Quantifying Mixed-Process Variability In Shallow-Marine Depositional Systems: What Are Sedimentary Structures Really Telling Us?

Research Article| October 23, 2017 Quantifying Mixed-Process Variability In Shallow-Marine Depositional Systems: What Are Sedimentary Structures Really Telling Us? Valentina M. Rossi; Valentina M. Rossi 1Jackson School of Geosciences, University of Texas at Austin, Austin, Texas 78712, U.S.A. Search for other works by this author on: GSW Google Scholar Mauricio M. Perillo; Mauricio M. Perillo 2ExxonMobil Upstream Research Company, 22777 Springwoods Village Parkway, Spring, Texas 77389, U.S.A. Search for other works by this author on: GSW Google Scholar Ronald J. Steel; Ronald J. Steel 1Jackson School of Geosciences, University of Texas at Austin, Austin, Texas 78712, U.S.A. Search for other works by this author on: GSW Google Scholar Cornel Olariu Cornel Olariu 1Jackson School of Geosciences, University of Texas at Austin, Austin, Texas 78712, U.S.A. 3National Institute for Marine Geology and Geoecology–GeoEcoMar, 23-25 Dimitrie Onciul Street, Bucharest 024053, Romania Search for other works by this author on: GSW Google Scholar Journal of Sedimentary Research (2017) 87 (10): 1060–1074. https://doi.org/10.2110/jsr.2017.49 Article history first online: 25 Oct 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Valentina M. Rossi, Mauricio M. Perillo, Ronald J. Steel, Cornel Olariu; Quantifying Mixed-Process Variability In Shallow-Marine Depositional Systems: What Are Sedimentary Structures Really Telling Us?. Journal of Sedimentary Research 2017;; 87 (10): 1060–1074. doi: https://doi.org/10.2110/jsr.2017.49 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyJournal of Sedimentary Research Search Advanced Search Abstract: Interpreting the whole range of fluvial, wave, and tidal interactions recorded in shallow-marine, coastal successions can be challenging. The complexity arises because sedimentary structures produced by all three processes can be fully or partially preserved in the same stratal packages, and many of these structures are not diagnostic of a specific process. We therefore need an improved method of capturing the internal facies complexity that characterizes mixed-process coastal systems.We propose a new methodology that assigns a percentage or probability to the likelihood for a bed or stratal unit to be formed by wave (w), tide (t), and fluvial (f) processes via a library of sedimentary structures and their non-unique generating processes. The library was generated through an intensive literature review of ancient, modern, and physical experiment works; the total frequency of association of each structure to each process (wave, tidal, fluvial) is used to calculate the percentage values. Each bed or bedset can be characterized by a specific structure or multiple structures (taking also into account lateral variations). Percentage values of wave/tide/fluvial processes of various structures can be averaged to create a final compound process probability for each bed. Vertical integration of process probability for individual beds in a rock succession creates probability graphs. This methodology has been tested on a 15-meters thick parasequence of the Jurassic Las Lajas Fm., Argentina, and on sedimentary logs of the Cretaceous lower Sego Sandstone, USA, and it is seen to efficiently couple classical facies analysis and surficial-process studies to quantify process variability in ancient systems. Additionally, we assessed the likelihood of association of sedimentary structures not only to hydrodynamic processes but also to depositional sub-environments, through a collection of published sedimentary logs (in modern and ancient deposits) from various basins worldwide. The methodology presented here better quantifies changing process dominance through time, improves the prediction of depositional environment evolution, and helps future studies that aim at a quantification of process variability. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.