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Graphite forms the endpoint for organic carbon metamorphism; it is extremely resilient to physical, biological and chemical degradation. Carbonaceous materials (CM) contained within sediments, collected across Taiwan and from the Gaoping submarine canyon, were analyzed using Raman spectroscopy to determine the crystallinity. This allowed the erosional and orogenic movements of petrogenic organic carbon (OCpetro) during the Taiwanese orogeny to be deduced. After automatically fitting and classifying spectra, the distribution of four groups of CM within the sediments provides evidence that many forms of OCpetro have survived at least one previous cycle of erosion, transport and burial before forming rocks in the Western Foothills of the island. There is extensive detrital graphite present in rocks that have not experienced high-grade metamorphism, and graphite flakes are also found in recently deposited marine sediments off Taiwan. The tectonic and geological history of the island shows that these graphite flakes must have survived at least three episodes of recycling. Therefore, transformation to graphite during burial and orogeny is a mechanism for stabilizing organic carbon over geological time, removing biospheric carbon from the active carbon cycle and protecting it from oxidation during future erosion events.
Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.