Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting
In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly...
Saved in:
| Published in | Applied geochemistry Vol. 158; p. 105807 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
01.11.2023
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly identifying prospective areas. Data sets with strongly imbalanced distributions of the target variable (deposits) and insufficient training data sets impose obstacles to these kinds of models which can significantly impact adversely on the performance of the models. Moreover, in some cases, negative training data sets as the non-deposit locations aren't really true negative data, which cause higher uncertainty in a mineral potential map. In this study, for handling these challenges the deep autoencoder neural network is adopted. The autoencoder can be trained to reconstruct geospatial data set in totally unsupervised manner and identify prospective areas based on the reconstruction error, where higher error corresponds with areas of higher mineral potential. In order to confirm the efficiency of the autoencoder algorithm in mineral potential modeling, the model was compared with a popular data-driven approach that assigned a weight to the evidence layer by using a concentration-area (C-A) fractal model and a prediction-area (P-A) plot, and combined them using a multi-class index overlay method. Receiver operating characteristic (ROC) curve, success-rate curve, and P-A plot were adopted to evaluate the predictive ability of Fe prospectivity models pertaining to the Esfordi district of Iran. Also, we use an area under the ROC curve (AUC) and partial AUC (pAUC) to quantitatively evaluate the overall and sensitivity performance of models, respectively. |
|---|---|
| AbstractList | In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly identifying prospective areas. Data sets with strongly imbalanced distributions of the target variable (deposits) and insufficient training data sets impose obstacles to these kinds of models which can significantly impact adversely on the performance of the models. Moreover, in some cases, negative training data sets as the non-deposit locations aren't really true negative data, which cause higher uncertainty in a mineral potential map. In this study, for handling these challenges the deep autoencoder neural network is adopted. The autoencoder can be trained to reconstruct geospatial data set in totally unsupervised manner and identify prospective areas based on the reconstruction error, where higher error corresponds with areas of higher mineral potential. In order to confirm the efficiency of the autoencoder algorithm in mineral potential modeling, the model was compared with a popular data-driven approach that assigned a weight to the evidence layer by using a concentration-area (C-A) fractal model and a prediction-area (P-A) plot, and combined them using a multi-class index overlay method. Receiver operating characteristic (ROC) curve, success-rate curve, and P-A plot were adopted to evaluate the predictive ability of Fe prospectivity models pertaining to the Esfordi district of Iran. Also, we use an area under the ROC curve (AUC) and partial AUC (pAUC) to quantitatively evaluate the overall and sensitivity performance of models, respectively. |
| ArticleNumber | 105807 |
| Author | Abedi, Maysam Mirzabozorg, Seyyed Ataollah Agha Seyyed |
| Author_xml | – sequence: 1 givenname: Seyyed Ataollah Agha Seyyed surname: Mirzabozorg fullname: Mirzabozorg, Seyyed Ataollah Agha Seyyed – sequence: 2 givenname: Maysam orcidid: 0000-0002-5365-0694 surname: Abedi fullname: Abedi, Maysam |
| BookMark | eNqFkDtPwzAUhT0UCQr8BjyypNhxGycDA0K8JCQkBLN161ynLokdbEc8Bn47KQUGFqYjnXvO0dU3JRPnHRJyxNmMM16crGfQN-j1CrtZznIxuouSyQnZY2UpsrzK5S6ZxrhmjC0ky_fIxz1q3zibrHfUG9pZhwFa-w4bJwvYQsKagvMdtG-0h5QwuEjTKvihWY0HCkPy6LSvMVCHw9geJb348ESNDz-LFF_71oevWZogNJisaw7IjoE24uG37pPHy4uH8-vs9u7q5vzsNgMhecrMvDCyKISW-bKYV9xoXi94mbOKlzXMARhiUdWyFmK-XGojDBgNlQANOa_MUuyT4-1uH_zzgDGpzkaNbQsO_RCV4AshRVFINkZPt1EdfIwBjdI2fb2dAthWcaY2rNVa_bJWG9Zqy3rsyz_9PtgOwtu_zU9hApFx |
| CitedBy_id | crossref_primary_10_1016_j_apgeochem_2024_106053 crossref_primary_10_1016_j_oregeorev_2025_106458 crossref_primary_10_1016_j_chemer_2024_126208 crossref_primary_10_1007_s12145_025_01708_0 crossref_primary_10_1016_j_chemer_2024_126207 crossref_primary_10_1007_s12145_024_01579_x crossref_primary_10_1016_j_cageo_2024_105785 crossref_primary_10_1007_s11053_024_10328_2 crossref_primary_10_1007_s12145_024_01481_6 crossref_primary_10_3390_min14101015 crossref_primary_10_1007_s12145_025_01843_8 crossref_primary_10_1007_s11004_024_10164_3 crossref_primary_10_1007_s12145_024_01404_5 crossref_primary_10_1016_j_oregeorev_2023_105859 crossref_primary_10_1016_j_rsase_2024_101343 |
