Exploration and discovery in Yellowstone Lake: results from high-resolution sonar imaging, seismic reflection profiling, and submersible studies

‘No portion of the American continent is perhaps so rich in wonders as the Yellow Stone’ (F.V. Hayden, September 2, 1874)Discoveries from multi-beam sonar mapping and seismic reflection surveys of the northern, central, and West Thumb basins of Yellowstone Lake provide new insight into the extent of post-collapse volcanism and active hydrothermal processes occurring in a large lake environment above a large magma chamber. Yellowstone Lake has an irregular bottom covered with dozens of features directly related to hydrothermal, tectonic, volcanic, and sedimentary processes. Detailed bathymetric, seismic reflection, and magnetic evidence reveals that rhyolitic lava flows underlie much of Yellowstone Lake and exert fundamental control on lake bathymetry and localization of hydrothermal activity. Many previously unknown features have been identified and include over 250 hydrothermal vents, several very large (>500 m diameter) hydrothermal explosion craters, many small hydrothermal vent craters (1–200 m diameter), domed lacustrine sediments related to hydrothermal activity, elongate fissures cutting post-glacial sediments, siliceous hydrothermal spire structures, sublacustrine landslide deposits, submerged former shorelines, and a recently active graben. Sampling and observations with a submersible remotely operated vehicle confirm and extend our understanding of the identified features. Faults, fissures, hydrothermally inflated domal structures, hydrothermal explosion craters, and sublacustrine landslides constitute potentially significant geologic hazards. Toxic elements derived from hydrothermal processes also may significantly affect the Yellowstone ecosystem.     

Asymmetric one‐storey elastic systems with non‐linear viscous and viscoelastic dampers: Simplified analysis and supplemental damping system design

Investigated is the accuracy in estimating the response of asymmetric one‐storey systems with non‐linear viscoelastic (VE) dampers by analysing the corresponding linear viscous system wherein all non‐linear VE dampers are replaced by their energy‐equivalent linear viscous dampers. The response of the corresponding linear viscous system is determined by response history analysis (RHA) and by response spectrum analysis (RSA) extended for non‐classically damped systems. The flexible and stiff edge deformations and plan rotation of the corresponding linear viscous system determined by the extended RSA procedure is shown to be sufficiently accurate for design applications with errors generally between 10 and 20%. Although similar accuracy is also shown for the ‘pseudo‐velocity’ of non‐linear VE dampers, the peak force of the non‐linear VE damper cannot be estimated directly from the peak damper force of the corresponding linear viscous system. A simple correction for damper force is proposed and shown to be accurate (with errors not exceeding 15%). For practical applications, an iterative linear analysis procedure is developed for determining the amplitude‐ and frequency‐dependent supplemental damping properties of the corresponding linear viscous system and for estimating the response of asymmetric one‐storey systems with non‐linear VE dampers from the earthquake design (or response) spectrum. Finally, a procedure is developed for designing non‐linear supplemental damping systems that satisfy given design criteria for a given design spectrum. Copyright © 2003 John Wiley & Sons, Ltd.     

Inelastic displacement ratios for evaluation of existing structures

Results of a detailed statistical study of constant relative strength inelastic displacement ratios to estimate maximum lateral inelastic displacement demands on existing structures from maximum lateral elastic displacement demands are presented. These ratios were computed for single‐degree‐of‐freedom systems with different levels of lateral strength normalized to the strength required to remain elastic when subjected to a relatively large ensemble of recorded earthquake ground motions. Three groups of soil conditions with shear wave velocities higher than 180m/s are considered. The influence of period of vibration, level of lateral yielding strength, site conditions, earthquake magnitude, distance to the source, and strain‐hardening ratio are evaluated and discussed. Mean inelastic displacement ratios and those associated with various percentiles are presented. A special emphasis is given to the dispersion of these ratios. It is concluded that distance to the source has a negligible influence on constant relative strength inelastic displacement ratios. However, for periods smaller than 1s earthquake magnitude and soil conditions have a moderate influence on these ratios. Strain hardening decreases maximum inelastic displacement at a fairly constant rate depending on the level of relative strength for periods of vibration longer than about 1.0s while it decreases maximum inelastic displacement non‐linearly as the period of vibration shortens and as the relative‐strength ratio increases for periods of vibration shorter than 1.0s. Finally, results from non‐linear regression analyses are presented that provide a simplified expression to be used to approximate mean inelastic displacement ratios during the evaluation of existing structures built on firm sites. Copyright © 2003 John Wiley & Sons, Ltd.     

Landslide susceptibility analysis using GIS and artificial neural network

The purpose of this study is to develop landslide susceptibility analysis techniques using an arti?cial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identi?ed in the study area from interpretation of aerial photographs, ?eld survey data, and a spatial database of the topography, soil type and timber cover. The landslide‐related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by arti?cial neural network methods. The landslide susceptibility index was calculated by the back‐propagation method, which is a type of arti?cial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were veri?ed using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to ef?ciently analyse the vast amount of data, and an arti?cial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction. Copyright © 2003 John Wiley & Sons, Ltd.     

