鄂尔多斯盆地南部的黄土塬区地震资料处理与解释,第一部分:地震资料处理

Branching river channels and the coexistence of valleys, ridges, hiils, and slopes＇as the result of long-term weathering and erosion form the unique loess topography. The Changqing Geophysical Company, working in these complex conditions, has established a suite of technologies for high-fidelity processing and fine interpretation of seismic data. This article introduces the processes involved in the data processing and interpretation and illustrates the results.     

Converted-wave seismology in anisotropic media revisited,Part I: Basic theory

We have developed new basic theories for calculating the conversion point and the travel time of the P-SV converted wave (C-wave) in anisotropic, inhomogeneous media. This enables the use of conventional procedures such as semblance analysis, Dix-type model building and Kirchhoff summation, to implement anisotropic processing, and makes anisotropic processing affordable. Here we present these new developments in two parts: basic theory and application to velocity analysis and parameter estimation. This part deals with the basic theory, including both conversion-point calculation and moveout analysis. Existing equations for calculating the PS-wave (C-wave) conversion point in layered media with vertical transverse isotropy (VTI) are strictly limited to offsets about half the reflector depth (an offset-depth ratio, xlz, of 0.5), and those for calculating the C-wave traveltimes are limited to offsets equal to the reflector depth (x/z=l.0). In contrast, the new equations for calculating the conversion-point extend into offsets about three-times the reflector depth (x/z=3.0), those for calculating the C-wave traveltimes extend into offsets twice the reflector depth (x/z=2.0). With the improved accuracy, the equations can help in C-wave data processing and parameter estimation in anisotropic, inhomogeneous media. This work is funded by the Edinburgh Anisotropy Project (EAP) of the British Geological Survey. First author: Xiangyang Li, Mr. Li is currently a professorial research seismologist (Grade 6) and technical director of the Edinburgh Anisotropy Project in the British Geological Survey. He also holds a honorary professorship in multicomponent seismology at the School of Geosciences, University of Edinburgh. He received his BSc(1982) in Geophysics from Changchun Geological Institute, China, an MSc (1984) in applied geophysics from East China Petroleum Institute (now known as the China University of Petroleum), and a PhD (1992) in seismology from the University of Edinburgh. During 1984–1987, he worked as a lecturer with the East China Petroleum Institute. Since 1991, he has been employed by the British Geological Survey. His research interests include seismic anisotropy and multicomponent seismology.     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

Observed Evolution of Linear and Nonlinear Effects at the Dahan Downhole Array,Taiwan: Analysis of the September 21, 1999 M 7.3 Chi-Chi Earthquake Sequence

— In this paper, the site characteristics of the Dahan downhole array are studied by analyzing the September 21, 1999 M 7.3 Chi-Chi earthquake sequence including the main shock and some aftershocks. The four-level array (0 m, 50 m, 100 m and 200 m) is located to the north of Hualien City in eastern Taiwan. Polarization analysis is used to check the orientation errors of the seismometers at different levels of depth. If the surface instrument is chosen as reference, the angle between the major polarization axes of the surface and any downhole records is the orientation error that must be corrected for the downhole accelerographs. The orientation errors at depths of 50 m, 100 m and 200 m are 32°, 120° and –84°. After the corrections, the coherency between the surface and downhole records is substantially improved. Spectral ratio analysis shows that the predominant frequency of the Chi-Chi main shock shifts to a lower frequency. We also simulate ground motions at different depths by using the Haskell method with a linear velocity structure model. The record at surface is chosen as the input motion. Compared with the observed data, ground acceleration can be well reproduced for the aftershocks (weak-motion events) of the September 21, 1999 M 7.3 Chi-Chi earthquake. However, for the Chi-Chi main shock, the synthetic waveform cannot match well with the observation neither in amplitude nor in phase. This indicates that large ground shaking probably induced the nonlinear site effect at that time, and the model used cannot support it.Acknowledgement. The authors would like to express their thanks to Dr. L.F. Bonilla and one anonymous reviewer for their valuable suggestions. This research was supported by the National Science Council under grant number NSC 89-2921-M-194-007. The Institute of Earth Sciences, Academia Sinica supplied the strong-motion data. The support of these organizations is gratefully acknowledged.     

