Recognition of geochemical anomalies using a deep autoencoder network
In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the tr...
Saved in:
| Published in: | Computers & geosciences Vol. 86; pp. 75 - 82 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.01.2016
|
| Subjects: | |
| ISSN: | 0098-3004, 1873-7803 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the trained model as anomalous samples due to their comparatively higher reconstructed errors. The southwestern Fujian district (China) is chosen as a case study area. A variety of learning rates, iterations, and the size of each hidden layer are constructing and training the deep autoencoder networks on all the geochemical samples. The reconstruction error (or, anomaly score) of each training sample is used to recognize multivariate geochemical anomalies associated with Fe polymetallic mineralization. By comparing the results obtained with a continuous restricted Boltzmann machine, we conclude that the autoencoder network can be trained to recognize multivariate geochemical anomalies. Most of the known skarn-type Fe deposits are located in areas with high reconstruction errors or anomaly scores in the anomaly map, indicating that these anomalies may be related to Fe mineralization.
•Recognition of geochemical anomalies using an autoencoder network.•Recognition of geochemical anomalies using continuous restricted Boltzmann machines.•Methods demonstrated in a case study southwestern Fujian district (China). |
|---|---|
| AbstractList | In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the trained model as anomalous samples due to their comparatively higher reconstructed errors. The southwestern Fujian district (China) is chosen as a case study area. A variety of learning rates, iterations, and the size of each hidden layer are constructing and training the deep autoencoder networks on all the geochemical samples. The reconstruction error (or, anomaly score) of each training sample is used to recognize multivariate geochemical anomalies associated with Fe polymetallic mineralization. By comparing the results obtained with a continuous restricted Boltzmann machine, we conclude that the autoencoder network can be trained to recognize multivariate geochemical anomalies. Most of the known skarn-type Fe deposits are located in areas with high reconstruction errors or anomaly scores in the anomaly map, indicating that these anomalies may be related to Fe mineralization. In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions. During the training, small probability samples contribute little to the autoencoder network. These samples can be recognized by the trained model as anomalous samples due to their comparatively higher reconstructed errors. The southwestern Fujian district (China) is chosen as a case study area. A variety of learning rates, iterations, and the size of each hidden layer are constructing and training the deep autoencoder networks on all the geochemical samples. The reconstruction error (or, anomaly score) of each training sample is used to recognize multivariate geochemical anomalies associated with Fe polymetallic mineralization. By comparing the results obtained with a continuous restricted Boltzmann machine, we conclude that the autoencoder network can be trained to recognize multivariate geochemical anomalies. Most of the known skarn-type Fe deposits are located in areas with high reconstruction errors or anomaly scores in the anomaly map, indicating that these anomalies may be related to Fe mineralization. •Recognition of geochemical anomalies using an autoencoder network.•Recognition of geochemical anomalies using continuous restricted Boltzmann machines.•Methods demonstrated in a case study southwestern Fujian district (China). |
| Author | Zuo, Renguang Xiong, Yihui |
| Author_xml | – sequence: 1 givenname: Yihui surname: Xiong fullname: Xiong, Yihui – sequence: 2 givenname: Renguang surname: Zuo fullname: Zuo, Renguang email: zrguang@cug.edu.cn |
| BookMark | eNqFkDtPwzAUhS0EEm3hF7BkZEnxI06cgQFV5SFVQkIwW7fOTXFJ7WInIP49LmVioNOVjs53dPWNybHzDgm5YHTKKCuv1lMDK_RTTplMyZTS8oiMmKpEXikqjsmI0lrlgtLilIxjXFNKOVdyROZPaPzK2d56l_k2SyvmFTfWQJeB8xvoLMZsiNatMsgaxG0GQ-_RGd9gyBz2nz68nZGTFrqI5793Ql5u58-z-3zxePcwu1nkIGre50YULWsl53VdgmRQpReWJWCxxKpoBQXBZa2MrJgCJhVHoSQUDdRKKlPBUkzI5X53G_z7gLHXGxsNdh049EPUnLFSpXKpDlZZlcwUQtA6Vet91QQfY8BWG9vDzkgfwHaaUb2zrNf6x7LeWd6FyXJixR92G-wGwtcB6npPYZL1YTHoaGxyio0NaHrdePsv_w2lk5iT |
| CitedBy_id | crossref_primary_10_1007_s11053_017_9345_4 crossref_primary_10_1016_j_gexplo_2021_106875 crossref_primary_10_1007_s11053_022_10038_7 crossref_primary_10_1016_j_gexplo_2021_106872 crossref_primary_10_1016_j_oregeorev_2024_106399 crossref_primary_10_1016_j_oregeorev_2022_105065 crossref_primary_10_1016_j_chemer_2024_126157 crossref_primary_10_1007_s11053_025_10464_3 crossref_primary_10_1007_s11053_024_10409_2 crossref_primary_10_1016_j_apgeochem_2020_104710 crossref_primary_10_1016_j_apgeochem_2023_105765 crossref_primary_10_1016_j_oregeorev_2018_10_006 crossref_primary_10_2478_jaiscr_2022_0017 crossref_primary_10_1029_2024JH000468 crossref_primary_10_3390_app121910029 crossref_primary_10_1016_j_apgeochem_2021_105072 crossref_primary_10_1016_j_oregeorev_2024_106049 crossref_primary_10_1016_j_gexplo_2017_05_008 crossref_primary_10_1016_j_rsase_2025_101561 crossref_primary_10_1007_s11053_022_10080_5 crossref_primary_10_1016_j_jafrearsci_2025_105854 crossref_primary_10_1007_s11004_024_10164_3 crossref_primary_10_1016_j_apgeochem_2020_104843 crossref_primary_10_1007_s12145_025_01811_2 crossref_primary_10_1016_j_cageo_2022_105075 crossref_primary_10_3390_app131910994 crossref_primary_10_1016_j_cageo_2019_01_016 crossref_primary_10_1016_j_cageo_2025_105913 crossref_primary_10_3390_s19071633 crossref_primary_10_1089_ast_2021_0062 crossref_primary_10_1144_geochem2016_024 crossref_primary_10_1016_j_cageo_2021_104817 crossref_primary_10_1016_j_gexplo_2025_107741 crossref_primary_10_1016_j_gexplo_2019_106431 crossref_primary_10_1007_s12665_020_09322_7 crossref_primary_10_1016_j_apgeochem_2025_106385 crossref_primary_10_1016_j_oregeorev_2021_104264 crossref_primary_10_1007_s11004_024_10153_6 crossref_primary_10_1007_s11053_017_9357_0 crossref_primary_10_1007_s11004_022_10024_y crossref_primary_10_1016_j_oregeorev_2025_106447 crossref_primary_10_3390_min15030222 crossref_primary_10_1016_j_chemer_2021_125794 crossref_primary_10_1016_j_oregeorev_2016_11_014 crossref_primary_10_1016_j_gexplo_2023_107272 crossref_primary_10_1016_j_gexplo_2023_107274 