We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure coreplatform@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Computational modeling and simulations are becoming popular test beds and proving grounds in several areas of science. The seismology community is no exception to this trend, and the increased capability and availability of computational power has fueled further interest in performing such simulations using models. Over the last two decades, scientists have been making steady improvements to computational models, and we are just beginning to see the results of their efforts. The current models are capable of running on a variety of computational resources at multiple scales and sizes. One approach for studying the simulation models is by creating probable earthquake scenarios. These simulations require considerable computational and storage capacity for a successful run, and the amount of data produced by these simulations is enormous, requiring a non-trivial amount of processing for analysis of the results. Traditional analysis of model outputs, using statistical summarizations or simple examination of output data is impractical. Visualization offers an alternative approach for analyzing such huge stockpiles of data.
Visualization characteristics
It is important to identify a few key characteristics that any visualization should possess which can help us in designing and implementing a visualization solution for a given problem. We discuss the role of desirable visualization characteristics including intuitiveness, trainability, focus, interactivity, and accessibility.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.