Conditional simulation of non-Gaussian random fields for earthquake monitoring systems

The problem of conditional simulation of random fields gained a significant interest recently due to its applications to urban earthquake monitoring. In this paper, for the first time in the literature, the method of conditional simulation of non-Gaussian random fields is developed. It combines previous techniques of iterative procedure of unconditional simulation of non-Gaussian fields, and the procedure of conditional simulation of Gaussian random fields. To contrast the agreement between the simulated correlation function and targeted correlation function, the numerical error is decomposed into two parts, namely, into simulation error and mapping error. Simulation error can be reduced by increasing number of samples while mapping error is eliminated by the suitable iteration procedure. In this paper univariate and time-independent random fields are considered. Numerical example shows that the correlation structure and probability distribution of the simulated random field have excellent agreements with given correlation structure and probability distribution, respectively.