Applying benefits and avoiding pitfalls of 3D computational modeling-based machine learning prediction for exploration targeting: Lessons from two mines in the Tongling-Anqing district, eastern China Article Swipe
Liangming Liu
,
Wei Cao
,
Hongsheng Liu
,
Alison Ord
,
Yaozu Qin
,
Feihu Zhou
,
Chenxi Bi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.oregeorev.2022.104712
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.oregeorev.2022.104712
The 3D computational modeling-based machine learning (ML) prediction is an innovative methodology for exploration-targeting. This paper presents our studies on its application in the Fenghuangshan (FHS) and Anqing (AQ) mines, its benefits and pitfalls and the strategies for applying benefits and avoiding pitfalls. The 3D geological models of the FHS and AQ orefields show that the topography and attitudes of the intrusions' contact zones have spatial constraints on orebodies. The 3D variation of resistivity can provide some ambiguous evidences for inferring contact zone and orebodies. The dynamic simulations of the intrusions' cooling processes suggest that the dilatant deformation produced by the coupled mechano-thermo-hydrological (MTH) processes is favor for mineralization. Based on the results of 3D geometric and geodynamic modeling, we conducted the random forest (RF) ML model in the FHS mine and the ML models respectively of artificial neural network (ANN), support vector machine (SVM) and RF in the AQ mine to predict mineral potentials. The RF prediction indicates that there is no significant potential in the FHS mine down to depth of 1400 m. The drills of the FHS Deep Drilling Program (FHSDDP) are all at the locations with low RF prediction probability (PP) of mineralization, which can explain why the FHSDDP had not discovered any orebodies and verifies the reliability of the RF prediction. The ANN, SVM and RF models of AQ mine gave different predictions, although the same data were imputed. The RF prediction indicates that there is no significant potential there down to −1520 m, whilst the SVM prediction shows a few small high potentials there, and ANN prediction shows prominent high potentials there. The drills of the AQ Mine Outer Drilling Program (AQMODP) that failed in ore discovery are all at the locations with low RF PP, but with high ANN PP, suggesting that the RF prediction is more reliable than that of ANN. Such inconsistent predictions of different algorithms and the unsuccessful exploration stories in FHS and AQ mines demonstrate that the 3D computational modeling-based ML technologies are a two edged sword. On the one hand, they can facilitate mineral exploration by characterizing the mineralization system in 3D visual and quantitative prediction of ore potentials. But on the other hand, they have some hidden pitfalls to impede or misguide mineral exploration, such as over extrapolation producing false model, over interpolation creating false big data, overfitting generating false knowledge and over simplification causing false prediction. The right strategies for applying benefits and avoiding pitfalls of 3D computational modeling-based ML prediction are pursuing high quality data, applying a capable ML algorithm, reinforcing field geology-based investigation and ratiocination, and keeping full feedback coupling with exploration processes to ensure correct decision-making and timely updating of data.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.oregeorev.2022.104712
- OA Status
- gold
- Cited By
- 17
- References
- 129
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205612322
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4205612322Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.oregeorev.2022.104712Digital Object Identifier
- Title
-
Applying benefits and avoiding pitfalls of 3D computational modeling-based machine learning prediction for exploration targeting: Lessons from two mines in the Tongling-Anqing district, eastern ChinaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-15Full publication date if available
- Authors
-
Liangming Liu, Wei Cao, Hongsheng Liu, Alison Ord, Yaozu Qin, Feihu Zhou, Chenxi BiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.oregeorev.2022.104712Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.oregeorev.2022.104712Direct OA link when available
- Concepts
-
