关于2008年5.12汶川大震若干热点问题探讨

依据史料考证了1327年天全MS≥6.0级地震,认为将其修订为MS7.75级合理.基于孕震断层多锁固段脆性破裂理论,从孕育周期界定与主震事件判识角度,分析了汶川地震区强(大)震事件的孕育过程.结果表明:汶川地震区至少已经历两轮孕育周期,1327年天全大震和2008年5.12汶川大震分别是第1和第2孕育周期主震事件,该区是一个MS7.75级地震危险区,其主震事件复发间隔上限值约为681年;2013年4.20芦山MS7.0级地震是主震后的一次大余震事件,当前孕育周期不会再次发生MS≥7.0级余震;汶川大震发生的直接导火索是1976年松潘-平武双震事件,与修建紫平铺水库并无因果关系.本文所得结论可供相关部门参考,以制定合理的灾后重建规划.     

磁暴月相二倍法的计算发震日期与全球MS ≥ 7.5大地震的对应关系

应用磁暴月相二倍法对1998年5月～2001年1月期间全球发生的MS≥7.5大地震进行研究.在16次大地震中发生于磁暴月相二倍法得出的计算发震日期(±5天)有13次;在15次计算发震日期中有11次对应MS≥7.5大地震,2次对应7.0≤MS≤7.4地震.研究表明K指数大的起倍磁暴日与MS≥7.8特大地震的发生有较好的相关性,发生在月相的磁暴与MS≥7.5大地震相关关系较好.     

2011年震情述评

1 SURVEY OF GLOBAL SEISMICITY IN 2011A total of 26 strong earthquakes with MS ≥ 7. 0 occurred in 2011 throughout the worldaccording to the China Seismic Network ( Table 1 ) . The strongest earthquake was the Japanearthquake measuring MS9. 0 on March 11 ( Fig. 1) . The frequency was slightly lower and the     

2003—2008年新疆区域构造应力场特征探讨

给出了2003—2008年新疆74次MS≥4.5地震的震源机制解。分析了该期间震源机制反映出的新疆区域应力场特征,并结合1990—2008年的地震活动图像和区域应力场进行了讨论。结果认为:1990—2003年与2004—2008年新疆MS≥5地震活动空间分布图像存在较大的差异性;震源机制得到的主压应力P轴方位为NNW向,与新疆近SN向的背景应力场和作者前期给出的NNE向结果略有差异,反映出不同时期新疆大区域应力场的变化特点;中强地震震源机制主压应力P轴仰角的大小变化与新疆地震活动的强弱相关性明显。     

南黄海地区强震趋势预测

对南黄海地区 1 846年以来MS≥ 6强震活动的可公度性进行了分析 .发现该区强震活动具有 6a、1 2a和 5 7a的基本周期 ,此外还有 6 3a、6 9a和 75a的周期 .根据强震活动的可公度性特征和丛集特征 ,对南黄海地区未来MS≥ 6地震活动趋势进行了预测 ,并对预测结果进行了论证 .     

四川盆地内部少震与弱震区MS≥4.5地震预测意义分析

自2008年汶川MS8.0地震发生以来,四川盆地内部及盆地边缘发生了一系列MS≥4.5地震,部分地震发生之后,四川及邻区又发生了MS≥6.0强震。2019年6月17日四川长宁MS6.0地震后,对四川盆地内部及盆地边缘MS≥4.5地震的预测意义进行了研究和疏理。通过给定不同的预测规则,对上述MS≥4.5地震的预测效能进行了统计检验,结果表明:仅四川盆地内部,以三台、梓潼等区域为代表的历史少震与弱震区MS≥4.5地震对四川及邻区未来半年内发生MS≥6.5地震具有显著的预测意义。     

2010年4月玉树M_S7.3地震序列的断层结构

利用双差定位方法对玉树地震序列2010年4月14日至10月31日间发生的ML≥1.0地震进行双差定位,得到1545个地震的重定位结果.综合分析地震双差定位结果和玉树地震序列中强地震震源机制解,发现玉树MS7.3地震发震构造由北西向和北东东向两条相交断层组成,主震发生在北西走向的甘孜—玉树断裂带上,5月29日的MS5.9余震序列发生在北东东走向的一条隐伏断裂上,两条断裂均接近直立.甘孜—玉树断裂是羌塘地块和巴彦喀拉地块的构造边界,由于羌塘地块和巴颜喀拉地块的差异运动使甘孜—玉树断裂强耦合段应力高度积累,在应变能超过岩石强度时破裂失稳发生了MS7.3地震.主震断层的左旋滑动导致北东东向断层的正应力减小,库伦应力增加,45天后触发了MS5.9余震序列的活动.     

