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The Jiaojia Fault (JJF) in the Jiaodong area of eastern China is an important NNE-trending structure that is subsidiary to the regional Tancheng–Lujiang (Tan-Lu) Fault Zone, and hosts >1200 t of gold reserves contained in disseminated and stockwork ore, dominantly in the footwall of the fault. We present new zircon U–Pb, apatite fission track, and illite K–Ar data along the JJF and have delineated its tectonic history focusing on its formation and reactivation. Zircon U–Pb dating shows that the Shangzhuang granite is a composite body with ages between 132 ± 1 and 127 ± 1 Ma. Illite K–Ar ages for the fault’s gouge range from 83 ± 2 to 68 ± 2 Ma, and the measured apatite fission track ages for ores are between 55 and 21 Ma. Previous zircon U–Pb geochronology and structural studies suggest that the JJF was originally activated in the Jurassic during 160–150 Ma as a sinistral fault. The JJF was a normal fault in the Early Cretaceous due to NW–SE orientated tension and NE–SW compression, which lasted from 135 to 120 Ma. This was followed by sinistral strike–slip faulting due to NW–SE compression and NE–SW tension during 120–110 Ma, and it changed to normal displacement at ca. 110 Ma. Our apatite fission track data analysis and thermal modeling of representative samples suggest that there was a subsequent dextral reactivation of the fault at ca. 55 Ma. Previous age data of ca. 130–110 Ma for gold mineralization along the JJF coincides with the Early Cretaceous magmatism and is coeval with the transition from normal faulting to sinistral strike–slip faulting of the JJF in Early Cretaceous, which is interpreted to be due to changing direction of the subducting Pacific Plate.

Selenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, a key area within the “Organic Valley” of Hubei Province, China. We employed fuzzy weights-of-evidence, backpropagation neural network, and support vector regression models to predict optimal planting zones for Selenium-rich crops. A comparative analysis indicated that the backpropagation neural network model provided the highest prediction accuracy (R2 = 0.77), identifying Selenium-rich crop zones covering 68.87% of the aera, where Selenium-rich crops made up 86.67% of all samples. Notably, the backpropagation neural network yielded excellent performance for rice and rapeseed, with R2 values of 0.95 and 0.99, respectively. The findings also indicate that the Selenium content in crops is closely linked to Selenium levels in the soil and is significantly influenced by synergistic and antagonistic interactions with other elements. This study provides scientific support for the cultivation of selenium-rich crops. It plays a positive role in promoting the development of the local selenium-rich industry and the sustainable utilization of soil selenium resources.

Measuring the relative importance and assigning weights to conditioning factors of land- slides occurrence are significant for landslide prevention and/or mitigation. In this contribution, a fractal method is introduced for measuring the spatial relationships between landslides and condition- ing factors （such as faults, rivers, geological boundaries, and roads）, and for assigning weights to condi- tioning factors for mapping of landslide susceptibility. This method can be expressed as p=C~, where d is the fractal dimension, and C is a constant. This relationship indicates a fractal relation between land- slide density （p） and distances to conditioning factors （6）. The case of d〉0 suggests a significant spatial correlation between landslides and conditioning factors. The larger the d （〉0） value, the stronger the spatial correlation is between landslides and a specific conditioning factor. Two case studies in South China were examined to demonstrate the usefulness of this novel method.

Identification of geochemical anomalies is of particular importance for tracing the footprints of anomalies. This can be implemented by advanced techniques of exploratory data analysis, such as fractal/multi-fractal approaches based on priori or posteriori distribution of geochemical elements. The latter workflow involves analysis of 2D/3D produced maps, which can be mostly obtained by geostatistical algorithms. There are two challenging issues for such an analysis. The first one corresponds to handling the cross-correlation structures among the data, and the second one relates to the compositional nature of data. To tackle these problems, this paper investigates the application of Gaussian co-simulation for modeling the cross-correlated compositional data in order to recognize the multivariate geochemical anomalies in integration with fractal analysis. In this context, an innovative algorithm, namely co-simulated size number (CoSS-N), is introduced for this purpose. The compositional nature of data is addressed by additive log-ratio transformation of original data while the Gaussian co-simulation handles the reproduction of cross-correlation among the components. The co-simulated outputs are then taken into account for capturing different geochemical populations, showing different levels of backgrounds and anomalies. The algorithm is illustrated via a real case study located in Philippine wherever seven geochemical components are required to be considered. The accuracy of results is examined by statistical validation techniques, indicating the capability of the CoSS-N algorithm for multivariate identification of geochemical anomalies.