Support vector machine, Random forest, Artificial neural network, Drilling, Predictive modelling, Mining engineering, Machine learning, Mineralization (soil science), Geology, Artificial intelligence, Soil science, Computer science, Engineering, Mechanical engineering, Soil waterTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 7, 2023: 5, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
129Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4205612322 |
|---|---|
| doi | https://doi.org/10.1016/j.oregeorev.2022.104712 |
| ids.doi | https://doi.org/10.1016/j.oregeorev.2022.104712 |
| ids.openalex | https://openalex.org/W4205612322 |
| fwci | 3.32857994 |
| type | article |
| title | Applying benefits and avoiding pitfalls of 3D computational modeling-based machine learning prediction for exploration targeting: Lessons from two mines in the Tongling-Anqing district, eastern China |
| awards[0].id | https://openalex.org/G6456151572 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 41372338 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| awards[1].id | https://openalex.org/G6975543506 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 41772351 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | |
| biblio.volume | 142 |
| biblio.last_page | 104712 |
| biblio.first_page | 104712 |
| topics[0].id | https://openalex.org/T12157 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Geochemistry and Geologic Mapping |
| topics[1].id | https://openalex.org/T10572 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9973000288009644 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1908 |
| topics[1].subfield.display_name | Geophysics |
| topics[1].display_name | Geophysical and Geoelectrical Methods |
| 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 | 0.9966999888420105 |
| 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 |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 2300 |
| apc_list.currency | USD |
| apc_list.value_usd | 2300 |
| apc_paid.value | 2300 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2300 |
| concepts[0].id | https://openalex.org/C12267149 |
| concepts[0].level | 2 |
| concepts[0].score | 0.767929196357727 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[0].display_name | Support vector machine |
| concepts[1].id | https://openalex.org/C169258074 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6700382232666016 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[1].display_name | Random forest |
| concepts[2].id | https://openalex.org/C50644808 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5963289141654968 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[2].display_name | Artificial neural network |
| concepts[3].id | https://openalex.org/C25197100 |
| concepts[3].level | 2 |
| concepts[3].score | 0.46483445167541504 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q890886 |
| concepts[3].display_name | Drilling |
| concepts[4].id | https://openalex.org/C45804977 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4583866000175476 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7239673 |
| concepts[4].display_name | Predictive modelling |
| concepts[5].id | https://openalex.org/C16674752 |
| concepts[5].level | 1 |
| concepts[5].score | 0.45531803369522095 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1370637 |
| concepts[5].display_name | Mining engineering |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43980610370635986 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C111696902 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4357408881187439 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q6864413 |
| concepts[7].display_name | Mineralization (soil science) |
| concepts[8].id | https://openalex.org/C127313418 |
| concepts[8].level | 0 |
| concepts[8].score | 0.4219183623790741 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[8].display_name | Geology |
| concepts[9].id | https://openalex.org/C154945302 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3932052552700043 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[9].display_name | Artificial intelligence |
| concepts[10].id | https://openalex.org/C159390177 |
| concepts[10].level | 1 |
| concepts[10].score | 0.276825875043869 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q9161265 |