Impact of warmer climate on melt and evaporation for the rainfed, snowfed and glacierfed basins in the Himalayan region

The impact of warmer climate on melt and evaporation was studied for rainfed, snowfed and glacierfed basins located in the western Himalayan region. Hydrological processes were simulated under current climatic conditions using a conceptual hydrological model, which accounts for the rainfall–runoff, evaporation losses, snow and glacier melt. After simulations of daily observed streamflow (R²=0.90) for 6 years, the model was used to study the impact of warmer climate on melt and evaporation. Based on the future projected climatic scenarios in the study region, three temperature scenarios (T+1, T+2 and T+3 °C) were adopted for quantifying the effect of warmer climate. The comparison of the effect of warmer climate on different types of basins indicated that the increase in evaporation was the maximum for snowfed basins. For a T+2 °C scenario, the annual evaporation for the rainfed basins increased by about 12%, whereas for the snowfed basins it increased by about 24%. The high increase of the evaporation losses would reduce the runoff. It was found that under a warmer climate, melt was reduced from snowfed basins, but increased from glacierfed basins. For a T+2 °C scenario, annual melt was reduced by about 18% for the studied snowfed basin, while it increased by about 33% for the glacierfed basin. Thus, impact of warmer climate on the melt from the snowfed and glacierfed basins was opposite to each other. The study suggests that out of three types of basins, snowfed basins are more sensitive in terms of reduction in water availability due to a compound effect of increase in evaporation and decrease in melt. For a complex type of basin, the decrease in melt from seasonal snow may be counterbalanced by increase in melt from glaciers. However, on long-term basis, when the areal extent of glaciers will decrease due to higher melt rate, the water availability from the complex basins will be reduced.     

Anisotropy from full waveform inversion of multicomponent seismic data using a hybrid optimization method

Seismic waveforms contain valuable information about the media, but the waveform inversion is a non‐linear problem. We present a waveform inversion method that combines a local optimization method with a genetic algorithm to determine the anisotropic parameters of a horizontally stratified medium. Synthetic seismograms for a horizontally stratified anisotropic medium are calculated using the reflectivity technique. In the initial stage of the inversion, the global space‐sampling properties of the genetic algorithm are used to direct the search to the region close to the global solution. This solution is then further improved using a conjugate‐gradient method. The numerical experiments performed with noisy synthetic data show that our hybrid optimization method satisfactorily reconstructs the anisotropic parameters at a reasonable computing cost while the range of slowness is adequate. We found that (i) for small‐angle data neither single‐ nor multiple‐component data are sufficient to determine the anisotropic parameters uniquely; (ii) for medium‐angle data the multiple‐component data are sufficient to determine the anisotropic parameters exactly whereas the single‐component data are not sufficient; and (iii) for wide‐angle data, either single‐ or multiple‐component data are sufficient to determine the anisotropic parameters accurately.     

Analysis of earth dams affected by the 2001 Bhuj Earthquake

An earthquake of magnitude of 7.6 (M_w 7.6) occurred in Bhuj, India on January 26, 2001. This event inflicted damages of varying extents to a large number of small to moderate size multi-zone earth dams in the vicinity of the epicenter. Some of the distress was due to the liquefaction of saturated alluvium in foundation. Liquefaction was relatively localized for the majority of these dams because the earthquake struck in the middle of a prolonged dry season when the reservoirs behind these dams were nearly empty and shallow alluvium soils underneath the downstream portions of the dams were partly dry. Otherwise, liquefaction of foundation soils would have been more extensive and damage to these dams more significant. Six such dams have been examined in this paper. Four of these facilities, Chang, Shivlakha, Suvi, and Tapar were within the 50 km of epicenter region. These dams underwent free-field ground motion with peak ground accelerations between 0.28g to 0.52g. Of these Chang Dam underwent severe slumping, whereas Shivlakha, Suvi, and Tapar Dams were affected severely especially over the upstream sections. Fatehgadh Dam and Kaswati Dam were affected relatively less severely. Foundation conditions underneath these dams were first examined for assessing liquefaction potential. A limited amount of subsurface information available from investigations undertaken prior to the earthquake indicates that, although the foundation soils within the top 2.0 to 2.5 m underneath these dams were susceptible to liquefaction, Bhuj Earthquake did not trigger liquefaction because of lack of saturation of these layers underneath the downstream portions of these dams. These dams were then analyzed using a simple sliding block procedure using appropriate estimates of undrained soil strength parameters. The results of this analysis for these structures were found to be in general agreement with the observed deformation patterns.     

Prediction of creep characteristic of rock under varying environment

The strain developed due to creep is mainly proportional to the logarithm of the time under load, and is mostly proportional to the stress and temperature. At higher temperature the creep rate falls slowly with respect to time, and the creep strain is proportional to a fractional power of time, with the exponent increasing as the temperature increases and reaching a value approximately one-third at temperatures of about 0.5°C. At these temperatures, the creep increases with stress according to a power greater than unity and possibly exponentially. It increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) including experimental methods, multivariate regression analysis, and by numerical simulation. These methods are less cumbersome and time consuming. In the present investigation, artificial neural network technique has been used for prediction of the creep strain and energy of Jog (B). Two different networks have been tested and validated. Both the networks have four input neurons and one hidden layer with five neurons, and one output neuron. The data for different rocks at temperatures up to 750°C under conditions of compressive or tortional stress are taken from the literatures. The training and testing data sets used were 163 and 14, respectively. To deal with the problem of overfitting of data, Bayesian regulation has been used and network is trained with suitable training epochs. The coefficients of correlation among the predicted and observed values are found high and they improve the confidence of the users. The mean absolute percentage error obtained are also very low.