The grossular–andradite garnet is an ideal mineral for indicating the formation age of skarn, which also pretends to constrain skarn processes because of its higher REE (rare earth elements) content. The Tongshanling deposit is a medium-sized reduced skarn Cu–W–Pb–Zn deposit associated with a highly evolved I-type granodiorite intrusion in the Nanling metallogenic belt, South China. Different mineral assemblages, microscopic characteristics, and BSE images distinguish two kinds of garnets in the prograde and retrograde skarn stages. The garnet grains from the prograde skarn stage have a U–Pb isochron age of 165.4 ± 3.8 Ma (MSWD = 0.7) and that from the retrograde skarn stage have a U–Pb isochron age of 159.5 ± 1.7 Ma (MSWD = 1.8), implying that the thermal metamorphism and hydrothermal metasomatism mainly occurred in the middle Upper Jurassic. The total amount of rare earth elements (∑REE+Y) in the garnet gradually decreased and the REE patterns shifted from enriched HREE with Eu negative anomaly to HREE-depleted with Eu positive anomaly. The decreasing U content and increasing Eu anomaly in the retrograde skarn stage indicate a redox environment change from oxidation to reduction. However, garnet from different elevations within the same stage (+90 m, +5 m, −35 m, −200 m, and −400 m) exhibit similar REE patterns, despite weak cooling and significant depressurization processes confirmed by fluid inclusion microthermography. As a result, the REE content and patterns are dominated by the REE species of parent fluids, which are changed over time by symbiotic REE-enriched mineral precipitation and the redox environment, while being slightly affected by the fluid pressure. Grossular garnets, rich in U and REEs, and found in reduced skarn deposits, can constrain chronology and reveal the spatio-temporal zonal characteristics.

The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal clustering method was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Secondly, within each spatio-temporal cluster, an ensemble learning strategy was employed to combine one time series prediction model and three spatio-temporal prediction models to reduce the prediction uncertainty of an individual model. Experiments on a land subsidence dataset from Cangzhou, China, show that the prediction accuracy of the proposed method is significantly higher than that of four individual prediction models.

The Qin-Hang Metallogenic Belt (QHMB), an important metallogenic belt in South China, hosts Cu and W–Sn polymetallic deposits. The Tongshanling ore field in the QHMB is characterized by the coexistence of Cu- and W-bearing polymetallic deposits, which are related to granodiorite and granite porphyry. This study examined whole-rock geochemistry, geochronology, and Sr–Nd–Hf isotopes to determine the genetic relationship between diverse ore-related granitoids (i.e., granodiorite and granite porphyry) and Cu–W metallogeny in the Tongshanling ore field. Zircon LA-ICP-MS U–Pb dating shows that the granodiorite and granite porphyry in the Tongshanling ore field were emplaced at 163.7 ± 0.4 Ma to 154.7 ± 0.6 Ma and 161.1 ± 0.3 Ma, respectively. Geochemically, the granodiorites are classified as oxidized I-type, while the highly evolved granite porphyry is reduced A-type. The Lu–Hf isotopic composition of the granodiorites is characterized by εHf(t) values ranging from –10.49 to –4.99 (average = –7.17), with corresponding T_DM^C ages ranging from 1524 to 1877 Ma (average = 1682 Ma). In contrast, the granite porphyry has higher εHf(t) values (–3.60 to –1.58, average = –2.78) and younger T_DM^C (1310–1438 Ma, average = 1387 Ma). The εNd(t) values of granodiorite are −8.06 to −7.37 and the two-stage model ages (T_DM2) are 1543–1598 Ma, while the granite porphyry has higher εNd(t) values (−3.0 to −3.4) and younger T_DM2 ages (1195–1223 Ma). The results show that the granodiorite and granite porphyry were formed from partial melting of different Mesoproterozoic basement rocks under varying degrees of crust–mantle interaction. Granite porphyry underwent well-recorded fractional crystallization. Compared to the Cu-forming granodiorite, the W-forming granite porphyry has a higher differentiation index, higher crystallization temperatures of zircon (average = 708 °C versus 631 °C), and lower oxygen fugacity (median ΔFMQ = –2.21 versus –1.77).

Understanding the effect mechanisms of various factors on land subsidence may help in the development of scientific measures to control land subsidence. Previous studies mainly focused on exploring local effect mechanisms, such as extracting hotspots and analyzing their spatiotemporal distribution characteristics and identifying the interaction mechanisms of the associated factors. However, the scarcely discussed structural effect mechanisms on a small scale suggests a need to further explore the effects on land subsidence. Therefore, in this paper, an analytical framework was proposed to elaborate the structural effect mechanisms of influencing factors on land subsidence. First, the local effect mechanisms were identified using the geographically and temporally weighted regression (GTWR) model, followed by a spatial clustering analysis and the detection of their aggregation pattern using the spatially constrained multivariate clustering (SCMC) model to show the structural mechanisms. Study datasets included the monitoring results of land subsidence during 2003–2010 and the related socioeconomic factors by using synthetic aperture radar (SAR) images from Beijing. Factors such as population, annual average rainfall, underground water, and static load were identified to measure the changes in land subsidence, and all of these had both negative and positive impacts. Among these, the annual average rainfall had the largest coefficient variation range. These four geographically associated factors revealed various spatiotemporal effects on land subsidence in Beijing, showing land subsidence changes resulting from the urbanization process of Beijing during that period.