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Your search keyword '"Khawaja A. M."' showing total 2 results
Start Over Author "Khawaja A. M." Topic statistical seismology
2 results on '"Khawaja A. M."'

1. pyCSEP: A Python Toolkit for Earthquake Forecast Developers

Author

Savran, William H., Bayona, José A., Iturrieta, Pablo, Khawaja, Asim M., Bao, Han, Bayliss, Kirsty, Herrmann, Marcus, Schorlemmer, Danijel, Maechling, Philip J., and Werner, Maximilian J.

Subjects
pycsep, statistical seismology, Geophysics, earthquake forecasting
Abstract

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an open and global community whose mission is to accelerate earthquake predictability research through rigorous testing of probabilistic earthquake forecast models and prediction algorithms. pyCSEP supports this mission by providing open-source implementations of useful tools for evaluating earthquake forecasts. pyCSEP is a Python package that contains the following modules: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines and various other utilities. Most significantly, pyCSEP contains several statistical tests needed to evaluate earthquake forecasts, which can be forecasts expressed as expected earthquake rates in space–magnitude bins or specified as large sets of simulated catalogs (which includes candidate models for governmental operational earthquake forecasting). To showcase how pyCSEP can be used to evaluate earthquake forecasts, we have provided a reproducibility package that contains all the components required to re-create the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this article. By providing useful tools to earthquake forecast modelers and facilitating an open-source software community, we hope to broaden the impact of the CSEP and further promote earthquake forecasting research.

Published
2022
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2. Statistical power of spatial earthquake forecast tests.

Author

Khawaja, Asim M, Hainzl, Sebastian, Schorlemmer, Danijel, Iturrieta, Pablo, Bayona, José A, Savran, William H, Werner, Maximilian, and Marzocchi, Warner

Subjects
*EARTHQUAKE prediction, *STATISTICAL power analysis, *FORECASTING, *EARTHQUAKES, *SPATIAL resolution
Abstract

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an international effort to evaluate earthquake forecast models prospectively. In CSEP, one way to express earthquake forecasts is through a grid-based format: the expected number of earthquake occurrences within 0.1° × 0.1° spatial cells. The spatial distribution of seismicity is thereby evaluated using the Spatial test (S-test). The high-resolution grid combined with sparse and inhomogeneous earthquake distributions leads to a huge number of cells causing disparity in the number of cells, and the number of earthquakes to evaluate the forecasts, thereby affecting the statistical power of the S-test. In order to explore this issue, we conducted a global earthquake forecast experiment, in which we computed the power of the S-test to reject a spatially non-informative uniform forecast model. The S-test loses its power to reject the non-informative model when the spatial resolution is so high that every earthquake of the observed catalog tends to get a separate cell. Upon analysing the statistical power of the S-test, we found, as expected, that the statistical power of the S-test depends upon the number of earthquakes available for testing, e.g. with the conventional high-resolution grid for the global region, we would need more than 32 000 earthquakes in the observed catalog for powerful testing, which would require approximately 300 yr to record M ≥ 5.95. The other factor affecting the power is more interesting and new; it is related to the spatial grid representation of the forecast model. Aggregating forecasts on multi-resolution grids can significantly increase the statistical power of the S-test. Using the recently introduced Quadtree to generate data-based multi-resolution grids, we show that the S-test reaches its maximum power in this case already for as few as eight earthquakes in the test period. Thus, we recommend for future CSEP experiments the use of Quadtree-based multi-resolution grids, where available data determine the resolution. [ABSTRACT FROM AUTHOR]

Published
2023
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