| concepts[10].display_name | Soil science |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2413044571876526 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.19261467456817627 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C78519656 |
| concepts[13].level | 1 |
| concepts[13].score | 0.08688846230506897 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[13].display_name | Mechanical engineering |
| concepts[14].id | https://openalex.org/C159750122 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q96621023 |
| concepts[14].display_name | Soil water |
| keywords[0].id | https://openalex.org/keywords/support-vector-machine |
| keywords[0].score | 0.767929196357727 |
| keywords[0].display_name | Support vector machine |
| keywords[1].id | https://openalex.org/keywords/random-forest |
| keywords[1].score | 0.6700382232666016 |
| keywords[1].display_name | Random forest |
| keywords[2].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[2].score | 0.5963289141654968 |
| keywords[2].display_name | Artificial neural network |
| keywords[3].id | https://openalex.org/keywords/drilling |
| keywords[3].score | 0.46483445167541504 |
| keywords[3].display_name | Drilling |
| keywords[4].id | https://openalex.org/keywords/predictive-modelling |
| keywords[4].score | 0.4583866000175476 |
| keywords[4].display_name | Predictive modelling |
| keywords[5].id | https://openalex.org/keywords/mining-engineering |
| keywords[5].score | 0.45531803369522095 |
| keywords[5].display_name | Mining engineering |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.43980610370635986 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/mineralization |
| keywords[7].score | 0.4357408881187439 |
| keywords[7].display_name | Mineralization (soil science) |
| keywords[8].id | https://openalex.org/keywords/geology |
| keywords[8].score | 0.4219183623790741 |
| keywords[8].display_name | Geology |
| keywords[9].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[9].score | 0.3932052552700043 |
| keywords[9].display_name | Artificial intelligence |
| keywords[10].id | https://openalex.org/keywords/soil-science |
| keywords[10].score | 0.276825875043869 |
| keywords[10].display_name | Soil science |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.2413044571876526 |
| keywords[11].display_name | Computer science |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.19261467456817627 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/mechanical-engineering |
| keywords[13].score | 0.08688846230506897 |
| keywords[13].display_name | Mechanical engineering |
| language | en |
| locations[0].id | doi:10.1016/j.oregeorev.2022.104712 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S29724373 |
| locations[0].source.issn | 0169-1368, 1872-7360 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 0169-1368 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Ore Geology Reviews |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Ore Geology Reviews |
| locations[0].landing_page_url | https://doi.org/10.1016/j.oregeorev.2022.104712 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5101514260 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0352-8949 |
| authorships[0].author.display_name | Liangming Liu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I139660479 |
| authorships[0].affiliations[1].raw_affiliation_string | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[0].institutions[0].id | https://openalex.org/I139660479 |
| authorships[0].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Central South University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Liangming Liu |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China, School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[1].author.id | https://openalex.org/A5030936507 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0904-1146 |
| authorships[1].author.display_name | Wei Cao |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[1].affiliations[0].raw_affiliation_string | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[1].institutions[0].id | https://openalex.org/I139660479 |
| authorships[1].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Central South University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Wei Cao |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[2].author.id | https://openalex.org/A5100709898 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6570-6627 |