crossref_primary_10_1016_j_gexplo_2023_107157 crossref_primary_10_1007_s11053_023_10227_y crossref_primary_10_1016_j_apgeochem_2020_104747 crossref_primary_10_1016_j_oregeorev_2023_105787 crossref_primary_10_1007_s11004_024_10141_w crossref_primary_10_1007_s11053_024_10433_2 crossref_primary_10_1016_j_oregeorev_2022_104693 crossref_primary_10_1016_j_geoen_2023_212132 crossref_primary_10_1007_s11053_019_09471_y crossref_primary_10_1016_j_oregeorev_2024_106352 crossref_primary_10_1016_j_gexplo_2022_106958 crossref_primary_10_3390_min14101021 crossref_primary_10_1016_j_chemer_2024_126197 crossref_primary_10_1016_j_cageo_2021_104717 crossref_primary_10_1007_s11004_021_09935_z crossref_primary_10_1007_s11053_019_09564_8 crossref_primary_10_1007_s12145_019_00412_0 crossref_primary_10_3390_s18030693 crossref_primary_10_1134_S0016702920040084 crossref_primary_10_1016_j_apgeochem_2024_106137 crossref_primary_10_1016_j_oregeorev_2023_105418 crossref_primary_10_1007_s11004_021_09979_1 crossref_primary_10_1016_j_oregeorev_2025_106627 crossref_primary_10_1016_j_cageo_2024_105703 crossref_primary_10_1016_j_gexplo_2018_06_013 crossref_primary_10_1016_j_oregeorev_2021_104282 crossref_primary_10_1016_j_cageo_2019_05_011 crossref_primary_10_1016_j_gexplo_2025_107772 crossref_primary_10_1007_s11004_025_10218_0 crossref_primary_10_1007_s10596_021_10104_8 crossref_primary_10_1016_j_apgeochem_2024_106146 crossref_primary_10_1016_j_rsase_2024_101343 crossref_primary_10_1029_2023GC011301 crossref_primary_10_1016_j_oregeorev_2021_104079 crossref_primary_10_3390_app12042247 crossref_primary_10_1016_j_oregeorev_2024_106215 crossref_primary_10_3390_s20216378 crossref_primary_10_3390_app10051657 crossref_primary_10_1007_s11053_022_10075_2 crossref_primary_10_1080_1206212X_2018_1475335 crossref_primary_10_1007_s11053_024_10334_4 crossref_primary_10_3390_su17073051 crossref_primary_10_1016_j_chemer_2023_125959 crossref_primary_10_1016_j_oregeorev_2025_106768 crossref_primary_10_1007_s11053_022_10088_x crossref_primary_10_1016_j_oregeorev_2019_102985 crossref_primary_10_1007_s11707_018_0704_1 crossref_primary_10_1007_s11053_021_09865_x crossref_primary_10_3390_app9194180 crossref_primary_10_1038_s41598_024_73357_0 crossref_primary_10_1016_j_cageo_2020_104667 crossref_primary_10_1016_j_earscirev_2025_105209 crossref_primary_10_1016_j_gexplo_2025_107671 crossref_primary_10_3390_min9050270 crossref_primary_10_1016_j_gexplo_2017_10_020 crossref_primary_10_1016_j_oregeorev_2022_104916 crossref_primary_10_1016_j_chemer_2022_125905 crossref_primary_10_1016_j_gexplo_2023_107326 crossref_primary_10_1016_j_gexplo_2020_106637 crossref_primary_10_5194_se_15_731_2024 crossref_primary_10_1186_s13174_019_0115_x crossref_primary_10_1007_s11053_022_10143_7 crossref_primary_10_1029_2021EA001927 crossref_primary_10_1016_j_cageo_2020_104484 crossref_primary_10_1007_s11053_020_09798_x crossref_primary_10_1007_s12145_025_01843_8 crossref_primary_10_1016_j_apgeochem_2020_104679 crossref_primary_10_1007_s11004_025_10190_9 crossref_primary_10_1109_ACCESS_2019_2948800 crossref_primary_10_1155_2018_3145947 crossref_primary_10_1016_j_oregeorev_2025_106705 crossref_primary_10_1007_s11004_024_10158_1 crossref_primary_10_1016_j_oregeorev_2024_106204 crossref_primary_10_1016_j_cageo_2022_105100 crossref_primary_10_1016_j_chemer_2024_126209 crossref_primary_10_1016_j_cageo_2025_106011 crossref_primary_10_1016_j_chemer_2024_126208 crossref_primary_10_3390_min13060730 crossref_primary_10_1007_s00521_019_04341_3 crossref_primary_10_1016_j_earscirev_2019_02_023 crossref_primary_10_1016_j_cageo_2017_10_005 crossref_primary_10_1038_s41598_024_56644_8 crossref_primary_10_1007_s00603_020_02314_w crossref_primary_10_1016_j_apgeochem_2021_105111 crossref_primary_10_1016_j_gexplo_2024_107393 crossref_primary_10_1007_s11053_023_10268_3 crossref_primary_10_1016_j_cageo_2023_105392 crossref_primary_10_1016_j_oregeorev_2025_106615 crossref_primary_10_1029_2022EA002626 crossref_primary_10_3390_min12091174 crossref_primary_10_1016_j_cageo_2024_105679 crossref_primary_10_1007_s11004_022_10038_6 crossref_primary_10_1371_journal_pone_0218930 crossref_primary_10_1007_s11004_022_10042_w crossref_primary_10_1007_s13762_016_1095_z crossref_primary_10_1007_s11004_023_10133_2 crossref_primary_10_1007_s12517_022_09536_y crossref_primary_10_1016_j_cageo_2019_104312 crossref_primary_10_1016_j_cageo_2021_104890 crossref_primary_10_1007_s11053_023_10279_0 crossref_primary_10_1016_j_apgeochem_2023_105807 crossref_primary_10_1038_s41598_025_07534_0 crossref_primary_10_1007_s11053_024_10321_9 crossref_primary_10_1007_s11053_024_10344_2 crossref_primary_10_1007_s11053_021_09934_1 crossref_primary_10_1016_j_pepi_2020_106584 crossref_primary_10_1016_j_oregeorev_2022_104955 crossref_primary_10_1016_j_jappgeo_2023_105046 crossref_primary_10_1016_j_oregeorev_2023_105706 crossref_primary_10_1016_j_oregeorev_2022_105024 crossref_primary_10_1016_j_cageo_2023_105490 crossref_primary_10_1016_j_oregeorev_2022_105265 crossref_primary_10_1007_s11053_023_10200_9 crossref_primary_10_1007_s11004_023_10059_9 crossref_primary_10_1016_j_cageo_2024_105657 crossref_primary_10_1016_j_earscirev_2016_04_006 crossref_primary_10_3390_min13101332 crossref_primary_10_1016_j_procs_2018_08_282 crossref_primary_10_1016_j_gexplo_2017_06_021 crossref_primary_10_1016_j_apgeochem_2021_104994 crossref_primary_10_1016_j_chemer_2024_126111 crossref_primary_10_1016_j_apgeochem_2023_105722 crossref_primary_10_3390_app13158992 crossref_primary_10_1109_ACCESS_2020_2978112 crossref_primary_10_1016_j_apgeochem_2021_105043 crossref_primary_10_1007_s40808_024_02157_2 crossref_primary_10_1007_s12583_020_1365_z crossref_primary_10_1007_s12583_020_1079_2 crossref_primary_10_1007_s12583_024_0090_4 crossref_primary_10_1007_s11053_024_10317_5 crossref_primary_10_1016_j_chemer_2024_126125 crossref_primary_10_1016_j_gexplo_2020_106704 crossref_primary_10_1007_s11053_019_09510_8 crossref_primary_10_1016_j_apgeochem_2020_104760 crossref_primary_10_1016_j_oregeorev_2021_104316 crossref_primary_10_3390_min13111384 crossref_primary_10_1016_j_gexplo_2019_04_007 crossref_primary_10_1007_s12583_021_1402_6 crossref_primary_10_1016_j_apgeochem_2022_105450 crossref_primary_10_1007_s11053_025_10496_9 crossref_primary_10_1016_j_cageo_2022_105153 crossref_primary_10_1177_16878132221081584 crossref_primary_10_3103_S0747923925700203 crossref_primary_10_1134_S0001433825700124 crossref_primary_10_1016_j_engappai_2025_112188 crossref_primary_10_1016_j_gexplo_2018_07_009 crossref_primary_10_1016_j_oregeorev_2019_02_027 crossref_primary_10_1016_j_oregeorev_2020_103611 crossref_primary_10_1007_s11004_025_10191_8 crossref_primary_10_1016_j_apgeochem_2023_105621 crossref_primary_10_1007_s12145_024_01611_0 crossref_primary_10_1007_s11053_017_9351_6 crossref_primary_10_1109_ACCESS_2022_3187961 crossref_primary_10_1016_j_oregeorev_2022_104765 crossref_primary_10_3390_min12050616 crossref_primary_10_1016_j_cageo_2023_105341 crossref_primary_10_3390_min11080816 crossref_primary_10_1016_j_gexplo_2021_106904 crossref_primary_10_1016_j_apgeochem_2020_104668 crossref_primary_10_1007_s11053_020_09789_y |
