Three models of resedimented (deep-water turbidite association) conglomerates are proposed, based on published data and on new observations in California. The descriptive features that contribute to the models are the presence or absence of a preferred clast fabric, stratification, and inverse and (or) normal graded bedding.

The first model is inverse-to-normally graded, characterized by the presence of both inverse and normal grading and the absence of stratification. A preferred fabric is present in about half of the examples studied. As the clast size becomes smaller, this model passes into the graded-stratified model, characterized by the absence of inverse grading and the presence of normal grading and stratification. Most examples exhibit a preferred clast fabric. A third model, disorganized-bed model, is characterized by the absence of normal and inverse grading and by stratification. Few examples contain a preferred fabric.

Where present, the preferred fabric has been shown to consist normally of long (a) axes parallel to flow and dipping upstream. This indicates direct deposition from dispersion without rolling on the bed. In examples showing a prominent stratification, however, it is postulated that the fabric changes to long (a) axes transverse to flow, intermediate (b) axes dipping upstream. This would indicate bed-load rolling and would allow use of standard bed-load formulae for calculation of rates of emplacement. Layers 20 m long parallel to flow, 10 cm thick, and of unit width would take between approximately one minute and one hour to emplace, depending on clast size (range 0.5 to 10 cm) and flow velocity (range 2.5 to 9 m/sec).

In basin analysis, the disorganized-bed model is probably characteristic of the feeder channels or canyons. The inverse-to-normally graded model reflects flow on a relatively steep slope, but nevertheless downstream from the disorganized-bed model. As the slopes flatten out in midfan areas, the inverse-to-normally graded model passes into the graded-stratified model.

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