| authorships[2].author.display_name | Hongsheng Liu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[2].affiliations[0].raw_affiliation_string | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I139660479 |
| authorships[2].affiliations[1].raw_affiliation_string | School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[2].institutions[0].id | https://openalex.org/I139660479 |
| authorships[2].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Central South University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hongsheng Liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China, School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[3].author.id | https://openalex.org/A5085144524 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4701-2036 |
| authorships[3].author.display_name | Alison Ord |
| authorships[3].countries | AU, CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I177877127 |
| authorships[3].affiliations[0].raw_affiliation_string | Centre for Exploration Targeting, University of Western Australia, Crawley 6008, Australia |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I16365422 |
| authorships[3].affiliations[1].raw_affiliation_string | School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui 230009, PR China |
| authorships[3].institutions[0].id | https://openalex.org/I177877127 |
| authorships[3].institutions[0].ror | https://ror.org/047272k79 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I177877127 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | The University of Western Australia |
| authorships[3].institutions[1].id | https://openalex.org/I16365422 |
| authorships[3].institutions[1].ror | https://ror.org/02czkny70 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I16365422 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Hefei University of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Alison Ord |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Centre for Exploration Targeting, University of Western Australia, Crawley 6008, Australia, School of Resources and Environmental Engineering, Hefei University of Technology, Hefei, Anhui 230009, PR China |
| authorships[4].author.id | https://openalex.org/A5022037397 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-1998-2916 |
| authorships[4].author.display_name | Yaozu Qin |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[4].affiliations[0].raw_affiliation_string | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[4].institutions[0].id | https://openalex.org/I139660479 |
| authorships[4].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Central South University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yaozu Qin |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[5].author.id | https://openalex.org/A5102504765 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Feihu Zhou |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[5].affiliations[0].raw_affiliation_string | School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I139660479 |
| authorships[5].affiliations[1].raw_affiliation_string | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[5].institutions[0].id | https://openalex.org/I139660479 |
| authorships[5].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Central South University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Feihu Zhou |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China, School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[6].author.id | https://openalex.org/A5039228339 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Chenxi Bi |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I139660479 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I139660479 |
| authorships[6].affiliations[1].raw_affiliation_string | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China |
| authorships[6].institutions[0].id | https://openalex.org/I139660479 |
