Kinematic earthquake source inversion and tsunami runup prediction with regional geophysical data

2015 ◽  
Vol 120 (5) ◽  
pp. 3324-3349 ◽  
Author(s):  
D. Melgar ◽  
Y. Bock
Keyword(s):  
Earthquake Source ◽  
Geophysical Data ◽  
Source Inversion ◽  
Tsunami Runup ◽  
Earthquake Source Inversion

scholarly journals Discriminating non-seismic long-period pulses and noise to improve earthquake source inversion

2016 ◽  
Vol 68 (1) ◽  
Author(s):  
Takahide Sakai ◽  
Hiroyuki Kumagai ◽  
Nelson Pulido ◽  
Jun Bonita ◽  
Masaru Nakano
Keyword(s):  
Earthquake Source ◽  
Source Inversion ◽  
Long Period ◽  
Earthquake Source Inversion

scholarly journals The Earthquake‐Source Inversion Validation (SIV) Project

2016 ◽  
Vol 87 (3) ◽  
pp. 690-708 ◽  
Author(s):  
P. Martin Mai ◽  
Danijel Schorlemmer ◽  
Morgan Page ◽  
Jean‐Paul Ampuero ◽  
Kimiyuki Asano ◽  
...  
Keyword(s):  
Earthquake Source ◽  
Source Inversion ◽  
Earthquake Source Inversion

scholarly journals Testing Earthquake Source Inversion Methodologies

Eos ◽  
2011 ◽  
Vol 92 (9) ◽  
pp. 75-75 ◽  
Author(s):  
Morgan Page ◽  
P. Martin Mai ◽  
Danijel Schorlemmer
Keyword(s):  
Earthquake Source ◽  
Source Inversion ◽  
Earthquake Source Inversion

Implications on 1 + 1 D Tsunami Runup Modeling due to Time Features of the Earthquake Source

2018 ◽  
Vol 175 (4) ◽  
pp. 1393-1404 ◽  
Author(s):  
M. Fuentes ◽  
S. Riquelme ◽  
J. Ruiz ◽  
J. Campos
Keyword(s):  
Earthquake Source ◽  
Tsunami Runup

scholarly journals Fully probabilistic seismic source inversion – Part 2: Modelling errors and station covariances

Solid Earth ◽  
2016 ◽  
Vol 7 (6) ◽  
pp. 1521-1536 ◽  
Author(s):  
Simon C. Stähler ◽  
Karin Sigloch
Keyword(s):  
Bayesian Inference ◽  
Cross Correlation ◽  
Likelihood Function ◽  
Body Wave ◽  
Source Parameters ◽  
Seismic Source ◽  
Earthquake Source ◽  
Source Inversion ◽  
Earthquake Source Parameters ◽  
Waveform Cross Correlation

Abstract. Seismic source inversion, a central task in seismology, is concerned with the estimation of earthquake source parameters and their uncertainties. Estimating uncertainties is particularly challenging because source inversion is a non-linear problem. In a companion paper, Stähler and Sigloch (2014) developed a method of fully Bayesian inference for source parameters, based on measurements of waveform cross-correlation between broadband, teleseismic body-wave observations and their modelled counterparts. This approach yields not only depth and moment tensor estimates but also source time functions. A prerequisite for Bayesian inference is the proper characterisation of the noise afflicting the measurements, a problem we address here. We show that, for realistic broadband body-wave seismograms, the systematic error due to an incomplete physical model affects waveform misfits more strongly than random, ambient background noise. In this situation, the waveform cross-correlation coefficient CC, or rather its decorrelation D = 1 − CC, performs more robustly as a misfit criterion than ℓp norms, more commonly used as sample-by-sample measures of misfit based on distances between individual time samples. From a set of over 900 user-supervised, deterministic earthquake source solutions treated as a quality-controlled reference, we derive the noise distribution on signal decorrelation D = 1 − CC of the broadband seismogram fits between observed and modelled waveforms. The noise on D is found to approximately follow a log-normal distribution, a fortunate fact that readily accommodates the formulation of an empirical likelihood function for D for our multivariate problem. The first and second moments of this multivariate distribution are shown to depend mostly on the signal-to-noise ratio (SNR) of the CC measurements and on the back-azimuthal distances of seismic stations. By identifying and quantifying this likelihood function, we make D and thus waveform cross-correlation measurements usable for fully probabilistic sampling strategies, in source inversion and related applications such as seismic tomography.

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