不同尺度地震层析层地质统计分析加强矿山勘探:以高分辨率结果改善低分辨率数据

IF 4.8 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
José Joaquín González, Nadia Mery, Felipe Navarro, Gonzalo Díaz, Diana Comte, Sergio Pichott
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引用次数: 0

摘要

在采矿勘探的背景下,局部地震层析成像是一种有价值的补充工具,适用于从绿地到棕地项目的不同规模。然而,在层析成像中解释体波速度异常是一项重大挑战,这在很大程度上取决于分析人员的专业知识和信息的可用性。针对这一挑战,本文提出了一种地质统计分析方法,以有效地比较和增强从低分辨率到高分辨率的层析成像中提取的信息。本研究使用的数据对应于位于智利北部Chuquicamata地区的Mantos Rojos (MR)和Radomiro Tomic (RT)斑岩铜矿床的层状反演值。MR的分辨率为2 × 2 km2,相对低于RT的1 × 1 km2,但两者共享相同的空间区域。本研究评估了低分辨率断层扫描(MR)与高分辨率断层扫描(RT)的识别能力。将MR模拟Vp/Vs值与RT地震层析成像数据进行比较。目视验证表明,MR纵横波速度值模拟的Vp/Vs值可以识别低Vp/Vs异常(<;1.7)。此外,空间分析比较了MR实现的实验方差和优先方向上Vp/Vs比率的RT值,发现两个空间工具之间存在对应关系。最后,将模拟结果与CODELCO提供的研究区地质图和钻探活动获得的铜品位进行对比,进行地质验证,识别出指示成矿的空间格局和西部断裂等更大规模的地质特征。我们的研究具有实际意义,因为通过地质统计学模拟,MR地震层析成像的网格尺寸可以减少,并且仍然可以识别研究区域内的低Vp/Vs异常,与rt的低分辨率验证网格一致。我们的研究结果证明了地质统计学方法通过提供对地下地质特征及其与成矿关系的见解,在加强勘探决策方面的有效性。这种方法不仅提高了矿产勘探项目的效率和成功率,而且通过允许更有针对性和更明智的勘探活动,将对环境的影响降到最低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Mining Exploration through Geostatistical Analysis of Seismic Tomographies at Different Scales: Improving Low-Resolution Data by High-Resolution Results

In the context of mining exploration, local earthquake tomography serves as a valuable complementary tool, applicable across varying scales from greenfield to brownfield projects. Nevertheless, interpreting body-wave velocity anomalies within tomographies poses a significant challenge, which largely depends on the expertise of the analyst and the availability of information. Addressing this challenge, this paper proposes a geostatistical analysis to effectively compare and enhance the information extracted from tomographies ranging from lower to higher resolutions. The data utilized in this study correspond to the tomographic inversion values of Mantos Rojos (MR) and Radomiro Tomic (RT) porphyry copper deposits situated within the Chuquicamata District in northern Chile. MR has a resolution of 2 × 2 km2, comparatively lower than RT’s resolution of 1 × 1 km2, yet both share the same spatial zone. This study evaluated the discernment capabilities of lower-resolution tomography (MR) in comparison to its higher-resolution counterpart (RT) using turning bands simulation. The simulated Vp/Vs values of MR were compared against RT seismic tomography data. Visual validation revealed that simulated Vp/Vs values from P- and S-wave velocity values of MR can identify the low Vp/Vs anomalies (< 1.7). Moreover, spatial analysis compared the experimental variograms for MR realizations and for RT values in preferential directions for Vp/Vs ratios, finding a correspondence between both spatial tools. Finally, geological validation was carried out by comparing the simulation results with geological maps of the study area and copper grades obtained through drilling campaigns provided by CODELCO, where spatial patterns indicative of mineralization and larger-scale geological features like the West Fault were identified. Our research has practical implications because, through geostatistical simulations, the grid dimensions of seismic tomography of MR can be reduced and still identify low Vp/Vs anomalies within the area of study, being consistent with the lower-resolution validation grid of RT. Our findings demonstrate the efficacy of geostatistical methods in enhancing exploration decision-making by providing insights into subsurface geological features and their relationship to mineralization. This approach not only improves the efficiency and success rate of mineral exploration projects but also minimizes environmental impact by allowing for more targeted and informed exploration activities.

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来源期刊
Natural Resources Research
Natural Resources Research Environmental Science-General Environmental Science
CiteScore
11.90
自引率
11.10%
发文量
151
期刊介绍: This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.
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