The Sequential Spectral Turning Bands Method (S-STBM) builds Gaussian random fields (GRF) calibrated to desired response functions. An interesting application of S-STBM concerns the simulation of GRF subject to inequality constraints. S-STBM works by choosing the phase of each cosine function of the STBM algorithm instead of perturbating nodes of the GRF many thousand times using conditional distributions as in Gibbs sampler. Each chosen phase increasingly constrains the nodes to the desired inequalities. A method based on the sequential Gaussian simulation is introduced to accelerate convergence at the end of the process. Examples shown compare S-STBM approach to Gibbs sampler. Orders of magnitude reduction in computation time is achieved with our spectral method. Furthermore, examples show that the phase selection has no significant influence on the spatial correlation. Our approach is easily generalized to pluriGaussian simulations. Compared to Gibbs sampler, S-STBM is not limited to small systems (no memory limitation) and its complexity of O(n) makes it an efficient tool to simulate large GRF subject to inequality constraints.

顺序频谱转向带方法（S-STBM）建立了已校准为所需响应函数的高斯随机场（GRF）。S-STBM的有趣应用涉及受不等式约束的GRF仿真。S-STBM通过选择STBM算法的每个余弦函数的相位来工作，而不是像Gibbs采样器中那样使用条件分布来扰动GRF的节点数千次。每个选择的阶段都会越来越多地将节点约束到所需的不等式。引入了一种基于顺序高斯仿真的方法来加速过程结束时的收敛。所示示例将S-STBM方法与Gibbs采样器进行了比较。使用我们的频谱方法，可将计算时间减少数量级。此外，实例表明，相位选择对空间相关性没有显着影响。我们的方法很容易推广到pluriGaussian模拟。与Gibbs采样器相比，S-STBM不仅限于小型系统（无内存限制），其复杂性还包括O（n）使其成为在不等式约束下模拟大型GRF的有效工具。