The new plotting strategies described in this paper provide promising new methods for estimating both porosity and saturation from seismic data as well as for distinguishing types of fluid saturation present in the earth. The methods will apply to low frequency (seismic) data whether or not they fit Gassmann's model (Gassmann, 1951) or a patchy saturation model (Berryman et al., 1988; Endres and Knight, 1989; Mavko and Nolen-Hoeksema, 1994; Dvorkin and Nur, 1998). At these low frequencies, the type of saturation present (well-segregated liquids and gases, homogeneous fluid mixtures, or some patchy saturation state intermediate between these two extremes) determines the location of data points on the (, )-plane. High frequency (ultrasonic) data are more likely to contain wave attenuation and dispersion effects that complicate our analyses, but as shown here they nevertheless do not seriously affect our interpretations based on Lamé's elastic parameter as long as the data are taken in a range of frequencies that avoids the very largest dispersive effects. We find porosity is correlated inversely with the slopes of the data-distribution lines in the (, )-plane (see Figure 3). This fact can be used to sort field data into subsets having similar material characteristics and porosities for display on the more sensitive (, ) plots. The main conclusion associated with Figure 2 is that saturation is an approximately (within normal data scatter) monotonic function of (as in Figure 1), and therefore also of (as in Figure 3). So saturation can be estimated from knowledge of location along the lines of Figure 3 and relative changes of saturation can be determined with a high level of confidence. After sorting seismic data by material characteristics and porosity using the (, )-diagram, the resulting data subsets can then be displayed in the (, )-plane and used to infer the local states of saturation.