| Cites_doi | 10.1016/j.oregeorev.2006.10.002 10.1007/s12040-013-0313-z 10.1186/s40537-019-0230-3 10.3390/min12050616 10.1145/2500853.2500857 10.1007/s11053-018-9425-0 10.1007/s11053-017-9335-6 10.3390/app10134497 10.1016/j.cageo.2019.01.016 10.3390/min11020159 10.1007/s11053-015-9268-x 10.1016/j.tecto.2013.12.011 10.1016/j.jseaes.2019.104152 10.2113/gsecongeo.89.8.1697 10.1007/s13755-018-0051-3 10.1007/s11053-020-09700-9 10.1016/j.oregeorev.2014.09.007 10.1016/j.earscirev.2019.02.023 10.1016/j.cageo.2010.09.014 10.1002/sim.2103 10.1016/j.cageo.2015.07.006 10.1007/s11053-020-09789-y 10.1016/j.apgeochem.2023.105561 10.1016/j.oregeorev.2015.12.005 10.1016/j.cageo.2015.10.006 10.1007/s11053-019-09510-8 10.1016/0375-6742(94)90013-2 10.2475/ajs.303.7.622 10.1007/s11053-014-9261-9 10.1016/j.oregeorev.2019.103005 10.1371/journal.pone.0272861 10.1109/ACCESS.2020.3001350 10.1016/j.apgeochem.2021.104940 10.1088/1742-6596/1142/1/012012 10.1016/S0883-2927(01)00066-X 10.1007/s11053-018-9375-6 10.1097/JTO.0b013e3181ec173d 10.1007/s12145-023-01019-2 10.1016/j.gexplo.2021.106904 10.1007/s11053-005-4674-0 10.1016/j.oregeorev.2020.103611 10.1016/j.oregeorev.2018.10.006 10.1016/j.jafrearsci.2014.06.015 10.3390/app12094184 10.1016/j.oregeorev.2007.07.001 10.1080/08120099.2017.1328705 10.1007/s10661-005-9067-7 10.1016/j.oregeorev.2022.104765 10.1007/s12517-014-1326-x 10.1016/j.gexplo.2017.05.008 10.1007/s12517-012-0615-5 10.2113/gsecongeo.102.6.1111 10.1016/j.oregeorev.2019.103115 10.1016/0301-9268(88)90053-8 10.1180/minmag.2007.071.3.347 10.1016/j.cageo.2015.03.007 10.3390/min9050270 10.1016/j.oregeorev.2014.07.013 10.1177/0962280217718866 10.1148/radiology.201.3.8939225 10.1016/j.oregeorev.2014.08.012 10.2113/0100165 10.1016/j.cageo.2005.03.018 10.1007/s11053-021-09842-4 10.1016/j.cageo.2011.12.014 10.1080/19479832.2019.1589585 10.1007/s11053-019-09564-8 10.1080/10106049.2021.1953617 10.1007/s11053-020-09742-z 10.1007/s11053-020-09668-6 10.1109/TKDE.2019.2947676 10.4236/ojg.2016.612112 10.1016/j.gexplo.2017.10.020 10.1016/j.jafrearsci.2013.05.003 10.1016/j.gexplo.2021.106839 10.1016/j.jseaes.2021.104828 10.1046/j.1440-0952.2000.00807.x 10.1016/j.oregeorev.2021.104054 10.1016/j.oregeorev.2015.01.001 10.1016/j.gexplo.2021.106923 10.1016/j.patrec.2013.11.015 10.1007/s11053-022-10050-x 10.1007/s11053-021-09934-1 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.apgeochem.2023.105807 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geology |
| ExternalDocumentID | 10_1016_j_apgeochem_2023_105807 |
| GeographicLocations | Iran |
| GeographicLocations_xml | – name: Iran |
| GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AAEDT AAEDW AAHBH AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYOK AAYWO AAYXX ABEFU ABFNM ABJNI ABLST ABMAC ABQEM ABQYD ABWVN ABXDB ACDAQ ACGFS ACLVX ACRLP ACRPL ACSBN ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO AEBSH AEGFY AEIPS AEKER AENEX AEUPX AFJKZ AFPUW AFTJW AFXIZ AGCQF AGHFR AGQPQ AGRNS AGUBO AGYEJ AHEUO AHHHB AI. AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKIFW AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU APXCP ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLECG BLXMC BNPGV CITATION CS3 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMA HMC HVGLF HZ~ H~9 IHE IMUCA J1W KCYFY KOM LY3 LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SDP SEN SEP SES SEW SPC SPCBC SSE SSH SSJ SSZ T5K TN5 VH1 WUQ XPP ZCA ZMT ~02 ~G- 7S9 L.6 |
| ID | FETCH-LOGICAL-a371t-f46f7663c72b6491fc1d51820918da4aa0ee69d7d334bbcf3fafca93aca219fb3 |
| ISSN | 0883-2927 |
| IngestDate | Fri Jul 11 16:14:38 EDT 2025 Tue Jul 01 01:59:45 EDT 2025 Thu Apr 24 23:01:28 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a371t-f46f7663c72b6491fc1d51820918da4aa0ee69d7d334bbcf3fafca93aca219fb3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-5365-0694 |
| PQID | 3153736670 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_3153736670 crossref_citationtrail_10_1016_j_apgeochem_2023_105807 crossref_primary_10_1016_j_apgeochem_2023_105807 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-11-00 20231101 |
| PublicationDateYYYYMMDD | 2023-11-01 |
| PublicationDate_xml | – month: 11 year: 2023 text: 2023-11-00 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied geochemistry |
| PublicationYear | 2023 |
| References | Ahmadi (10.1016/j.apgeochem.2023.105807_bib5) 2022; 7 Karar (10.1016/j.apgeochem.2023.105807_bib57) 2006; 120 Cheng (10.1016/j.apgeochem.2023.105807_bib35) 2007; 32 Rodriguez-Galiano (10.1016/j.apgeochem.2023.105807_bib84) 2015; 71 Gao (10.1016/j.apgeochem.2023.105807_bib44) 2016; 75 Zuo (10.1016/j.apgeochem.2023.105807_bib114) 2011; 37 Nayebi (10.1016/j.apgeochem.2023.105807_bib72) 2021; 132 Yousefi (10.1016/j.apgeochem.2023.105807_bib107) 2019; 111 Ghezelbash (10.1016/j.apgeochem.2023.105807_bib47) 2020; 134 Goel (10.1016/j.apgeochem.2023.105807_bib48) 2013; vol. 9 Ghasemzadeh (10.1016/j.apgeochem.2023.105807_bib45) 2019; 113 Zuo (10.1016/j.apgeochem.2023.105807_bib116) 2020; 29 Shin (10.1016/j.apgeochem.2023.105807_bib88) 2020; 10 Granek (10.1016/j.apgeochem.2023.105807_bib49) 2016 Chen (10.1016/j.apgeochem.2023.105807_bib34) 2018; vols. 1–5 Malainine (10.1016/j.apgeochem.2023.105807_bib65) 2022; 37 Chen (10.1016/j.apgeochem.2023.105807_bib30) 2019; 125 Lin (10.1016/j.apgeochem.2023.105807_bib64) 2021; 11 Chen (10.1016/j.apgeochem.2023.105807_bib31) 2017; 64 Bradley (10.1016/j.apgeochem.2023.105807_bib19) 2014; 38 Jami (10.1016/j.apgeochem.2023.105807_bib55) 2007; 102 Cao (10.1016/j.apgeochem.2023.105807_bib22) 2022; 12 Guan (10.1016/j.apgeochem.2023.105807_bib50) 2021; 156 Calvert (10.1016/j.apgeochem.2023.105807_bib21) 2019; 6 Yang (10.1016/j.apgeochem.2023.105807_bib101) 2021; 30 Carranza (10.1016/j.apgeochem.2023.105807_bib23) 2001; 10 Carranza (10.1016/j.apgeochem.2023.105807_bib26) 2008 Carranza (10.1016/j.apgeochem.2023.105807_bib25) 2016; 25 Mokhtari (10.1016/j.apgeochem.2023.105807_bib71) 2013; 122 Zuo (10.1016/j.apgeochem.2023.105807_bib115) 2022; 31 Daviran (10.1016/j.apgeochem.2023.105807_bib41) 2023; 16 Chen (10.1016/j.apgeochem.2023.105807_bib32) 2015; 71 Torab (10.1016/j.apgeochem.2023.105807_bib90) 2007; 71 Sadeghi (10.1016/j.apgeochem.2023.105807_bib86) 2013; 85 Prado (10.1016/j.apgeochem.2023.105807_bib77) 2020; 124 Rezapour (10.1016/j.apgeochem.2023.105807_bib83) 2020; 10 Hariri (10.1016/j.apgeochem.2023.105807_bib52) 