Partitioning Ground Motion Uncertainty When Conditioned on Station Data

ABSTRACT Rapid estimation of earthquake ground shaking and proper accounting of associated uncertainties in such estimates when conditioned on strong-motion station data or macroseismic intensity observations are crucial for downstream applications such as ground failure and loss estimation. The U.S. Geological Survey ShakeMap system is called upon to fulfill this objective in light of increased near-real-time access to strong-motion records from around the world. Although the station data provide a direct constraint on shaking estimates at specific locations, these data also heavily influence the uncertainty quantification at other locations. This investigation demonstrates methods to partition the within- (phi) and between-event (tau) uncertainty estimates under the observational constraints, especially when between-event uncertainties are heteroscedastic. The procedure allows the end users of ShakeMap to create separate between- and within-event realizations of ground-motion fields for downstream loss modeling applications in a manner that preserves the structure of the underlying random spatial processes.

Effect of Base Conditions in One-Dimensional Numerical Simulation of Seismic Site Response: A Technical Note for Best Practice

The effects induced by the choice of numerical base conditions for evaluating local seismic response are investigated in this technical note, aiming to provide guidelines for professional applications. A numerical modelling of the seismic site response is presented, assuming a one-dimensional scheme. At first, with reference to the case of a homogeneous soil layer overlying a half-space, two different types of numerical base conditions, named rigid and elastic, were adopted to analyse the seismic site response. Then, geological setting, physical and mechanical properties were selected from Italian case studies. In detail, the following stratigraphic successions were considered: shallow layer 1 (shear wave velocity, VS, equal to 400 m/s), layer 2 (VS equal to 600 m/s) and layer 3 (VS equal to 800 m/s). In addition, real signals were retrieved from the web site of the Italian accelerometric strong motion network. Rigid and elastic base conditions were adopted to estimate the ground motion modifications of the reference signals. The results are presented in terms of amplification factors (i.e., ratio of integral quantities referred to free-field and reference response spectra) and are compared between the adopted numerical models.

CHINGIZ EARTHQUAKE on January 20, 2015, KR=12.2, mb=5.6, I_0^P=5–6 NEAR the SEMIPALATINSK POLYGON (Eastern Kazakhstan)

The work presents the results of data analysis of the earthquake on January 20, 2015, mb=5.6 occurred at the low-active region of Eastern Kazakhstan near the border of the former Semipalatinsk Test Site. The work briefly describes the seismicity of the Semipalatinsk Test Site related to aseismic regions according to the acting map of general seismic zoning. The earthquake was recorded by permanent seismic stations of the Institute of Geophysical Research of the Ministry of Energy of the Republic of Kazakhstan and by two strong-motion accelerometers. The earthquake was felt at distances up to 300 km from the epicentre, its maximum intensity was noted at Medeu settlement where the shaking intensity was 56 by the MSK-64 scale. The earthquake focal mechanism is shown, the parameters of seismic effects and response spectra are calculated.

Arias Intensity Attenuation Relationship in Sichuan-yunnan Region, China

Arias intensity is an essential ground motion measure correlating with the potential for earthquake-induced landslides. The Sichuan-Yunnan region, which is primarily mountainous, is a high incidence region of earthquake-induced landslides in China. However, there is no available attenuation relationship for this intensity measure due to the backward construction of the stations. In this study, we developed a region-specific Arias intensity attenuation relationship using the China Strong-Motion Networks Center (CSMNC) database which was established in 2008. We recommend this relationship be applied in the Sichuan-Yunnan region for moment magnitudes ranging between 4.2 and 7.9, distances ranging between 0 and 400 km and with Vs30 (the average shear-wave velocity in the upper 30 meters of a soil profile) ranging between 128 and 760 m/s. The current study finds that this relationship’s intra-event, inter-event, and total standard deviations are greater than for other regions. This is likely caused by the complicated seismotectonic activities, nonlinear site effects, error from inferring Vs30, basin effects, etc. However, this relationship has the best performance in fitting and predicting the data from the Sichuan-Yunnan region.

INSTANCE – the Italian seismic dataset for machine learning

The Italian earthquake waveform data are collected here in a dataset suited for machine learning analysis (ML) applications. The dataset consists of nearly 1.2 million three-component (3C) waveform traces from about 50 000 earthquakes and more than 130 000 noise 3C waveform traces, for a total of about 43 000 h of data and an average of 21 3C traces provided per event. The earthquake list is based on the Italian Seismic Bulletin (http://terremoti.ingv.it/bsi, last access: 15 February 2020​​​​​​​) of the Istituto Nazionale di Geofisica e Vulcanologia between January 2005 and January 2020, and it includes events in the magnitude range between 0.0 and 6.5. The waveform data have been recorded primarily by the Italian National Seismic Network (network code IV) and include both weak- (HH, EH channels) and strong-motion (HN channels) recordings. All the waveform traces have a length of 120 s, are sampled at 100 Hz, and are provided both in counts and ground motion physical units after deconvolution of the instrument transfer functions. The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities. This rich set of metadata allows the users to target the data selection for their own purposes. Much of these metadata can be used as labels in ML analysis or for other studies. The dataset, assembled in HDF5 format, is available at http://doi.org/10.13127/instance (Michelini et al., 2021).