The Yellowstone caldera, like many other later Quaternary calderas of the world, exhibits dramatic unrest. Between 1923 and, the center of the Yellowstone caldera rose nearly one meter along an axis between its two resurgent domes Smith, 1979, Dzurisin andYamashita, 1987). From 1985From until 1995, it subsided at about two cm/yr (Dzurisin and others, 1990). More recent radar interferometry studies show renewed inflation of the northeastern resurgent dome between 1995 and 1996; this inflation migrated to the southwestern resurgent dome from 1996 to 1997 (Wicks and others, 1998).We extend this record back in time using dated geomorphic evidence of postglacial Yellowstone Lake shorelines around the northern shore, and Yellowstone River levels in the outlet area. We date these shorelines using carbon isotopic and archeological methods. Following Meyer and Locke (1986) and Locke and Meyer (1994), we identify the modern shoreline as S1 (1.9 ± 0.3 m above the lake gage datum), map paleoshoreline terraces S2 to S6, and infer that the prominent shorelines were cut during intracaldera uplift episodes that produced rising water levels. Doming along the caldera axis reduces the gradient of the Yellowstone River from Le Hardys Rapids to the Yellowstone Lake outlet and ultimately causes an increase in lake level. The 1923 doming is part of a longer uplift episode that has reduced the Yellowstone River gradient to a "pool" with a drop of only 0.25 m over most of this 5 km reach. We also present new evidence that doming has caused submergence of some Holocene lake and river levels.Shoreline S5 is about 14 m above datum and estimated to be ~12.6 ka, because it post-dates a large hydrothermal explosion deposit from the Mary Bay area (MB-II) that occurred ~13 ka. S4 formed about 8 m above datum ~10.7 ka as dated by archeology and 14 C, and was accompanied by offset on the Fishing Bridge fault. About 9.7 ka, the Yellowstone River eroded the "S-meander", followed by a ~5 m rise in lake level to S2. The lowest generally recognizable shoreline is S2. It is ~5 m above datum (3 m above S1) and is ~8 ka, as dated on both sides of the outlet. Yellowstone Lake and the river near Fishing Bridge were 5-6 m below their present level about 3-4 ka, as indicated by 14 C ages from submerged beach deposits, drowned valleys, and submerged Yellowstone River gravels. Thus, the lake in the outlet region has been below or near its present level for about half the time since a 1 km-thick icecap melted from the Yellowstone Lake basin about 16 ka.The amplitude of two rises in lake and river level can be estimated based on the altitude of LeHardys Rapids, indicators of former lake and river levels, and reconstruction of the river gradient from the outlet to Le Hardys Rapids. Both between ~9.5 ka and ~8.5 ka, and after ~3 ka, Le Hardys Rapids (LHR) was uplifted about 8 meters above the outlet, suggesting a cyclic deformation process. Older possible rises in lake level are suggested by locations where the ~10.7 ka S4 truncates older shorelines, an...
The purpose of this article is to explore the effect of forest fires on the archaeological context in a mountainous environment. As Schiffer (1987) has pointed out, understanding environmental formation processes is integral to understanding site formation. Regional-scale processes, such as forest fires, have important site-level effects. By examining these effects in areas burned during the 1988 Yellowstone fires and by concurrently excavating nearby sites, site formation processes related to forest fires were examined. Important effects of fire which may be noted at the site-level include: 1) the mosaic burn pattern, where sharp boundaries are present between burned and unburned areas, 2) morphological changes to stone or bone should be limited to the charred layer representing the burn or within several centimeters below it, 3) specific oxidized soil features, and 4) ash pockets.
Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis
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