| authorships[6].institutions[0].ror | https://ror.org/00f1zfq44 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I139660479 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Central South University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Chenxi Bi |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Computational Geosciences Research Centre, Central South University, Changsha 410083, PR China, School of Geoscience and Info-Physics, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Education Ministry, Central South University, Changsha 410083, PR China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.oregeorev.2022.104712 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Applying benefits and avoiding pitfalls of 3D computational modeling-based machine learning prediction for exploration targeting: Lessons from two mines in the Tongling-Anqing district, eastern China |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12157 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Geochemistry and Geologic Mapping |
| related_works | https://openalex.org/W1526398043, https://openalex.org/W2027141232, https://openalex.org/W4386259002, https://openalex.org/W4200112873, https://openalex.org/W2955796858, https://openalex.org/W4224941037, https://openalex.org/W2004826645, https://openalex.org/W3135818052, https://openalex.org/W3191198889, https://openalex.org/W2802491896 |
| cited_by_count | 17 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 5 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 3 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.oregeorev.2022.104712 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S29724373 |
| best_oa_location.source.issn | 0169-1368, 1872-7360 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 0169-1368 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Ore Geology Reviews |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Ore Geology Reviews |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.oregeorev.2022.104712 |
| primary_location.id | doi:10.1016/j.oregeorev.2022.104712 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S29724373 |
| primary_location.source.issn | 0169-1368, 1872-7360 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 0169-1368 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Ore Geology Reviews |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Ore Geology Reviews |
| primary_location.landing_page_url | https://doi.org/10.1016/j.oregeorev.2022.104712 |
| publication_date | 2022-01-15 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W1980789580, https://openalex.org/W2923222994, https://openalex.org/W131195880, https://openalex.org/W4212883601, https://openalex.org/W2911964244, https://openalex.org/W2057591731, https://openalex.org/W3012148969, https://openalex.org/W2061716532, https://openalex.org/W1978053173, https://openalex.org/W342324839, https://openalex.org/W2072504176, https://openalex.org/W2039814260, https://openalex.org/W1984270108, https://openalex.org/W2933807147, https://openalex.org/W3005046502, https://openalex.org/W2040358312, https://openalex.org/W2017368933, https://openalex.org/W6603548763, https://openalex.org/W2024876931, https://openalex.org/W2906876500, https://openalex.org/W2907617649, https://openalex.org/W2087018845, https://openalex.org/W11840688, https://openalex.org/W2606355498, https://openalex.org/W2024657179, https://openalex.org/W1487926254, https://openalex.org/W1866781782, https://openalex.org/W2924231381, https://openalex.org/W2040925926, https://openalex.org/W1420785588, https://openalex.org/W1988277234, https://openalex.org/W2171234904, https://openalex.org/W6734139733, https://openalex.org/W1963580067, https://openalex.org/W2774264647, https://openalex.org/W1194229105, https://openalex.org/W2134112716, https://openalex.org/W6773634589, https://openalex.org/W2043191555, https://openalex.org/W2022415789, https://openalex.org/W2149163236, https://openalex.org/W2905422867, https://openalex.org/W2004121791, https://openalex.org/W2392684658, https://openalex.org/W2116656904, https://openalex.org/W7036970817, https://openalex.org/W2086072411, https://openalex.org/W2033631878, https://openalex.org/W2080238745, https://openalex.org/W1989744718, https://openalex.org/W4233857083, https://openalex.org/W2001119618, https://openalex.org/W2481514139, https://openalex.org/W2792898375, https://openalex.org/W2371963126, https://openalex.org/W2024697317, https://openalex.org/W2169638223, https://openalex.org/W1995341919, https://openalex.org/W866664596, https://openalex.org/W1996843479, https://openalex.org/W2153471137, https://openalex.org/W2055804300, https://openalex.org/W2141885723, https://openalex.org/W2415328666, https://openalex.org/W1969185593, https://openalex.org/W2768818485, https://openalex.org/W3171942522, https://openalex.org/W6804846292, https://openalex.org/W2548695521