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Xiaoshan gold–polymetallic concentrated area is located in Henan Province, central China. The area is principally underlain by typical metamorphic core complexes (MCCs), along with essential components of the basins and ranges of western Henan. Mineralization occurs in the regions where the geochemical anomalies of Au and related elements are widely distributed and consist predominantly of mineralized altered rocks and auriferous quartz veins, both of which are hosted within shear zones or fractures of varying sizes and orientations. Several small‐ to medium‐sized gold–polymetallic ore deposits, along with numerous clustered occurrences, have been found in the area in recent years. However, despite the fact that the Xiaoshan District has traditionally been deemed as an excellent geological endowment area with enormous potential for large‐sized deposits, no significant discoveries have been made to date. It is suggested that the main reason responsible for the present exploration situation could be the scarcity of applications of deep‐penetration geophysical prospecting in the area. It is anticipated that integrating geological and geophysical data would be crucial to enhance the successful mineral explorations. In this contribution, a conceptual geological model for the study area was initially reviewed so that the corresponding electromagnetic responses could then be modelled, which would simulate a geophysical type of survey, with appropriate uncertainties added into the analyses. Then, based on such an integrated approach, the required precision of detecting deep‐buried deposits could then be evaluated. In addition, by employing supplementary geophysical data acquired over known deposits, it is possible to gain more instructive insights into the practical geological model. The viabilities of this approach were demonstrated through a case study in which a hidden gold deposit in the studied area was successfully detected. Geophysical surveys were carried out by employing a grounded‐wire short‐offset TEM (SOTEM) technique and revealed two new anomalies (one of which was subsequently tested by drilling). The drilling then confirmed that this anomaly was caused by an ore body 30 m in thickness occurring at about 550 m below surface. [ABSTRACT FROM AUTHOR]

• A new wetland mapping approach by combing hierarchical classification framework and optimal feature selection. • A coastal wetlands map of Yellow River Delta with 11 detailed wetland types. • Feature selection and separability analysis for accurately extraction of wetland. • Comprehensive analysis of uncertainty assessment for wetland classification. Wetlands play an important role in ecological health and sustainable development, their spatial distribution and explicit thematic information are crucial for developing management and conservation measures. The Yellow River Delta is an important coastal wetland reserve in China, its wetland types are complex and diverse, natural and artificial wetlands are easily confused, making refined classification more difficult. To address this challenge, we proposed a new wetland mapping approach by combing hierarchical classification framework (HCF) and optimal feature selection. First, inheritance-based multiscale segmentation was carried out to obtain object-oriented images, and decision tree classification was used for preliminarily identify wetland and non-wetland. Second, recursive feature elimination and cross-validation (RFECV) was used to select optimal features, which was then utilized for wetland refinement extraction by using random forest (RF) algorithm. The experiments were performed based on Sentinel-1, Sentinel-2 and NASADEM datasets. The results show that effective wetland classification features can be selected by using RFECV. The feature scores are as follows, red edge index > spectral features > vegetation/water body index > backscatter coefficient > topographic features > texture features > location feature > urban index > geometric feature. The overall accuracy and Kappa coefficient of the method in this paper are 92.36 % and 0.915, which are 14.62 % and 6.68 % higher than using only HCF or only RFECV. Compared with the GlobeLand30 and CAS_Wetlands datasets, the refinement of wetland mapping in this paper is higher. This study provides a new idea in methodological selection for wetland information extraction, and the resulting coastal wetland map can be used for sustainable management, ecological assessment and conservation of the Yellow River Delta. [ABSTRACT FROM AUTHOR]

• FDCSEM has high signal-to-noise ratio and greatly improved exploration resolution. • Detectibility of the ore-forming rock masses is realized by 3D forward modeling. • The inversion results are coincided with the borehole results. • The research idea points out a new prospecting direction for sulfide deposits. Several magmatic Ni-Cu deposits associated with Permian-Triassic mafic-ultramafic intrusions have been discovered during the last three decades in the southern part of the Central Asian Orogenic Belt, the northeastern margin of Tarim, and the margin of the North China Craton. The accurate delineations of the large resistive rock masses are crucial for locating potential ore bodies when they are located beneath rock masse. The controlled-source electromagnetic method (CSEM) is an important tool for investigating resistivity contrast in earth subsurface. In this study, we present a comprehensive example of using frequency domain CSEM survey to delineate the resistive rock masses that are related to the forming of orebody. The horizontal electric field, which is more sensitive to resistive target than the other components, is used in our study. We first built a geophysical model of the survey area based on prior confirmed geophysical and geological information. Then, both forward and inverse modeling were performed in order to assess the detectability of the resistive ore-forming rock masses in the model. Finally, this study acquired field data in the Kalatongke Ni-Cu Deposit, which is located in the Kalatongke District of the southern section of the Central Asian Orogenic Belt, and is specifically part of China's southern Altai Orogenic Belt. The resistive ore-forming rock masses were successfully identified during the survey process and further verified by the drilling results. It was observed that both the synthetic and field examples indicated that the adopted frequency domain CSEM method successfully measured the electrical fields, and could be utilized to explore deeply buried resistive targets in future prospecting activities. [ABSTRACT FROM AUTHOR]

Land cover change affects the carbon emissions of ecosystems in some way. The qualitative and quantitative understanding of carbon emissions from human activities (e.g., land cover change, industrial production, etc.) is highly significant for realizing the objective of carbon neutrality. Therefore, this paper used GlobeLand30 land cover maps, annual average normalised difference vegetation index (NDVI) data, annual average net ecosystem productivity (NEP) data and statistical yearbook data from 2000 to 2020 to explore the relationship between land cover change and carbon emissions. Specifically, it included land cover change, carbon storage changes influenced by land cover change, spatial and temporal analysis of carbon sources and sinks, land use intensity change and anthropogenic carbon emissions. The results of the study show that the main land cover changes in Shandong province during 2000-2020 was cultivated land conversion to artificial surfaces. Among them, the area of cultivated land converted to artificial surfaces from 2000 to 2010 was 4930.62 km 2 , and the proportion of cultivated land converted to artificial surfaces from 2010 to 2020 was as high as 78.35%. The total carbon stock of vegetation affected by land cover change decreased by 463.96 × 10 4  t and 193.50 × 10 4  t in 2000-2010 and 2010-2020 respectively. The spatial and temporal distribution of carbon sources and sinks differed more markedly from 2000 to 2020, and land use intensity changes in Shandong Province showed an upward trend. Of the total energy production, industry has the largest energy consumption, followed closely by total energy consumption in transportation, storage and postal services., (© 2022. The Author(s).)