| Middle-Lower Yangtze Valley is an important poly-metallic mineral deposits base ineastern China. Prospecting the deep structure in typical deposits by advanced geophysicalsurvey methods, forming geological and geophysical model to prognosis of concealed oresbased on the differences between ore-body and surrounding rock is necessary for findingout the origin of deep deposit and strengthening different geophysics technologies inmineral exploration.In this paper, based on the present situation of deep exploration of mineral resources,we study the geologic and geophysical survey of Nihe porphyrite iron deposit, which is atypical concealed deposit in Middle-Lower Yangtze Valley. Summarized the regionalgeological background and mineralizing setting, analyzed the metallogeny, formed themetallogenic model of Nihe iron deposit; and did integrative geophysical prospecting suchas gravity, magnetic and electromagnetic prospecting, did data processing, inversion andinterpretation to every method; finally, did gravity and magnetic3D joint inversion andresistivity3D imaging, formed3D geologic-geophysical model of Nihe iron deposit, putforward prospecting model of Nihe iron deposit. After the study, this paper shows thepractice and cognition as follows:⑴Summarized geological background of mineralization and metallogeny of Niheiron deposit, established metallogenic model of Nihe porphyrite iron according topredecessor’s research achievements. Nihe iron deposit is a volcanic-subvolcanicmagmatic hydrothermal deposit which in connection with Yanshanian Early Cretaceousdiorite porphyrite, the form of deposit is controlled by volcanic structure and basement saltbed.⑵Did second-order tendency analysis, reduction to the pole and analyticalcontinuation to large scale gravity data and high-accuracy ground magnetic survey data ofNihe deposit, obtained the local bouguer gravity anomaly map and reduction magneticanomaly map. Combined the both results, the region of high value of gravity and magneticcoinciding good.⑶Conducted multiple magnetotelluric sounding method (AMT,CSAMT,TEM)prospecting, did denoising, static correction, near-field sources correction, and didtwo-dimension or0ne0dimension to the correction data. Interpreted the results ofmagnetotelluric prospecting combined with geology, gravity and magnetic data, thecharacteristics of resistivity profile traced the distribution of zhuanqiao bridge volcanicrock formation, identified the uplift position and shape of diorite porphyrite, predicted thepossibility position of ore-body.⑷Constraint with geological data and well log, did gravity and magnetic3D joint inversion to Nihe iron ore-body by human-computer interaction model. The result ofinversion reflected the distribution of ore-body in three dimension, displayed the position,shape and tendency of ore-body objectively. It shows the tendency of ore-body isconsistent with course of seam, both are north-east. The depth of magnetite in thewest-south part extending from600m to1100m underground, pyrites are mainly in thenorth-east part, anhydrite is mainly in the middle and lower than magnetite.⑸Interpolated and smoothed in3D with the results of28AMT profiles, establishedthe3D resistivity model of mining area beyond the1500m under the ground. Accordingthe different range of resistivity data volume, inferred the distribution of different rockmass combined with gravity and magnetic. The3D model of resistivity higher than150ohm.m shows the shape and position of uplift, which is in accordance with the3Dresults of gravity and magnetic, we inferred it as the position of rock body, and it in favorof mineralization.⑹Based on the research of metallogenic regularity by predecessor, combining withthe study to the geophysical in this paper, established3D geologic-geophysical model ofNihe iron-ore deposit, discussed the prospecting model: guided by metallogenic theory,multiple geophysical exploration method (such as gravity, magnetic, electromagnetic)associated use, comprehensive interpretation by multiple physical property and parameteris an efficient deep prospecting model. |
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