Multi-element geochemical data mining: Implications for block boundaries and deposit distributions in South China

Liu, Weiqiang; Lü, Qingtian; Cheng, Zhizhong; Xing, Guangfu; Yan, Jiayong; Yuan, Lulu; Chen, Changxin

doi:10.1016/j.oregeorev.2021.104063

Cited by 11 publications

(5 citation statements)

References 57 publications

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“…The dash line represents the new segment boundary of the Yangtze and Cathaysia blocks. (b) The relationship between the new boundary and the Mn geochemical anomalies (modified from Liu et al., 2021). (c) The relationship between the new boundary and the outcrops of volcanoes in Daoxian (modified from Liu et al., 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The geophysical features are not the only evidence for the remarkable differences along this boundary. The Mn geochemical anomalies are different on the two sides of this boundary (less than 0.3 on the east side of this boundary, and greater than 0.6 on the west) (Figure 2b, Liu et al, 2021). Moreover, the middle Paleozoic and Jurassic granites are mainly distributed on the east side (Wang et al, 2013;Yang et al, 2018).…”

Section: The Southwestern Boundary Between the Yangtze And Cathaysia ...mentioning

confidence: 93%

“…Such models include an Andean-type active continental margin (Hsü et al, 1990) and lithospheric subduction (Zhou & Li, 2000) with the PYF as the boundary; and a crustal detachment model with the CLF as the boundary (Wang et al, 2003). However, these proposed boundaries are inconsistent with some characteristics of the SCB, for example, the Mn geochemical anomalies (Liu et al, 2021) and the outcrops of volcanoes in Daoxian (Liu et al, 2020). Therefore, unraveling the exact location and spatial extent of this boundary is critical to understanding the tectonic evolution of SCB.…”

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confidence: 99%

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Lithospheric Structure Near Jiuyishan, South China: Implications for Asthenospheric Upwelling and Lithospheric Modification

Sun

et al. 2021

Geophysical Research Letters

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The South China Block (SCB) is an important part of the Asian continent, and its constituent lithotectonic fragments record a long history of assembly and dispersal within the major supercontinents through the Earth's history (Cawood et al., 2017;Cocks & Torsvik, 2013). The SCB is composed of two major blocks: the Yangtze block in the northwest, and the Cathaysia block in the southeast, which were accreted in the early Neoproterozoic (Li et al., 2009) (Figure 1a). The northeastern segment of the tectonic boundary between the Yangtze and Cathaysia blocks is well exposed and is represented by the Jiangnan-Shaoxing fault (Figure 1a) (Chen et al., 2008;Liu et al., 2012;Wang et al., 2012). However, the location of the boundary in the southwestern segment remains ambiguous because of the widely covered Phanerozoic sediments (Shu, 2012). In the past 30 years, many faults have been proposed as representing the southwestern boundary. Liu et al. (2012) considered the Pingxiang-Yushan fault (PYF) as the boundary based on lithological information. He et al. (2013) proposed that the concealed Jiujiang-Shitai fault represents the southwestern boundary of the Yangtze and Cathaysia blocks. The Chenzhou-Linwu fault (CLF) has been also proposed as the boundary between the two blocks based on various evidences. For example, Deng et al. ( 2014) observed an abrupt variation in gravity and density structure across the CLF from a linear array, suggesting that the CLF may represent the southwestern boundary between the two blocks; Wang et al. (2003) showed Sr-Nd-Pb isotopic differences around the CLF, suggesting that the fault represents the Mesozoic lithospheric boundary between the two blocks; From the South China crustal lithology

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Section: Resultsmentioning

confidence: 99%

Section: The Southwestern Boundary Between the Yangtze And Cathaysia ...mentioning

confidence: 93%

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confidence: 99%

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Lithospheric Structure Near Jiuyishan, South China: Implications for Asthenospheric Upwelling and Lithospheric Modification

Sun

et al. 2021

Geophysical Research Letters

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“…Previous studies (Xu et al 2014;Lin et al 2021;Liu et al 2021;Zhang et al 2021a,c;Hou et al 2022) suggest that the crustal compositional structure (especially in lower crust) is likely the fundamental factor affecting the regional metallogenic differences. However, considering the close association between the deposit types and the regional faults in Wuyishan and the impacted scope and pattern of the fault zones constrained by the obtained models (Fig.…”

