Abstract

Abundant data on sediment accumulation rates exist for the continental margin in the region of the Mississippi Delta. They were obtained during numerous studies by many institutions and researchers using a range of methods: radioisotope and other tracers, identified events, and biostratigraphy. For several reasons, it is necessary to integrate the data across the region: to test and validate numerical models of sedimentation, and to devise methods for a Big Data integration of similar rates on a global scale. We have collated over 700 records of sediment accumulation rate in the Mississippi region. They were taken over time intervals (viz. integration times, measurement intervals) that range over five orders of magnitude, from 2 days to 1000 years. This range stems from use of diverse analytical methods such as tracers with different half-lives, and also from varying sample lengths and times.

A Sadler Effect relationship is found to apply across the data: intercept 1.71 in log₁₀(cm/yr), slope –0.55, R² 0.66, N 717. It corroborates ad hoc observations in the area by analysts, that short-period rates tend to be higher than long-term rates. Investigations are made on how the Sadler Effect coefficients vary by environment: by water depth, physiographic province, and sediment mud contents. For most environments the coefficients cluster around the general result, though coefficients for prodelta, deep basin, bay, canyon, and upper slope areas are separate enough to be remarked on.

The statistics indicate that measurement interval is responsible for most of the inter-analysis variability. Therefore a transform of the data to a 1-year basis (the Sadler Effect intercept) is proposed, removing measurement-interval effects and providing a general framework for integrating the rates. This is in conformance with a random-walk model for the Sadler Effect of steady, constant deposition plus a spectrum of unsteady nondeposition events. Precise values of the coefficients are different in some environments. After the transform, variations in the rates by spatial and environmental factors are revealed more clearly.