2019; 33 Chen (10.1016/j.apgeochem.2023.105807_bib28) 2022; 143 Nykänen (10.1016/j.apgeochem.2023.105807_bib74) 2015; 71 Yousefi (10.1016/j.apgeochem.2023.105807_bib105) 2021; 229 Daliran (10.1016/j.apgeochem.2023.105807_bib40) 2010; 20 Yousefi (10.1016/j.apgeochem.2023.105807_bib106) 2023; 149 Bauder (10.1016/j.apgeochem.2023.105807_bib14) 2018; 6 Bonyadi (10.1016/j.apgeochem.2023.105807_bib18) 2020; 189 Porwal (10.1016/j.apgeochem.2023.105807_bib76) 2006; 32 Daliran (10.1016/j.apgeochem.2023.105807_bib39) 2002; 2 Chen (10.1016/j.apgeochem.2023.105807_bib29) 2019; 9 Carranza (10.1016/j.apgeochem.2023.105807_bib24) 2008; 33 Agterberg (10.1016/j.apgeochem.2023.105807_bib3) 2005; 14 Finke (10.1016/j.apgeochem.2023.105807_bib42) 2021 Xia (10.1016/j.apgeochem.2023.105807_bib95) 2015 Kingma (10.1016/j.apgeochem.2023.105807_bib60) 2014 Yousefi (10.1016/j.apgeochem.2023.105807_bib102) 2015; 83 Zavrak (10.1016/j.apgeochem.2023.105807_bib108) 2020; 8 Zuo (10.1016/j.apgeochem.2023.105807_bib113) 2020; 29 Ahmadi (10.1016/j.apgeochem.2023.105807_bib4) 2022; 16 Cevik (10.1016/j.apgeochem.2023.105807_bib27) 2020 Zumlot (10.1016/j.apgeochem.2023.105807_bib111) 2012; 4 Kheyrollahi (10.1016/j.apgeochem.2023.105807_bib59) 2021; 217 Meigooni (10.1016/j.apgeochem.2023.105807_bib68) 2021; 11 Walter (10.1016/j.apgeochem.2023.105807_bib92) 2005; 24 Mhangara (10.1016/j.apgeochem.2023.105807_bib69) 2005 Yousefi (10.1016/j.apgeochem.2023.105807_bib104) 2016; 25 Blakely (10.1016/j.apgeochem.2023.105807_bib16) 1996 Ghezelbash (10.1016/j.apgeochem.2023.105807_bib46) 2021; 30 Ammar (10.1016/j.apgeochem.2023.105807_bib9) 2014; 100 Li (10.1016/j.apgeochem.2023.105807_bib62) 2021; 30 Xiong (10.1016/j.apgeochem.2023.105807_bib98) 2018; 102 Yang (10.1016/j.apgeochem.2023.105807_bib100) 2019; 28 Torab (10.1016/j.apgeochem.2023.105807_bib91) 2008 Ramezani (10.1016/j.apgeochem.2023.105807_bib81) 2003; 303 Hariharan (10.1016/j.apgeochem.2023.105807_bib51) 2017; 26 Rahimi (10.1016/j.apgeochem.2023.105807_bib79) 2021; 128 Xiong (10.1016/j.apgeochem.2023.105807_bib96) 2016; 86 Li (10.1016/j.apgeochem.2023.105807_bib63) 2020; 29 Zuo (10.1016/j.apgeochem.2023.105807_bib117) 2019; 192 Samani (10.1016/j.apgeochem.2023.105807_bib87) 1988; 39 Taghipour (10.1016/j.apgeochem.2023.105807_bib89) 2015; 8 Abedi (10.1016/j.apgeochem.2023.105807_bib1) 2012; 46 Rajan Girija (10.1016/j.apgeochem.2023.105807_bib80) 2019; 10 Porwal (10.1016/j.apgeochem.2023.105807_bib75) 2015; vol. 71 Mandrekar (10.1016/j.apgeochem.2023.105807_bib66) 2010; 5 Abedi (10.1016/j.apgeochem.2023.105807_bib2) 2013; 6 Rajabi (10.1016/j.apgeochem.2023.105807_bib78) 2015; 64 Reimann (10.1016/j.apgeochem.2023.105807_bib82) 2002; 17 Chen (10.1016/j.apgeochem.2023.105807_bib33) 2019; 28 Khalid (10.1016/j.apgeochem.2023.105807_bib58) 2014 Wang (10.1016/j.apgeochem.2023.105807_bib93) 2020; 29 Ahmadi (10.1016/j.apgeochem.2023.105807_bib6) 2021; 55 Moeini (10.1016/j.apgeochem.2023.105807_bib70) 2017; 180 Wu (10.1016/j.apgeochem.2023.105807_bib94) 2022; 232 Mangortey (10.1016/j.apgeochem.2023.105807_bib67) 2020 Neukirchen (10.1016/j.apgeochem.2023.105807_bib73) 2019 Zuo (10.1016/j.apgeochem.2023.105807_bib112) 2018; 184 Alzubi (10.1016/j.apgeochem.2023.105807_bib7) 2018; 1142 Zhang (10.1016/j.apgeochem.2023.105807_bib110) 2019; 28 Li (10.1016/j.apgeochem.2023.105807_bib61) 2022; 12 Foerster (10.1016/j.apgeochem.2023.105807_bib43) 1994; 89 Amer (10.1016/j.apgeochem.2023.105807_bib8) 2013 Zhang (10.1016/j.apgeochem.2023.105807_bib109) 2021; 30 Azadi (10.1016/j.apgeochem.2023.105807_bib12) 2021 Bonham-Carter (10.1016/j.apgeochem.2023.105807_bib17) 1994 Clark (10.1016/j.apgeochem.2023.105807_bib37) 2014; 624 Xiong (10.1016/j.apgeochem.2023.105807_bib97) 2021; 147 Xu (10.1016/j.apgeochem.2023.105807_bib99) 2015 Ikotun (10.1016/j.apgeochem.2023.105807_bib54) 2022; 17 Bigdeli (10.1016/j.apgeochem.2023.105807_bib15) 2022; 233 Brown (10.1016/j.apgeochem.2023.105807_bib20) 2000; 47 Ayodele (10.1016/j.apgeochem.2023.105807_bib10) 2010; 3 Daliran (10.1016/j.apgeochem.2023.105807_bib38) 2007 Yousefi (10.1016/j.apgeochem.2023.105807_bib103) 2015; 79 Cheng (10.1016/j.apgeochem.2023.105807_bib36) 1994; 51 Bakhtiyari (10.1016/j.apgeochem.2023.105807_bib13) 2016; 6 Azadi (10.1016/j.apgeochem.2023.105807_bib11) 2021; 12 Jiang (10.1016/j.apgeochem.2023.105807_bib56) 1996; 201 |
| References_xml | – volume: 32 start-page: 314 issue: 1–2 year: 2007 ident: 10.1016/j.apgeochem.2023.105807_bib35 article-title: Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2006.10.002 – volume: 122 start-page: 795 issue: 3 year: 2013 ident: 10.1016/j.apgeochem.2023.105807_bib71 article-title: Genesis of iron-apatite ores in Posht-e-Badam Block (Central Iran) using REE geochemistry publication-title: Journal of Earth System Science doi: 10.1007/s12040-013-0313-z – volume: 10 start-page: 149 issue: 1 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib83 article-title: A clustering approach for mineral potential mapping: a deposit-scale porphyry copper exploration targeting publication-title: Geopersia – volume: 6 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib21 article-title: Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data publication-title: J. Big Data doi: 10.1186/s40537-019-0230-3 – volume: 12 start-page: 616 issue: 5 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib61 article-title: Overview on the development of intelligent methods for mineral resource prediction under the background of geological big data publication-title: Minerals doi: 10.3390/min12050616 – start-page: 8 year: 2013 ident: 