GSA Connects 2024 Meeting in Anaheim, California

Paper No. 13-6
Presentation Time: 9:25 AM

TSUNAMIS AND EARTHQUAKES IN THE COASTAL STRATIGRAPHY: A MONTE-CARLO-TYPE SIMULATION FRAMEWORK TO UNDERSTAND UNCERTAINTY AND IMPROVE INTERPRETATION


WEISS, Robert, Geoscience, Virginia Tech, 4044 Derring Hall, Blacksburg, VA 24061-0001 and DURA, Tina, Geosciences, Virginia Tech, 4044 Derring Hall, 926 W Campus Dr, Blacksburg, VA 24061-1040

Coastal stratigraphy along active margins can be complicated, as the interface between the ocean and land creates complex systems for formational processes influenced by geographic location, regional geologic context, and the interplay of marine and terrestrial processes. This complexity has fundamental consequences for the interpretation of stratigraphic data.

To form stratigraphy, we assume the deposit type is linked to the amount of available accommodation space. In our model, mud, marsh, and soil accumulate depending on the water level and accommodation space. Furthermore, we can intersperse a number of far-field tsunami or storm layers during the considered time interval if needed. The most consequential feature of our model is the implementation of earthquakes as subsidence events with subsequent crustal recovery. Each subsidence event can, but does not have to, be accompanied by a sand layer to indicate tsunami deposition. We employ the logistic equation to govern crustal recovery after an earthquake. For sediment deposition, we track both recovery and past sediment generation that might impact accommodation space. To form the deposits, we consider temporal changes in accommodation space according to the logistic assumption and apply sedimentation rates and behaviors for different coastal stratigraphy components: mud, marsh units, soil, and sand layers.

For the simulations, we follow a subsidence-budget approach that predetermines the total subsidence "spent" in events over a given period. We employ ranges of deposition rates (from literature) for different stratigraphic units and generate a database of stratigraphic columns. Given a real-world stratigraphic section, we identify which simulated stratigraphic columns match it. Our search algorithm identifies a match based on parts and the whole column to increase statistical robustness. The result is a frequency density plot for each considered parameter, providing a statistically robust depiction of the depositional environment, number of earthquakes, and their subsidence values.