Testing a statistical curve‐fitting procedure for quantifying sediment populations within multi‐modal particle‐size distributions

This paper describes a series of tests designed to evaluate the capacity of a personal computer (PC) based statistical curve‐fitting program called MIX to quantify composite populations within multi‐modal particle‐size distributions. Three natural soil samples were analysed by a Coulter Multisizer, and their particle‐size distributions analysed using MIX software to identify the modes, standard deviations and proportions of their composite populations. The particle‐size distributions of the three natural soil samples were then numerically combined in equal proportions using a spreadsheet program to create synthetic particle‐size distributions of known populations. MIX was then tested on the synthetic particle‐size distributions to see if the modes and proportions it identified were similar to those modes and proportions known to characterize the synthetic particle‐size distributions. The main outcome is that MIX can very accurately describe the modal particle size and proportions of the major composite populations within a particle‐size distribution. However MIX has difficulty in identifying small populations (those contributing <10 per cent of a total particle‐size distribution), particularly when they are located in the central sections of particle‐size distributions, overlain by larger populations, or when positioned in the fine tails of distributions. Despite these minor shortcomings, MIX is a valuable tool for the examination and interpretation of particle‐size data. Copyright © 2005 John Wiley & Sons, Ltd.     

Spatial damage distribution of August 16,2003,Inner Mongolia,China,M_S=5.9 earth-quake and analysis

Introduction The ground motion is generally estimated by attenuation relation in seismic hazard assesment.The attenuation relation is usually the function of earthquake magnitude and distance.Actually the focal mechanism and other source parameters may also have significant impacts on the ground motion,especially in the near-source region.Recent post-earthquake investigations show that the damages have close relation with the closest fault-plane distance.On August16,2003,a MS=5.9earthquake o…     

Geostatistical simulation of random fields with bivariate isofactorial distributions by adding mosaic models

This work deals with the geostatistical simulation of a family of stationary random field models with bivariate isofactorial distributions. Such models are defined as the sum of independent random fields with mosaic-type bivariate distributions and infinitely divisible univariate distributions. For practical applications, dead leaf tessellations are used since they provide a wide range of models and allow conditioning the realizations to a set of data via an iterative procedure (simulated annealing). The model parameters can be determined by comparing the data variogram and madogram, and enable to control the spatial connectivity of the extreme values in the realizations. An illustration to a forest dataset is presented, for which a negative binomial model is used to characterize the distribution of coniferous trees over a wooded area.     

Kinetic energy–rainfall intensity relationship for Central Cebu, Philippines for soil erosion studies

In the study of soil erosion, specifically on detachment of soil particles by raindrop impact, kinetic energy is a commonly suggested indicator of the raindrop's ability to detach soil particles from the soil mass. Since direct measurement of kinetic energy requires sophisticated and costly instruments, the alternative approach is to estimate it from rainfall intensity. The present study aims at establishing a relationship between rainfall intensity and kinetic energy for rainfalls in Central Cebu, Philippines as a preface of a wider regional investigation.

Drop size distributions of rainfalls were measured using the disdrometer RD-80. There are two forms of kinetic energy considered here. One is kinetic energy per unit area per unit time (KE_R, J m⁻² h⁻¹) and the other is kinetic energy per unit area per unit depth (KE, J m⁻² mm⁻¹). Relationships between kinetic energy per unit area per unit time (KE_R) and rainfall intensity (I) were obtained using linear and power relations. The exponential model and the logarithmic model were fitted to the KE–I data to obtain corresponding relationships between kinetic energy per unit area per unit depth of rainfall (KE) and rainfall intensity (I). The equation obtained from the exponential model produced smaller standard error of estimates than the logarithmic model.     

基于正交多项式逼近法的岩土参数概率分布推断

针对岩上参数样本容量较大的情况,基于数值分析中的逼近原理,直接根据试验样本值,运用勒让德正交多项式来拟合岩土参数的概率密度函数,并用K-S检验法从理论上证明所求的密度函数的正确性和实用性。     

Soil liquefaction during the Arequipa Mw 8.4, June 23, 2001 earthquake, southern coastal Peru