10.1016/j.apgeochem.2023.105807_bib8 article-title: Enhancing one-class support vector machines for unsupervised anomaly detection publication-title: Proc. ACM SIGKDD Workshop Outlier Detect. Descrip. doi: 10.1145/2500853.2500857 – volume: 28 start-page: 645 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib110 article-title: Maximum entropy and random forest modeling of mineral potential: analysis of gold prospectivity in the Hezuo--Meiwu district, west Qinling Orogen, China publication-title: Natural Resources Research doi: 10.1007/s11053-018-9425-0 – volume: vols. 1–5 year: 2018 ident: 10.1016/j.apgeochem.2023.105807_bib34 – volume: 26 start-page: 489 issue: 4 year: 2017 ident: 10.1016/j.apgeochem.2023.105807_bib51 article-title: Random forest-based prospectivity modelling of greenfield terrains using sparse deposit data: an example from the Tanami Region, Western Australia publication-title: Natural Resources Research doi: 10.1007/s11053-017-9335-6 – year: 2016 ident: 10.1016/j.apgeochem.2023.105807_bib49 – volume: 10 issue: 13 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib88 article-title: Extended autoencoder for novelty detection with reconstruction along projection pathway publication-title: Applied Sciences doi: 10.3390/app10134497 – volume: 125 start-page: 43 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib30 article-title: A spatially constrained multi-autoencoder approach for multivariate geochemical anomaly recognition publication-title: Computers \& Geosciences doi: 10.1016/j.cageo.2019.01.016 – volume: 11 start-page: 159 issue: 2 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib64 article-title: A comparative study of machine learning models with hyperparameter optimization algorithm for mapping mineral prospectivity publication-title: Minerals doi: 10.3390/min11020159 – volume: 25 start-page: 35 year: 2016 ident: 10.1016/j.apgeochem.2023.105807_bib25 article-title: Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines) publication-title: Nat. Resour. Res. doi: 10.1007/s11053-015-9268-x – volume: 624 start-page: 46 year: 2014 ident: 10.1016/j.apgeochem.2023.105807_bib37 article-title: Magnetic effects of hydrothermal alteration in porphyry copper and iron-oxide copper--gold systems: a review publication-title: Tectonophysics doi: 10.1016/j.tecto.2013.12.011 – volume: 189 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib18 article-title: Hydrothermal alteration associated with magnetite mineralization in the Bafq iron deposits, Iran publication-title: J. Asian Earth Sci. doi: 10.1016/j.jseaes.2019.104152 – volume: 89 start-page: 1697 issue: 8 year: 1994 ident: 10.1016/j.apgeochem.2023.105807_bib43 article-title: The Bafq mining district in central Iran; a highly mineralized Infracambrian volcanic field publication-title: Economic Geology doi: 10.2113/gsecongeo.89.8.1697 – volume: 7 start-page: 1 issue: 2 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib5 article-title: Mineral potential mapping using satellite images of sentinel-2, landsat-8 and ASTER for iron ore at Esfordi 1: 100000 sheet publication-title: J. Min. Resour. Eng. – start-page: 1501 year: 2007 ident: 10.1016/j.apgeochem.2023.105807_bib38 article-title: Multistage metasomatism and mineralization at hydrothermal Fe oxide-REE-apatite deposits and “apatitites” of the Bafq District, Central-East Iran publication-title: Digging Deeper. Proceedings of the 9th Biennial SGA Meeting Dublin – start-page: 1 year: 2014 ident: 10.1016/j.apgeochem.2023.105807_bib60 – volume: 6 start-page: 1 year: 2018 ident: 10.1016/j.apgeochem.2023.105807_bib14 article-title: The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data publication-title: Health Inf. Sci. Syst. doi: 10.1007/s13755-018-0051-3 – volume: 29 start-page: 3415 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib113 article-title: Geodata science-based mineral prospectivity mapping: a review publication-title: Natural Resources Research doi: 10.1007/s11053-020-09700-9 – volume: 71 start-page: 853 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib74 article-title: Receiver operating characteristics (ROC) as validation tool for prospectivity models—a magmatic Ni--Cu case study from the Central Lapland Greenstone Belt, Northern Finland publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.09.007 – volume: 192 start-page: 1 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib117 article-title: Deep learning and its application in geochemical mapping publication-title: Earth-Science Reviews doi: 10.1016/j.earscirev.2019.02.023 – volume: 37 start-page: 1967 issue: 12 year: 2011 ident: 10.1016/j.apgeochem.2023.105807_bib114 article-title: Support vector machine: a tool for mapping mineral prospectivity publication-title: Computers \& Geosciences doi: 10.1016/j.cageo.2010.09.014 – volume: 24 start-page: 2025 issue: 13 year: 2005 ident: 10.1016/j.apgeochem.2023.105807_bib92 article-title: The partial area under the summary ROC curve publication-title: Statistics in Medicine doi: 10.1002/sim.2103 – volume: 12 start-page: 547 issue: 2 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib11 article-title: Stable downward continuation of airborne potential field geophysical data: an investigation of stabilizer family publication-title: J. Min. Environ. – volume: 2 start-page: 303 year: 2002 ident: 10.1016/j.apgeochem.2023.105807_bib39 article-title: Kiruna-type iron oxide-apatite ores and apatitites of the Bafq district, Iran, with an emphasis on the REE geochemistry of their apatites publication-title: Hydrothermal Iron Oxide Copper Gold