{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:55:50Z","timestamp":1778576150256,"version":"3.51.4"},"reference-count":46,"publisher":"Society of Exploration Geophysicists","issue":"3","funder":[{"DOI":"10.13039\/100012994","name":"Polytechnique Montr\u00e9al","doi-asserted-by":"publisher","award":["2021 New Faculty Start-up Grant"],"award-info":[{"award-number":["2021 New Faculty Start-up Grant"]}],"id":[{"id":"10.13039\/100012994","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012994","name":"Polytechnique Montr\u00e9al","doi-asserted-by":"publisher","award":["2021 New Faculty Start-up Grant"],"award-info":[{"award-number":["2021 New Faculty Start-up Grant"]}],"id":[{"id":"10.13039\/100012994","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["library.seg.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Induced polarization (IP) measurements are affected by various types of noise, which should be removed prior to data interpretation. However, existing data processing methods often rely on empirical assumptions about the standard shape of IP decay curves. Our goal is to introduce a data-driven approach for modeling and processing time-domain IP measurements. To reach this goal, we train a variational autoencoder (VAE) on 1,600,319 IP decays collected in Canada, the United States, and Kazakhstan. The proposed deep learning approach is unsupervised and avoids the pitfalls of IP parameterization with empirical Cole-Cole and Debye decomposition models, simple power-law models, or mechanistic models. Four applications of VAEs are key to modeling and processing IP data: (1)\u00a0synthetic data generation, (2)\u00a0Bayesian denoising, (3)\u00a0evaluation of signal-to-noise ratio, and (4)\u00a0outlier detection. Furthermore, we interpret the IP data compilation\u2019s latent representation and reveal a correlation between its first dimension and the average chargeability. Finally, we determine that a single real-valued scalar parameter contains sufficient information to encode IP data. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pretrained implementation of the VAE model is available as open-source Python code.<\/jats:p>","DOI":"10.1190\/geo2021-0497.1","type":"journal-article","created":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T08:10:43Z","timestamp":1644480643000},"page":"E135-E146","update-policy":"https:\/\/doi.org\/10.1190\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Data-driven modeling of time-domain induced polarization"],"prefix":"10.1190","volume":"87","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5438-0382","authenticated-orcid":false,"given":"Charles L.","family":"B\u00e9rub\u00e9","sequence":"first","affiliation":[{"name":"Polytechnique Montr\u00e9al 1 , Department of Civil, Geological and Mining Engineering, Montr\u00e9al, Quebec, Canada. charles.berube@polymtl.ca (corresponding author)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"B\u00e9rub\u00e9","sequence":"additional","affiliation":[{"name":"Abitibi Geophysics Inc 2 ., Val-d\u2019Or, Quebec, Canada. pberube@ageophysics.com"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"186","published-online":{"date-parts":[[2022,4,12]]},"reference":[{"key":"2025121211234088400_r1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1071\/EG15077","article-title":"Relationship between bulk mineralogy and induced polarisation responses in iron oxide-copper-gold and porphyry copper mineralisation, northern Chile","volume":"48","author":"Aguilef","year":"2017","journal-title":"Exploration Geophysics"},{"key":"2025121211234088400_r2","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1093\/gji\/ggaa460","article-title":"Automatic processing of time domain induced polarization data using supervised artificial neural networks","volume":"224","author":"Barfod","year":"2021","journal-title":"Geophysical Journal 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Learning structured output representation using deep conditional generative models: Advances in Neural Information Processing Systems, 1\u20139."},{"key":"2025121211234088400_r44","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1111\/1365-2478.12363","article-title":"Deep massive sulphide exploration using 2D and 3D geoelectrical and induced polarization data in Skellefte mining district, Northern Sweden","volume":"64","author":"Tavakoli","year":"2016","journal-title":"Geophysical Prospecting","ISSN":"https:\/\/id.crossref.org\/issn\/0016-8025","issn-type":"print"},{"key":"2025121211234088400_r45","doi-asserted-by":"crossref","unstructured":"Vincent\n              P.\n            \n            \n              Larochelle\n              H.\n            \n            \n              Bengio\n              Y.\n            \n            \n              Manzagol\n              P.-A.\n            \n          , 2008, Extracting and composing robust features with denoising 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