Determining Q of near-surface materials from Rayleigh waves

High-frequency (≥2 Hz) Rayleigh wave phase velocities can be inverted to shear (S)-wave velocities for a layered earth model up to 30 m below the ground surface in many settings. Given S-wave velocity (V_S), compressional (P)-wave velocity (V_P), and Rayleigh wave phase velocities, it is feasible to solve for P-wave quality factor Q_P and S-wave quality factor Q_S in a layered earth model by inverting Rayleigh wave attenuation coefficients. Model results demonstrate the plausibility of inverting Q_S from Rayleigh wave attenuation coefficients. Contributions to the Rayleigh wave attenuation coefficients from Q_P cannot be ignored when Vs/V_P reaches 0.45, which is not uncommon in near-surface settings. It is possible to invert Q_P from Rayleigh wave attenuation coefficients in some geological setting, a concept that differs from the common perception that Rayleigh wave attenuation coefficients are always far less sensitive to Q_P than to Q_S. Sixty-channel surface wave data were acquired in an Arizona desert. For a 10-layer model with a thickness of over 20 m, the data were first inverted to obtain S-wave velocities by the multichannel analysis of surface waves (MASW) method and then quality factors were determined by inverting attenuation coefficients.     

Testing a model for predicting the timing and location of shallow landslide initiation in soil‐mantled landscapes

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever‐expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km² catchment a near‐annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an in?nite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process‐based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and ?ve in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide‐producing storms. Landslide and soil production parameters were ?xed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were ?rst calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to de?ne whether a rainstorm was a signi?cant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identi?ed the 1982 storm as the only landslide‐producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identi?ed the three events, but also predicted that two other landslide‐producing events should have occurred. This performance is signi?cantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non‐producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to signi?cant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

Mercury mobility at the Carson River Superfund Site, west-central Nevada, USA: Interpretation of mercury speciation data in mill tailings, soils, and sediments

The Carson River Superfund Site in west-central Nevada is an area of Hg-contaminated soil, sediment, water, air, and biola resulting from the amalgamation milling of Ag-Au ores of the Comstock lode worked approximately a century ago. In order to develop an understanding of the behavior, transport, and fate of Hg at this site, a technique was developed to estimate the proportions of total, elemental, exchangeable, organic, and sulfide Hg in soils, sediments, and tailings.Results of this analysis performed on active Carson River sediments indicate that Hg is selectively dissolved out of Hg-Au amalgam particles and subsequently adsorbed to fine-grained sediments which are then deposited in downstream, low-energy reaches of the Carson River and Labontan Reservoir. In the relatively more-reducing environment of the reservoir Hg appears to be converted, in large part, to relatively-insoluble HgS.The original elemental form of Hg released to the environment is the chemical form which is still dominant in most highly-contaminated soils, sediments, and tailings. Deeper, more-reducing soil horizons, however, appear to fix a significant portion of the Hg as HgS, analogous to the Lahontan Reservoir example described above. This fixation as HgS is documented to be largely limited to higher-sulfur areas where sulfide minerals from the Comstock ores increase the total sulfur concentrations of contaminated soils, sediments, and tailings.     

Composition of hydrous melts in equilibrium with quartz eclogites

Summary Compositions of the hydrous melts in equilibrium with garnet, omphacitic clinopyroxene and quartz have been investigated experimentally at 28.5 and 35 kbar. They are represented by silica-rich liquids (> 70% SiO₂) with low MgO, FeO and CaO contents. The removal of ca 10–15% of the magma of this composition may be sufficient to convert quartz eclogite formed after subduction of altered MORB into a quartz-free bimineralic eclogite assemblage, which is a common type of xenoliths in kimberlites.At 28.5 kbar the solidus temperature is between 700 and 750° C in the system quartz eclogite—water, and the high pressure amphibole-out boundary lies at ca 25 kbar in accord with the previous studies.

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Die Zusammensetzung wasserhältiger Schmelzen im Gleichgewicht mit Quarz-Eklogiten

Zusammenfassung Um Prozesse zu simulieren, die bei der Subduktion von Ozeanbodenbasalten durch partielle Anatexis im Stabilitätsfeld von Eklogiten ablaufen, wurde die Zusammensetzung wasserhältiger Schmelzen in Gleichgewicht mit Granat, Omphacit und Quarz bei 28.5 und 35 Kbar experimentell untersucht. Diese Schmelzen sind reich an SiO₂ (> 70 Gew%) und arm an Mg0, Fe0 and CaO. Die Extraktion von ca. 10–15% derartiger Schmelzen würde genügen, um quarzführende Eklogite, die durch die Subduktion von alteriertem MORB Material entstanden sind, in quarzfreie bimineralische Eklogite umzuwandeln wie sie häufig als Xenolithe in Kimberliten beobachtet werden.Im System Quarz-Eklogit-Wasser liegt die Solidustemperatur bei 28.5 Kbar zwischen 700 und 750°C. Die obere Stabilitätsgrenze von Amphibol liegt in diesem Temperaturbereich bei ca. 25 Kbar.

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With 1 Figures