A procedure for evaluating seismic energy demand of framed structures

Energy serves as an alternative index to response quantities like force or displacement to include the duration‐related seismic damage effect. A procedure to evaluate the absorbed energy in a multistorey frame from energy spectra was developed. For low‐ to medium‐rise frames, it required a static pushover analysis of the structure to determine the modal yield force and ductility factor of an equivalent single‐degree‐of‐freedom system for the first two modes. The energy spectra were then used to determine the energy contribution of each mode. A procedure was also developed to distribute the energy along the frame height based on energy shapes. This study showed that the second‐mode response in some cases needs to be considered to reflect the energy (or damage) concentration in the upper floors. Copyright © 2002 John Wiley & Sons, Ltd.     

System identification of the Vincent Thomas suspension bridge using earthquake records

The Vincent Thomas Bridge in the Los Angeles metropolitan area, is a critical artery for commercial traffic flow in and out of the Los Angeles Harbor, and is at risk in the seismically active Southern California region, particularly because it straddles the Palos Verdes fault zone. A combination of linear and non‐linear system identification techniques is employed to obtain a complete reduced‐order, multi‐input–multi‐output (MIMO) dynamic model of the Vincent Thomas Bridge based on the dynamic response of the structure to the 1987 Whittier and 1994 Northridge earthquakes. Starting with the available acceleration measurements (which consists of 15 accelerometers on the bridge structure and 10 accelerometers at various locations on its base), an efficient least‐squares‐based time‐domain identification procedure is applied to the data set to develop a reduced‐order, equivalent linear, multi‐degree‐of‐freedom model. Although not the main focus of this study, the linear system identification method is also combined with a non‐parametric identification technique, to generate a reduced‐order non‐linear mathematical model suitable for use in subsequent studies to predict, with good fidelity, the total response of the bridge under arbitrary dynamic environments. Results of this study yield measurements of the equivalent linear modal properties (frequencies, mode shapes and non‐proportional damping) as well as quantitative measures of the extent and nature of non‐linear interaction forces arising from strong ground shaking. It is shown that, for the particular subset of observations used in the identification procedure, the apparent non‐linearities in the system restoring forces are quite significant, and they contribute substantially to the improved fidelity of the model. Also shown is the potential of the identification technique under discussion to detect slight changes in the structure's influence coefficients, which may be indicators of damage and degradation in the structure being monitored. Difficulties associated with accurately estimating damping for lightly damped long‐span structures from their earthquake response are discussed. The technical issues raised in this paper indicate the need for added spatial resolution in sensor instrumentation to obtain identified mathematical models of structural systems with the broadest range of validity. Copyright © 2003 John Wiley & Sons, Ltd.     

Surface analysis of pyrite in the No. 9 coal seam, Wuda Coalfield, Inner Mongolia, China, using high-resolution time-of-flight secondary ion mass-spectrometry

The chemical composition of pyrite in coal can be used to investigate its geological and mineralogical origin. In this paper, high-resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used to study the chemical composition of various pyrite forms in the No. 9 coal seam (S_t,d=3.46%) from the Wuda Coalfield, Inner Mongolia, northern China. These include bacteriogenic, framboidal, massive, cell-filling, fracture-filling, and nodular pyrites. In addition to Fe⁺ (⁵⁴Fe⁺, ⁵⁶Fe⁺, ⁵⁷Fe⁺), other fragment ions were detected in bacteriogenic pyrites, such as ²⁷Al⁺, Si⁺ (²⁸Si⁺, ²⁹Si⁺, ³⁰Si⁺), ⁴⁰Ca⁺, Cu⁺ (⁶³Cu⁺, ⁶⁵Cu⁺), Zn⁺ (⁶⁴Zn⁺, ⁶⁶Zn⁺, ⁶⁷Zn⁺, ⁶⁸Zn⁺), Ni⁺ (⁵⁸Ni⁺, ⁶⁰Ni⁺, ⁶²Ni⁺), and C₃H₇⁺. TOF-SIMS images show bacteriogenic pyrites are relatively rich in Cu, Zn, and Ni, suggesting that bacteria probably play an important role in the enrichment of Cu, Zn, and Ni during their formation. Intense positive secondary ion fragments from framboidal aggregates, such as ²⁷Al⁺, ²⁸Si⁺, ²⁹Si⁺, AlO⁺, CH₂⁺, C₃H₃⁺, C₃H₅⁺, and C₄H₇⁺, indicate that formation of the framboidal aggregates may have occurred together with clay mineral and organic matter, which probably serve as the binding substance. The intense ions of ²⁸Si⁺ and ²⁷Al⁺ from massive pyrites also suggest that their pores incorporated clay minerals during crystallization. Together with the lowest ²⁸Si⁺/²³Na⁺ value, the intense organic positive secondary ion peaks from cell-filling pyrites, such as C₃H₃⁺, C₃H₅⁺, C₃H₇⁺, and C₄H₇⁺, indicate that pyrite formation may have accompanied dissolution or disintegration of the cell. The intense P⁺ peak was observed only in the fracture-filling pyrite and the highest ²⁸Si⁺/²³Na⁺ value of fracture-filling pyrite reflects its epigenetic origin. Together with XRD and REEs data, the stronger ⁴⁰Ca⁺ in nodular pyrite than other pyrite forms shows seawater influence during its formation.