index.spectral | 57 |
| abstract_inverted_index.vertical | 62, 113 |
| abstract_inverted_index.walkaway | 76, 183 |
| abstract_inverted_index.Zechstein | 157 |
| abstract_inverted_index.compared, | 42 |
| abstract_inverted_index.different | 38, 61 |
| abstract_inverted_index.formation | 158, 163 |
| abstract_inverted_index.full-wave | 89 |
| abstract_inverted_index.including | 43 |
| abstract_inverted_index.iterative | 45 |
| abstract_inverted_index.mudstone, | 150 |
| abstract_inverted_index.performed | 145 |
| abstract_inverted_index.presented | 173 |
| abstract_inverted_index.reservoir | 18 |
| abstract_inverted_index.algorithms | 40, 97 |
| abstract_inverted_index.claystone) | 152 |
| abstract_inverted_index.clustering | 39, 48 |
| abstract_inverted_index.multilevel | 182 |
| abstract_inverted_index.parameters | 23, 71 |
| abstract_inverted_index.properties | 34 |
| abstract_inverted_index.resolution | 114 |
| abstract_inverted_index.techniques | 16 |
| abstract_inverted_index.validation | 119 |
| abstract_inverted_index.(claystone, | 149 |
| abstract_inverted_index.acquisition | 185 |
| abstract_inverted_index.anhydrite). | 161 |
| abstract_inverted_index.attenuating | 168 |
| abstract_inverted_index.clustering, | 100 |
| abstract_inverted_index.clustering. | 58 |
| abstract_inverted_index.geophysical | 26 |
| abstract_inverted_index.possibility | 10 |
| abstract_inverted_index.properties. | 171 |
| abstract_inverted_index.resolutions | 63 |
| abstract_inverted_index.Measurements | 59 |
| abstract_inverted_index.hierarchies, | 50 |
| abstract_inverted_index.lithological | 129 |
| abstract_inverted_index.measurements | 27, 143 |
| abstract_inverted_index.overburdened | 156 |
| abstract_inverted_index.unsupervised | 13 |
| abstract_inverted_index.identification | 130 |
| abstract_inverted_index.interpretation | 19 |
| abstract_inverted_index.pre-processing | 103 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5031743013 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I686019 |
| citation_normalized_percentile.value | 0.79336908 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |