On checking through data on the KEYW seismometer which miraculously kept going all through the Christmas break  I spotted a nice record from a small event (Mw=5.3) in Northern Italy on 23rd Dec.   Very nice example of a regional event with a short duration and clearly identifiable P S and Surface waves.   Because this event is so close it contains lots of high frequency energy and shows up best with a high-pass filter (set to 2sec to remove all the 6sec microseismic noise) instead of the usual 10sec low pass filters used for teleseismic events

The City of Manokwari,West Papua, was struck by a M 7.6 earthquake at 19:43:54 UT on the 3rd of January. The earthquake was caused by the thrust faulting as the Pacific plate subducted beneath the Australia plate along the NW coast of New Guinea. Several (5) aftershocks of between M4.8 and M5.6 followed. A M7.3 earthquake occured at 22:33:42 UT and is thought to have been triggered by the ealier event producing a "doublet". The helicorder trace on PAUL shows the two events, the red dots represent the occurence of each event.

The trace for each earthquake is shown below;

0901031943PAUL.sac

0901032233PAUL.sac

The BBC world news channel reports 4 dead and 37 injured, with the total destruction of 10 buildings including several hotels. Many people fled from the costal area in fear of a Tsunami. An initial warning was issued, indicating a tsunami had been generated (based on gauge readings of sea level changes), the threat however was to the local area around the epicentre and only over a two hour period, there was no threat to other coastal areas in the pacific. The area has been subjected to 19 aftershocks of between M4.3 and M6.0 since.

Merry Christmas all....

I've not been successful with ftp uploads to IRIS, for at least a week, - does anyone know if the site is down for any especial reason ?

The White Rose Grid iRODS Evaluation and Demonstrator Project This project will provide an in-depth evaluation and demonstration of the iRODS system, assessing its capabilities and role within a distributed data management scheme and complex virtual organisation driven by real-world requirements from the CARMEN e-Science project. Distributed data management issues are still a major challenge in addressing the ubiquity and usability of Grid systems, particularly within the context of complex virtual organisations (VO) where flexible security models and fine -grained role based access are a pre-requisite. The Storage Request Broker (SRB) software from SDSC has established itself as one of the leading Grid middleware applications to support the management of highly distributed large scale datasets for science applications. SRB provides the capability to virtualise distributed datasets, and to provide standardised access to a broad range of underlying storage technologies, spanning flat file systems through to database servers and tape archiving systems. Through the use of SRB, end-users are freed from concerns about the location of data and determining the correct procedures to recall or transfer data to their local or host compute environment. SRB abstracts these challenging aspects of distributed data management away from the end-user, and provides a simplified and uniform way to recall data via indexing systems (metacatalogs) which keep a logical mapping of the underlying distributed data. SRB has been widely adopted within large-scale Grid applications, particularly in the science communities, and provides the data management backbone for the National Grid Service (NGS). However, SRB does have limitations particularly in regard to integration with complex VO’s. These have been recognised by the developers of SRB at SDSC and a new version, iRODS, has recently been released to address the short-comings within SRB. Many of the limitations within SRB relate to the metadata schemes used to describe and annotate the data collections. In many cases these metadata mechanisms are too restrictive and inflexible to support complex meta-data schemes for complex role-based Grid systems. iRODS is dowloadable at release 1.1, and as part of the objectives of the JISC e-infrastructure imitative, this project will assess the impact that the new features and functions within iRODS will have on the UK e-Science community, and assess its potential for deployment within the NGS. [Note that the current iREAD demonstration uses an iRODS pre-release of 1.2 kindly provided by UCSD.]

This is a paper describing research undertaken by Clemson University exploring thought processes and problem solving. The paper describes the use of the Bloom taxonomy to understand the cognitive processes for computer scientists solving a data structure problem. Understanding this process is a key part of teaching, training and evan enabling machinie learning.

The paper provides a good introduction, overview and description of related work. In the concluding remarks it is acknowledged by the researchers that Bloom's taxonomy is difficult to use for the classification of cognitive processes.

The results show timelines for the Bloom classification contrasting the least successful and the most successful these amazing results would need to be supported by the provision of a confidence level. As the authors point out the small number of transcripts and the number of problems make it difficult to provide general characterisations.

The results of the analysis indicate the insight that has been gained into these processes. Amongst these ideas is the conclusion that successful problem solvers move more frequently between the different cognitive processes. It also lends support to  the idea that when we become stuck with solving a problem it is best to leave the problem and return at a later time. The research methodology identified here will find it difficult to quantify this technique.

Two areas in common with the White Rose Grid are as follows.

1. Using analysis techniques of the CARMEN [6] project to classify cognitive processes.
2. Automation of analysis techniques using structured Transcriptions and Information Extraction work undertaken at Sheffield [5]

Recommendations

1. In the methodology quantify how much effort undertaken by investigators to transcribe and classify the trannscripts.
2. Add a suitable legend to figure 1, make this clearer.
3. Identify candidate methods for improving the analysis work.

Links

1. Using Bloom’s Taxonomy To Code Verbal Protocols of Students Solving a Data Structure Problem
2. Wikipedia article on Taxonomy of educational objectives
3. Article on Bloom's taxonomy
4. Writing learning objectives using blooms taxonomy
5. Large vocabulary speech recognition systems and their applications
6. The CARMEN project
7. Research Methods: Verbal Protocols

We have added a quake locator tool to www.bgs.ac.uk/schoolseismology .  An instruction video for using this tool is available at youtube

12 hours after eth damaging Mw6.4 event in Pakistan a second event occurred

08:30 UT today M 6.4 earthquake in Eastern Xiang (Tibet), 50 miles west of the capital Lhasa, this was followed by another quake of M 5.1 15 mins later. The bbc world news reports the collapse of houses in Damxung county with the death of approximately 30 people according to chinese sources.

The recent update on the Kyrgyzstan earthquake puts the death toll at 65 and the village of Nura close to the chinese border has been completely destroyed.

There are no reports of casualties form the M5.9 Afghanistan earthquake 22:56 UT 5th October.

Trying to download the data from the IRIS site yesterday was impossible as it appeared that no data had been upoladed via "upload", it is now available apart from 00 and 01 files for the 5th Oct, any body else experience this problem?

During August PAUL registered the following earthquakes, I've included the .sac files.

03/08/08 Aegean Sea M 5.4 0808030039PAUL.sac

 05/08/08 China, Sichuan M 6.0 0808050949PAUL.sac  

16/08/08 Siberia M 5.1 0808160401PAUL.sac  

25/08/08 Western Xiang (Tibet) M 6.6 0808251322PAUL.sac  

26/08/08 Northern Peru M 6.4 0808262100PAUL.sac  

27/08/08 Russia, Lake Baykal M 6.2 0808270135PAUL.sac  

The Norther Peru trace originally looked like a random blip but comparison with the Keyworth trace on the IRIS schools site showed the same blip, more than coincidence!  The "blip" was in fact the S wave arrival, very little was discernible of the P wave or surface waves as this event occured at a depth of 153Km

It is always rewarding to visualise data sets, particularly those visualisations that exhibit characteristics of the systems that we are attempting to model. I've had such an experience today.  

Data visualisation is a key part of the scientific research process it is,

• an important stage in the knowledge creation and discovery  process
  
• aids the validation of data sets

The model described in the previous post describes some of the MHD modelling we are attempting to undertake. The simulation generated 400 time series points from a total of 80000 iterations and the stored configuration size was an array of 3x 2-vectors and 4 scalars. For each time step there is a total of 1976x400 points. Visualisation of the raw data can be acheived using tools such as Matlab, IDL, AVS or IBM Data Explorer.

For the case considered visualising the raw data is quite challenging and a recommended approach is to undertake some post processing of the data to take averaging or data samples from the computed mesh.  The first stage in this process was the use of a covertdata routine provided with VAC.

 The advantages of using data explorer include

• Well supported with good documentation and an active user forum
• A visual programming environment enabling rapid development of applications
• A powerful data buffering capability, once a large data set has been read it is possible to modify and reexecute the program without having to reload the data set.
• Each of the modules feature useful descriptions providing user guidelines

For the data set considered here we generated two applications using data explorer. The first application reduces and selects the data to a manageable volume. Very often there is a need to preserve the original "raw" data, this comes at a storage cost. This application can be run non-interactively and submitted to a job queue, given the size of the raw data it can take a few hours.

The second application is the application used to visualise the data this application is run in interactive mode and is used by the researcher for generating images and movies that might be shared with the community.

The main IBM data explorer modules used for the post processing stage are as follows

• construct
  
• regrid
• CollectNamed
• Connect
• Export
  

Having imported a data set and selected a member of a data set that data can be mapped onto a new grid for this purpose we use the construct and regrid modules. The construct module is used to specify the form of the new grid the counts enable us to specify the number of items in the new grid, the deltas enablem us to select the correct items from the input data field. The output from the construct module feeds into the grid input of regrid module. The selected and imported data feeds into the input of the regrid module.

Having regridded data we can collect together all the selected data fields using the CollectNamed module, the resulting data object may then be exported to an output file for use in the final visualisation stage. We mention here the use of the connect module which is used to provide connection information for the data set, this information enables data interapolation.   

At each stage of the research process, from preparation of the model to the generation of the raw data and the final post processing stages, there are quite a few data transformations.  For studies where the researcher investigates a range of state points in the modelling phase space  this can lead to an overwhelming collection of data in the problems are increased as the number of compute nodes and storage resources are increased. This problem requires the utilisation of metadata capture techniques, this includes suitable storage, search and querying mechanisms.

In later posts we will review metadata, capture techniques and search and querying mechanisms. We will also look at the data provenance capture and querying mechanisms. We add here that, given the nature of the research process and the exploration of generated data sets provenance capture may provide an imporatnt research tool, we can envisage paralles with social book marking and "MyExperiment"  style environments.