, https://openalex.org/W98624355, https://openalex.org/W3033765727, https://openalex.org/W1969898025, https://openalex.org/W2883595196, https://openalex.org/W1982998540, https://openalex.org/W1983865151, https://openalex.org/W1498436455, https://openalex.org/W2112865076, https://openalex.org/W6681742681, https://openalex.org/W2166730155, https://openalex.org/W1561553493, https://openalex.org/W2091743083, https://openalex.org/W1965167226, https://openalex.org/W2936859456, https://openalex.org/W2022153142, https://openalex.org/W6642004991, https://openalex.org/W6780376396, https://openalex.org/W1966599063, https://openalex.org/W2004938785, https://openalex.org/W2968343715, https://openalex.org/W2068168052, https://openalex.org/W2981149068, https://openalex.org/W2982159501, https://openalex.org/W1970486594, https://openalex.org/W3036260856, https://openalex.org/W1801002936, https://openalex.org/W3171799684, https://openalex.org/W1997728085, https://openalex.org/W1988041076, https://openalex.org/W2151724806, https://openalex.org/W2139637740, https://openalex.org/W2006139708, https://openalex.org/W6987205149, https://openalex.org/W1975507031, https://openalex.org/W2069875833, https://openalex.org/W1991871589, https://openalex.org/W2615492166, https://openalex.org/W3025212340, https://openalex.org/W2018366608, https://openalex.org/W1968642437, https://openalex.org/W2751092619, https://openalex.org/W2018857446, https://openalex.org/W2751060233, https://openalex.org/W4292079189, https://openalex.org/W588919155, https://openalex.org/W259288501, https://openalex.org/W3216830627, https://openalex.org/W2613327815, https://openalex.org/W2894158799, https://openalex.org/W2792125314, https://openalex.org/W2365866504, https://openalex.org/W2887129834, https://openalex.org/W2025357764, https://openalex.org/W2468964845, https://openalex.org/W4241686659, https://openalex.org/W1514338110, https://openalex.org/W1528620860, https://openalex.org/W3041370484, https://openalex.org/W2043959951, https://openalex.org/W3004790706 |
| referenced_works_count | 129 |
| abstract_inverted_index.a | 254, 334, 421 |
| abstract_inverted_index.3D | 1, 44, 70, 114, 328, 353, 410 |
| abstract_inverted_index.AQ | 51, 149, 223, 272, 323 |
| abstract_inverted_index.ML | 125, 133, 331, 413, 423 |
| abstract_inverted_index.On | 338 |
| abstract_inverted_index.RF | 146, 156, 191, 214, 220, 235, 290, 300 |
| abstract_inverted_index.an | 9 |
| abstract_inverted_index.as | 378 |
| abstract_inverted_index.at | 186, 285 |
| abstract_inverted_index.by | 99, 347 |
| abstract_inverted_index.in | 22, 127, 147, 165, 280, 320, 352 |
| abstract_inverted_index.is | 8, 105, 161, 240, 302 |
| abstract_inverted_index.m, | 248 |
| abstract_inverted_index.m. | 174 |
| abstract_inverted_index.no | 162, 241 |
| abstract_inverted_index.of | 47, 59, 72, 88, 113, 136, 172, 177, 195, 212, 222, 270, 307, 312, 358, 409, 446 |
| abstract_inverted_index.on | 19, 67, 110, 362 |
| abstract_inverted_index.or | 373 |
| abstract_inverted_index.to | 151, 170, 246, 371, 439 |
| abstract_inverted_index.we | 119 |
| abstract_inverted_index.ANN | 261, 295 |
| abstract_inverted_index.But | 361 |
| abstract_inverted_index.FHS | 49, 129, 167, 179, 321 |
| abstract_inverted_index.PP, | 291, 296 |
| abstract_inverted_index.SVM | 218, 251 |
| abstract_inverted_index.The | 0, 43, 69, 85, 155, 175, 216, 234, 268, 400 |
| abstract_inverted_index.all | 185, 284 |
| abstract_inverted_index.and | 26, 32, 34, 40, 50, 57, 83, 116, 131, 145, 208, 219, 260, 315, 322, 355, 394, 406, 429, 431, 443 |
| abstract_inverted_index.any | 206 |
| abstract_inverted_index.are | 184, 283, 333, 415 |
| abstract_inverted_index.big | 388 |
| abstract_inverted_index.but | 292 |
| abstract_inverted_index.can | 74, 198, 343 |
| abstract_inverted_index.few | 255 |
| abstract_inverted_index.for | 12, 37, 79, 107, 403 |
| abstract_inverted_index.had | 203 |
| abstract_inverted_index.its | 20, 30 |
| abstract_inverted_index.low | 190, 289 |
| abstract_inverted_index.not | 204 |
| abstract_inverted_index.one | 340 |
| abstract_inverted_index.ore | 281, 359 |
| abstract_inverted_index.our | 17 |
| abstract_inverted_index.the | 23, 35, 48, 55, 60, 89, 95, 100, 111, 121, 128, 132, 148, 166, 178, 187, 201, 210, 213, 229, 250, 271, 286, 299, 316, 327, 339, 349, 363 |
| abstract_inverted_index.two | 335 |
| abstract_inverted_index.why | 200 |