and Related Deposits: A Global Perspective – volume: 83 start-page: 72 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib102 article-title: Geometric average of spatial evidence data layers: a GIS-based multi-criteria decision-making approach to mineral prospectivity mapping publication-title: Computers \& Geosciences doi: 10.1016/j.cageo.2015.07.006 – volume: 30 start-page: 1011 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib109 article-title: Data-driven mineral prospectivity mapping by joint application of unsupervised convolutional auto-encoder network and supervised convolutional neural network publication-title: Natural Resources Research doi: 10.1007/s11053-020-09789-y – volume: 149 year: 2023 ident: 10.1016/j.apgeochem.2023.105807_bib106 article-title: Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2023.105561 – volume: 55 start-page: 171 issue: 2 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib6 article-title: Geochemical potential mapping of iron-oxide targets by Prediction-Area plot and Concentration-Number fractal model in Esfordi, Iran publication-title: Int. J. Min. Geol. Eng. – volume: 75 start-page: 16 year: 2016 ident: 10.1016/j.apgeochem.2023.105807_bib44 article-title: Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2015.12.005 – volume: 86 start-page: 75 year: 2016 ident: 10.1016/j.apgeochem.2023.105807_bib96 article-title: Recognition of geochemical anomalies using a deep autoencoder network publication-title: Computers \& Geosciences doi: 10.1016/j.cageo.2015.10.006 – volume: 29 start-page: 189 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib93 article-title: Mapping mineral prospectivity via semi-supervised random forest publication-title: Natural Resources Research doi: 10.1007/s11053-019-09510-8 – volume: 51 start-page: 109 issue: 2 year: 1994 ident: 10.1016/j.apgeochem.2023.105807_bib36 article-title: The separation of geochemical anomalies from background by fractal methods publication-title: Journal of Geochemical Exploration doi: 10.1016/0375-6742(94)90013-2 – volume: 303 start-page: 622 issue: 7 year: 2003 ident: 10.1016/j.apgeochem.2023.105807_bib81 article-title: The Saghand region, central Iran: U-Pb geochronology, petrogenesis and implications for Gondwana tectonics publication-title: American Journal of Science doi: 10.2475/ajs.303.7.622 – volume: 25 start-page: 3 year: 2016 ident: 10.1016/j.apgeochem.2023.105807_bib104 article-title: Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration publication-title: Natural Resources Research doi: 10.1007/s11053-014-9261-9 – volume: 111 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib107 article-title: Exploration information systems--A proposal for the future use of GIS in mineral exploration targeting publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2019.103005 – volume: 17 issue: 8 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib54 article-title: Boosting k-means clustering with symbiotic organisms search for automatic clustering problems publication-title: Plos One doi: 10.1371/journal.pone.0272861 – volume: 8 start-page: 108346 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib108 article-title: Anomaly-based intrusion detection from network flow features using variational autoencoder publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3001350 – volume: 128 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib79 article-title: Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof publication-title: Applied Geochemistry doi: 10.1016/j.apgeochem.2021.104940 – volume: 1142 year: 2018 ident: 10.1016/j.apgeochem.2023.105807_bib7 article-title: Machine learning from theory to algorithms: an overview publication-title: J. Phys. Conf. doi: 10.1088/1742-6596/1142/1/012012 – volume: 17 start-page: 185 issue: 3 year: 2002 ident: 10.1016/j.apgeochem.2023.105807_bib82 article-title: Factor analysis applied to regional geochemical data: problems and possibilities publication-title: Applied Geochemistry doi: 10.1016/S0883-2927(01)00066-X – volume: 28 start-page: 31 issue: 1 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib33 article-title: Isolation forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency publication-title: Natural Resources Research doi: 10.1007/s11053-018-9375-6 – volume: 5 start-page: 1315 issue: 9 year: 2010 ident: 10.1016/j.apgeochem.2023.105807_bib66 article-title: Receiver operating characteristic curve in diagnostic test assessment publication-title: Journal of Thoracic Oncology doi: 10.1097/JTO.0b013e3181ec173d – volume: 16 start-page: 2143 year: 2023 ident: 10.1016/j.apgeochem.2023.105807_bib41 article-title: Hybridizing K-means clustering algorithm with harmony search and artificial bee colony optimizers for intelligence mineral prospectivity mapping publication-title: Earth Science Informatics doi: 10.1007/s12145-023-01019-2 – volume: 232 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib94 article-title: Transfer learning and siamese neural network based identification of geochemical anomalies for mineral exploration: a case study from the CuAu deposit in the NW Junggar area of northern Xinjiang Province, China publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2021.106904 – start-page: 1850 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib67 – volume: 14 start-page: 1 issue: 1 year: 2005 ident: 10.1016/j.apgeochem.2023.105807_bib3 article-title: Measuring the performance of mineral-potential maps publication-title: Nat. Resour. Res. doi: 10.1007/s11053-005-4674-0 – volume: 124 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib77 