Identifying autogenic sedimentation in fluvial-deltaic stratigraphy: Evaluating the effect of outcrop-quality data on the compensation statistic

S. M. Trampush, E. A. Hajek, K. M. Straub, E. P. Chamberlin

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Stratigraphy preserves an extensive record of Earth-surface dynamics acting over a range of scales in a variety of environments. To take advantage of this record, we first must distinguish depositional patterns that arise due to intrinsic (i.e., autogenic) landscape dynamics from sedimentation that results from changes in climate, tectonic, or eustatic boundary conditions. The compensation statistic is a quantitative tool that has been used to estimate scales and patterns of autogenic sedimentation in experimental deposits; it has been applied to a few outcrop studies, but its sensitivity to data limitations common in natural deposits remains unconstrained. To explore how the compensation statistic may be applied to outcrop data, we evaluate the sensitivity of the tool to stratigraphic data sets limited in extent and resolution by subsampling an autogenic experimental deposit to create pseudo-outcrop-scale data sets. Results show that for data sets more than 3 times thicker than a characteristic depositional element (e.g., channel or lobe), the compensation statistics that can be used reliably constrain the maximum scale of autogenic sedimentation even for low-resolution data sets. Additionally, we show that autogenic sedimentation patterns may be characterized as persistent, random, or compensational using the compensation statistic when data sets are high resolution. We demonstrate how these measurements can be applied to natural data sets with comparative case studies of two fluvial and two deltaic outcrops. These case studies show how the compensation statistic can provide insight into what controls the maximum scale of autogenic sedimentation in different systems and how landscape dynamics can produce organized sedimentation patterns over long time scales.

Original languageEnglish (US)
Pages (from-to)91-113
Number of pages23
JournalJournal of Geophysical Research: Earth Surface
Volume122
Issue number1
DOIs
StatePublished - Jan 1 2017

All Science Journal Classification (ASJC) codes

  • Earth-Surface Processes
  • Geophysics

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