| abstract_inverted_index.(AQ) | 28 |
| abstract_inverted_index.(ML) | 6 |
| abstract_inverted_index.(PP) | 194 |
| abstract_inverted_index.(RF) | 124 |
| abstract_inverted_index.1400 | 173 |
| abstract_inverted_index.ANN, | 217 |
| abstract_inverted_index.ANN. | 308 |
| abstract_inverted_index.Deep | 180 |
| abstract_inverted_index.Mine | 273 |
| abstract_inverted_index.Such | 309 |
| abstract_inverted_index.This | 14 |
| abstract_inverted_index.data | 231 |
| abstract_inverted_index.down | 169, 245 |
| abstract_inverted_index.full | 433 |
| abstract_inverted_index.gave | 225 |
| abstract_inverted_index.have | 64, 367 |
| abstract_inverted_index.high | 257, 265, 294, 417 |
| abstract_inverted_index.mine | 130, 150, 168, 224 |
| abstract_inverted_index.more | 303 |
| abstract_inverted_index.over | 379, 384, 395 |
| abstract_inverted_index.same | 230 |
| abstract_inverted_index.show | 53 |
| abstract_inverted_index.some | 76, 368 |
| abstract_inverted_index.such | 377 |
| abstract_inverted_index.than | 305 |
| abstract_inverted_index.that | 54, 94, 159, 238, 278, 298, 306, 326 |
| abstract_inverted_index.they | 342, 366 |
| abstract_inverted_index.were | 232 |
| abstract_inverted_index.with | 189, 288, 293, 436 |
| abstract_inverted_index.zone | 82 |
| abstract_inverted_index.(FHS) | 25 |
| abstract_inverted_index.(MTH) | 103 |
| abstract_inverted_index.(SVM) | 144 |
| abstract_inverted_index.Based | 109 |
| abstract_inverted_index.Outer | 274 |
| abstract_inverted_index.data, | 389, 419 |
| abstract_inverted_index.data. | 447 |
| abstract_inverted_index.depth | 171 |
| abstract_inverted_index.edged | 336 |
| abstract_inverted_index.false | 382, 387, 392, 398 |
| abstract_inverted_index.favor | 106 |
| abstract_inverted_index.field | 426 |
| abstract_inverted_index.hand, | 341, 365 |
| abstract_inverted_index.mines | 324 |
| abstract_inverted_index.model | 126 |
| abstract_inverted_index.other | 364 |
| abstract_inverted_index.paper | 15 |
| abstract_inverted_index.right | 401 |
| abstract_inverted_index.shows | 253, 263 |
| abstract_inverted_index.small | 256 |
| abstract_inverted_index.there | 160, 239, 244 |
| abstract_inverted_index.which | 197 |
| abstract_inverted_index.zones | 63 |
| abstract_inverted_index.(ANN), | 140 |
| abstract_inverted_index.Anqing | 27 |
| abstract_inverted_index.FHSDDP | 202 |
| abstract_inverted_index.drills | 176, 269 |
| abstract_inverted_index.ensure | 440 |
| abstract_inverted_index.failed | 279 |
| abstract_inverted_index.forest | 123 |
| abstract_inverted_index.hidden | 369 |
| abstract_inverted_index.impede | 372 |
| abstract_inverted_index.mines, | 29 |
| abstract_inverted_index.model, | 383 |
| abstract_inverted_index.models | 46, 134, 221 |
| abstract_inverted_index.neural | 138 |
| abstract_inverted_index.random | 122 |
| abstract_inverted_index.sword. | 337 |
| abstract_inverted_index.system | 351 |
| abstract_inverted_index.there, | 259 |
| abstract_inverted_index.there. | 267 |
| abstract_inverted_index.timely | 444 |
| abstract_inverted_index.vector | 142 |
| abstract_inverted_index.visual | 354 |
| abstract_inverted_index.whilst | 249 |
| abstract_inverted_index.Program | 182, 276 |
| abstract_inverted_index.capable | 422 |
| abstract_inverted_index.causing | 397 |
| abstract_inverted_index.contact | 62, 81 |
| abstract_inverted_index.cooling | 91 |
| abstract_inverted_index.correct | 441 |
| abstract_inverted_index.coupled | 101 |
| abstract_inverted_index.dynamic | 86 |
| abstract_inverted_index.explain | 199 |
| abstract_inverted_index.keeping | 432 |
| abstract_inverted_index.machine | 4, 143 |
| abstract_inverted_index.mineral | 153, 345, 375 |
| abstract_inverted_index.network | 139 |
| abstract_inverted_index.predict | 152 |
| abstract_inverted_index.provide | 75 |
| abstract_inverted_index.quality | 418 |
| abstract_inverted_index.results | 112 |
| abstract_inverted_index.spatial | 65 |
| abstract_inverted_index.stories | 319 |
| abstract_inverted_index.studies | 18 |
| abstract_inverted_index.suggest | 93 |
| abstract_inverted_index.support | 141 |
| abstract_inverted_index.−1520 | 247 |
| abstract_inverted_index.(AQMODP) | 277 |
| abstract_inverted_index.(FHSDDP) | 183 |
| abstract_inverted_index.Drilling | 181, 275 |
| abstract_inverted_index.although | 228 |
| abstract_inverted_index.applying | 38, 404, 420 |
| abstract_inverted_index.avoiding | 41, 407 |
| abstract_inverted_index.benefits | 31, 39, 405 |