| Cites_doi | 10.1561/2200000006 10.1016/0375-6742(91)90071-2 10.1016/j.gexplo.2014.10.010 10.1016/j.apgeochem.2009.04.024 10.1016/j.gexplo.2012.02.002 10.1016/j.gexplo.2014.05.005 10.1016/j.gexplo.2015.06.004 10.1016/j.gexplo.2011.06.012 10.1007/BF00890195 10.1162/neco.2006.18.7.1527 10.1016/j.apgeochem.2013.02.009 10.1016/j.gexplo.2014.02.013 10.1016/j.gexplo.2013.08.013 10.1049/ip-vis:20030362 10.1162/089976602760128018 10.1016/S0375-6742(97)00029-0 10.1007/s00254-006-0528-2 10.1111/rge.12070 10.1016/j.oregeorev.2006.10.002 10.1145/2689746.2689747 10.1016/j.gexplo.2012.09.009 10.1144/1467-7873/06-109 10.1016/S0169-1368(99)00027-X 10.1016/j.gexplo.2012.07.007 10.1016/S0375-6742(02)00276-5 10.1016/0375-6742(74)90030-2 10.1126/science.1127647 10.1016/S0893-6080(01)00097-1 10.1016/j.neucom.2012.11.050 10.1016/S0375-6742(99)00077-1 10.1016/0375-6742(94)90013-2 10.1016/j.oregeorev.2014.08.012 10.1016/j.gexplo.2008.08.003 10.1016/j.oregeorev.2014.09.024 10.1111/j.1365-246X.2012.05429.x 10.1007/s11053-013-9217-5 10.1016/j.oregeorev.2013.09.009 10.1023/A:1007529726302 10.1023/A:1023818214614 10.1007/BF02080498 10.1023/A:1010109829861 10.1007/s11004-007-9141-5 10.1007/s002549900081 10.1144/1467-7873/09-224 10.1002/env.966 10.1007/s00531-014-1096-4 10.1109/ACPR.2013.37 10.1016/j.gexplo.2011.05.007 10.1016/j.gexplo.2015.04.013 |
| ContentType | Journal Article |
| Copyright | 2015 Elsevier Ltd |
| Copyright_xml | – notice: 2015 Elsevier Ltd |
| DBID | AAYXX CITATION 7SC 8FD FR3 H8D JQ2 KR7 L7M L~C L~D 7S9 L.6 |
| DOI | 10.1016/j.cageo.2015.10.006 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database Engineering Research Database Aerospace Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Aerospace Database Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA Aerospace Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geology |
| EISSN | 1873-7803 |
| EndPage | 82 |
| ExternalDocumentID | 10_1016_j_cageo_2015_10_006 S0098300415300728 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFS ACLVX ACNNM ACRLP ACSBN ACZNC ADBBV ADEZE ADJOM ADMUD AEBSH AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HMA HVGLF HZ~ IHE IMUCA J1W KOM LG9 LY3 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SEP SES SEW SPC SPCBC SSE SSV SSZ T5K TN5 WUQ ZCA ZMT ~02 ~G- 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADXHL AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 8FD FR3 H8D JQ2 KR7 L7M L~C L~D 7S9 L.6 |
| ID | FETCH-LOGICAL-a392t-c34f1f522996a51a7285b6ae4be74f30a32598c5718a1582e385a4da9858c7ab3 |
| ISICitedReferencesCount | 234 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000366770500008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0098-3004 |
| IngestDate | Sat Sep 27 19:27:53 EDT 2025 Sun Sep 28 05:50:53 EDT 2025 Sat Nov 29 03:42:10 EST 2025 Tue Nov 18 21:46:53 EST 2025 Fri Feb 23 02:34:00 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Skarn-type iron deposits Multivariate geochemical data Autoencoder network Geochemical exploration |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a392t-c34f1f522996a51a7285b6ae4be74f30a32598c5718a1582e385a4da9858c7ab3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1778043309 |
| PQPubID | 23500 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_2116885868 proquest_miscellaneous_1778043309 crossref_citationtrail_10_1016_j_cageo_2015_10_006 crossref_primary_10_1016_j_cageo_2015_10_006 elsevier_sciencedirect_doi_10_1016_j_cageo_2015_10_006 |
| PublicationCentury | 2000 |
| PublicationDate | January 2016 2016-01-00 20160101 |
| PublicationDateYYYYMMDD | 2016-01-01 |
| PublicationDate_xml | – month: 01 year: 2016 text: January 2016 |
| PublicationDecade | 2010 |
| PublicationTitle | Computers & geosciences |
| PublicationYear | 2016 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Zhang, Zuo, Cheng (bib58) 2015; 65 Carranza (bib8) 2010; 10 Zuo, Zhang, Zhang, Carranza, Wang (bib67) 2015; 71 Zhang, Zuo, Xiong (bib59) 2015 Zuo (bib61) 2014; 139 Wang, Zuo (bib50) 2015 Harris, Wilkinson, Grunsky (bib26) 2000; 16 Govett, Goodfellow, Chapman, Chork (bib22) 1975; 7 Sakurada, M., Yairi, T., 2014. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the Second Workshop on Machine Learning for Sensory Data Analysis, pp. 4. Chen, Lu, Li (bib12) 2014; 140 Chen, Murray (bib10) 2003; 150 Harris, Wilkinson, Grunsky, Heather, Ayer (bib27) 1999; 67 Sun, Steinecker, Glocker (bib43) 2014 Zhang, Wu, Di, Wang, Yao, Zhang, Lv, Yuan, Shi (bib55) 2012; 37 Bengio, Lamblin, Popovici, Larochelle (bib6) 2007; 19 Hinton (bib29) 2002; 14 Chen (bib11) 2014; 71 Wackernagel (bib46) 2003 Cheng (bib13) 2007; 32 Fiore, Palmieri, Castiglione, De Santis (bib20) 2013; 122 Agterberg, Bonham-Carter, Wright (bib1) 1990 Yang, Zhang, Feng, She, Li (bib52) 2008; 27 Grunsky, Drew, Sutphin (bib24) 2009; 24 Luz, Mateus, Matos, Gonçalves (bib35) 2014; 23 Zuo, Xia, Zhang (bib66) 2013; 33 Valentine, Trampert (bib45) 2012; 189 Yousefi, Kamkar-Rouhani, Carranza (bib53) 2012; 115 Carranza (bib9) 2011; 110 Zhang, Zuo (bib56) 2014; 57 Reimann, Filzmoser (bib37) 2000; 39 Gałuszka (bib21) 2007; 52 Ali, Cheng, Li, Chen (bib4) 2006; 6 Cheng, Xu, Grunsky (bib16) 2000; 9 Yuan, Feng, Zhang, Di, Wang, Ni (bib54) 2013 Sahasrabudhe, M., Namboodiri, A.M., 2013. Learning fingerprint orientation fields using continuous restricted Boltzmann machines. In: Proceedings of the Second IAPR Asian Conference on Pattern Recognition, pp. 351–355. Zuo, Carranza, Cheng (bib62) 2012; 122 Cheng, Agterberg, Ballantyne (bib15) 1994; 51 Tukey (bib44) 1997 Zuo (bib60) 2011; 111 Rumelhart, Hinton, Williams (bib38) 1988 Sinclair (bib42) 1991; 41 Zuo, Wang (bib64) 2015 Sinclair (bib41) 1974; 3 Hawkes, Webb (bib28) 1962 Grunsky, Agterberg (bib23) 1988; 20 Zuo, Cheng, Agterberg, Xia (bib63) 2009; 101 Wang, Cheng, Zuo (bib48) 2015; 157 Filzmoser, Hron, Reimann (bib19) 2009; 20 Han, Ge (bib25) 1983; 7 Hinton (bib30) 2010 Xie, Mu, Ren (bib51) 1997; 60 Egozcue, Pawlowsky-Glahn, Mateu-Figueras, Barcelo-Vidal (bib17) 2003; 35 Wang, Cheng, Zuo (bib49) 2015; 148 Li, Ma, Shi (bib34) 2003; 77 Filzmoser, Hron (bib18) 2008; 40 Zuo, Wang, Chen, Yang (bib65) 2015; 148 Aitchison (bib2) 1986 Aitchison, Barceló-Vidal, Martín-Fernández, Pawlowsky-Glahn (bib3) 2000; 32 Bonham-Cater (bib7) 1994 Wang, Zuo (bib47) 2015; 155 Bengio (bib5) 2009; 2 Cheng (bib14) 2012; 122 Murray (bib36) 2001; 14 Hinton, Salakhutdinov (bib32) 2006; 313 Larochelle, Bengio, Louradour, Lamblin (bib33) 2009; 10 Zhang, Zuo, Cheng (bib57) 2015; 104 Hinton, Osindero, Teh (bib31) 2006; 18 Fiore (10.1016/j.cageo.2015.10.006_bib20) 2013; 122 Bengio (10.1016/j.cageo.2015.10.006_bib5) 2009; 2 Zhang (10.1016/j.cageo.2015.10.006_bib55) 2012; 37 10.1016/j.cageo.2015.10.006_bib39 Gałuszka (10.1016/j.cageo.2015.10.006_bib21) 2007; 52 Rumelhart (10.1016/j.cageo.2015.10.006_bib38) 1988 Zhang (10.1016/j.cageo.2015.10.006_bib59) 2015 Chen (10.1016/j.cageo.2015.10.006_bib10) 2003; 150 Chen (10.1016/j.cageo.2015.10.006_bib12) 2014; 140 Hinton (10.1016/j.cageo.2015.10.006_bib29) 2002; 14 Zuo (10.1016/j.cageo.2015.10.006_bib64) 2015 Wang (10.1016/j.cageo.2015.10.006_bib50) 2015 Xie (10.1016/j.cageo.2015.10.006_bib51) 1997; 60 Grunsky (10.1016/j.cageo.2015.10.006_bib24) 2009; 24 Carranza (10.1016/j.cageo.2015.10.006_bib9) 2011; 110 Chen (10.1016/j.cageo.2015.10.006_bib11) 2014; 71 Luz (10.1016/j.cageo.2015.10.006_bib35) 2014; 23 Zuo (10.1016/j.cageo.2015.10.006_bib67) 2015; 71 Harris (10.1016/j.cageo.2015.10.006_bib26) 2000; 16 Zuo (10.1016/j.cageo.2015.10.006_bib60) 2011; 111 Agterberg (10.1016/j.cageo.2015.10.006_bib1) 1990 Hinton (10.1016/j.cageo.2015.10.006_bib30) 2010 Hinton (10.1016/j.cageo.2015.10.006_bib31) 2006; 18 Hinton (10.1016/j.cageo.2015.10.006_bib32) 2006; 313 Zuo (10.1016/j.cageo.2015.10.006_bib65) 2015; 148 Cheng (10.1016/j.cageo.2015.10.006_bib14) 2012; 122 Yang (10.1016/j.cageo.2015.10.006_bib52) 2008; 27 Li (10.1016/j.cageo.2015.10.006_bib34) 2003; 77 Zuo (10.1016/j.cageo.2015.10.006_bib62) 2012; 122 Carranza (10.1016/j.cageo.2015.10.006_bib8) 2010; 10 Zhang (10.1016/j.cageo.2015.10.006_bib57) 2015; 104 Zuo (10.1016/j.cageo.2015.10.006_bib63) 2009; 101 Grunsky (10.1016/j.cageo.2015.10.006_bib23) 1988; 20 Sun (10.1016/j.cageo.2015.10.006_bib43) 2014 Egozcue (10.1016/j.cageo.2015.10.006_bib17) 2003; 35 Cheng (10.1016/j.cageo.2015.10.006_bib15) 1994; 51 Aitchison (10.1016/j.cageo.2015.10.006_bib3) 2000; 32 Han (10.1016/j.cageo.2015.10.006_bib25) 1983; 7 Reimann (10.1016/j.cageo.2015.10.006_bib37) 2000; 39 Sinclair (10.1016/j.cageo.2015.10.006_bib42) 1991; 41 Murray (10.1016/j.cageo.2015.10.006_bib36) 2001; 14 Bonham-Cater (10.1016/j.cageo.2015.10.006_bib7) 1994 Tukey (10.1016/j.cageo.2015.10.006_bib44) 1997 Aitchison (10.1016/j.cageo.2015.10.006_bib2) 1986 Yousefi (10.1016/j.cageo.2015.10.006_bib53) 2012; 115 Yuan (10.1016/j.cageo.2015.10.006_bib54) 2013 Filzmoser (10.1016/j.cageo.2015.10.006_bib19) 2009; 20 Sinclair (10.1016/j.cageo.2015.10.006_bib41) 1974; 3 10.1016/j.cageo.2015.10.006_bib40 Cheng (10.1016/j.cageo.2015.10.006_bib16) 2000; 9 Bengio (10.1016/j.cageo.2015.10.006_bib6) 2007; 19 Wackernagel (10.1016/j.cageo.2015.10.006_bib46) 2003 Wang (10.1016/j.cageo.2015.10.006_bib47) 2015; 155 Wang (10.1016/j.cageo.2015.10.006_bib49) 2015; 148 Harris (10.1016/j.cageo.2015.10.006_bib27) 1999; 67 Valentine (10.1016/j.cageo.2015.10.006_bib45) 2012; 189 Zuo (10.1016/j.cageo.2015.10.006_bib66) 2013; 33 Hawkes (10.1016/j.cageo.2015.10.006_bib28) 1962 Zhang (10.1016/j.cageo.2015.10.006_bib58) 2015; 65 Filzmoser (10.1016/j.cageo.2015.10.006_bib18) 2008; 40 Cheng (10.1016/j.cageo.2015.10.006_bib13) 2007; 32 Larochelle (10.1016/j.cageo.2015.10.006_bib33) 2009; 10 Zhang (10.1016/j.cageo.2015.10.006_bib56) 2014; 57 Ali (10.1016/j.cageo.2015.10.006_bib4) 2006; 6 Wang (10.1016/j.cageo.2015.10.006_bib48) 2015; 157 Govett (10.1016/j.cageo.2015.10.006_bib22) 1975; 7 Zuo (10.1016/j.cageo.2015.10.006_bib61) 2014; 139 |
| References_xml | – volume: 7 start-page: 415 year: 1975 end-page: 446 ident: bib22 article-title: Exploration geochemistry distribution of elements and recognition of anomalies publication-title: Math. Geol. – volume: 115 start-page: 24 year: 2012 end-page: 35 ident: bib53 article-title: Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping publication-title: J. Geochem. Explor. – volume: 6 start-page: 341 year: 2006 end-page: 348 ident: bib4 article-title: Multi-element association analysis of stream sediment geochemistry data for predicting gold deposits in south-central Yunnan Province, China publication-title: Geochem. Explor. Environ. Anal. – volume: 7 start-page: 1 year: 1983 end-page: 118 ident: bib25 article-title: Geological and geochemical features of submarine volcanic hydrothermal-sedimentary mineralization of Makeng iron deposit, Fujian Province publication-title: Bull. Inst. Miner. Deposits Chin. Acad. Geol. Sci. – reference: Sakurada, M., Yairi, T., 2014. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the Second Workshop on Machine Learning for Sensory Data Analysis, pp. 4. – volume: 111 start-page: 13 year: 2011 end-page: 22 ident: bib60 article-title: Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China) publication-title: J. Geochem. Explor. – volume: 110 start-page: 167 year: 2011 end-page: 185 ident: bib9 article-title: Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values publication-title: J. Geochem. Explor. – volume: 189 start-page: 1183 year: 2012 end-page: 1202 ident: bib45 article-title: Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data publication-title: Geophys. J. Int. – volume: 104 start-page: 663 year: 2015 end-page: 682 ident: bib57 article-title: The mineralization age of the Makeng Fe deposit, South China: implications from U–Pb and Sm–Nd geochronology publication-title: Int. J. Earth Sci. – reference: Sahasrabudhe, M., Namboodiri, A.M., 2013. Learning fingerprint orientation fields using continuous restricted Boltzmann machines. In: Proceedings of the Second IAPR Asian Conference on Pattern Recognition, pp. 351–355. – volume: 122 start-page: 13 year: 2013 end-page: 23 ident: bib20 article-title: Network anomaly detection with the restricted Boltzmann machine publication-title: Neurocomputing – start-page: 398 year: 1994 ident: bib7 article-title: Geographic Information Systems for Geoscientist: Modeling with GIS – volume: 19 start-page: 153 year: 2007 end-page: 160 ident: bib6 article-title: Greedy layer-wise training of deep networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 40 start-page: 233 year: 2008 end-page: 248 ident: bib18 article-title: Outlier detection for compositional data using robust methods publication-title: Math. Geosci. – volume: 150 start-page: 153 year: 2003 end-page: 158 ident: bib10 article-title: Continuous restricted Boltzmann machine with an implementable training algorithm publication-title: Vision Image Signal Process. IEEE Process. – volume: 16 start-page: 107 year: 2000 end-page: 143 ident: bib26 article-title: Effective use and interpretation of lithogeochemical data in regional mineral exploration programs: application of Geographic Information Systems (GIS) technology publication-title: Ore Geol. Rev. – volume: 10 start-page: 365 year: 2010 end-page: 381 ident: bib8 article-title: Catchment basin modelling of stream sediment anomalies revisited: incorporation of EDA and fractal analysis publication-title: Geochem. Explor. Environ. Anal. – volume: 39 start-page: 1001 year: 2000 end-page: 1014 ident: bib37 article-title: Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data publication-title: Environ. Geol. – start-page: 381 year: 2003 ident: bib46 article-title: Multivariate Geostatistics: An Introduction with Applications – volume: 122 start-page: 55 year: 2012 end-page: 70 ident: bib14 article-title: Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas publication-title: J. Geochem. Explor. – volume: 14 start-page: 1771 year: 2002 end-page: 1800 ident: bib29 article-title: Training products of experts by minimizing contrastive divergence publication-title: Neural Comput. – volume: 157 start-page: 110 year: 2015 end-page: 119 ident: bib48 article-title: Quantifying the spatial characteristics of geochemical patterns via GIS-based geographically weighted statistics publication-title: J. Geochem. Explor. – volume: 27 start-page: 329 year: 2008 end-page: 335 ident: bib52 article-title: SHRIMP zircon U–Pb dating of quartz porphyry from Zhongjia tin–polymetallic deposit in Longyan area, Fujian province, and its geological significance publication-title: Miner. Depos. – volume: 71 start-page: 749 year: 2014 end-page: 760 ident: bib11 article-title: Mineral potential mapping with a restricted Boltzmann machine publication-title: Ore Geol. Rev. – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: bib31 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 10 start-page: 1 year: 2009 end-page: 40 ident: bib33 article-title: Exploring strategies for training deep neural networks publication-title: J. Mach. Learn. Res. – year: 2015 ident: bib59 article-title: A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China publication-title: Sci. China Earth Sci. – year: 2015 ident: bib64 article-title: Fractal/multifractal modeling of geochemical data: a review publication-title: J. Geochem. Explor. – volume: 77 start-page: 167 year: 2003 end-page: 175 ident: bib34 article-title: Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background publication-title: J. Geochem. Explor. – volume: 148 start-page: 12 year: 2015 end-page: 24 ident: bib65 article-title: Identification of weak anomalies: a multifractal perspective publication-title: J. Geochem. Explor. – volume: 41 start-page: 1 year: 1991 end-page: 22 ident: bib42 article-title: A fundamental approach to threshold estimation in exploration geochemistry: probability plots revisited publication-title: J. Geochem. Explor. – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib32 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 60 start-page: 99 year: 1997 end-page: 113 ident: bib51 article-title: Geochemical mapping in China publication-title: J. Geochem. Explor. – volume: 122 start-page: 1 year: 2012 end-page: 3 ident: bib62 article-title: Fractal/multifractalmodelling of geochemical exploration data publication-title: J. Geochem. Explor. – volume: 155 start-page: 84 year: 2015 end-page: 90 ident: bib47 article-title: A comparative study of trend surface analysis and spectrum-area multifractal model to identify geochemical anomalies publication-title: J. Geochem. Explor. – start-page: 416 year: 1986 ident: bib2 article-title: The statistical analysis of compositional data – volume: 57 start-page: 53 year: 2014 end-page: 60 ident: bib56 article-title: Sr–Nd–Pb isotope systematics of magnetite: implications for the genesis of Makeng Fe deposit, southern China publication-title: Ore Geol. Rev. – volume: 37 start-page: 1217 year: 2012 end-page: 1231 ident: bib55 article-title: Geochronology of diagenesis and mineralization of the Luoyang iron deposit in Zhangping city, Fujian province and its geological significance publication-title: Earth Sci. J. China Univ. Geosci. – volume: 71 start-page: 502 year: 2015 end-page: 515 ident: bib67 article-title: Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: a case study with skarn-type Fe deposits in Southwestern Fujian Province, China publication-title: Ore Geol. Rev. – year: 1997 ident: bib44 article-title: Exploratory Data Analysis – volume: 52 start-page: 861 year: 2007 end-page: 870 ident: bib21 article-title: A review of geochemical background concepts and an example using data from Poland publication-title: Environ. Geol. – volume: 14 start-page: 1257 year: 2001 end-page: 1264 ident: bib36 article-title: Novelty detection using products of simple experts—a potential architecture for embedded systems publication-title: Neural Netw. – volume: 3 start-page: 129 year: 1974 end-page: 149 ident: bib41 article-title: Selection of threshold values in geochemical data using probability graphs publication-title: J. Geochem. Explor. – volume: 101 start-page: 225 year: 2009 end-page: 235 ident: bib63 article-title: Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China publication-title: J. Geochem. Explor. – volume: 9 start-page: 43 year: 2000 end-page: 52 ident: bib16 article-title: Integrated spatial and spectrum method for geochemical anomaly separation publication-title: Nat. Resour. Res. – volume: 33 start-page: 165 year: 2013 end-page: 172 ident: bib66 article-title: A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas publication-title: Appl. Geochem. – volume: 32 start-page: 271 year: 2000 end-page: 275 ident: bib3 article-title: Logratio analysis and compositional distance publication-title: Math. Geol. – year: 2010 ident: bib30 article-title: A practical guide to training restricted Boltzmann machines publication-title: Technical Report, University of Toronto – volume: 51 start-page: 109 year: 1994 end-page: 130 