Differential Tectonic Deformation of the Longmen Mountain Thrust Belt,Western Sichuan Basin,China

Field investigation and seismic section explanation showed that the Longmen Mountain Thrust Belt has obvious differential deformation： zonation, segmentation and stratification. Zonation means that, from NW to NE, the Longmen Mountain Thrust Belt can be divided into the Songpan- Garz~ Tectonic Belt, ductile deformation belt, base involved thrust belt, frontal fold-thrust belt, and foreland depression. Segmentation means that it can be divided into five segments from north to south： the northern segment, the Anxian Transfer Zone, the center segment, the Guanxian Transfer Zone and the southern segment. Stratification means that the detachment layers partition the structural styles in profile. The detachment layers in the Longmen Mountain Thrust Belt can be classified into three categories： the deep-level detachment layers, including the crust-mantle system detachment layer, intracrustal detachment layer, and Presinian system basal detachment layer; the middle-level detachment layers, including Cambrian-Ordovician detachment layer, Silurian detachment layer, etc.; and shallow-level detachment layers, including Upper Triassic Xujiahe Formation detachment layer and the Jurassic detachment layers. The multi-level detachment layers have a very important effect on the shaping and evolution of Longmen Mountain Thrust Belt.

Vertical Differential Structural Deformation of the Main Strike-slip Fault Zones in the Shunbei Area,Central Tarim Basin:Structural Characteristics,Deformation Mechanisms,and Hydrocarbon Accumulation Significance

Vertical differential structural deformation(VDSD),one of the most significant structural characteristics of strike-slip fault zones(SSFZs)in the Shunbei area,is crucial for understanding deformation in the SSFZ and its hydrocarbon accumulation significance.Based on drilling data and high-precision 3-D seismic data,we analyzed the geometric and kinematic characteristics of the SSFZs in the Shunbei area.Coupled with the stratification of the rock mechanism,the structural deformations of these SSFZs in different formations were differentiated and divided into four deformation layers.According to comprehensive structural interpretations and comparisons,three integrated 3-D structural models could describe the VDSD of these SSFZs.The time-space coupling of the material basis(rock mechanism stratification),changing dynamic conditions(e.g.,changing stress-strain states),and special deformation mechanism of the en echelon normal fault array uniformly controlled the formation of the VDSD in the SSFZs of the Shunbei area.The VDSD of the SSFZs in this area controlled the entire hydrocarbon accumulation process.Multi-stage structural superimposing deformation influenced the hydrocarbon migration,accumulation,distribution,preservation,and secondary adjustments.

Floor heave characteristics and control technology of the roadway driven in deep inclined-strata

Based on in-mine instrumentation and theoretical analysis of the unsymmetrical large-deformation that occurred in the roadway after excavation,Differential Floor Heave(DFH)was found to be the main reason for roadway failure.It needs to be pointed out that the specific roadway was driven in inclined rock strata.In addition,the factors that contribute to the occurrence of DFH are discussed in detail.It is believed that DFH is triggered by the unsymmetrical stress distribution in the floor and the different rock types encountered near the two floor corners.Hence,DFH control should be focused on the left floor corner where shearing failure occurs initially and the left floor surface where tensile failure is more severe.The proposed DFH control strategies include unsymmetrical grouting for the whole roadway,re-design of the roof and ribs support,reinforcement of the weak zones,and release of the concentrated stress in the earlier stage.Meanwhile,it is recommended that in the later stage,both bolts and cable bolts with higher strength and the backfilling technique using the coal measure rocks and concrete should be employed in the reversed-arch floor.The field instrumentation results,after using the proposed control strategies,indicate that large deformation in a DFH roadway has been successfully controlled.

New method for analyzing recrystallization kinetics of deformed metal by differential scanning calorimeter

The drawn copper wires have been analyzed by differential scanning calorimeter(DSC) and a new method, which uses DSC measurements to determine the Johnson-Mehl-Avrami-Kolmogorov(JMAK) exponent via introducing Arrhenius behavior and modifying the baseline of DSC curves, has been proposed. The results show that JMAK exponent and recrystallization activation energy of the drawn copper wires with a strain of 2.77 are about 2.39 and 125 k J/mol, respectively. The line linking the tangency points of DSC curve hypotenuse can be used as the baseline when calculating recrystallization fraction. The JMAK exponent obtained by the DSC method is in a good agreement with that obtained by microhardness measurements. Compared to traditional methods to measure the exponent, the proposed method is faster and less labor intensive.

Differentiable Deformation Graph-Based Neural Non-rigid Registration

The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target surface.Among the pipeline,the correspondence construction and iterative manner are key to the results,while existing strategies might result in local optima.In this paper,we adopt the widely used deformation graph-based representation,while replacing some key modules with neural learning-based strategies.Specifically,we design a neural network to predict the correspondence and its reliability confidence rather than the strategies like nearest neighbor search and pair rejection.Besides,we adopt the GRU-based recurrent network for iterative refinement,which is more robust than the traditional strategy.The model is trained in a self-supervised manner and thus can be used for arbitrary datasets without ground-truth.Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin.