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Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world‐class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans‐North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas. [ABSTRACT FROM AUTHOR]

Application of (supervised and unsupervised) machine learning algorithms to big geoscience data can facilitate intelligent lithological mapping and interpretation in a data-driven fashion. Among them, unsupervised machine learning could play more important role in lithological classification because lithological labels are often missed in covered places. In this study, principal component analysis (PCA) and self-organizing maps (SOM) were applied to delineate major lithologies in the Jining region, Inner Mongolia, China, by integrating ten major-element stream sediment geochemical (K2O, Na2O, CaO, MgO, Al2O3, ΣFeO, MnO2, SiO2, P2O5 and TiO2) datasets. The results suggest that unsupervised machine learning using major-element stream sediment geochemical data is effective in capturing main lithological variations in a covered area. In detail, the PCA method identified only three interpretable components among nine principal components, whereas the SOM clustering recognized four categories including Archean TTG (tonalite–trondhjemite–granodiorite) gneisses besides felsic series, Cenozoic clastics and Cenozoic basalts. Apart from coinciding well with the pre-existing geological map in outcropping areas, the mapping results added new details regarding spatial distributions of felsic and basaltic rocks, which were previously recognized as Cenozoic clastics. This study highlights the ability of unsupervised learning for intelligent lithological mapping in covered areas using major-element stream sediment geochemical data. [ABSTRACT FROM AUTHOR]

• Seismic reflection data were acquired and analyzed for investigating deep-seated ore-controlling structures. • Joint modeling of potential field and seismic data images Datong graben and identifies three buried granitic intrusions. • Hongniangyu district has high potential of hosting porphyry-type Mo deposits. Since the discovery of several large porphyry Mo deposits (>0.1 Mt Mo) in the Jining region of Inner Mongolia, China, during the past decades, the area has been receiving wide interest for mining activities. However, mineral exploration in this area is challenging because most of it is thickly covered by Cenozoic sediments. Geophysical surveys, especially the seismic method, are becoming increasingly important for investigating deep-seated ore-controlling structures (e.g., porphyry granites, faults and even orebodies), supporting the discovery of buried ore deposits. Recently, potential field data and seismic reflection data have been acquired for the Hongniangyu area, south of Jining region. In this study, we mainly use seismic reflection and potential field data to obtain subsurface geological model in support of mineral exploration targeting. Gravity and magnetic data were firstly used for spatial analysis of mineral system by mapping the distribution of the faults, concealed granites, and the sedimentary basin (through wavelet analysis, tilt angle, and Euler deconvolution). To address the common problem of low signal-to-noise ratio of seismic reflection data obtained in hard-rock terranes, we applied a sophisticated workflow including refraction static correction, noise filtering, surface-consistent deconvolution, and velocity analysis. The obtained post-stack migrated seismic profile clearly imaged the geometry of the concealed graben located at the north end of the Datong Basin, and the South-East dipping Kouquan normal fault controls the depression of the graben and offsets Cenozoic sediments by several hectometers up to ~500 m. Joint modeling of seismic, gravity and magnetic data further provided evidence of the three buried belts of granites. Intriguingly, one of the concealed granitic belts located in the north of Hongniangyu area was found to have both significant chargeability and Mo geochemical anomalies, indicating a high potential of hosting porphyry-type Mo ore deposits. As expected, Mo and W mineralized cores were discovered in this target area but limited to only one deep drilling hole. The presented results provide the geological framework of Hongniangyu district and guidelines for delineating new potential Mo mineralization in the Jining region. [ABSTRACT FROM AUTHOR]