Bin Li

Underground mining disturbance alters the initial stress state of rock masses and is an important factor triggering large-scale slope failures in karst mountainous areas. In the karst mountains of southwestern China, dual-layered geological structures composed of carbonate rocks and clastic rocks are widely developed. However, studies integrating lithological combinations, structural discontinuities, and the transmission of mining-induced disturbances into a hierarchical identification framework remain limited. Based on data from 29 representative cases, the deformation and failure characteristics of mining-affected slopes are analyzed, and a hierarchical failure-mode identification framework controlled by dual-layered geological structures is proposed. First, according to stratigraphic structural characteristics, mining-affected slopes are divided into two basic structural types: hard-over-soft and alternating hard–soft strata. These types are further classified according to the development of karst-related structural discontinuities, deformation characteristics of the basal layer, and failure evolution sequence, resulting in nine typical failure modes. Case analyses indicate that lithological combinations and structural discontinuities jointly control slope deformation under underground mining, while deformation of weak basal layers and propagation of karst-related structural discontinuities are key factors governing different failure modes. The identification framework provides a theoretical basis for hazard investigation and identification, mechanism analysis, and risk prevention and control of mining-induced slope failure hazards.

Deep excavation reduces lateral confinement and can increase tangential stress, promoting brittle failure in highly stressed rock. However, the effects of bolt number and spacing on mechanical response and damage evolution under this stress path remain insufficiently understood. In this study, parallel RFPA^3D simulations were conducted for true-triaxial single-face unloading followed by continued axial loading, assuming perfect bonding at the bolt–rock interface. Two parameter series examined bolt numbers from zero to four (M0–M4) and bolt spacings from 10 to 80 mm. Stress–strain response, acoustic emission activity, failure morphology, damage area ratio (A_d), and box-counting fractal dimension (D_f) were analysed. Bolt reinforcement increased post-unloading peak stress and restricted the inward extent of damage, while damage persisted near the unloaded face and within the inter-bolt regions. Peak stress increased from approximately 345 MPa in M0 to 363 MPa in M3, then decreased slightly to 358 MPa in M4, showing that reinforcement gains were not proportional to bolt number. Bolt spacing had a modest effect on peak stress but a clearer influence on damage continuity and inward extent. Among the tested spacings, the 40 mm case (D40) achieved the highest peak stress of 370.0 MPa and the lowest A_d of 23.85%, with a low D_f of 1.431. In contrast, the 80 mm case (D80) exhibited more extensive and spatially complex damage (A_d = 30.78%, D_f = 1.677). A_d and D_f provided complementary descriptions of damage extent and spatial complexity. These numerical results provide a quantitative basis for comparing reinforcement configurations under the investigated specimen-scale conditions.

1. As shallow resources are depleted and underground construction extends to greater depths, the exploitation of deep resources has become increasingly important [1–3]. Deep underground hard-rock e...

Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

Keywords: DEM Coregistration, DEM Intensity Simulation, Cross-correlation, 2D Polynomial Fitting Due to the systematic error, especially the horizontal deviation that exists in the multi-source, multi-temporal DEMs (Digital Elevation Models), a method for high precision coregistration is needed. This paper presents a new fast DEM coregistration method based on a given SAR (Synthetic Aperture Radar) imaging geometry to overcome the divergence and time-consuming problem of the conventional DEM coregistration method. First, intensity images are simulated for two DEMs under the given SAR imaging geometry. 2D (Two-dimensional) offsets are estimated in the frequency domain using the intensity cross-correlation operation in the FFT (Fast Fourier Transform) tool, which can greatly accelerate the calculation process. Next, the transformation function between two DEMs is achieved via the robust least-square fitting of 2D polynomial operation. Accordingly, two DEMs can be precisely coregistered. Last, two DEMs, i.e., one high-resolution LiDAR (Light Detection and Ranging) DEM and one low-resolution SRTM (Shutter Radar Topography Mission) DEM, covering the Yangjiao landslide region of Chongqing are taken as an example to test the new method. The results indicate that, in most cases, this new method can achieve not only a result as much as 80 times faster than the minimum elevation difference (Least Z-difference, LZD) DEM registration method, but also more accurate and more reliable results.