Integrated geophysical prospecting for deep ore detection in the Yongxin gold mining area, Heilongjiang, China

Integrated geophysical prospecting for deep ore detection in the Yongxin gold mining area, Heilongjiang, China | 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 Article Integrated geophysical prospecting for deep ore detection in the Yongxin gold mining area, Heilongjiang, China Yechang Yin, Jun Chen, Zhonghai Zhao, Yuanjiang Yang, Chenglu Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4247453/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Geophysical exploration techniques are crucial in facilitating accurate mineral prospecting predictions, but relying solely on single methods can introduce uncertainties. In this study, we employed audio-frequency magnetotelluric (AMT) methods, complemented by gravimetric surveying and high-resolution magnetic profiling in the Yongxin gold deposit. Utilizing advanced three-dimensional modeling techniques, we were able to precisely delineate the lithological variations and deep-seated mineralization features inherent to the area. The inversion and interpretation of cross-sectional AMT data illuminated the subsurface structure down to a depth of 1.5 kilometers. This enhanced data reliability was achieved through an integrated interpretation constrained by gravity, magnetic, and electrical datasets, thereby enabling a more accurate inference of the deeper geological framework. Furthermore, by amalgamating regional geological data, drilling information, and additional datasets, we uncovered the characteristics of deep mineralization, the three-dimensional configuration of mineralization-related rock masses, and the spatial orientation of known ore deposits. This approach facilitated a transparent visualization of the deeper geological formations. A thorough analysis was conducted on the distribution patterns of ore-controlling structures and exploration markers. Consequently, we have established a geological-geophysical model tailored for mineral exploration within the study area, which provides an effective reference for future deep exploration work. Earth and environmental sciences/Solid earth sciences/Geology Earth and environmental sciences/Solid earth sciences/Geophysics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Sep, 2024 Reviews received at journal 14 Jun, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviews received at journal 20 May, 2024 Reviewers agreed at journal 03 May, 2024 Reviewers invited by journal 19 Apr, 2024 Editor assigned by journal 19 Apr, 2024 Editor invited by journal 16 Apr, 2024 Submission checks completed at journal 16 Apr, 2024 First submitted to journal 10 Apr, 2024 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. 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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-4247453","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292227332,"identity":"321dc563-7857-4ed5-bb00-0b0ae9c38e06","order_by":0,"name":"Yechang Yin","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Yechang","middleName":"","lastName":"Yin","suffix":""},{"id":292227336,"identity":"c5ada14d-9ef5-4fce-8522-2b666d837c03","order_by":1,"name":"Jun Chen","email":"","orcid":"","institution":"Liaoning Technical 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