Geochemical pattern recognition and anomaly mapping are always involved in the fields of environmental and exploration geochemistry. Principal component analysis (PCA) and factor analysis (FA) are most commonly used to reveal underlying geochemical associations for the purpose of spatial distribution pattern analysis. However, the methods of PCA and FA cannot eliminate correlations between different principal components/factors, meaning that geochemical associations revealed by PCA or FA could be simultaneously influenced by two or more principal components/factors, as can be observed from biplot analysis. Such problem provides a challenge for interpretation of geochemical/geological processes. In the present study, we demonstrated a simple method, termed compositional balance analysis (CoBA), to interpret critical geochemical/geological processes. Comparative studies between CoBA and compositional factor analysis, as well as data- and knowledge-driven CoBA, were considered to discuss the advantage and practicability of the CoBA in geochemical pattern recognition and anomaly mapping based on a case study in the Nanling belt, South China. The results indicate that the CoBA has greater efficiency in enhancing weak or concealed geochemical anomalies and suppressing spurious geochemical anomalies relative to multivariate dimensionality reduction analysis; especially, knowledge-driven CoBA provides more robust interpretation of geochemical/geological processes relative to data-driven CoBA.

This paper demonstrates knowledge-guided fuzzy logic modeling of regional-scale surficial uranium (U) prospectivity in British Columbia (Canada). The deposits/occurrences of surficial U in this region vary from those in Western Australia and Namibia; thus, requiring innovative and carefully-thought techniques of spatial evidence generation and integration. As novelty, this papers introduces a new weighted fuzzy algebraic sum operator to combine certain spatial evidence layers. The analysis trialed several layers of spatial evidence based on conceptual mineral system model of surficial U in British Columbia (Canada) as well as tested various models of evidence integration. Non-linear weighted functions of (a) spatial closeness to U-enriched felsic igneous rocks was employed as U-source spatial evidence, (b) spatial closeness to paleochannels as fluid pathways spatial evidence, and (c) surface water U content as chemical trap spatial evidence. The best models of prospectivity created by integrating the layers of spatial evidence for U-source, pathways and traps predicted at least 85% of the known surficial U deposits/occurrences in >10% of the study region with the highest prospectivity fuzzy scores. The results of analyses demonstrate that, employing the known deposits/occurrences of surficial U for scrutinizing the spatial evidence layers and the final models of prospectivity can pinpoint the most suitable critical processes and models of data integration to reduce bias in the analysis of mineral prospectivity.

The operation of large-scale ore-forming processes triggers the development of neighboring mineral deposits of the same or related types in a metallogenic province. While these deposits often bear striking similarities, variations in local geological settings cause differences in many deposit features. Therefore, in a metallogenic province, geochemical, geophysical, and geological signatures of local areas mineralized with a certain deposit type can show considerable inherent differences. The application of deposit-type locations as training sites, thus, introduces a type of stochastic uncertainty into data-driven mineral prospectivity mapping (MPM), impairing the predictive capability of this activity. This study delves into this type of uncertainty and applies an ensemble technique combining bootstrapping and naïve Bayes classifiers to measure this uncertainty and lessen its impact on the MPM-generated exploration targets. Two components, one representing the quantified uncertainty and the other a modulated predictive model, are retained by the proposed framework. This framework was applied to a suite of mineral-systems derived targeting criteria of skarn-type Cu mineralization in the Alborz–Azerbaijan magmatic belt of northern Iran. The predictive results derived by the proposed technique outperformed those derived using a single classifier, showcasing its efficacy. In addition, a novel approach is described and applied to demarcating exploration targets marked by low uncertainty.

