Development of double-pair double difference earthquake location algorithm for improving earthquake locations

2016 ◽  
Vol 208 (1) ◽  
pp. 333-348 ◽  
Author(s):  
Hao Guo ◽  
Haijiang Zhang
Keyword(s):  
Earthquake Location ◽  
Double Difference ◽  
Earthquake Locations ◽  
Location Algorithm

scholarly journals APPLICATION OF 3-D VELOCITY MODELS AND RAY TRACING IN DOUBLE DIFFERENCE EARTHQUAKE LOCATION ALGORITHMS: APPLICATION TO THE MYGDONIA BASIN (NORTHERN GREECE)

2004 ◽  
Vol 36 (3) ◽  
pp. 1396 ◽  
Author(s):  
O. C. Galanis ◽  
C. B. Papazachos ◽  
P. M. Hatzidimitriou ◽  
E. M. Scordilis
Keyword(s):  
Ray Tracing ◽  
Velocity Structure ◽  
Test Site ◽  
Earthquake Location ◽  
Double Difference ◽  
Northern Greece ◽  
Local Networks ◽  
Velocity Models ◽  
Earthquake Locations

In the past years there has been a growing demand for precise earthquake locations for seismotectonic and seismic hazard studies. Recently this has become possible because of the development of sophisticated location algorithms, as well as hardware resources. This is expected to lead to a better insight of seismicity in the near future. A well-known technique, which has been recently used for relocating earthquake data sets is the double difference algorithm. In its original implementation it makes use of a one-dimensional ray tracing routine to calculate seismic wave travel times. We have modified the implementation of the algorithm by incorporating a three-dimensional velocity model and ray tracing in order to relocate a set of earthquakes in the area of the Mygdonia Basin (Northern Greece). This area is covered by a permanent regional network and occasionally by temporary local networks. The velocity structure is very well known, as the Mygdonia Basin has been used as an international test site for seismological studies since 1993, which makes it an appropriate location for evaluating earthquake location algorithms, with the quality of the results depending only on the quality of the data and the algorithm itself. The new earthquake locations reveal details of the area's seismotectonic structure, which are blurred, if not misleading, when resolved by standard (routine) location algorithms.

Relocation of the 1998 Zhangbei-Shangyi earthquake sequence using the double difference earthquake location algorithm

2004 ◽  
Vol 17 (2) ◽  
pp. 125-130 ◽  
Author(s):  
Yang Zhi-xian ◽  
Chen Yun-tai
Keyword(s):  
Earthquake Sequence ◽  
Earthquake Location ◽  
Double Difference ◽  
Location Algorithm

Velocity models for routine earthquake monitoring at volcanoes: from deterministic to Bayesian

2021 ◽  
Author(s):  
Jeremy Pesicek ◽  
Trond Ryberg ◽  
Roger Machacca ◽  
Jaime Raigosa
Keyword(s):  
Velocity Structure ◽  
Full Range ◽  
Earthquake Location ◽  
Monitoring Networks ◽  
Unknown Parameters ◽  
Widespread Application ◽  
Starting Values ◽  
Velocity Models ◽  
Earthquake Locations ◽  
Station Corrections

<p>Earthquake location is a primary function of volcano observatories worldwide and the resulting catalogs of seismicity are integral to interpretations and forecasts of volcanic activity.  Ensuring earthquake location accuracy is therefore of critical importance.  However, accurate earthquake locations require accurate velocity models, which are not always available.  In addition, difficulties involved in applying traditional velocity modeling methods often mean that earthquake locations are computed at volcanoes using velocity models not specific to the local volcano.   </p><p>Traditional linearized methods that jointly invert for earthquake locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, which are then iteratively updated to minimize the data misfit.  However, these deterministic methods are susceptible to local minima and divergence, issues exacerbated by sparse seismic networks and/or poor data quality common at volcanoes.  In cases where independent prior constraints on local velocity structure are not available, these methods may result in systematic errors in velocity models and hypocenters, especially if the full range of possible starting values is not explored.  Furthermore, such solutions depend on subjective choices for model regularization and parameterization.</p><p>In contrast, Bayesian methods promise to avoid all these pitfalls.  Although these methods traditionally have been difficult to implement due to additional computational burdens, the increasing use and availability of High-Performance Computing resources mean widespread application of these methods is no longer prohibitively expensive.  In this presentation, we apply a Bayesian, hierarchical, trans-dimensional Markov chain Monte Carlo method to jointly solve for hypocentral parameters, 1D velocity structure, and station corrections using data from monitoring networks of varying quality at several volcanoes in the U.S. and South America.  We compare the results with those from a more traditional deterministic approach and show that the resulting velocity models produce more accurate earthquake locations.  Finally, we chart a path forward for more widespread adoption of the Bayesian approach, which may improve catalogs of volcanic seismicity at observatories worldwide. </p>

scholarly journals Relative earthquake location procedure for clustered seismicity with a single station

2020 ◽  
Author(s):  
Francesco Grigoli ◽  
William Ellsworth ◽  
Miao Zhang ◽  
Mostafa Mousavi ◽  
Simone Cesca ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Distance Geometry ◽  
Synthetic Data ◽  
Earthquake Location ◽  
Double Difference ◽  
Monitoring Networks ◽  
Seismic Stations ◽  
Single Station ◽  
Active Research ◽  
Distance Geometry Problem

Summary Earthquake location is one of the oldest problems in seismology, yet remains an active research topic. With dense seismic monitoring networks it is possible to obtain reliable locations for microearthquakes; however, in many cases dense networks are lacking, limiting the location accuracy, or preventing location when there are too few observations. For small events in all settings, recording may be sparse and location may be difficult due to low signal-to-noise ratio. In this work we introduce a new, distance-geometry-based method to locate seismicity clusters using only one or two seismic stations. A Distance Geometry Problem determines the location of sets of points based only on the distances between some member pairs. Applied to seismology, our approach allows earthquake location using the inter-event distance between earthquakes pairs, which can be estimated using only one or two seismic stations. We first validate the method with synthetic data that resemble common cluster shapes, and then test the method with two seismic sequences in California: the August 2014 Mw 6.0 Napa earthquake band the July 2019 Mw 6.4 Ridgecrest earthquake sequence. We demonstrate that our approach provides robust and reliable results even for a single station. When using two seismic stations, the results capture the same structures recovered with high resolution Double Difference locations based on multiple stations. The proposed method is particularly useful for poorly monitored areas, where only a limited number of stations are available.

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