Section: Fault System In Wuyishan and Its Effect On Regional Metallogenymentioning

confidence: 93%

Upper crustal structure beneath the Qin-Hang and Wuyishan metallogenic belts in Southeast China as revealed by a joint active and passive seismic experiment

Zheng

Lü

et al. 2022

Geophysical Journal International

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Summary Southeast China is one of the most important polymetallic metallogenic provinces in the world. However, distinct differences in the type and origin of the mineral deposits are present between the major metallogenic belts in this province. To deepen our knowledge of this regional metallogenic setting, investigate the factors controlling its metallogenic differences, and explore the feasibility of active and passive seismic imaging, we performed a multibatch seismic experiment using active and passive sources with the help of 437 short-period seismographs deployed across the Qin-Hang and Wuyishan metallogenic belts in Southeast China. We resolved the S-wave velocity structure beneath the profile using ambient noise tomography and derived a Vp/Vs model by combining with a published P-wave model. A good correlation between the obtained seismic velocity models and geological observations are observed. The Proterozoic volcanic-sedimentary rock series and the Fuzhou-Yongfeng sedimentary basin are identified by low P- and S-wave velocities, high Vp/Vs ratios, and low S-wave velocity perturbations. Within the core of the Wuyishan Metallogenic Belt, several narrow, elongated, and low seismic velocity anomalies are considered to be signs of the nearby fault zones, and these anomalies provide reference information on the extension and pattern of these faults at depth. Combined with the geological and mineralogical evidence, we propose that theses faults function as ore-guiding structures and facilitate the formation of regionally representative mineral species and metal assemblages.

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“…In addition, Wan et al proposed a multisource target data fusion tracking method based on data mining in heterogeneous networks, in order to improve the detection and recognition ability of heterogeneous network multisource target data [5]. In addition, in the study of Liu et al, by extracting multilayer convolution features, the target data represented by it is more comprehensive, and calculate the correlation response of the data; then, dynamically fuse all the historical data and the target position in the real-time data response, so as to realize the positioning and dynamic tracking of target data [6]. In the study of Yi et al, using the fusion redetection mechanism to track the network target data and integrating the correlation filter into the network in the training phase, extract multisource data features through end-to-end training.…”

Section: Related Workmentioning

confidence: 99%

Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining

Guo¹,

Li²

2022

Wireless Communications and Mobile Computing

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This research is on heterogeneous network fusion method of multisource target data based on data mining. Firstly, it is a distributed storage structure model for building heterogeneous network multisource target data. Then, using the phase space reconstruction method, a grid distribution structure model for data fusion tracking is constructed, and realize visual scheduling and automatic monitoring of multisource target data. Finally, according to the feature extraction results, analyze the statistical characteristics of multisource target data in heterogeneous networks, combined with the fuzzy tomographic analysis method, multilevel fusion, and adaptive mining of multisource target data, extract the associated feature quantities in it, and realize the fusion tracking of data. The simulation results show that, in relatively simple heterogeneous networks, the feature mining error of the proposed method is nearly 2.11% lower than the two traditional methods. In relatively complex heterogeneous networks, the feature mining error of the proposed method is nearly 6.48% lower than the two traditional methods. It can be seen that this method has better adaptability for fusion tracking of heterogeneous network multisource target data, the anti-interference ability is strong, and the tracking accuracy in the data fusion tracking process is also improved.

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Multi-element geochemical data mining: Implications for block boundaries and deposit distributions in South China

Cited by 11 publications

References 57 publications

Lithospheric Structure Near Jiuyishan, South China: Implications for Asthenospheric Upwelling and Lithospheric Modification

Lithospheric Structure Near Jiuyishan, South China: Implications for Asthenospheric Upwelling and Lithospheric Modification

Upper crustal structure beneath the Qin-Hang and Wuyishan metallogenic belts in Southeast China as revealed by a joint active and passive seismic experiment

Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining

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