SAMSI workshop focused on a quantitative understanding of earthquake dynamics

people working with change

Antoinette Tordesillas, U. Melbourne (L), Nicholas van der Elst, Columbia U. and Karen Daniels, NCSU (R).

The following was written by: Yehuda Ben-Zion, University of Southern California, Jörn Davidsen, University of Calgary, Egill Hauksson, California Institute of Technology and Ilya Zaliapin, University of Nevada, Reno.

A quantitative understanding of earthquake dynamics that can improve the ability to forecast large earthquakes remains one of the major unsolved challenges in geophysics despite considerable research. Mathematical and statistical methods that are generally indispensable in the Earth studies (think of seismic tomography or seismic invisibility cloak) play an increasingly important role in understanding the dynamics of seismicity on the scales of days to tens of years, which are most relevant to earthquake forecasting and seismic hazard assessment.

Yehuda Ben Zion talking to someone at reception

Michael Le Blanc, U. Illinois, Urbana-Champaign (L), Karen Daniels, NCSU and Yehuda Ben-Zion, USC, (R).

A workshop on Dynamics of Seismicity, Earthquake Clustering and Patterns in Fault Networks that has recently taken place at SAMSI in Research Triangle Park, NC aimed to achieve progress in this field by taking advantage of the newly available data sets and statistical techniques. The workshop was organized as a part of the international program Mathematics of Planet Earth 2013, in cooperation with the Bernoulli Society for Mathematical Statistics and Probability via the Committee on Probability and Statistics in Physical Sciences and the International Union of Geodesy and Geophysics via the Commission on Mathematical Geophysics.

Joern Davidsen, U. Calgary talking with someone

Joern Davidsen, U. Calgary talking with someone about a research poster.

The main goal of the workshop was to build and strengthen newly emerging links between active research groups in different scientific areas – statistics, probability, mathematics, physics, seismology and computer science – toward achieving a solid understanding of seismicity patterns and structures and a physical theory for earthquake dynamics. The workshop had highlight the key role of the mathematical sciences in studying seismicity dynamics in relation to properties of faults and the crust as an essential component of this interdisciplinary research endeavor.

Ilya Zaliapin with another researcher

Lee Long, U. Wyoming (L) and Ilya Zaliapin, U. Nevada, Reno, (R).

Quality and availability of seismic data was one of the workshop topics. Many studies of seismicity are based on regional or global earthquake catalogs. Regional seismic networks in the US usually produce these catalogs. The global ComCat catalog of USGS/ANSS is a merged version of all catalogs produced in the US, including the global NEIC catalog.  Most seismic networks also produce real-time catalogs automatically but these are usually of lesser quality than the human reviewed catalogs. As real-time catalogs improve and the cost of producing human reviewed catalogs increases, network operators and the researchers are faced with the questions if the real-time catalogs are sufficient. This question is particularly pressing in the current budget climate, which already had a negative impact on catalog production. The real-time catalogs meet the need for rapid notification to emergency managers. However, they may not provide accurate count of small earthquakes and in some cases magnitudes for events of less than M3 may be incorrect or a few events may be mislocated. The research community that works with seismicity catalogs could provide minimum quality criteria for seismicity catalogs, which can be used to judge what catalogs are of sufficient quality to be used for seismicity research.

Egil Hauksson sitting at table

Michael LeBlanc, U. Illinois, Urbana-Champaign (L) and Egill Hauksson, California Institute of Technology, (R).

The workshop participants discussed several key topics related to earthquake dynamics: (i) State-of-the-art seismic data and its complexity (presented by Egill Hauksson from Caltech and Yehuda Ben-Zion from USC), (ii) Earthquake clustering and triggering (Zhigang Peng – Georgia Tech, Ilya Zaliapin – U of Nevada, Reno, and Joern Davidsen – U of Calgary), (iii) Statistical and mathematical modeling and forecasting (Antoinette Tordesillas – U of Melbourne, Bala Rajaratnam – Stanford, Philip Stark – Berkeley, Dave Harte – Statistical Research Associates, New Zealand, and Karin Dahmen, University of Illinois – Urbana-Champaign).

Further information can be found at the workshop site:

Impressions from the SAMSI LDHD Opening Workshop

Photo of Heather with some other people in background

Heather Harrington.

The following was written by Heather A. Harrington, Hooke Research Fellow at the Mathematical Institute at the University of Oxford.

three people sitting in front of room

panel discussion.

The SAMSI LDHD workshop covered a range of topics with the overarching theme of using mathematical and statistical techniques to analyze high dimensional systems/data. Many of the talks focused on exploiting features of such systems (e.g., sparity, structure etc.) to compute high dimensional covariances, perform convex optimization, classify images, and solve inverse and inference problems using Bayesian statistics and graphical models.  I came to the conference very open and interested in many aspects of capturing the low dimensional features of high dimensional data and I learned the many advances in approaches as well as the current challenges in the field.

I particularly found the talks on computational geometry and topology fascinating. Recently I have became interested in persistent homology for analyzing networks, but I hadn’t considered some of the open problems on the statistical side. I found the most interesting talks during the LDHD conference were by Sayan Mukherjee on sufficient statistics for shapes and graph Laplacians, Ingrid Daubechies on conformal mapping to define a metric between surfaces, as well as David Dunson’s introduction on Bayesian inference, manifold learning, and geometric type models for exploiting the low dimensional structure in high dimensional data.

people around a poster

poster session.

Overall, the workshop provided a broad overview of the field, but more specifically, I learned about current research in combining probabilistic and statistical models for geometric/topological structures, and I’ve now joined two working groups aiming to understand how to put a likelihood function on surfaces and/or the meaning of noise in computational topology diagrams (summaries).

group of people talking about LDHD

LDHD working group.