云南地区Ms≥6地震预报实践中地震非均匀度的应用

我们知道,地震活动性主要是研究地震活动在时间、空间和强度等方面的规律。一些学者对地震活动时、空、强方面的统计规律还作了不少研究。本文利用MAPSIS系统工具中GL值计算方法对云南地区(21°N~29°N,97°E~107°E)1972年以来21次MS≥6地震前三年GL值进行时间变化扫描,结果发现该地区MS≥6地震前两年,在震中200 km范围内,3.0级以上地震活动均显示出明显的非均匀状态(GL>1);同时,MS≥6中强震前GL值空间扫描图象显示,GL值的高值区(GL>1)与未来MS≥6地震震中密切相关。因此GL值在云南地区MS≥6地震预报(特别是中期预报)中具有一定的实际意义。     

近40年菲律宾M_S≥7级地震时空结构及未来趋势再判断

整理了近40年菲律宾MS≥7级地震公开的灾害数据,采用可公度计算、蝴蝶结构图、可公度结构系等方法分析判断未来地震的时间;利用相邻地震震源经、纬度差变化分析震中的空间迁移;并利用震中经向、纬向迁移进行佐证,实现对其时空结构的未来趋势判断。通过判断发现菲律宾MS≥7级地震在2014年、2015年发生的信号较强;未来地震震中可能会在2013年10月15日菲律宾地震震中的东北方向,空间位置大致在9.8°N以北,126.704°E以东。菲律宾MS≥7级地震与地球自转速度变化有一定的关系,减速期发生地震的可能性更大,且速率转换的时间越长越易发生地震;同时发现太阳活动处于活跃强烈期内时,太阳黑子峰值、次峰值前后发生地震的概率很大。目前地球自转正处于减速期、太阳活动处于太阳黑子第24周期极大年附近,所以2014—2015年菲律宾发生MS≥7级地震的可能性很大。     

中国大陆强震活动对称性研究

利用中国大陆MS≥8.0地震基本信息,研究其时空对称性,并对未来可能发生MS≥8.0地震的时间和地点进行趋势判断,目的是探索对称性在地震灾害预测中的应用价值。采用时间对称性的三元、四元、五元可公度方法和蝴蝶结构图法,对1500年以来中国大陆MS≥8.0地震进行趋势分析,结果表明,我国大陆未来5年内可能在2012年和2015年发生MS≥8.0地震。空间对称性方面,通过对每一期地震震源点位置统计综合构建多边形结构考虑其空间分布对称性,对历史MS≥8.0地震震源分布规律进行分析,结果表明,下一(几)次中国大陆发生MS≥8.0地震大致在105°E以西、34°N以南位置。     

东北第五活跃期中强震前的矩释放空间扫描特征研究

为考察中国东北地区中强震前是否存在与地震孕育过程物理相关的地震矩加速释放现象,利用"地震矩释放程度"m值对该地区第五活跃期内的5例中强震进行了时-空扫描研究.通过考察删除余震的影响,提取了MS5.0以上地震与地震矩加速释放相关的特征时、空尺度,并作为时-空扫描的模型参数.采用震前5个月逐月扫描的方式,得到了震前m值随时间变化的图像.结果显示,其中4例在未来的震中附近出现稳定和集中分布的地震矩释放加速区.这表明东北地区中强震前的地震矩加速释放现象具有一定的普遍性,矩释放加速扫描方法对判断未来可能的中强震破裂成核点有一定的指导意义.采用相同的计算参数设置,还考察了震前破裂成核点的Benioff应变释放情况.结果显示,有3例在破裂成核点的地震矩加速释放现象明显.这表明,对于东北地区的中强地震,利用矩释放程度时-空扫描寻找未来中强震的破裂成核点,尚需其它方法作为辅助.     

Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance. Forest aboveground biomass (AGB) is an important component in the land surface carbon cycle. In this study, using forest inventory data and forest distribution data, the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device (CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast (HJ-1) and Geoscience Laser Altimeter System (GLAS) waveform data from the Ice, Cloud and land Elevation Satellite (ICESat). The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing (CQ) Forest Center, which was calculated from forest inventory data. The resulted statistical regression models have a R ²=0.68 for conifer and R ²=0.71 for broadleaf forests. These models were used to estimate biomass for all GLAS footprints of forest located in the study area. All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models (R ²=0.55 and R ²=0.52 for needle and broadleaf respectively). Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data. Biomass data obtained from forest inventory data of the Zhuanglin (ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data (R ²=0.71). About 80% of biomass samples had an error less than 20 t ha⁻¹, and the mean error of all validation samples is 5.74 t ha⁻¹. The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area. It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision, and the biomass data can be used as input data for future carbon budget analysis.

     

Geostatistical Mapping of Salinity Conditioned on Borehole Logs,Montebello Oil Field,California

We present a geostatistics-based stochastic salinity estimation framework for the Montebello Oil Field that capitalizes on available total dissolved solids (TDS) data from groundwater samples as well as electrical resistivity (ER) data from borehole logging. Data from TDS samples (n = 4924) was coded into an indicator framework based on falling below four selected thresholds (500, 1000, 3000, and 10,000 mg/L). Collocated TDS-ER data from the surrounding groundwater basin were then employed to produce a kernel density estimator to establish conditional probabilities for ER data (n = 8 boreholes) falling below the selected TDS thresholds within the Montebello Oil Field area. Directional variograms were estimated from these indicator coded data, and 500 TDS realizations from conditional indicator simulation were generated for the subsurface region above the Montebello Oil Field reservoir. Simulations were summarized as 3D maps of median TDS, most likely salinity class, and probability for exceeding each of the specified TDS thresholds. Results suggested TDS was below 500 mg/L in most of the study area, with a trend toward higher values (500 to 1000 mg/L) to the southwest; consistent with the average regional groundwater flow direction. Discrete localized zones of TDS greater than 1000 mg/L were observed, with one of these zones in the greater than 10,000 mg/L range; however, these areas were not prevalent. The probabilistic approach used here is adaptable and is readily modified to include additional data and types and can be employed in time-lapse salinity modeling through Bayesian updating.     

Reassembling the Paleogene–Eocene North Atlantic igneous province: New paleomagnetic constraints from the Isle of Mull, Scotland

The paleomagnetic data sets from the British Tertiary Igneous Province (BTIP) have recently been criticized as being unreliable and discordant with data from elsewhere in the North Atlantic Igneous Province (NAIP) [Riisager et al. Earth Planet. Sci. Lett. 201 (2002) 261–276; Riisager et al. Earth Planet. Sci. Lett. 214 (2003) 409–425]. We offer new paleomagnetic data for the extensive lava flow sequence on the Isle of Mull, Scotland, and can confirm the paleomagnetic pole positions emanating from important earlier studies. Our new north paleomagnetic pole position for Eurasia at 59 ± 0.2 Ma has latitude 73.3°N, longitude 166.2°E (dp/dm = 5.2/7.0).A re-evaluation and an inter-comparison of the paleomagnetic database emanating from the NAIP were carried out to test for sub-province consistency. We find a general agreement between the Eurasian part of NAIP (BTIP and Faeroes) and East Greenland data. However a compilation of West Greenland data displays a large and unexplained dispersion. We speculate on if this is related to different sense of block rotation of the Tertiary West Greenland constituents. Combining all data from the NAIP constituents, give a pole position at 75.0°N, 169.9°E (N = 25, K = 84.3, A₉₅ = 3.2) in Eurasian reference frame.     

Correlation dimension estimation of hydrological series and data size requirement: myth and reality/Estimation de la dimension de corrélation de séries hydrologiques et taille nécessaire du jeu de données: mythe et réalité

Abstract

The issue of data size (length) requirement for correlation dimension estimation continues to be the nucleus of criticisms on the (low) correlation dimensions reported for hydrological series. The present study addresses this issue from the viewpoints of both the existing theoretical guidelines and the practical reality. For this purpose, correlation dimension analysis is carried out for various data sizes from each of three types of series: (a) stochastic series (artificially generated using a random number generation technique); (b) chaotic series (artificially generated using the Henon map equation); and (c) hydrological series (real flow data observed on the Göta River in Sweden). The outcomes of the analysis of the (artificial) stochastic and chaotic series are used as a basis for interpreting the outcomes of the hydrological series. It is found that reliable dimension results for the stochastic and chaotic series are obtained even when the data size is only a few hundred points (i.e. no underestimation of dimension for small data sizes is visible), with no significant change in the scaling regimes (of the dimension plots) with respect to data size. This implies that the dimension results obtained for the hydrological series even with a few hundred points are also close to the actual ones. The insignificant difference in the scaling regimes for the various data sizes further supports this point. These results lead to the conclusions that: (1) the issue of data size requirement for correlation dimension estimation is more of a myth than reality; (2) the dimension estimates reported thus far for hydrological series could indeed be close to the actual ones (unless influenced by factors other than data size, e.g. delay time, noise, zeros, intermittency).     

Further evaluation of the Sim-ReSET model for ET estimation driven by only satellite inputs

Abstract

A simple remote sensing evapotranspiration (ET) model (Sim-ReSET) has been proposed but only tested using field measurements at a site with a semi-arid climate. Its performance for mapping ET using only satellite data remained unknown. In this study, the Sim-ReSET model was further evaluated for ET estimation driven by only MODIS data products. The estimated ET rates were compared with ground-based observational data from a variety of ecosystems and climates across China. The results show that MODIS-based ET estimates are consistent with both the ET measurements from eddy covariance flux towers and those from the Penman-Monteith method combined with micrometeorological data. Evaporation fraction (EF) is indicative of land surface moisture. The derivative EF maps demonstrate that the proposed ET data set obtained from the Sim-ReSET model and MODIS data is capable of capturing the spatio-temporal pattern of land surface moisture for different land covers with different climates.

Editor Z.W. Kundzewicz

Citation Sun, Z.G., Wang, Q.X., Matsushita, B., Fukushima, T., Ouyang, Z., Watanabe, M., and Gebremichael, M., 2013. Further evaluation of the Sim-ReSET model for ET estimation driven by only satellite inputs. Hydrological Sciences Journal, 58 (5), 994–1012.     

Detection of the urban heat island in Beijing using HJ-1B satellite imagery

Satellite images are used extensively in studying the urban heat island (UHI) phenomenon. We evaluated the suitability of thermal infrared (TIR) data from the HJ-1B satellite for detecting UHI using a case study in Beijing. Two modified algorithms for retrieving the land surface temperature (LST) from HJ-1B data were tested. The results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST output. The spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS data. Of the two algorithms, the mono-window algorithm performed better but further tests are necessary. With more frequent coverage than TM and higher spatial resolution than MODIS, the HJ-1B TIR data present a unique opportunity to study thermal environments in cities in China and neighboring countries.

     

Water balance of the Sudanese savannah woodland region

Abstract

The annual water balance for 39 grid cells covering the savannah woodland region of Sudan (10–16°N; 21–36°E) was determined and regional maps produced. Long-term (1961–1990) mean monthly climate data, National Forest Inventory data and Harmonized World Soil Database data for arenosols and vertisols, the two dominant soil types in the region, were used. Model validation was performed using daily data from a site in one of the grid cells and inter-annual (1961–1990) variation examined for another grid cell. Rainfall varied from 147 to 732 mm and only exceeded evapotranspiration for 18 of the grid cells, resulting in a small increase in soil moisture and runoff. Evapotranspiration accounted for, on average, 96% of rainfall and there was little difference between soil types. Drainage only occurred from AR soils and for four of the grid cells. Runoff varied from 0 to 89 mm for arenosols and from 0 to 109 mm for vertisols. The study provided useful insights into the spatial variability in water balance components across the region.

Editor D. Koutsoyiannis; Associate editor D. Gerten     

Mass loss of the Greenland ice sheet from GRACE time-variable gravity measurements

The Gravity Recovery and Climate Experiment (GRACE) satellite data is used to estimate the rate of ice mass variability over Greenland. To do this, monthly GRACE level 2 Release-04 (RL04) data from three different processing centers, Center for Space Research (CSR), German Research Center for Geosciences (GFZ) and Jet Propulsion Laboratories (JPL) were used during the period April 2002 to February 2010. It should be noted that some months are missing for all three data sets. Results of computations provide a mass decrease of −163 ± 20 Gigaton per year (Gt/yr) based on CSR-RL04 data, −161 ± 21 Gt/yr based on GFZ-RL04 data and −84 ± 26 Gt/yr based on JPL RL04.1. The results are derived by the application of a non-isotropic filter whose degree of smoothing corresponds to a Gaussian filter with a radius of 340 km. Striping effects in the GRACE data, C₂₀ effect, and leakage effects are taken into the consideration in the computations. There is some significant spread of the results among different processing centers of GRACE solutions; however, estimates achieved in this study are in agreement with the results obtained from alternative GRACE solutions.     

Estimation of the elastic earth parameters using the SLR LAGEOS 1 and LAGEOS 2 data

We present results for the global elastic parameters (k ₂, k ₃) and (h ₂, l ₂) derived from the analysis of Satellite Laser Ranging (SLR) data. The SLR data for two satellites, LAGEOS 1 and LAGEOS 2, collected over a period of two years, from 1 January 2005 to 1 January 2007, from 18 globally distributed ground stations are analyzed using different approaches. The analysis is performed for the two satellites jointly, and the four elastic parameters are estimated. We carry out a sequential analysis and study the stability of the estimates as a function of the length of the data set used. The adjusted final values for k ₂ (0.3011±0.0001) and k ₃ (0.0896±0.0037) and those for h ₂ (0.6146±0.0006) and l ₂ (0.0883±0.0003) for LAGEOS 1 and LAGEOS 2 tracking data are compared to the estimates derived in other studies.