Varve analysis is the process of counting varves or annually laminated sediments to determine the rates of change in climate and various ecosystems. Varves form when glacial advances come in contact with bodies of water such as lakes. When this process occurs, layers of sediment form on the floor of the body of water. This technique provides an opportunity to acquire detailed chronological information about the composition, displacement, and climate of that region, at that time. It was first developed by the Swedish scientist Baron de Geer in 1878.

Varves form distinctive layers. A varve consists of two layers; a thick light colored layer of silt and fine sand which forms in the spring and summer and a thin dark colored layer of clay forming in the fall and winter. Since low temperatures are necessary in delaying the settling of clay particles, it is assumed that varve formation can occur only in glacial waters, particularly lakes on the margins of glaciers. The salt and electrolytes in seawater cause a homogenous mass, preventing the formation of varves.

Traditionally, when one "counted" varves, it proved to be a fairly monotonous and tiresome task resulting in much time lost and much subjectivity due to determining layers. One had to take microscopic evaluations of the material and form some sort of hypothesis or interpretation from the shape and thickness of the layers. This provided inaccurate data because different varve types can cause mistakes in the algorithm that is used and produce miscounts. For example, sub-annual laminations can be counted as extra years, and certain years may not deposit a carbonate layer, which often would not be counted. Thus, this technique is much criticized for providing insufficient data to determine such changes.

Significant advances have been made to improve accuracy in determining varves. Using audio/visual equipped computers and digital video cameras, differences in hues can be determined and analyzed. Through such a procedure, accurate counts are made to determine different layers, which in turn gives varve analysis more credibility. Also, with the aid of computers and digitally enhanced photography, content and classification are accurate and efficient.