|
Journal: |
Bulletin of Seismological Society of America 2018 No.4
clicks:180 |
|
Title:
|
|
|
Author:
|
Naeem Khoshnevis ; Ricardo Taborda |
|
Adress: |
Center for Earthquake Research and Information, The University of Memphis, 3890 Central Avenue, Memphis, Tennessee 38152, nkhshnvs@memphis.edu |
|
Abstract:
|
It has become common practice to validate ground‐motion simulations based on a variety of time and frequency metrics scaled to quantify the level of agreement between synthetics and data or other reference solutions. There is, however, no agreement about the importance or weight that it ought to be given to each metric. This leads to their selection often being subjective, either based on intended applications or personal preferences. As a consequence, it is difficult for simulators to identify what modeling improvements are needed, which would be easier if they could focus on a reduced number of metrics. We present an analysis that looks into 11 ground‐motion validation metrics using semisupervised and supervised machine learning techniques. These techniques help label and classify goodness‐of‐fit results with the objective of prioritizing and narrowing the choice of these metrics. In particular, we use a validation dataset of a series of physics‐based ground‐motion simulations done for the 2008
|
|
Comment: |
|
|
|
|