article-title: Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: dealing with imbalanced training data publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2020.103611 – volume: 102 start-page: 811 year: 2018 ident: 10.1016/j.apgeochem.2023.105807_bib98 article-title: Mapping mineral prospectivity through big data analytics and a deep learning algorithm publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2018.10.006 – volume: 100 start-page: 81 year: 2014 ident: 10.1016/j.apgeochem.2023.105807_bib9 article-title: Hydro-geochemical processes in the Complexe Terminal aquifer of southern Tunisia: an integrated investigation based on geochemical and multivariate statistical methods publication-title: J. Afr. Earth Sci. doi: 10.1016/j.jafrearsci.2014.06.015 – volume: 12 start-page: 4184 issue: 9 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib22 article-title: Network intrusion detection model based on CNN and GRU publication-title: Appl. Sci. doi: 10.3390/app12094184 – year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib99 – volume: 33 start-page: 536 issue: 3–4 year: 2008 ident: 10.1016/j.apgeochem.2023.105807_bib24 article-title: Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2007.07.001 – volume: 16 issue: 62 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib4 article-title: Integration of airborne magnetic and satellite imagery data to identify potential zones of iron occurrences using the prediction-area plot in the Esfordi area publication-title: Iranian J. Geol. – volume: 64 start-page: 639 issue: 5 year: 2017 ident: 10.1016/j.apgeochem.2023.105807_bib31 article-title: Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data publication-title: Australian Journal of Earth Sciences doi: 10.1080/08120099.2017.1328705 – volume: 120 start-page: 347 issue: 1 year: 2006 ident: 10.1016/j.apgeochem.2023.105807_bib57 article-title: Characterization and identification of the sources of chromium, zinc, lead, cadmium, nickel, manganese and iron in PM10 particulates at the two sites of Kolkata, India publication-title: Environmental Monitoring and Assessment doi: 10.1007/s10661-005-9067-7 – volume: 11 start-page: 115 issue: 1 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib68 article-title: Detection of rare earth element anomalies in Esfordi phosphate deposit of Central Iran, using geostatistical-fractal simulation publication-title: Geopersia – volume: 143 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib28 article-title: Mineral prospectivity mapping based on wavelet neural network and Monte Carlo simulations in the Nanling W-Sn metallogenic province publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2022.104765 – volume: 8 start-page: 2967 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib89 article-title: Skarn mineral assemblages in the Esfordi iron oxide--apatite deposit, Bafq district, Central Iran publication-title: Arabian Journal of Geosciences doi: 10.1007/s12517-014-1326-x – volume: vol. 9 start-page: 392 year: 2013 ident: 10.1016/j.apgeochem.2023.105807_bib48 – volume: 180 start-page: 15 year: 2017 ident: 10.1016/j.apgeochem.2023.105807_bib70 article-title: Comparing compositional multivariate outliers with autoencoder networks in anomaly detection at Hamich exploration area, east of Iran publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2017.05.008 – volume: 134 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib47 article-title: Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm publication-title: Computers \& Geosciences – volume: 6 start-page: 3601 issue: 10 year: 2013 ident: 10.1016/j.apgeochem.2023.105807_bib2 article-title: Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit publication-title: Arabian J. Geosci. doi: 10.1007/s12517-012-0615-5 – start-page: 372 year: 2014 ident: 10.1016/j.apgeochem.2023.105807_bib58 – volume: 102 start-page: 1111 issue: 6 year: 2007 ident: 10.1016/j.apgeochem.2023.105807_bib55 article-title: Fluid inclusion and stable isotope study of the Esfordi apatite-magnetite deposit, Central Iran publication-title: Economic Geology doi: 10.2113/gsecongeo.102.6.1111 – volume: 113 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib45 article-title: Stream sediment geochemical data analysis for district-scale mineral exploration targeting: measuring the performance of the spatial U-statistic and CA fractal modeling publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2019.103115 – volume: 39 start-page: 85 year: 1988 ident: 10.1016/j.apgeochem.2023.105807_bib87 article-title: Metallogeny of the precambrian in Iran publication-title: Precambrian Research doi: 10.1016/0301-9268(88)90053-8 – volume: 71 start-page: 347 issue: 3 year: 2007 ident: 10.1016/j.apgeochem.2023.105807_bib90 article-title: Magnetite-apatite deposits of the Bafq district, Central Iran: apatite geochemistry and monazite geochronology publication-title: Mineralogical Magazine doi: 10.1180/minmag.2007.071.3.347 – volume: 79 start-page: 69 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib103 article-title: Prediction--area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling publication-title: Computers \& Geosciences doi: 10.1016/j.cageo.2015.03.007 – start-page: 1 issue: 6 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib42 article-title: Autoencoders for unsupervised anomaly detection in high energy physics publication-title: Journal of High Energy Physics – year: 2008 ident: 10.1016/j.apgeochem.2023.105807_bib91 – volume: 9 start-page: 270 issue: 5 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib29 