ident: bib15 article-title: The separation of geochemical anomalies from background by fractal methods publication-title: J. Geochem. Explor. – volume: 20 start-page: 825 year: 1988 end-page: 861 ident: bib23 article-title: Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario publication-title: Math. Geol. – volume: 20 start-page: 621 year: 2009 end-page: 632 ident: bib19 article-title: Principal component analysis for compositional data with outliers publication-title: Environmetrics – volume: 65 start-page: 266 year: 2015 end-page: 284 ident: bib58 article-title: Geological features and formation processes of the Makeng Fe deposit, China publication-title: Resour. Geol. – volume: 139 start-page: 170 year: 2014 end-page: 176 ident: bib61 article-title: Identification of geochemical anomalies associated with mineralization in the Fanshan district, Fujian, China publication-title: J. Geochem. Explor. – year: 2015 ident: bib50 article-title: A MATLAB-based program for processing geochemical data using fractal/multifractal modeling publication-title: Earth Sci. Inform. – volume: 32 start-page: 314 year: 2007 end-page: 324 ident: bib13 article-title: Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China publication-title: Ore Geol. Rev. – volume: 148 start-page: 259 year: 2015 end-page: 269 ident: bib49 article-title: Spatial characteristics of geochemical patterns related to Fe mineralization in the southwestern Fujian province (China) publication-title: J. Geochem. Explor. – volume: 67 start-page: 301 year: 1999 end-page: 334 ident: bib27 article-title: Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario publication-title: J. Geochem. Explor. – year: 1988 ident: bib38 article-title: Learning Representations by Back-propagation Errors – volume: 2 start-page: 1 year: 2009 end-page: 127 ident: bib5 article-title: Learning deep architectures for AI publication-title: Found. Trends Mach. Learn. – volume: 35 start-page: 279 year: 2003 end-page: 300 ident: bib17 article-title: Isometric logratio transformations for compositional data analysis publication-title: Math. Geol. – volume: 140 start-page: 56 year: 2014 end-page: 63 ident: bib12 article-title: Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly publication-title: J. Geochem. Explor. – volume: 23 start-page: 195 year: 2014 end-page: 215 ident: bib35 article-title: Cu- and Zn-soil anomalies in the NE border of the South Portuguese Zone (Iberian Variscides, Portugal) identified by multifractal and geostatistical analyses publication-title: Nat. Resour. Res. – start-page: 1 year: 1990 end-page: 21 ident: bib1 article-title: Statistical pattern integration for mineral exploration publication-title: Computer Applications in Resource Estimation Prediction and Assessment for Metals and Petroleum – start-page: 87 year: 2014 end-page: 93 ident: bib43 article-title: Application of deep belief networks for precision mechanism quality inspection publication-title: Precision Assembly Technologies and Systems – year: 1962 ident: bib28 article-title: Geochemistry in Mineral Exploration – start-page: S73 year: 2013 end-page: S75 ident: bib54 article-title: Geochronology of Dapai iron–polymetallic deposit in Yongding city, Fujian province and its geological significance publication-title: Acta Miner. Sin. – volume: 24 start-page: 1602 year: 2009 end-page: 1616 ident: bib24 article-title: Process recognition in multi-element soil and stream-sediment geochemical data publication-title: Appl. Geochem. – volume: 2 start-page: 1 year: 2009 ident: 10.1016/j.cageo.2015.10.006_bib5 article-title: Learning deep architectures for AI publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000006 – volume: 41 start-page: 1 year: 1991 ident: 10.1016/j.cageo.2015.10.006_bib42 article-title: A fundamental approach to threshold estimation in exploration geochemistry: probability plots revisited publication-title: J. Geochem. Explor. doi: 10.1016/0375-6742(91)90071-2 – volume: 148 start-page: 259 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib49 article-title: Spatial characteristics of geochemical patterns related to Fe mineralization in the southwestern Fujian province (China) publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2014.10.010 – volume: 24 start-page: 1602 year: 2009 ident: 10.1016/j.cageo.2015.10.006_bib24 article-title: Process recognition in multi-element soil and stream-sediment geochemical data publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2009.04.024 – volume: 10 start-page: 1 year: 2009 ident: 10.1016/j.cageo.2015.10.006_bib33 article-title: Exploring strategies for training deep neural networks publication-title: J. Mach. Learn. Res. – volume: 115 start-page: 24 year: 2012 ident: 10.1016/j.cageo.2015.10.006_bib53 article-title: Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2012.02.002 – volume: 148 start-page: 12 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib65 article-title: Identification of weak anomalies: a multifractal perspective publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2014.05.005 – volume: 157 start-page: 110 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib48 article-title: Quantifying the spatial characteristics of geochemical patterns via GIS-based geographically weighted statistics publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2015.06.004 – volume: 111 start-page: 13 year: 2011 ident: 10.1016/j.cageo.2015.10.006_bib60 article-title: Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China) publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2011.06.012 – volume: 20 start-page: 825 year: 1988 ident: 10.1016/j.cageo.2015.10.006_bib23 article-title: Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario publication-title: Math. Geol. doi: 10.1007/BF00890195 – volume: 18 start-page: 1527 year: 2006 ident: 10.1016/j.cageo.2015.10.006_bib31 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – volume: 33 start-page: 165 year: 2013 ident: 10.1016/j.cageo.2015.10.006_bib66 article-title: A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas publication-title: Appl. Geochem. doi: 10.1016/j.apgeochem.2013.02.009 – volume: 140 start-page: 56 year: 2014 ident: 10.1016/j.cageo.2015.10.006_bib12 article-title: Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2014.02.013 – year: 2010 ident: 10.1016/j.cageo.2015.10.006_bib30 article-title: A practical guide to training restricted Boltzmann machines – volume: 139 start-page: 170 year: 2014 ident: 10.1016/j.cageo.2015.10.006_bib61 