The Arequipa June 23, 2001, earthquake with a moment magnitude of Mw 8.4 struck southern Peru, northern Chile and western Bolivia. This shallow (29 km deep) interplate event, occurring in the coupled zone of the Nazca subduction next to the southeast of the subducting Nazca ridge, triggered very localized but widely outspread soil liquefaction. Although sand blows and lateral spreading of river banks and road bridge abutments were observed 390 km away from the epicenter in the southeast direction (nearing the town of Tacna, close to the Chile border), liquefaction features were only observed in major river valleys and delta and coastal plains in the meizoseismal area. This was strongly controlled by the aridity along the coastal strip of Southern Peru. From the sand blow distribution along the coastal area, a first relationship of isolated sand blow diameter versus epicentral distance for a single event is ever proposed. The most significant outcome from this liquefaction field reconnaissance is that energy propagation during the main June 23, 2001, event is further supported by the distribution and size of the isolated sand blows in the meizoseismal area. The sand blows are larger to the southeast of the epicenter than its northwestern equivalents. This can be stated in other words as well. The area affected by liquefaction to the northwest is less spread out than to the southeast. Implications of these results in future paleoliquefaction investigations for earthquake magnitude and epicentral determinations are extremely important. In cases of highly asymmetrical distribution of liquefaction features such as this one, where rupture propagation tends to be mono-directional, it can be reliably determined an epicentral distance (between earthquake and liquefaction evidence) and an earthquake magnitude only if the largest sand blow is found. Therefore, magnitude estimation using this uneven liquefaction occurrence will surely lead to underrating if only the shortest side of the meizoseismal area is unluckily studied, which can eventually be the only part exhibiting liquefaction evidence, depending on the earthquake location and the distribution of liquefaction-prone environments.     

Conditional Simulation of Random Fields with Bivariate Gamma Isofactorial Distributions

This work focuses on a random function model with gamma marginal and bivariate isofactorial distributions, which has been applied in mining geostatistics for estimating recoverable reserves by disjunctive kriging. The objective is to widen its use to conditional simulation and further its application to the modeling of continuous attributes in geosciences. First, the main properties of the bivariate gamma isofactorial distributions are analyzed, with emphasis in the destructuring of the extreme values, the presence of a proportional effect (higher variability in high-valued areas), and the asymmetry in the spatial correlation of the indicator variables with respect to the median threshold. Then, we provide examples of stationary random functions with such bivariate distributions, for which the shape parameter of the marginal distribution is half an integer. These are defined as the sum of squared independent Gaussian random fields. An iterative algorithm based on the Gibbs sampler is proposed to perform the simulation conditional to a set of existing data. Such ‘multivariate chi-square’ model generalizes the well-known multigaussian model and is more flexible, since it allows defining a shape parameter which controls the asymmetry of the marginal and bivariate distributions.     

“雪龙号”2003北极航次气相多环芳烃纬度分布观测

2003年“雪龙号”北极科学考察期间,对沿途海洋大气进行采样,分析其中气相多环芳烃的空间分布。结果显示,气相中主要是2-4环的多环芳烃,其中菲为主要的化合物,平均占到总多环芳烃的55.1%。在整个航程的广泛区域尺度内,气相总多环芳烃浓度在1043.9-92993.1pg/m3。空间分布上,远东亚的海面>北太平洋海面>北极圈以内海面;总多环芳烃的浓度随纬度升高呈现显著降低的趋势。通过Clausius-Clapeyron方程对浓度和温度相互关系的分析表明,温度是控制气相多环芳烃长距离传输的主要因素。     

Fingerprinting the 8.2 ka event climate response in a coupled climate model

Using results from coupled climate model simulations of the 8.2 ka climate event that produced a cold period over Greenland in agreement with the reconstructed cooling from ice cores, we investigate the typical pattern of climate anomalies (fingerprint) to provide a framework for the interpretation of global proxy data for the 8.2 ka climate event. For this purpose we developed an analysis method that isolates the forced temperature response and provides information on spatial variations in magnitude, timing and duration that characterise the detectable climate event in proxy archives. Our analysis shows that delays in the temperature response to the freshwater forcing are present, mostly in the order of decades (30 a over central Greenland). The North Atlantic Ocean initially cools in response to the freshwater perturbation, followed in certain parts by a warm response. This delay, occurring more than 200 a after the freshwater pulse, hints at an overshoot in the recovery from the freshwater perturbation. The South Atlantic and the Southern Ocean show a warm response reflecting the bipolar seesaw effect. The duration of the simulated event varies for different areas, and the highest probability of recording the event in proxy archives is in the North Atlantic Ocean area north of 40° N. Our results may facilitate the interpretation of proxy archives recording the 8.2 ka event, as they show that timing and duration cannot be assumed to correspond with the timing and duration of the event as recorded in Greenland ice cores. Copyright © 2011 John Wiley & Sons, Ltd.