Use of geostatistics in hydrology, reservoir engineering, soil physics, and environmental science has expanded vastly in the last few years. Since the semivariance is the backbone of geostatistics, it is very important to estimate it accurately. Building on Matheron's classical formula, there are several improved semivariance estimators, but they are only reliable at small lag distances. For large lag distances all estimators can produce erratic results that may lead to a misinterpretation of the spatial structure of a given data set. The lack of precision is primarily caused by a shrinking number of observation pairs as lag distance increases. This paper presents a new semivariance estimator that uses all given data at all lag distances. In addition, the new estimator is not restricted to samples taken along lines, and it easily accounts for missing data. We compare the new estimator to existing ones on simulated data sets as well as three outcrop data sets. The results show that the new estimator is unbiased, robust, and resistant to contamination. ¿ American Geophysical Union 1994