Seismic data from Colle Gnifetti glacie saddle around borehole KCI, Swiss Alps

Two seismic surveys were carried out on the high-altitude glacier saddle, Colle Gnifetti, Monte Rosa, Italy/Switzerland. Explosive and vibroseismic sources were tested to explore the best way to generate seismic waves to deduce shallow and intermediate properties (<100 m) of firn and ice. The explosive source (SISSY) excites strong surface and diving waves, degrading data quality for processing; no englacial reflections besides the noisy bed reflector are visible. However, the strong diving waves are analyzed to derive the density distribution of the firn pack, yielding results similar to a nearby ice core. The vibrator source (ElViS), used in both P- and SH-wave modes, produces detectable laterally coherent reflections within the firn and ice column. We compare these with ice-core and radar data. The SH-wave data are particularly useful in providing detailed, high-resolution information on firn and ice stratigraphy. Our analyses demonstrate the potential of seismic methods to determine physical properties of firn and ice, particularly density and potentially also crystal-orientation fabric.

Fragilariopsis kerguelensis images from sediment core PS1768-8

Background: Light microscopic analysis of diatom frustules is widely used both in basic and applied research, notably taxonomy, morphometrics, water quality monitoring and paleo-environmental studies. In these applications, usually large numbers of frustules need to be identified and / or measured. Although there is a need for automation in these applications, and image processing and analysis methods supporting these tasks have previously been developed, they did not become widespread in diatom analysis. While methodological reports for a wide variety of methods for image segmentation, diatom identification and feature extraction are available, no single implementation combining a subset of these into a readily applicable workflow accessible to diatomists exists. Results: The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of object outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to ease of use, applicability to a broad range of data and problems, and supporting high throughput analyses with minimal manual intervention. Conclusions: Tested with several diatom datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. SHERPA is unique in combining the following features: application of multiple segmentation methods and selection of the one giving the best result for each individual object; identification of shapes of interest based on outline matching against a template library; quality scoring and ranking of resulting outlines supporting quick quality checking; extraction of a wide range of outline shape descriptors widely used in diatom studies and elsewhere; minimizing the need for, but enabling manual quality control and corrections. Although primarily developed for analyzing images of diatom valves originating from automated microscopy, SHERPA can also be useful for other object detection, segmentation and outline-based identification problems.