Geology topics

Battalgazi Yildirim

Publications and source records attributed to Battalgazi Yildirim.

3 recordsLinked to original sources

USGS2015

Improved rapid magnitude estimation for a community-based, low-cost MEMS accelerometer network

Immediately following the M w 7.2 Darfield, New Zealand, earthquake, over 180 Quake‐Catcher Network (QCN) low‐cost micro‐electro‐mechanical systems accelerometers were deployed in the Canterbury region. Using data recorded by this dense network from 2010 to 2013, we significantly improved the QCN rapid magnitude estimation relationship. The previous scaling relationship ( Lawrence et al. , 2014 ) did not accurately estimate the magnitudes of nearby (<35 km) events. The new scaling relationship estimates earthquake magnitudes within 1 magnitude unit of the GNS Science GeoNet earthquake catalog magnitudes for 99% of the events tested, within 0.5 magnitude units for 90% of the events, and within 0.25 magnitude units for 57% of the events. These magnitudes are reliably estimated within 3 s of the initial trigger recorded on at least seven stations. In this report, we present the methods used to calculate a new scaling relationship and demonstrate the accuracy of the revised magnitude estimates using a program that is able to retrospectively estimate event magnitudes using archived data.

Bulletin of the Seismological Society of America↗

USGS2015

On the reliability of Quake-Catcher Network earthquake detections

Over the past two decades, there have been several initiatives to create volunteer‐based seismic networks. The Personal Seismic Network, proposed around 1990, used a short‐period seismograph to record earthquake waveforms using existing phone lines ( Cranswick and Banfill, 1990 ; Cranswick et al. , 1993 ). NetQuakes ( Luetgert et al. , 2010 ) deploys triaxial Micro‐Electromechanical Systems (MEMS) sensors in private homes, businesses, and public buildings where there is an Internet connection. Other seismic networks using a dense array of low‐cost MEMS sensors are the Community Seismic Network ( Clayton et al. , 2012 ; Kohler et al. , 2013 ) and the Home Seismometer Network ( Horiuchi et al. , 2009 ). One main advantage of combining low‐cost MEMS sensors and existing Internet connection in public and private buildings over the traditional networks is the reduction in installation and maintenance costs ( Koide et al. , 2006 ). In doing so, it is possible to create a dense seismic network for a fraction of the cost of traditional seismic networks ( D’Alessandro and D’Anna, 2013 ; D’Alessandro, 2014 ; D’Alessandro et al. , 2014 ).

Seismological Research Letters↗

USGS2015

The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.

The Quake‐Catcher Network (QCN) is an expanding seismic array made possible by thousands of participants who volunteered time and resources from their computers to record seismic data using low‐cost accelerometers (http://qcn.stanford.edu/; last accessed December 2014). Sensors based on Micro‐Electromechanical Systems (MEMS) technology have rapidly improved over the last few years due to the demand of the private sector (e.g., automobiles, cell phones, and laptops). For strong‐motion applications, low‐cost MEMS accelerometers have promising features due to an increasing resolution and near‐linear phase and amplitude response ( Cochran, Lawrence, Christensen, and Jakka, 2009 ; Clayton et al. , 2011 ; Evans et al. , 2014 ). Each volunteer computer monitors ground motion and communicates using the Berkeley Open Infrastructure for Network Computing (BOINC, Anderson, 2004 ). Using a standard short‐term average, long‐term average (STLA) algorithm ( Earle and Shearer, 1994 ; Cochran, Lawrence, Christensen, Chung, 2009 ; Cochran, Lawrence, Christensen, and Jakka, 2009 ), volunteer computer and sensor systems detect abrupt changes in the acceleration recordings. Each time a possible trigger signal is declared, a small package of information containing sensor and ground‐motion information is streamed to one of the QCN servers ( Chung et al. , 2011 ). Trigger signals, correlated in space and time, are then processed by the QCN server to look for potential earthquakes.

Seismological Research Letters↗