PurposeNatural organic acids, such as humic acid (HA), play crucial roles in biogeochemistry of anions and cations in soil due to their numerous functional groups on their surfaces. Selenium (Se) and cadmium (Cd) could bind strongly to HA; nevertheless, it is still unclear as to the effects of HA on Se and Cd uptake in rice which will be focused on in this paper.Materials and methodsPot experiments were carried out at Huazhong Agricultural University, Wuhan City, Hubei Province, China. Agricultural soils were treated with different concentrations of HA (0, 4, and 8 g kg−1 soil) and Se (SeIV or SeVI) (0 and 2 mg kg−1 soil) as well as with base fertilizer 3 days prior to planting. For Cd treatment, experimental soils were treated with Cd (0 and 2 mg kg−1 soil) 1 month before sowing. For element determination, root (after DCB extraction) and shoot samples were digested with a mixed solution of HNO3-HClO4, and the Se and Cd in digest solution were measured by HG-AFS and ICP-MS, respectively. Fe, Se, and Cd in iron plaque were extracted by DCB extraction and measured by AAS, HG-AFS, and ICP-MS, respectively.Results and discussionHA reduced Se (or Cd)-induced growth stimulation and Se and Cd uptake in rice seedlings, whereas iron plaque formation varied little with different treatments. HA inhibited SeIV (or SeVI) uptake in rice seedlings by reducing Se translocations from soil to iron plaque (or by increasing Se adsorption capacity of iron plaque and decreasing Se transport from iron plaque to root). HA reduced Cd uptake in rice seedlings by reducing Cd transport from soil to iron plaque and from iron plaque to root. Compared with single addition of SeIV or SeVI or HA, adding HA combined with SeIV or SeVI could further reduce Cd uptake in rice seedlings, whereas Se contents of aerial tissues did not change obviously.ConclusionsHA inhibited the accumulation of Se (SeIV or SeVI) and Cd in rice seedlings; nevertheless, the mechanism was different. Compared with adding Se (or HA) alone, application of Se mixed with HA might be a more effective way to produce Se-enriched and Cd-deficient crop in Cd-contaminated soil.

Whereas the mechanism of fluid flow, and thus structural control, linked with mineral deposit formation is quite understood, the specific structures that likely provided controls on mineralization at certain geographic scales are not readily known for a given region unless it is well-explored. This contributes uncertainty in mineral prospectivity analysis in poorly-explored regions (or greenfields). Here, because the spatial distribution of mineral deposits has been postulated to be fractals (i.e., the patterns of these features are self-similar across a range of spatial scales), we show for the first time that micro-geochemical anomalies (as proxies of micro-scale patterns of ore minerals), from few discrete parts of the Sossego iron-oxide copper-gold (IOCG) deposit in the Carajás Mineral Province (CMP) of Brazil, exhibit trends of macro-scale faults that are known to have controlled IOCG mineralization in the CMP. The methodology described here, which led to this novel finding, would help towards detecting mineral exploration targets as well as help towards understanding structural controls on mineralization in greenfields.

Selenium (Se), iron (Fe), and humic acid (HA) are beneficial fertilizers that inhibit cadmium (Cd) uptake in crops and are crucial for agricultural yields as well as human health. However, the joined effect of Se, Fe, and HA on Cd uptake in rice are still poorly understood. Therefore, a hydroponic culture experiment was established to evaluate the combined effect of Se (Se 4+ or Se 6+ ), Fe, and HA on the biomass, Cd uptake, and Cd translocation of/in rice seedlings. Compared to Se 6+ application, Se 4+ application in most treatments resulted in lower Cd translocations from roots to shoots, leading to a significant decrease in shoot Cd concentrations. Compared to the treatments with Se 4+ or Fe 2+ application, joined application of Se 4+ and Fe 2+ inhibited Cd uptake in shoots by decreasing Cd adsorption onto (iron plaque) and uptake by roots, and alleviating Cd translocation from root to shoot. Compared to the treatments with Se 6+ or Fe 2+ application, joined application of Se 6+ and Fe 2+ inhibited Cd uptake in shoots by sequestering (retaining) Cd onto root surface (iron plaque). HA inhibited Cd uptake in all treatments by decreasing the bioavailability of Cd in the nutrient solution through complexation. The simultaneous application of Se, Fe, and HA decreased the shoot Cd concentrations the most, followed by the combined application of two fertilizers and their individual application; the mean shoot Cd concentration in the Fe-SeIV-HA2 treatment was the lowest among all the treatments, at only 11.39 % of those in the control treatments. The 3-way ANOVA results indicated that the Cd concentrations in shoots were significantly affected by Se, Fe, HA, and certain of their interactions (Fe×Se and Se×HA) ( p < 0.05). The above findings suggest that the joined application of Se, Fe, and HA ameliorated Cd uptake mainly by inhibiting Cd adsorption onto (iron plaque) and uptake by roots and the translocation from roots to shoots (Fe×Se 4+ ), retaining (sequestering) Cd in iron plaque (Fe×Se 6+ ), and decreasing Cd availability in nutrient solution (HA). Graphical abstract