article-title: A multi-convolutional autoencoder approach to multivariate geochemical anomaly recognition publication-title: Minerals doi: 10.3390/min9050270 – start-page: 1511 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib95 article-title: Learning discriminative reconstructions for unsupervised outlier removal publication-title: Proceedings of the IEEE International Conference on Computer Vision – volume: 4 start-page: 756 issue: 7 year: 2012 ident: 10.1016/j.apgeochem.2023.105807_bib111 article-title: Multivariate statistical approach to geochemical methods in water quality factor identification; application to the shallow aquifer system of the Yarmouk Basin of north Jordan publication-title: Research Journal of Environmental and Earth Sciences – volume: 64 start-page: 328 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib78 article-title: Basin evolution and stratigraphic correlation of sedimentary-exhalative Zn–Pb deposits of the early Cambrian Zarigan–Chahmir Basin, Central Iran publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.07.013 – volume: 28 start-page: 184 issue: 1 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib100 article-title: Two-way partial AUC and its properties publication-title: Statistical Methods in Medical Research doi: 10.1177/0962280217718866 – volume: 201 start-page: 745 issue: 3 year: 1996 ident: 10.1016/j.apgeochem.2023.105807_bib56 article-title: A receiver operating characteristic partial area index for highly sensitive diagnostic tests publication-title: Radiology doi: 10.1148/radiology.201.3.8939225 – volume: 71 start-page: 749 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib32 article-title: Mineral potential mapping with a restricted Boltzmann machine publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2014.08.012 – volume: 10 start-page: 165 issue: 3 year: 2001 ident: 10.1016/j.apgeochem.2023.105807_bib23 article-title: Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines publication-title: Explor. Min. Geol. doi: 10.2113/0100165 – volume: 32 start-page: 1 issue: 1 year: 2006 ident: 10.1016/j.apgeochem.2023.105807_bib76 article-title: Bayesian network classifiers for mineral potential mapping publication-title: Computers \& Geosciences doi: 10.1016/j.cageo.2005.03.018 – volume: 30 start-page: 1977 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib46 article-title: Regional-scale mineral prospectivity mapping: support vector machines and an improved data-driven multi-criteria decision-making technique publication-title: Natural Resources Research doi: 10.1007/s11053-021-09842-4 – volume: 46 start-page: 272 year: 2012 ident: 10.1016/j.apgeochem.2023.105807_bib1 article-title: Support vector machine for multi-classification of mineral prospectivity areas publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2011.12.014 – start-page: 1 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib12 article-title: Two-step inversion of airborne geophysical data: a stable downward continuation approach for physical modelling publication-title: Acta Geophys. – volume: 10 start-page: 79 issue: 2 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib80 article-title: Mapping of mineral resources and lithological units: a review of remote sensing techniques publication-title: International Journal of Image and Data Fusion doi: 10.1080/19479832.2019.1589585 – volume: 29 start-page: 203 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib63 article-title: Prospectivity mapping for tungsten polymetallic mineral resources, Nanling metallogenic belt, south China: use of random forest algorithm from a perspective of data imbalance publication-title: Natural Resources Research doi: 10.1007/s11053-019-09564-8 – volume: 37 start-page: 6579 issue: 22 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib65 article-title: An integrated ASTER-based approach for mapping carbonatite and iron oxide-apatite deposits publication-title: Geocarto International doi: 10.1080/10106049.2021.1953617 – volume: 30 start-page: 27 issue: 1 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib62 article-title: Random-drop data augmentation of deep convolutional neural network for mineral prospectivity mapping publication-title: Natural Resources Research doi: 10.1007/s11053-020-09742-z – volume: 29 start-page: 3443 year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib116 article-title: Effects of random negative training samples on mineral prospectivity mapping publication-title: Natural Resources Research doi: 10.1007/s11053-020-09668-6 – volume: vol. 71 start-page: 477 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib75 article-title: Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration – volume: 33 start-page: 1479 issue: 4 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib52 article-title: Extended isolation forest publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2019.2947676 – volume: 6 start-page: 1580 year: 2016 ident: 10.1016/j.apgeochem.2023.105807_bib13 article-title: Economic geology of the χіv iron-oxide prospect, Bafq mining district, Central Iran: a preliminary approach publication-title: Open J. Geol. doi: 10.4236/ojg.2016.612112 – volume: 147 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib97 article-title: A positive and unlabeled learning algorithm for mineral prospectivity mapping publication-title: Computers \& Geosciences – volume: 184 start-page: 150 year: 2018 ident: 10.1016/j.apgeochem.2023.105807_bib112 article-title: Selection of an elemental association related to