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The exploration of podiform chromites in the Indus Yarlong Zangbo suture zone of southern Tibet has proved difficult because most known deposits pinch out and then reappear in the same direction. Several ground-based geophysical approaches such as gravity, magnetic, and controlled-source audio-frequency magnetotelluric (CSAMT) methods have been applied to explore for these chromite deposits but have mostly failed to delineate prospective areas. We have evaluated a successful podiform chromite exploration case history that is based on AMT. More than 8000 AMT stations were used in this study within a [Formula: see text] area of the ophiolite belt. Line separations were 80 or 40 m, and the station separation was 20 m. We implemented Bostick conversion and nonlinear conjugate gradient inversions for data interpretation, whereas 2D resistivity sections and 3D resistivity imaging were used to elucidate the inner structure and distribution of rock faces within the Luobusa ophiolite. Results from rock physics and drilling further indicate that resistivity-anomaly domains from these AMT results are correlated with rock faces in terms of fresh harzburgite, altered harzburgite and dunite, and they can thus be connected to concealed deposits. Therefore, we have developed three resistivity-anomaly models for chromite exploration, and we delineated several prospective regions containing exploitable deposits within the Luobusa ophiolite. Seven of the nine verified boreholes discussed in this paper intersected with chromite deposits; one comprises the largest and highest grade chromite deposit in China to date. Our AMT results provide the impetus for future chromite exploration in Tibet and enable a refined understanding of the structure and distribution of rock faces within the Luobusa ophiolite.

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Background: The conventional FFRct numerical calculation method uses a model with a multi-scale geometry based upon CFD, and rigid walls. Therefore, important interactions between the elastic vessel wall and blood flow are not routinely considered. Changes in the resistance of coronary microcirculation during hyperaemia are likewise not typically incorporated using a fluid–structure interaction (FSI) algorithm. It is likely that both have resulted in FFRct calculation errors.Objective: In this study we incorporated both the influence of vascular elasticity and coronary microcirculatory structure on FFR, to improve the accuracy of FFRct calculation. Thus, in this study, a physics-driven 3D–0D coupled model including fluid–structure interaction was established to calculate accurate FFRct values.Methods: Based upon a novel geometric multi-scale modeling technology, a FSI simulation approach was used. A lumped parameter model (0D) was used as the outlet boundary condition for the 3D FSI coronary artery model to incorporate physiological microcirculation, with bidirectional coupling between the two models.Results: The accuracy, sensitivity, specificity, and both positive and negative predictive values of FFRDC calculated based upon the coupled 3D–0D model were 86.7, 66.7, 84.6, 66.7, and 91.7%, respectively. Compared to the calculated value using the basic CFD model (MSE = 5.9%, accuracy rate = 80%), the FFRCFD calculated based on the coupled 3D–0D model has a smaller MSE of 1.9%.Conclusion: The physics-driven coupled 3D–0D model that incorporates fluid–structure interactions not only consider the influence of the elastic vessel wall on blood flow, but also provides reliable microvascular resistance boundary conditions for the 3D FSI model. This allows for a calculation that is based upon conditions that are closer to the physiological environment, and thus improves the accuracy of FFRct calculation. It is likely that more accurate information will provide an enhanced recommendation regarding percutaneous coronary intervention (PCI) in the clinic.

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