Geology topics

Megan Gillen

Publications and source records attributed to Megan Gillen.

2 recordsLinked to original sources

USGS2023

Incorporating wave climate complexity into modeling lower shoreface morphology and transport

The lower shoreface, a transitional subaqueous region extending from the seaward limit of the surf zone to beyond the closure depth, serves as a sediment reservoir and pathway in sandy beach environments over annual to millennial time scales. Despite the important role this region plays in shoreline dynamics, the morphodynamics of the lower shoreface remain poorly quantified and understood. To better understand controls on shoreface morphology, here we combine energetics-based suspended sediment transport formulae (Ortiz & Aston 2016) with empirical wave climate data to incorporate temporal complexity in modeled equilibrium profiles and sediment flux rates. The equilibrium shoreface shape computed using a full wave climate is steeper in shallower water and less steep in the deeper reaches compared to profiles computed using single wave characteristics. Using a full wave climate to simulate steady-state morphology will yield steeper profiles in shallow water. Suspended sediment transport rates also vary in direction and magnitude at different equilibrium profile depths and can potentially inform the location of morphodynamic boundaries in the shoreface. Our results reveal how infrequent storm waves affect shoreface slopes, with large events tending to drive sediment onshore in the deeper portions of the profile. This work explores a few ways to add complexity to simple energetics-based frameworks to reproduce empirical bathymetric data more accurately and provides insight toward refining coastal source-to-sink models.

Conference Paper↗

USGS2021

Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.

Earth and Space Science↗