Parsimonious 3D post-stack Kirchhoff depth migration

Parsimonious post‐stack migration is extended to three dimensions. By tracing single rays back along each incident wave direction (as determined by a local slant stack at the receivers), the ray tracing can be embedded in the migration. This approach significantly reduces the computer time and disk space needed because it is not necessary to build and save image time maps; 3D migration can be performed on a workstation or personal computer rather than using a supercomputer or cluster. The location of a reflector in the output image is defined by tracing a zero‐offset ray to the one‐way traveltime (the image condition); the orientation of the reflector is defined as a surface perpendicular to the raypath. The migration impulse response operator is confined to the first Fresnel zone around the estimated reflection point, which is much smaller than the large isochronic surface in traditional Kirchhoff depth migration. Additional efficiency is obtained by applying an amplitude threshold to reduce the amount of data to be migrated. Tests on synthetic data show that the proposed implementation of parsimonious 3D post‐stack Kirchhoff depth migration is at least two orders of magnitude faster than traditional Kirchhoff migration, at the expense of slightly degraded migration image coherence. The proposed migration is expected to be a useful complement to conventional time migrations for fast initial imaging of subsurface structures and for real‐time imaging of near‐offset sections during data acquisition for quality control.     

Wave propagation and soil–structure interaction on a cliff crest during the 1999 Athens Earthquake

During the 1999 Athens Earthquake the town of Adàmes, located on the eastern cliff of the Kifissos river canyon, experienced unexpectedly heavy damage. Despite the significant amplification potential of the slope geometry, topography effects cannot alone explain the uneven damage distribution within a 300 m zone behind the crest, characterized by a rather uniform structural quality. This paper illustrates the important role of soil stratigraphy, material heterogeneity, and soil–structure interaction on the characteristics of ground surface motion. For this purpose, we first perform elastic two-dimensional wave propagation analyses utilizing available geotechnical and seismological data, and validate our results by comparison with aftershock recordings. We then conduct non-linear time-domain simulations that include spatial variability of soil properties and soil–structure interaction effects, to reveal their additive contribution in the topographic motion aggravation.     

ERRATUM Estimation of peak relative velocity and peak absolute acceleration of linear SDOF systems

We have found some mistakes in the article by Jianwei Song et al. (2007). The revisions are given below:     

Weather observations on Whistler Mountain during five storms

A greater understanding of precipitation formation processes over complex terrain near the west coast of British Colombia will contribute to many relevant applications, such as climate studies, local hydrology, transportation, and winter sport competition. The phase of precipitation is difficult to determine because of the warm and moist weather conditions experienced during the wintertime in coastal mountain ranges. The goal of this study is to investigate the wide range of meteorological conditions that generated precipitation on Whistler Mountain from 4–12 March 2010 during the SNOW-V10 field campaign. During this time period, five different storms were documented in detail and were associated with noticeably different meteorological conditions in the vicinity of Whistler Mountain. New measurement techniques, along with the SNOW-V10 instrumentation, were used to obtain in situ observations during precipitation events along the Whistler mountainside. The results demonstrate a high variability of weather conditions ranging from the synoptic-scale to the macro-scale. These weather events were associated with a variation of precipitation along the mountainside, such as events associated with snow, snow pellets, and rain. Only two events associated with a rain–snow transition along the mountainside were observed, even though above-freezing temperatures along the mountainside were recorded 90 % of the time. On a smaller scale, these events were also associated with a high variability of snowflake types that were observed simultaneously near the top of Whistler Mountain. Overall, these detailed observations demonstrate the importance of understanding small-scale processes to improve observational techniques, short-term weather prediction, and longer-term climate projections over mountainous regions.     

Identification of Glacial Isostatic Adjustment in Eastern Canada Using S Transform Filtering of GPS Observations

Over the years, a number of different models and techniques have been proposed to both quantify and explain the glacial isostatic adjustment (GIA) process. There are serious challenges, however, to obtaining accurate results from measurements, due to noise in the data and the long periods of time necessary to identify the relatively small-magnitude signal in certain regions. The primary difficulty, in general, is that most of the geophysical signals that occur in addition to GIA are nonstationary in nature. These signals are also corrupted by random as well as correlated noise added during data acquisition. The nonstationary characteristic of the data makes it difficult for traditional frequency-domain denoising approaches to be effective. Time–frequency filters present a more robust and reliable alternative to deal with this problem. This paper proposes an extended S transform filtering approach to separate the various signals and isolate that associated with GIA. Continuous global positioning system (GPS) data from eastern Canada for the period from June 2001 to June 2006 are analyzed here, and the vertical velocities computed after filtering are consistent with the GIA models put forward by other researchers.     

Travel times ofP-waves in North-East India

Summary Based on 90 accurately localized earthquakes in and around North-East India and the local crustal velocity model of Gupta et al. [4], the travel times of P-waves have been determined from the foci of these earthquakes at arbitrarily selected depths of 5, 13, 25, 41 and 50 km to the sites of the seismic stations operated by the Regional Research Laboratory, Jorhat and the National Geophysical Research Institute, Hyderabad, and also to the sites of the Shillong and Tura seismic stations run by the India Meteorological Department, New Delhi. The travel times of P-waves fit a straight line very well with velocities of 5·97 ± 0·31, 6·18 ± 0·01, 6·41 ± 0·03, 7·82 ± 0·07 and 7·95 ± 0·01 km/sec at each of the depths under study. Similar investigations of P* and Pg-waves of 16 earthquakes at a depth of 10 km have revealed velocities of 6·53 ± 0·31 and 5·64 ± 0·34 km/sec, respectively. A simplified two-layered crustal model consisting of an average crustal thickness of 41·5 km with 22·2 and 19·3 km thick layers has been obtained.     

A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan

Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and sub-tropical areas. During 2007, in particular, there were over 2,000 DF cases in Taiwan, which was the highest number of cases in the recorded history of Taiwan epidemics. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas they have understated spatial DF patterns (spatial dependence and clustering) and composite space–time effects. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space–time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007. The results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required “one-week-ahead” outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed approach can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space–time.     

Modelling spatio-temporal patterns of sediment yield and connectivity in a semi-arid catchment with the WASA-SED model

Abstract

Rainfall–runoff induced soil erosion causes important environmental degradation by reducing soil fertility and impacting on water availability as a consequence of sediment deposition in surface reservoirs used for water supply, particularly in semi-arid areas. However, erosion models developed on experimental plots cannot be directly applied to estimate sediment yield at the catchment scale, since sediment redistribution is also controlled by the transport conditions along the landscape. In particular, representation of landscape connectivity relating to sediment transfer from upslope areas to the river network is required. In this study, the WASA-SED model is used to assess the spatial and temporal patterns of water and sediment connectivity for a semi-arid meso-scale catchment (933 km²) in Brazil. It is shown how spatial and temporal patterns of sediment connectivity within the catchment change as a function of landscape and event characteristics. This explains the nonlinear catchment response in terms of sediment yield at the outlet.

Citation Medeiros, P. H. A., Güntner, A., Francke, T., Mamede, G. L. & de Araújo, J. C. (2010) Modelling spatio-temporal patterns of sediment yield and connectivity in a semi-arid catchment with the WASA-SED model. Hydrol. Sci. J. 55(4), 636–648.