article-title: Identification of geochemical anomalies associated with mineralization in the Fanshan district, Fujian, China publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2013.08.013 – volume: 150 start-page: 153 year: 2003 ident: 10.1016/j.cageo.2015.10.006_bib10 article-title: Continuous restricted Boltzmann machine with an implementable training algorithm publication-title: Vision Image Signal Process. IEEE Process. doi: 10.1049/ip-vis:20030362 – start-page: 416 year: 1986 ident: 10.1016/j.cageo.2015.10.006_bib2 – volume: 14 start-page: 1771 year: 2002 ident: 10.1016/j.cageo.2015.10.006_bib29 article-title: Training products of experts by minimizing contrastive divergence publication-title: Neural Comput. doi: 10.1162/089976602760128018 – year: 1997 ident: 10.1016/j.cageo.2015.10.006_bib44 – volume: 60 start-page: 99 year: 1997 ident: 10.1016/j.cageo.2015.10.006_bib51 article-title: Geochemical mapping in China publication-title: J. Geochem. Explor. doi: 10.1016/S0375-6742(97)00029-0 – volume: 52 start-page: 861 year: 2007 ident: 10.1016/j.cageo.2015.10.006_bib21 article-title: A review of geochemical background concepts and an example using data from Poland publication-title: Environ. Geol. doi: 10.1007/s00254-006-0528-2 – volume: 65 start-page: 266 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib58 article-title: Geological features and formation processes of the Makeng Fe deposit, China publication-title: Resour. Geol. doi: 10.1111/rge.12070 – volume: 32 start-page: 314 year: 2007 ident: 10.1016/j.cageo.2015.10.006_bib13 article-title: Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2006.10.002 – year: 1962 ident: 10.1016/j.cageo.2015.10.006_bib28 – year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib64 article-title: Fractal/multifractal modeling of geochemical data: a review publication-title: J. Geochem. Explor. – ident: 10.1016/j.cageo.2015.10.006_bib40 doi: 10.1145/2689746.2689747 – volume: 122 start-page: 1 year: 2012 ident: 10.1016/j.cageo.2015.10.006_bib62 article-title: Fractal/multifractalmodelling of geochemical exploration data publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2012.09.009 – start-page: S73 issue: Suppl. year: 2013 ident: 10.1016/j.cageo.2015.10.006_bib54 article-title: Geochronology of Dapai iron–polymetallic deposit in Yongding city, Fujian province and its geological significance publication-title: Acta Miner. Sin. – year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib50 article-title: A MATLAB-based program for processing geochemical data using fractal/multifractal modeling publication-title: Earth Sci. Inform. – volume: 6 start-page: 341 year: 2006 ident: 10.1016/j.cageo.2015.10.006_bib4 article-title: Multi-element association analysis of stream sediment geochemistry data for predicting gold deposits in south-central Yunnan Province, China publication-title: Geochem. Explor. Environ. Anal. doi: 10.1144/1467-7873/06-109 – volume: 16 start-page: 107 year: 2000 ident: 10.1016/j.cageo.2015.10.006_bib26 article-title: Effective use and interpretation of lithogeochemical data in regional mineral exploration programs: application of Geographic Information Systems (GIS) technology publication-title: Ore Geol. Rev. doi: 10.1016/S0169-1368(99)00027-X – start-page: 87 year: 2014 ident: 10.1016/j.cageo.2015.10.006_bib43 article-title: Application of deep belief networks for precision mechanism quality inspection – volume: 19 start-page: 153 year: 2007 ident: 10.1016/j.cageo.2015.10.006_bib6 article-title: Greedy layer-wise training of deep networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 122 start-page: 55 year: 2012 ident: 10.1016/j.cageo.2015.10.006_bib14 article-title: Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2012.07.007 – volume: 77 start-page: 167 year: 2003 ident: 10.1016/j.cageo.2015.10.006_bib34 article-title: Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background publication-title: J. Geochem. Explor. doi: 10.1016/S0375-6742(02)00276-5 – volume: 3 start-page: 129 year: 1974 ident: 10.1016/j.cageo.2015.10.006_bib41 article-title: Selection of threshold values in geochemical data using probability graphs publication-title: J. Geochem. Explor. doi: 10.1016/0375-6742(74)90030-2 – volume: 313 start-page: 504 year: 2006 ident: 10.1016/j.cageo.2015.10.006_bib32 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 14 start-page: 1257 year: 2001 ident: 10.1016/j.cageo.2015.10.006_bib36 article-title: Novelty detection using products of simple experts—a potential architecture for embedded systems publication-title: Neural Netw. doi: 10.1016/S0893-6080(01)00097-1 – volume: 122 start-page: 13 year: 2013 ident: 10.1016/j.cageo.2015.10.006_bib20 article-title: Network anomaly detection with the restricted Boltzmann machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.11.050 – volume: 67 start-page: 301 year: 1999 ident: 10.1016/j.cageo.2015.10.006_bib27 article-title: Techniques for analysis and visualization of lithogeochemical data with applications to the Swayze greenstone belt, Ontario publication-title: J. Geochem. Explor. doi: 10.1016/S0375-6742(99)00077-1 – volume: 51 start-page: 109 year: 1994 ident: 10.1016/j.cageo.2015.10.006_bib15 article-title: The separation of geochemical anomalies from background by fractal methods publication-title: J. Geochem. Explor. doi: 10.1016/0375-6742(94)90013-2 – volume: 71 start-page: 749 year: 2014 ident: 10.1016/j.cageo.2015.10.006_bib11 article-title: Mineral potential mapping with a restricted Boltzmann machine publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2014.08.012 – volume: 101 start-page: 225 year: 2009 ident: 10.1016/j.cageo.2015.10.006_bib63 article-title: Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2008.08.003 – volume: 37 start-page: 1217 year: 2012 ident: 10.1016/j.cageo.2015.10.006_bib55 article-title: Geochronology of diagenesis and mineralization of the Luoyang iron deposit in Zhangping city, Fujian province and its geological significance publication-title: Earth Sci. J. China Univ. Geosci. – volume: 71 start-page: 502 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib67 article-title: Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: a case study with skarn-type Fe deposits in Southwestern Fujian Province, China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2014.09.024 – year: 1988 ident: 10.1016/j.cageo.2015.10.006_bib38 – start-page: 381 year: 2003 ident: 10.1016/j.cageo.2015.10.006_bib46 – volume: 189 start-page: 1183 year: 2012 