| abstract_inverted_index.coupling | 435 |
| abstract_inverted_index.creating | 386 |
| abstract_inverted_index.dilatant | 96 |
| abstract_inverted_index.feedback | 434 |
| abstract_inverted_index.imputed. | 233 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.misguide | 374 |
| abstract_inverted_index.pitfalls | 33, 370, 408 |
| abstract_inverted_index.presents | 16 |
| abstract_inverted_index.produced | 98 |
| abstract_inverted_index.pursuing | 416 |
| abstract_inverted_index.reliable | 304 |
| abstract_inverted_index.updating | 445 |
| abstract_inverted_index.verifies | 209 |
| abstract_inverted_index.ambiguous | 77 |
| abstract_inverted_index.attitudes | 58 |
| abstract_inverted_index.conducted | 120 |
| abstract_inverted_index.different | 226, 313 |
| abstract_inverted_index.discovery | 282 |
| abstract_inverted_index.evidences | 78 |
| abstract_inverted_index.geometric | 115 |
| abstract_inverted_index.indicates | 158, 237 |
| abstract_inverted_index.inferring | 80 |
| abstract_inverted_index.knowledge | 393 |
| abstract_inverted_index.locations | 188, 287 |
| abstract_inverted_index.modeling, | 118 |
| abstract_inverted_index.orebodies | 207 |
| abstract_inverted_index.orefields | 52 |
| abstract_inverted_index.pitfalls. | 42 |
| abstract_inverted_index.potential | 164, 243 |
| abstract_inverted_index.processes | 92, 104, 438 |
| abstract_inverted_index.producing | 381 |
| abstract_inverted_index.prominent | 264 |
| abstract_inverted_index.variation | 71 |
| abstract_inverted_index.algorithm, | 424 |
| abstract_inverted_index.algorithms | 314 |
| abstract_inverted_index.artificial | 137 |
| abstract_inverted_index.discovered | 205 |
| abstract_inverted_index.facilitate | 344 |
| abstract_inverted_index.generating | 391 |
| abstract_inverted_index.geodynamic | 117 |
| abstract_inverted_index.geological | 45 |
| abstract_inverted_index.innovative | 10 |
| abstract_inverted_index.orebodies. | 68, 84 |
| abstract_inverted_index.potentials | 258, 266 |
| abstract_inverted_index.prediction | 7, 157, 192, 236, 252, 262, 301, 357, 414 |
| abstract_inverted_index.strategies | 36, 402 |
| abstract_inverted_index.suggesting | 297 |
| abstract_inverted_index.topography | 56 |
| abstract_inverted_index.application | 21 |
| abstract_inverted_index.constraints | 66 |
| abstract_inverted_index.deformation | 97 |
| abstract_inverted_index.demonstrate | 325 |
| abstract_inverted_index.exploration | 318, 346, 437 |
| abstract_inverted_index.intrusions' | 61, 90 |
| abstract_inverted_index.methodology | 11 |
| abstract_inverted_index.overfitting | 390 |
| abstract_inverted_index.potentials. | 154, 360 |
| abstract_inverted_index.prediction. | 215, 399 |
| abstract_inverted_index.predictions | 311 |
| abstract_inverted_index.probability | 193 |
| abstract_inverted_index.reinforcing | 425 |
| abstract_inverted_index.reliability | 211 |
| abstract_inverted_index.resistivity | 73 |
| abstract_inverted_index.significant | 163, 242 |
| abstract_inverted_index.simulations | 87 |
| abstract_inverted_index.exploration, | 376 |
| abstract_inverted_index.inconsistent | 310 |
| abstract_inverted_index.predictions, | 227 |
| abstract_inverted_index.quantitative | 356 |
| abstract_inverted_index.respectively | 135 |
| abstract_inverted_index.technologies | 332 |
| abstract_inverted_index.unsuccessful | 317 |
| abstract_inverted_index.Fenghuangshan | 24 |
| abstract_inverted_index.computational | 2, 329, 411 |
| abstract_inverted_index.extrapolation | 380 |
| abstract_inverted_index.geology-based | 427 |
| abstract_inverted_index.interpolation | 385 |
| abstract_inverted_index.investigation | 428 |
| abstract_inverted_index.characterizing | 348 |
| abstract_inverted_index.mineralization | 350 |
| abstract_inverted_index.modeling-based | 3, 330, 412 |
| abstract_inverted_index.ratiocination, | 430 |
| abstract_inverted_index.simplification | 396 |
| abstract_inverted_index.decision-making | 442 |
| abstract_inverted_index.mineralization, | 196 |
| abstract_inverted_index.mineralization. | 108 |
| abstract_inverted_index.exploration-targeting. | 13 |
| abstract_inverted_index.mechano-thermo-hydrological | 102 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5101514260 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I139660479 |
| citation_normalized_percentile.value | 0.90297258 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |