Data archives of seismic fault-induced damage

The study of earthquake engineering requires an understanding of various processes by which structures are seriously damaged. For this, archiving available data in a systematic way will be quite necessary. Taskforces of both Japan Society of Civil Engineers (JSCE) and Japan Geotechnical Society (JGS), with a mission of establishing remedial measures for fault-inflicted damage to civil infrastructures, have been investigating previous examples of fault-related damages. Some of their important activities were further expanded as a subject of EqTAP project (Development of Earthquake and Tsunami Disaster Mitigation Technologies and Their Integration for the Asia-Pacific Region). This paper introduces some examples from the JSCE/JGS/EqTAP Data Archives, which can give some hints for rational remedial or design measures for civil-infrastructures whose constructions across faults were/are unavoidable.     

The International Laser Ranging Service and its support for IGGOS

The International Laser Ranging Service (ILRS) was established in September 1998 as a service within the IAG to support programs in geodetic, geophysical, and lunar research activities and to provide data products to the International Earth Rotation Service (IERS) in support of its prime objectives. Now in operation for 5 years, the ILRS develops: (1) the standards and specifications necessary for product consistency and (2) the priorities and tracking strategies required to maximize network efficiency. The service collects, merges, analyzes, archives and distributes satellite and lunar laser ranging data to satisfy a variety of scientific, engineering, and operational needs and encourages the application of new technologies to enhance the quality, quantity, and cost effectiveness of its data products. The ILRS works with: (1) the global network to improve station performance; (2) new satellite missions in the design and building of retroreflector targets to maximize data quality and quantity and (3) science programs to optimize scientific data yield. The ILRS Central Bureau maintains a comprehensive web site as the primary vehicle for the distribution of information within the ILRS community. The site, which can be accessed at: http://ilrs.gsfc.nasa.gov is also available at mirrored sites at the Communications Research Laboratory (CRL) in Tokyo and the European Data Center (EDC) in Munich.During the last 2 years, the ILRS has addressed very important challenges: (1) data from the field stations are now submitted hourly and made available immediately through the data centers for access by the user community; (2) tracking on low satellites has been significantly improved through the sub-daily issue of predictions, drag functions, and the real-time exchange of time biases; (3) analysis products are now submitted in SINEX format for compatibility with the other space geodesy techniques; (4) the Analysis Working Group is heavily engaged in Pilot Projects as it works toward an ILRS “standard” global solution and (5) SLR has significantly increased its participation in the International Terrestrial Reference Frame (ITRF) activity, which is important to the success of IGGOS.     

Primary component analysis method and reduction of seismicity parameters

Introduction Data mining (SHAO and YU, 2003) is a new kind of technique developed with database and artificial intelligence in recent years, which processes the data in the database to abstract the im- plied and pre-unknown, but potentially useful information and knowledge from large amounts of incomplete, noisy, blurring and stochastic data. For data mining, data purging is an important link beforehand that includes eliminating noise, making up lost domain, and deleting ineffective data, as…     

Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion

In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid.     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.     

东北地区矿业开发态势与矿山环境地质问题综合研究

在论述东北地区矿业开发态势的同时,阐述了区内能源矿产、金属矿产及非金属矿产开采引发的主要环境地质问题,针对区内复杂的矿山环境地质问题现状,提出了开展东北地区矿山环境地质综合研究问题的工作思路.     

Water pollution in gold mining industry: a case study in Roşia Montană district, Romania

The preliminary study of streams and rivers from the Roşia Montană area revealed that the concntration of heavy metals— Cd, Mn, Cu, Pb, and Zn—are above accepted limits. The gold extraction method is based on flotation. The most important pollution sources are mine tailings. The determinations were performed for samples collected in: April 2004, July 2004, September 2004, November 2004, February 2005 and May 2005. The highest concentrations were found for cadmium in September 2004: 0.17 mg/L; for copper in September 2004: 1.38 mg/L; for manganese in July 2004: 239.4 mg/L; for lead in May 2005: 0.54 mg/L; and for zinc in September 2004: 35.37 mg/L;. This study involved three small rivers (streams) that flow into the Mureş River and finally into the Danube River, having a great impact on human health and environmental stability in the area. In May 2005, a sample of drinking water from the mining district was also collected.     

Localization of mining-induced horizontal fractures along rock layer interfaces in overburden: field measurements and prediction

The locations of mining-induced horizontal fractures along rock interfaces in the overburden of Donetsk Coal Basin were identified using an original experimental device. The device traps methane from horizontal fracture zone (100–fold coal seam thickness) over an active longwall mining excavation. Presence or absence of horizontal fractures along rock layer interfaces is correlated with physical characteristics of the overburden, such as thickness, uniaxial compressive strength of overburden rock layers, location of rock layer interfaces and thickness of extracted coal seams. As a result, a combined criterion based on these physical characteristics is proposed to predict the presence of overburden horizontal fracturing in coal mine operations.     

Soil property variation mapping through data mining of soil category maps

Spatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.