ident: 10.1016/j.cageo.2015.10.006_bib45 article-title: Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data publication-title: Geophys. J. Int. doi: 10.1111/j.1365-246X.2012.05429.x – year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib59 article-title: A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China publication-title: Sci. China Earth Sci. – volume: 23 start-page: 195 year: 2014 ident: 10.1016/j.cageo.2015.10.006_bib35 article-title: Cu- and Zn-soil anomalies in the NE border of the South Portuguese Zone (Iberian Variscides, Portugal) identified by multifractal and geostatistical analyses publication-title: Nat. Resour. Res. doi: 10.1007/s11053-013-9217-5 – start-page: 398 year: 1994 ident: 10.1016/j.cageo.2015.10.006_bib7 – volume: 57 start-page: 53 year: 2014 ident: 10.1016/j.cageo.2015.10.006_bib56 article-title: Sr–Nd–Pb isotope systematics of magnetite: implications for the genesis of Makeng Fe deposit, southern China publication-title: Ore Geol. Rev. doi: 10.1016/j.oregeorev.2013.09.009 – start-page: 1 year: 1990 ident: 10.1016/j.cageo.2015.10.006_bib1 article-title: Statistical pattern integration for mineral exploration – volume: 32 start-page: 271 year: 2000 ident: 10.1016/j.cageo.2015.10.006_bib3 article-title: Logratio analysis and compositional distance publication-title: Math. Geol. doi: 10.1023/A:1007529726302 – volume: 35 start-page: 279 year: 2003 ident: 10.1016/j.cageo.2015.10.006_bib17 article-title: Isometric logratio transformations for compositional data analysis publication-title: Math. Geol. doi: 10.1023/A:1023818214614 – volume: 7 start-page: 415 year: 1975 ident: 10.1016/j.cageo.2015.10.006_bib22 article-title: Exploration geochemistry distribution of elements and recognition of anomalies publication-title: Math. Geol. doi: 10.1007/BF02080498 – volume: 9 start-page: 43 year: 2000 ident: 10.1016/j.cageo.2015.10.006_bib16 article-title: Integrated spatial and spectrum method for geochemical anomaly separation publication-title: Nat. Resour. Res. doi: 10.1023/A:1010109829861 – volume: 40 start-page: 233 year: 2008 ident: 10.1016/j.cageo.2015.10.006_bib18 article-title: Outlier detection for compositional data using robust methods publication-title: Math. Geosci. doi: 10.1007/s11004-007-9141-5 – volume: 39 start-page: 1001 year: 2000 ident: 10.1016/j.cageo.2015.10.006_bib37 article-title: Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data publication-title: Environ. Geol. doi: 10.1007/s002549900081 – volume: 27 start-page: 329 year: 2008 ident: 10.1016/j.cageo.2015.10.006_bib52 article-title: SHRIMP zircon U–Pb dating of quartz porphyry from Zhongjia tin–polymetallic deposit in Longyan area, Fujian province, and its geological significance publication-title: Miner. Depos. – volume: 10 start-page: 365 year: 2010 ident: 10.1016/j.cageo.2015.10.006_bib8 article-title: Catchment basin modelling of stream sediment anomalies revisited: incorporation of EDA and fractal analysis publication-title: Geochem. Explor. Environ. Anal. doi: 10.1144/1467-7873/09-224 – volume: 7 start-page: 1 year: 1983 ident: 10.1016/j.cageo.2015.10.006_bib25 article-title: Geological and geochemical features of submarine volcanic hydrothermal-sedimentary mineralization of Makeng iron deposit, Fujian Province publication-title: Bull. Inst. Miner. Deposits Chin. Acad. Geol. Sci. – volume: 20 start-page: 621 year: 2009 ident: 10.1016/j.cageo.2015.10.006_bib19 article-title: Principal component analysis for compositional data with outliers publication-title: Environmetrics doi: 10.1002/env.966 – volume: 104 start-page: 663 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib57 article-title: The mineralization age of the Makeng Fe deposit, South China: implications from U–Pb and Sm–Nd geochronology publication-title: Int. J. Earth Sci. doi: 10.1007/s00531-014-1096-4 – ident: 10.1016/j.cageo.2015.10.006_bib39 doi: 10.1109/ACPR.2013.37 – volume: 110 start-page: 167 year: 2011 ident: 10.1016/j.cageo.2015.10.006_bib9 article-title: Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2011.05.007 – volume: 155 start-page: 84 year: 2015 ident: 10.1016/j.cageo.2015.10.006_bib47 article-title: A comparative study of trend surface analysis and spectrum-area multifractal model to identify geochemical anomalies publication-title: J. Geochem. Explor. doi: 10.1016/j.gexplo.2015.04.013 |
| SSID | ssj0002285 |
| Score | 2.562533 |
| Snippet | In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 75 |
| SubjectTerms | Anomalies Autoencoder network case studies China computers Deposition Errors Geochemical exploration Geochemistry iron learning Mineralization Multivariate geochemical data Networks probability probability distribution Reconstruction Skarn-type iron deposits Training |
| Title | Recognition of geochemical anomalies using a deep autoencoder network |
| URI | https://dx.doi.org/10.1016/j.cageo.2015.10.006 https://www.proquest.com/docview/1778043309 https://www.proquest.com/docview/2116885868 |
| Volume | 86 |
| WOSCitedRecordID | wos000366770500008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-7803 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002285 issn: 0098-3004 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLagA4kXxFWMm4LEnkqm2o5j53GClouqglAnhafoJHG2TpCUtUHj33Mc21kEbBoPvESV5Vx6Psfn8_HJdwh5GXMteFWosErUJIyEyENgMgllxVkBDMrIquvP5WKh0jT55CoVbrpyArKu1dlZsv6vUGMbgm0-nf0HuPuLYgP-RtDxiLDj8UrAf_YpQZYJHmlTE8uLAjTfkHfrzbjtQgQwLrVej6HdNkbP0shK1DYtfMhZfeGHTTdM8HrOa_ZsPF25vN4vq-N21Yei28biZwKizj-68AIdhhfclJmo0MhyDadMFY_X-1KEtm6Qm_tsBRTvRdlf52cbKjjBtfdR9-klFfs2t-7cHfkt-MXHbHY4n2fLabrc47P199CUCjNb6nv8jYXtOtlhUiRqRHYO3k_TD70LZkwJL5Zqnt3LTXWJfX_c-yJK8ptz7hjH8g657ZYKwYGF-C65put75ObbrhTzz_tkOgA6aKpgAHTQAx10QAcQGKCDAdCBA_oBOZxNl6_fha4oRghIZbdhwaOKVsiacaEKgoLEv5rHoKNcy6jiE-C4oFWFQM4BVCimuRIQlZAooQoJOX9IRnVT60ckYDynUMaqKkUZQcQg4UXB41iWCgRQukuYt0tWOMV4U7jka-ZTA0-yzpiZMaZpRGPuklf9SWsrmHJ599gbPHOj13K5DIfM5Se-8PBkOCOabS6oddNuMiqNqBbnk-TiPozSWKFFYvX4Cn2ekFvnr8ZTMtqetvoZuVH82K42p8_d4PsFYVaN1A |
| 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+geochemical+anomalies+using+a+deep+autoencoder+network&rft.jtitle=Computers+%26+geosciences&rft.au=Xiong%2C+Yihui&rft.au=Zuo%2C+Renguang&rft.date=2016-01-01&rft.issn=0098-3004&rft.volume=86+p.75-82&rft.spage=75&rft.epage=82&rft_id=info:doi/10.1016%2Fj.cageo.2015.10.006&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0098-3004&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0098-3004&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0098-3004&client=summon |