Abstract Identification of an output error model using the prediction error method leads to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because in most cases both the corresponding objective function and the search space are nonconvex. The difficulty in solving the optimization problem depends mainly on the experimental conditions, more specifically on the spectra of the input/output data collected from the system. It is therefore possible to improve the convergence of the algorithms by properly choosing the data prefilters; in this paper we show how to perform this choice. We present the application of the proposed approach to case studies where the standard algorithms tend to fail to converge to the global minimum.

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Fossil assemblages are expected to be time-averaged as a result of biological and physical processes that mix skeletal remains. Our quantitative understanding of time-averaging derives primarily from actualistic studies, in which direct numerical dating of individual specimens is used to assess the scale and structure of age mixing in death assemblages (incipient fossil assemblages). Here we examine the age, and the time-averaging of Mactra shells (Bivalvia: Mollusca) gathered from surface mixed siliciclastic-bioclastic sands at three sites on a passive-margin subtropical shelf (the Southern Brazilian Shelf; ∼ 33°S). Sixty Mactra specimens were individually dated using amino acid racemization (AAR) calibrated using radiocarbon ages (n=15). The time-averaging and the total age variability was based on a Bayesian approach that integrates the estimation errors and uncertainties derived from the posterior distribution associated with the AAR calibration average model. The 14C-calibrated AAR ages, pooled across all three sites, are strongly right-skewed with 97% of the individual mollusk shell age estimates ranging from 0 to 6 cal kyr BP. The magnitude of time-averaging varied inversely with the water depth, from < 15 yr at the deepest site (21 m) up to 1020-1250 yr at the shallowest site (7 m). The substantial variation in the temporal resolution across nearby sites, which are located in a seemingly homogenous depositional setting, indicates the presence of notable (if cryptic) spatial heterogeneities in local sedimentation, production, and exhumation, all increasing with water depth. Copyright © 2017, SEPM (Society for Sedimentary Geology).