FDEM数值模拟提升露天矿滑坡稳定性评估

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FDEM模型在矿山测量滑坡中的应用主要体现在对复杂地质环境下的露天煤矿边坡稳定性评估和数值模拟计算上。本文以山西某露天煤矿的下盘岩质高边坡为例,针对"2011-1005滑坡"事件,通过对边坡工程地质的深入理解和现场实地考察,结合实时监测数据,采用了一种结合有限元方法(FE)和离散元方法(DEM)的混合模型——FDEM。这种方法的优势在于它能够在滑坡的整个生命周期,从稳定状态到失稳破坏,模拟边坡的连续和不连续变形过程。 在计算模型的建立过程中,关键在于考虑边坡的产状和岩层的分布特性,如顺层石质边坡的特征,包括边坡走向(298°)、倾向一致以及特定的坡角(平均46°)和岩层倾角(平均48°)。选择的计算区域集中在12号勘探线附近的台阶,即发生滑坡的具体位置。 FDEM模型的优势在于其能够细致地模拟岩石微裂纹的聚合、发展以及破碎岩石之间的动态交互,如接触、碰撞和反弹等,这些都是传统方法难以捕捉的细节。通过与实际边坡破坏的物理特征进行对比分析,研究者验证了FDEM模型在预测滑坡风险和模拟滑坡过程中的有效性。这种方法的应用有助于减少监测和加固的成本,提高滑坡预警的精度和速度,从而满足滑坡地质灾害应急治理的需求。 此外,通过不断优化计算参数和模型,FDEM方法有望形成适用于该区域边坡破坏的通用模型、参数和方法,这对于其他类似地区的滑坡危险区预测具有重要的指导意义。这种通用模型不仅具有可靠性,而且在适用性和准确性方面表现出色,能够为露天煤矿的边坡安全管理提供科学依据和技术支持。 FDEM模型在矿山测量滑坡中的应用展示了其在复杂地质条件下滑坡防治中的潜力,对于提升边坡工程的安全管理实践具有显著的价值。通过实际案例的研究和模型验证,FDEM方法已经成为边坡工程领域研究的重要工具。

解释:target = self.survey.source.target collection = self.survey.source.collection '''Mesh''' # Conductivity in S/m (or resistivity in Ohm m) background_conductivity = 1e-6 air_conductivity = 1e-8 # Permeability in H/m background_permeability = mu_0 air_permeability = mu_0 dh = 0.1 # base cell width dom_width = 20.0 # domain width # num. base cells nbc = 2 ** int(np.round(np.log(dom_width / dh) / np.log(2.0))) # Define the base mesh h = [(dh, nbc)] mesh = TreeMesh([h, h, h], x0="CCC") # Mesh refinement near transmitters and receivers mesh = refine_tree_xyz( mesh, collection.receiver_location, octree_levels=[2, 4], method="radial", finalize=False ) # Refine core mesh region xp, yp, zp = np.meshgrid([-1.5, 1.5], [-1.5, 1.5], [-6, -4]) xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] mesh = refine_tree_xyz(mesh, xyz, octree_levels=[0, 6], method="box", finalize=False) mesh.finalize() '''Maps''' # Find cells that are active in the forward modeling (cells below surface) ind_active = mesh.gridCC[:, 2] < 0 # Define mapping from model to active cells active_sigma_map = maps.InjectActiveCells(mesh, ind_active, air_conductivity) active_mu_map = maps.InjectActiveCells(mesh, ind_active, air_permeability) # Define model. Models in SimPEG are vector arrays N = int(ind_active.sum()) model = np.kron(np.ones((N, 1)), np.c_[background_conductivity, background_permeability]) ind_cylinder = self.getIndicesCylinder( [target.position[0], target.position[1], target.position[2]], target.radius, target.length, [target.pitch, target.roll], mesh.gridCC ) ind_cylinder = ind_cylinder[ind_active] model[ind_cylinder, :] = np.c_[target.conductivity, target.permeability] # Create model vector and wires model = mkvc(model) wire_map = maps.Wires(("sigma", N), ("mu", N)) # Use combo maps to map from model to mesh sigma_map = active_sigma_map * wire_map.sigma mu_map = active_mu_map * wire_map.mu '''Simulation''' simulation = fdem.simulation.Simulation3DMagneticFluxDensity( mesh, survey=self.survey.survey, sigmaMap=sigma_map, muMap=mu_map, Solver=Solver ) '''Predict''' # Compute predicted data for your model. dpred = simulation.dpred(model) dpred = dpred * 1e9 # Data are organized by frequency, transmitter location, then by receiver. # We had nFreq transmitters and each transmitter had 2 receivers (real and # imaginary component). So first we will pick out the real and imaginary # data bx_real = dpred[0: len(dpred): 6] bx_imag = dpred[1: len(dpred): 6] bx_total = np.sqrt(np.square(bx_real) + np.square(bx_imag)) by_real = dpred[2: len(dpred): 6] by_imag = dpred[3: len(dpred): 6] by_total = np.sqrt(np.square(by_real) + np.square(by_imag)) bz_real = dpred[4: len(dpred): 6] bz_imag = dpred[5: len(dpred): 6] bz_total = np.sqrt(np.square(bz_real) + np.square(bz_imag)) mag_data = np.c_[mkvc(bx_total), mkvc(by_total), mkvc(bz_total)] if collection.SNR is not None: mag_data = self.mag_data_add_noise(mag_data, collection.SNR) data = np.c_[collection.receiver_location, mag_data] # data = (data, ) return data

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