Seismics in Mineral Exploration

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3633

Special Issue Editors


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Guest Editor
‘Multi-Wave & Multi-component’ (MWMC) Seismic Group, State Key Laboratory of Geological Processes and Mineral Resources, School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
Interests: seismic anisotropy; multi-component seismic; multi-wave seismic; rotational seismology; deep underground geophysical observation; ocean geophysics; mineral seismic

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Guest Editor
Institutions of Earth Science, Chinese Academy of Sciences, Beijing 100029, China
Interests: artificial intelligence geophysics; compressive sensing seismic exploration; seismic while drilling; numerical simulation and FWI

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Guest Editor
School of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing 100083, China
Interests: seismic exploration; mineral exploration; passive reflection seismic; seismic imaging; seismic migration; GPU
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: geo-electromagnetic induction methods for mineral exploration; joint inversions for minerals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main problem encountered by mineral seismic exploration is accurately imaging concealed rock mass, faults and ore bodies with limited investment. Therefore, how to decrease the seismic acquisition cost is the most important point of concern. Promoted by rapid development of modern information science and technology, artificial intelligence, multi-component seismometers or nodes, and remote sensing techniques are widely applied in exploration seismology. However, mineral seismic is not a simple duplication of oil and gas seismics or engineering seismics. In recent years, much progress involving compressive sensing, multi-component seismics, active and passive source techniques, joint geophysical inversion and field cases has been achieved. For this Special Issue, submitted papers should be focused on a feasible, cost-effective seismic solution for mineral exploration to further improve the seismic precision, resolution and reliability. At least one of the following four topics should be covered:

(1) New theories, methods and techniques about active and passive source seismic and geo-electromagnetic induction methods, including significant or defeated cases. It should be noted that research on active reflection seismics, scattering seismics and geo-electromagnetic induction prospecting is not welcomed here, while joint active and passive seismic, natural and artificial source of geo-electromagnetic methods are favored topics.

(2) Seismic acquisition techniques assisted with artificial intelligence aimed at characterizing the complex topography and underground structures of mineral deposits, spare and non-regular grid seismic sampling, and active and passive seismics with multi-component sensors, especially methods about wave field construction with compressive sensing, and methods about separation of hybrid source and denoising. If there is no real mineral application, fossil energy models or cases can be supplemented.

(3) Multi-component seismics. Whether the source is active or passive, multi-component geophones and data processing methods, especially methods about vector denoising, multi-wave imaging, joint imaging with body and surface waves, should be given enough emphases; wide-frequency seismic migration joined with active and passive sources, and relatively new inversion methods and applications are particularly welcomed.

(4) Integrated geophysics. Seismic constrained geophysical interpretation and inversion, such as gravity-seismic, electro-seismic, and magnetic-seismic inversion, including joint imaging and inversion with surface and vertical logging are worth considering.

Prof. Dr. Yun Wang
Dr. Shoudong Huo
Prof. Dr. Guofeng Liu
Prof. Dr. Zhengyong Ren
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mineral seismic
  • compressive sensing
  • artificial intelligence
  • multi-component seismic
  • active seismic
  • passive seismic
  • joint imaging and inversion
  • integrated geophysics

Published Papers (2 papers)

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Research

15 pages, 12681 KiB  
Article
Real-Time Ambient Seismic Noise Tomography of the Hillside Iron Oxide–Copper–Gold Deposit
by Timothy Jones, Gerrit Olivier, Bronwyn Murphy, Lachlan Cole, Craig Went, Steven Olsen, Nicholas Smith, Martin Gal, Brooke North and Darren Burrows
Minerals 2024, 14(3), 254; https://doi.org/10.3390/min14030254 - 28 Feb 2024
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Abstract
We conduct an exploration-scale ambient noise tomography (ANT) survey over the Hillside Iron Oxide–Copper–Gold (IOCG) deposit in South Australia, leveraging Fleet’s direct-to-satellite technology for real-time data analysis. The acquisition array consisted of 100 sensors spaced 260 m apart which recorded continuous vertical-component seismic [...] Read more.
We conduct an exploration-scale ambient noise tomography (ANT) survey over the Hillside Iron Oxide–Copper–Gold (IOCG) deposit in South Australia, leveraging Fleet’s direct-to-satellite technology for real-time data analysis. The acquisition array consisted of 100 sensors spaced 260 m apart which recorded continuous vertical-component seismic ambient noise for 14 days. High quality Rayleigh wave signals, with a mean signal-to-noise ratio (SNR) of 40, were recovered in the frequency band 1–4 Hz after processing the recorded data between 0.1–9 Hz. Our modelling results capture aspects of the deposit’s known geology, including depth of cover, structures linked to mineralisation, and the mineralised host rock, down to approximately 1 km depth. We compare our velocity model with existing magnetic, gravity, induced polarisation and drilling data, showing strong correlation with each. We identify several new features of the local geology, including the behaviour of key structures down to 1 km, and highlight the significance of a Cambrian-age dolomite that cuts across the main structural corridor that hosts the Hillside deposit. An analysis of model convergence rates with respect to Rayleigh wave SNRs shows that real-time data analysis can reduce recording duration at the site by 65% compared to traditional deployment durations, from ∼14 days to ∼5 days. Finally, we conclude by commenting on the efficacy of the ANT technique for the exploration of IOCG systems more broadly. Full article
(This article belongs to the Special Issue Seismics in Mineral Exploration)
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25 pages, 20194 KiB  
Article
Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China
by Yuxuan Zhou, Yuyong Yang, Zhengyang Wang, Bing Zhang, Huailai Zhou and Yuanjun Wang
Minerals 2024, 14(2), 159; https://doi.org/10.3390/min14020159 - 31 Jan 2024
Viewed by 833
Abstract
Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil [...] Read more.
Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir. Full article
(This article belongs to the Special Issue Seismics in Mineral Exploration)
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