In the mid-1980s, still in his young 40s, André Journel was already recognized as one of the giants of geostatistics. Many of the contributions from his new research program at Stanford University had centered around the indicator methods that he developed: indicator kriging and multiple indicator kriging. But when his second crop of graduate students arrived at Stanford, indicator methods still lacked an approach to conditional simulation that was not tainted by what André called the ‘Gaussian disease’; early indicator simulations went through the tortuous path of converting all indicators to Gaussian variables, running a turning bands simulation, and truncating the resulting multi-Gaussian realizations. When he conceived of sequential indicator simulation (SIS), even André likely did not recognize the generality of an approach to simulation that tackled the simulation task one step at a time. The early enthusiasm for SIS was its ability, in its multiple-indicator form, to cure the Gaussian disease and to build realizations in which spatial continuity did not deteriorate in the extreme values. Much of Stanford’s work in the 1980s focused on petroleum geostatistics, where extreme values (the high-permeability fracture zones and the low-permeability shale barriers) have much stronger anisotropy, and much longer ranges of correlation in the maximum continuity direction, than mid-range values. With multi-Gaussian simulations necessarily imparting weaker continuity to the extremes, SIS was an important breakthrough. The generality of the sequential approach was soon recognized, first through its analogy with multi-variate unconditional simulation achieved using the lower triangular matrix of an LU decomposition of the covariance matrix as the multiplier of random normal deviates. Modifying LU simulation so that it became conditional gave rise to sequential Gaussian simulation (SGS), an algorithm that shared much in common with SIS. With nagging implementation details like the sequential path and the search neighborhood being common to both methods, improvements in either SIS or SGS often became improvements to the other. Almost half of the contributors to this Special Issue became students of André in the classes of 1984–1988, and several are the pioneers of SIS and SGS. Others who studied later with André explored and developed the first multipoint statistics simulation procedures, which are based on the same concept that underlies sequential simulation. Among his many significant intellectual accomplishments, one of the cornerstones of André Journel’s legacy was sequential simulation, built one step at a time.

在1980年代中期，仍然年仅40岁的安德烈·乔纳尔（AndréJournel）被公认为地统计学的巨人之一。他在斯坦福大学新研究计划的许多贡献都围绕着他开发的指标方法：指标克里金法和多指标克里金法。但是，当他的第二批研究生到达斯坦福大学时，指标方法仍然缺乏条件模拟的方法，而这种方法并没有被安德烈所说的“高斯病”所困扰。早期的指标模拟经历了将所有指标转换为高斯变量，进行转弯带模拟并截断所得的多高斯实现的曲折路径。当他想到顺序指标模拟（SIS）时，甚至安德烈（André）也可能不认识到一次只能一步完成模拟任务的模拟方法的普遍性。SIS的早期热情在于其以多指标形式出现的能力，能够治愈高斯疾病，并建立实现空间连续性在极值不变的情况下的能力。斯坦福大学（Stanford）在1980年代的大部分工作都集中在石油地统计学上，与中部地区相比，极端值（高渗透性裂缝带和低渗透性页岩屏障）具有更大的各向异性，并且在最大连续性方向上的相关范围更长。范围值。由于多高斯模拟必然会使极端情况的连续性较弱，因此SIS是一项重要的突破。很快就认识到顺序方法的普遍性，首先通过类比，使用协方差矩阵的LU分解的下三角矩阵作为随机法线的乘数来实现多变量无条件仿真。修改LU仿真，使其成为有条件的，就产生了顺序高斯仿真（SGS），该算法与SIS有很多共同点。由于两种方法都采用了诸如顺序路径和搜索邻域这样的implementation琐实现细节，因此SIS或SGS的改进通常会成为其他方法的改进。该特刊的近一半贡献者是1984-1988届安德烈的学生，其中几位是SIS和SGS的开拓者。其他后来与André学习的人探索并开发了第一个多点统计模拟程序，它们基于顺序仿真基础上的相同概念。在他的许多重要的学术成就中，安德烈·乔纳尔（AndréJournel）的遗产的基石之一是顺序模拟，一次完成了一步。