A Magnitude Attenuation Function Derived for the 2014 Pisagua (Chile) Sequence Using Strong-Motion Data

Bindi, Dino; Schurr, Bernd; Puglia, Rodolfo; Russo, Emiliano; Strollo, Angelo; Cotton, Fabrice; Parolai, Stefano

doi:10.1785/0120140152

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…For example, bootstrapping was used to determine the standard deviation of the parameters of the GMPE model and the estimation of confidence intervals [11][12][13][14][15][16][17]. Another application of bootstrapping in seismology is for determining the uncertainty of estimated seismic deformations [18], determining the confidence intervals of the location of historical earthquakes [19], calculating the horizontal component of the seismic slowness vector [20], calculating the b parameter of the Gutenberg-Richter law [21], as well as estimating the uncertainty of the values describing seismic magnitude attenuation function [22]. There are fewer research studies on the use of bootstrapping to calculate the confidence intervals of GMPE parameters for seismic ground vibrations caused by induced seismicity [23], or blasting works [24].…”

Section: Introductionmentioning

confidence: 99%

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Bańka,

Lurka,

Szuła

2023

Energies

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Induced seismicity has been a serious problem for many coal mines in the Upper Silesian Coal Basin in Poland for many decades. The occurring mining tremors of the rock mass generate seismic vibrations that cause concern to the local population and in some rare cases lead to partial damage to buildings on the surface. The estimation of peak ground acceleration values caused by high energy mining seismic tremors is an important part of seismic hazard assessment in mining areas. A specially designed bootstrapping procedure has been applied to estimate the ground motion prediction model and makes it possible to calculate the confidence intervals of these peak ground acceleration values with no assumptions about the statistical distribution of the recorded seismic data. Monte Carlo sampling with the replacement for 132 seismic records measured for mining seismic tremors exceeding 150 mm/s2 have been performed to estimate the mean peak ground acceleration values and the corresponding upper limits of 95% confidence intervals. The specially designed bootstrap procedure and obtained ground motion prediction model reflect much better the observed PGA values and therefore provide more accurate PGA estimators compared to the GMPE model from multiple regression analysis. The bootstrap analysis of recorded peak ground acceleration values of high-energy mining tremors provides significant information on the level of seismic hazard on the surface infrastructure. A new tool has been proposed that allows for more reliable determination of PGA estimators and identification in the areas in coal mines that are prone to high-energy seismic activity.

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Section: Introductionmentioning

confidence: 99%

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Bańka,

Lurka,

Szuła

2023

Energies

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“…Savage & Anderson (1995) propose a simple 1-D model with linear interpolation that can be fit to data using quadratic optimization. A similar model, covering the same area as in this study, was applied to strong motion data from 106 events in the Pisagua sequence 2014 by Bindi et al (2014). More complex attenuation functions have been proposed in the context of ground motion prediction.…”

Section: Multifeature Magnitude Estimation 143 1 Introductionmentioning

confidence: 99%

Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile

Münchmeyer

Bindi

Sippl

et al. 2019

Geophysical Journal International

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SUMMARY Magnitude estimation is a central task in seismology needed for a wide spectrum of applications ranging from seismicity analysis to rapid assessment of earthquakes. However, magnitude estimates at individual stations show significant variability, mostly due to propagation effects, radiation pattern and ambient noise. To obtain reliable and precise magnitude estimates, measurements from multiple stations are therefore usually averaged. This strategy requires good data availability, which is not always given, for example for near real time applications or for small events. We developed a method to achieve precise magnitude estimations even in the presence of only few stations. We achieve this by reducing the variability between single station estimates through a combination of optimization and machine learning techniques on a large catalogue. We evaluate our method on the large scale IPOC catalogue with >100 000 events, covering seismicity in the northern Chile subduction zone between 2007 and 2014. Our aim is to create a method that provides low uncertainty magnitude estimates based on physically meaningful features. Therefore we combine physics based correction functions with boosting tree regression. In a first step, we extract 110 features from each waveform, including displacement, velocity, acceleration and cumulative energy features. We correct those features for source, station and path effects by imposing a linear relation between magnitude and the logarithm of the features. For the correction terms, we define a non-parametric correction function dependent on epicentral distance and event depth and a station specific, adaptive 3-D source and path correction function. In a final step, we use boosting tree regression to further reduce interstation variance by combining multiple features. Compared to a standard, non-parametric, 1-D correction function, our method reduces the standard deviation of single station estimates by up to $57\, {\rm per\, cent}$, of which $17\, {\rm per\, cent}$ can be attributed to the improved correction functions, while boosting tree regression gives a further reduction of $40\, {\rm per\, cent}$. We analyse the resulting magnitude estimates regarding their residuals and relation to each other. The definition of a physics-based correction function enables us to inspect the path corrections and compare them to structural features. By analysing feature importance, we show that envelope and P wave derived features are key parameters for reducing uncertainties. Nonetheless the variety of features is essential for the effectiveness of the boosting tree regression. To further elucidate the information extractable from a single station trace, we train another boosting tree on the uncorrected features. This regression yields magnitude estimates with uncertainties similar to the single features after correction, but without using the earthquake location as required for applying the correction terms. Finally, we use our results to provide high precision magnitudes and their uncertainties for the IPOC catalogue.

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A Magnitude Attenuation Function Derived for the 2014 Pisagua (Chile) Sequence Using Strong-Motion Data

Cited by 2 publications

References 11 publications

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile

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