The efficiency of an optimization method for acoustic emission/microseismic(AE/MS) source location is determined by the compatibility of its error definition with the errors contained in the input data.This compatibility can be examined in terms of the distribution of station residuals.For an ideal distribution,the input error is held at the station where it takes place as the station residual and the error is not permitted to spread to other stations.A comparison study of two optimization methods,namely the least squares method and the absolute value method,shows that the distribution with this character constrains the input errors and minimizes their impact,which explains the much more robust performance by the absolute value method in dealing with large and isolated input errors.When the errors in the input data are systematic and/or extreme in that the basic data structure is altered by these errors,none of the optimization methods are able to function.The only means to resolve this problem is the early detection and correction of these errors through a data screening process.An efficient data screening process is of primary importance for AE/MS source location.In addition to its critical role in dealing with those systematic and extreme errors,data screening creates a favorable environment for applying optimization methods. The efficiency of an optimization method for acoustic emission / microseismic (AE / MS) source location is determined by the compatibility of its error definition with the errors contained in the input data. This compatibility can be examined in terms of the distribution of station residuals. For an ideal distribution, the input error is held at the station where it takes place as the station residual and the error is not permitted to spread to other stations. A comparison study of two optimization methods, namely the least squares method and the absolute value method, shows that the distribution with this character constrains the input errors and minimizes their impact, which explains the much more robust performance by the absolute value method in dealing with large and isolated input errors. Where the errors in the input data are systematic and / or extreme in that the basic data structure is altered by these errors, none of the optimization methods are able to function. The only means to resolve thi s problem is the early detection and correction of these errors through a data screening process. An efficient data screening process is of primary importance for AE / MS source location. addition to its critical role in dealing with those systematic and extreme errors, data screening creates a favorable environment for applying optimization methods.