mineralization using spatial analysis publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2017.10.020 – volume: 85 start-page: 103 year: 2013 ident: 10.1016/j.apgeochem.2023.105807_bib86 article-title: Using ETM+ and ASTER sensors to identify iron occurrences in the Esfordi 1: 100,000 mapping sheet of Central Iran publication-title: Journal of African Earth Sciences doi: 10.1016/j.jafrearsci.2013.05.003 – volume: 229 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib105 article-title: Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: state-of-the-art and outlook publication-title: Journal of Geochemical Exploration doi: 10.1016/j.gexplo.2021.106839 – volume: 3 start-page: 19 year: 2010 ident: 10.1016/j.apgeochem.2023.105807_bib10 article-title: Types of machine learning algorithms publication-title: New Adv. Mach. Learn. – year: 2008 ident: 10.1016/j.apgeochem.2023.105807_bib26 – volume: 156 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib50 article-title: A spatial-compositional feature fusion convolutional autoencoder for multivariate geochemical anomaly recognition publication-title: Computers \& Geosciences – volume: 217 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib59 article-title: Regional magnetic and gravity structures and distribution of mineral deposits in Central Iran: implications for mineral exploration publication-title: Journal of Asian Earth Sciences doi: 10.1016/j.jseaes.2021.104828 – year: 1994 ident: 10.1016/j.apgeochem.2023.105807_bib17 – volume: 47 start-page: 757 issue: 4 year: 2000 ident: 10.1016/j.apgeochem.2023.105807_bib20 article-title: Artificial neural networks: a new method for mineral prospectivity mapping publication-title: Aust. J. Earth Sci. doi: 10.1046/j.1440-0952.2000.00807.x – volume: 132 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib72 article-title: Geochronological and geochemical evidence for multi-stage apatite in the Bafq iron metallogenic belt (Central Iran), with implications for the Chadormalu iron-apatite deposit publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2021.104054 – volume: 71 start-page: 804 year: 2015 ident: 10.1016/j.apgeochem.2023.105807_bib84 article-title: Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines publication-title: Ore Geology Reviews doi: 10.1016/j.oregeorev.2015.01.001 – volume: 233 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib15 article-title: Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2021.106923 – volume: 38 start-page: 93 year: 2014 ident: 10.1016/j.apgeochem.2023.105807_bib19 article-title: Half-AUC for the evaluation of sensitive or specific classifiers publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2013.11.015 – volume: 20 start-page: 143 year: 2010 ident: 10.1016/j.apgeochem.2023.105807_bib40 article-title: Early Cambrian iron oxide-apatite-REE (U) deposits of the Bafq district, east-central Iran publication-title: Exploring for Iron Oxide Copper--Gold Deposits: Canada and Global Analogues. Geol Assoc Canada, Short Course Notes – volume: 31 start-page: 1121 issue: 3 year: 2022 ident: 10.1016/j.apgeochem.2023.105807_bib115 article-title: A geologically constrained variational autoencoder for mineral prospectivity mapping publication-title: Natural Resources Research doi: 10.1007/s11053-022-10050-x – volume: 30 start-page: 3905 issue: 6 year: 2021 ident: 10.1016/j.apgeochem.2023.105807_bib101 article-title: A convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation publication-title: Natural Resources Research doi: 10.1007/s11053-021-09934-1 – year: 1996 ident: 10.1016/j.apgeochem.2023.105807_bib16 – year: 2020 ident: 10.1016/j.apgeochem.2023.105807_bib27 – year: 2005 ident: 10.1016/j.apgeochem.2023.105807_bib69 – start-page: 371 year: 2019 ident: 10.1016/j.apgeochem.2023.105807_bib73 |
| SSID | ssj0005702 |
| Score | 2.510118 |
| Snippet | In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 105807 |
| SubjectTerms | algorithms data collection geochemistry Iran mineralization spatial data uncertainty |
| Title | Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting |
| URI | https://www.proquest.com/docview/3153736670 |
| Volume | 158 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ba9swFBZbx2AvY1fW7oIGeysOjiXfHsNoV0bXwZZA3syRLfVCa4fWeUge-tt3dI0DgXV7MUGxFKPzRedYOuf7CPki46bkOVeRSnEJxIgAIijNNkfDU1kCs1XvP86ykxn_Pk_nG_1OU13Si1G93llX8j9WxTa0q66S_QfLhkGxAT-jffGKFsbrg2z8y2f_2KDv5tJQSLvKysiUqUhNxdrdgN7EMFSa7V3Q5sG_Niz7TlNZakYJTW2JBmttYrjJP3Qjah2Aa48VmzvuPZ4nsHXB7LnUClxWQi7Y8vJ2jVBbdzaR7LdcrfDOSQ8agheHk_MLcI2bsyh0qbaSaGV1bcLORMJcid5wAdM6cbb2P6y2aXG40OrCRZxHO9dwu51wNYKFe-aRHtx12bgtf1R_9rM6np2eVtOj-fQxeZLg64Je70b3g1Sf3OSehufZyvPb-TPbUcq2kzaRx_QFee5eGejE2v8leSTbV-TpNyPJvHpN7gcooJ2iu1FAHQqoRwF1KMAv6AAF1KKAOhRQRIEfkQ5QQAMK3pDZ8dH060nkVDUiYPm4jxTPVI5xZp0nIuPlWNXjJtU0_uW4aIADxFJmZZM3jHEhasUUqBpKBjWgd1OCvSV7bdfKd4TKMkmkYEUtZMk1xXHaAGjhIygEFzXsk8zPYlU7ynmtfHJd-dzCqypMf6Wnv7LTv0_i0HFhWVf-3uWzN1OFKNfHXtDKbnlXMXTqOcuyPD54wD3vybMNlj-Qvf52KT9i3NmLTwZUfwDRN46t |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Recognition+of+mineralization-related+anomaly+patterns+through+an+autoencoder+neural+network+for+mineral+exploration+targeting&rft.jtitle=Applied+geochemistry&rft.au=Mirzabozorg%2C+Seyyed+Ataollah+Agha+Seyyed&rft.au=Abedi%2C+Maysam&rft.date=2023-11-01&rft.issn=0883-2927&rft.volume=158+p.105807-&rft_id=info:doi/10.1016%2Fj.apgeochem.2023.105807&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0883-2927&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0883-2927&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0883-2927&client=summon |