Optimizing geophysical anomaly detection using combined electrode arrays through image processing

Optimizing geophysical anomaly detection using combined electrode arrays through image processing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing geophysical anomaly detection using combined electrode arrays through image processing Mohammadreza Yousefi, Mohamad Ghayeghi Zarinjoy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4092953/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The use of a single electrode array in geophysical surveys can lead to increased uncertainty in anomaly detection. To address this, we took advantage of image processing to produce a wide range of data from a limited one and subsequently enhance data manipulation capabilities. Three geophysical anomalies—rectangle block, dyke, and fault—were modelled using four typical electrode arrays (Wenner, Pole-Pole, Dipole-Dipole, and Schlumberger) with RES2DMOD software. The synthetic data were then inverted using RES2DINV software. Python software facilitated the conversion of inverted resistivity models from RGB to real resistivity values, enabling the application of various statistical approaches. We plotted the resistivity sections using the reconstructed data to ensure that our conversion procedure was correct. Next, the resulting models were evaluated using mean resistivity value (MRV), mean absolute error (MAE), and mean absolute percentage error (MAPE) criteria. Our study revealed that combined models outperformed individual arrays in detecting underground anomalies. However, increasing the number of electrode arrays combined does not necessarily give rise to an ideal result. By identifying and eliminating an electrode array that significantly deviated from real resistivity values, new combined models were plotted. In all three models, we can see that three combined electrode arrays provided more accurate results than the four conventional ones if the less relevant array was properly recognized. Geophysical anomaly detection Synthetic data modelling Combination of electrode arrays Image processing Forward modelling Data inversion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4092953","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282616680,"identity":"30e062fa-4a7a-4d8f-b739-0e6715f11f4f","order_by":0,"name":"Mohammadreza Yousefi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDAD9mbmgw8+ABls7MRq4TnMlmw4A6SFmWgtB3jMpHlALEJadGcfYJP42FYnx8POYyBt82ubPB8zA+OHjzm4tZidS2CTnNl22JiHma3AOLfvtmEbMwOz5MxteLScYWCT5m07kLifmXlDcm7PbUagFjZmXsJa6up7mBkMDlv23LYnVgtzAg8zi2Ezw4/biURoYWy2nHHusGEPM1syY2/D7eQ2ZsZmAn5hPnjjQ1mdPA//4eM/fvy5bTu/vfngh494tDAwMLZIINhtYLIBn3oQYP6AYP8hpHgUjIJRMApGIgAA/BRJorMbM9EAAAAASUVORK5CYII=","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Yousefi","suffix":""},{"id":282616681,"identity":"4e20753f-9883-42bd-9a6c-3345cb151eb9","order_by":1,"name":"Mohamad Ghayeghi Zarinjoy","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"Ghayeghi","lastName":"Zarinjoy","suffix":""}],"badges":[],"createdAt":"2024-03-13 13:01:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4092953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4092953/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53793304,"identity":"60dc5b8b-48e3-4109-8cef-1c1167f47151","added_by":"auto","created_at":"2024-03-30 20:37:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2299800,"visible":true,"origin":"","legend":"","description":"","filename":"YousefiCombinationofelectrodeconfigurations.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4092953/v1_covered_a3949ab7-70ab-4d14-a8f6-5d60bfa69921.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing geophysical anomaly detection using combined electrode arrays through image processing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geophysical anomaly detection, Synthetic data modelling, Combination of electrode arrays, Image processing, Forward modelling, Data inversion","lastPublishedDoi":"10.21203/rs.3.rs-4092953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4092953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of a single electrode array in geophysical surveys can lead to increased uncertainty in anomaly detection. To address this, we took advantage of image processing to produce a wide range of data from a limited one and subsequently enhance data manipulation capabilities. Three geophysical anomalies\u0026mdash;rectangle block, dyke, and fault\u0026mdash;were modelled using four typical electrode arrays (Wenner, Pole-Pole, Dipole-Dipole, and Schlumberger) with RES2DMOD software. The synthetic data were then inverted using RES2DINV software. Python software facilitated the conversion of inverted resistivity models from RGB to real resistivity values, enabling the application of various statistical approaches. We plotted the resistivity sections using the reconstructed data to ensure that our conversion procedure was correct. Next, the resulting models were evaluated using mean resistivity value (MRV), mean absolute error (MAE), and mean absolute percentage error (MAPE) criteria. Our study revealed that combined models outperformed individual arrays in detecting underground anomalies. However, increasing the number of electrode arrays combined does not necessarily give rise to an ideal result. By identifying and eliminating an electrode array that significantly deviated from real resistivity values, new combined models were plotted. In all three models, we can see that three combined electrode arrays provided more accurate results than the four conventional ones if the less relevant array was properly recognized.\u003c/p\u003e","manuscriptTitle":"Optimizing geophysical anomaly detection using combined electrode arrays through image processing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 15:54:36","doi":"10.21203/rs.3.rs-4092953/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fbb0d45-bcda-41a3-84a1-2146206c857d","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-30T20:29:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 15:54:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4092953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4092953","identity":"rs-4092953","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}