Site-specific Seismic Hazard Assessment Research Articles

Ground-Motion Model for Hard-Rock Sites by Correction of Surface Recordings (Part 2): Correction, Mixed-Effects Regressions, and Results

ABSTRACT In the framework of site-specific seismic hazard assessment, the definition of reference motion is a crucial step. Reference motion is generally associated with hard-rock conditions, characterized by S-wave velocity exceeding 1500 m/s. However, ground motion recorded at sites with such conditions is poorly represented in existing strong-motion databases. Thus, the validity domains of most empirical ground-motion prediction equations (GMPEs) are not representative of reference rock conditions. To overcome this limitation and assess ground motion at reference conditions, the so-called “deconvolution approach” was proposed by Laurendeau et al. (2018) to correct surface recordings from theoretical 1DSH site response before GMPE developments. With the same purpose, in this article, we propose to apply the deconvolution approach using empirical site-response estimates as an alternative to theoretical ones. Using the Kiban–Kyoshin network (KiK-net) data, we estimate empirical site responses at KiK-net stations using generalized inversion techniques in addition to those from 1DSH numerical simulations, as presented in the companion article. Finally, a reference ground-motion model (RGMM) is determined based on empirically deconvolved ground motions. The advantage of using empirical rather than 1DSH site responses in the deconvolution approach is that in the former case the RGMM can be built based on records from an extensive set of sites, whereas the latter case is restricted to well-characterized sites with dominant 1D behavior. This makes the proposed approach easily exportable to different regions of the world, where precise site characterizations are not systematically available, and the knowledge of site behavior is limited.

Frequency-dependent site amplification functions for key geological units in Iceland from a Bayesian hierarchical model for earthquake strong-motions

A fundamental modeling assumption in engineering seismology is that seismic ground motion is affected by source, propagation path and site effects. Contrary to the first two, comprehensive studies on site effects, the localized amplification effects on seismic ground motion due to the uppermost geological structure, have been limited in Iceland. As a result, site amplification has been modeled in ground motion models in a simplistic and qualitative way. In this study, we address this limitation by using the new Bayesian hybrid GMM proposed by Kowsari et al. (2020) to model the recorded ground motions at regional strong-motion stations in southwest Iceland, specifically the peak ground acceleration and pseudo-acceleration response spectra as a function of oscillator period (i.e., PGA and PSA at T=0.01-3.0 s), earthquake magnitude, distance and hypocentral depth, and at first, we omit a site-term. Then, we incorporate the GMM in a Bayesian Hierarchical Model (BHM) that allows prior distributions of model parameters to be used and we partition the model residuals into various terms associated with the source, propagation path and station terms. Through Bayesian inference of the model parameters the results show that the station term residuals reveal a frequency-dependent site amplification at the recording sites. By inspecting this systematic behaviour, we propose four groups of station terms by their characteristics. We then carefully inspect the station locations in terms of their associated geological unit and observe that the geological units and station term groups are qualitatively correlated. By combining the two, we propose the first frequency-dependent average site amplification functions associated with key geological units in Iceland based on age and formation. Provisionally we refer to them as hard rock, rock, (Holocene) lava rock, and (unspecified) soil site classes, respectively. We then update the GMM by incorporating these amplification functions so that it now enables the prediction of PGA and PSA at these four site classes. As a result, the new model standard deviations are considerably lower, with those associated with the event term being τ = 0.05, station terms φS2S = 0.10, intra-event φ = 0.17 and total σ = 0.18 in common logarithm units for PGA. The new site class-specific GMM now enables the site-specific ground motion for separate geological units which in turn improves site-specific seismic hazard assessment and informed site-specific engineering decision making in Iceland.

Site amplification prediction model of shallow bedrock sites based on machine learning models

Prediction of the site amplification is of primary importance for a site-specific seismic hazard assessment. A large suite of both empirical and simulation-based site amplification models has been proposed. Because they are conditioned on a few simplified site proxies including time-averaged shear wave velocity up to a depth of 30 m (VS30) and site period (TG), they only provide approximate estimates of the site amplification. In this study, site amplification prediction models are developed using two machine learning algorithms, which are random forest (RF) and deep neural network (DNN). A comprehensive database of site response analysis outputs obtained from simulations performed on shallow bedrock profiles is used. Instead of simplified site proxies and ground motion intensity measures, matrix data which include the response spectrum of the input ground motion and shear wave velocity profile. Both machine learning based models provide exceptional prediction accuracies of both the linear and nonlinear amplifications compared with the regression-based model, producing accurate predictions of both binned mean and standard deviation of the site amplification. Among two machine learning techniques, DNN-based model is revealed to produce better predictions.

HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations

ABSTRACT Seismic site classification, which is fundamental for site-specific seismic hazard assessment, also plays an important role in accurate interpretation of ground motion data. However, detailed borehole information is not always available in many countries, e.g., China. Therefore, this study investigated application of the generalized regression neural network (GRNN) method to seismic site classification using China strong motion stations as example case. First, stations from KiK-net in Japan were classified based on their borehole information and individually assigned to I, II, III, and IV site classes as defined in Chinese seismic code. Then, mean horizontal-to-vertical spectral ratio (HVSR) curves for each site class were calculated as reference patterns. The overall recall rates for I, II, and III sites could reach 66.60%, 67.57%, and 68.42%, respectively, regarding use of KiK-net stations. The GRNN site classification scheme was validated using borehole information of K-NET stations, with recall rates for I and II site classes reaching 68% and 60%, respectively. Finally, based on HVSR curves calculated using strong ground motion data acquired during 2007–2015 in China, the site conditions of 167 National Strong Motion Observation Network System stations were estimated using the GRNN classification scheme. The results were partially validated using borehole information of 73 stations. The similarity between the mean HVSR curves and reference pattern curves indicated that the GRNN seismic site classification scheme is robust and could produce plausible results succinctly.

A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering

Investigations made to evaluate the site-effect characteristics and to develop a reliable site classification scheme have received paramount importance for the urban areas planning and reliable site-specific seismic hazard assessment. This paper presents a novel non-objective and data-driven approach for preliminary seismic site-specific classification maps using machine learning (ML) based on affinity propagation (AP) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves inside King Saud University (KSU) campus, which is among the main areas in Saudi Arabia. Besides, the proposed model aims to overcome the clustering error due to the dependency of the interpreter’s experience. Measurements of the ambient vibrations were performed to cover the entire campus area by about 307 stations. Recording at each station lasted for 20 minute length and a sample rate of 128 Hz for each station to satisfy the criteria for reliable and unambiguous HVSR results. Frequency and amplification values were used for subsequent site classification by passing messages between data points. The obtained results illustrate that the microtremor spectral ratio can be a remarkably robust tool in determining site effects. Accordingly, the proposed methodology can assist the decision-makers to set the priorities of managing land uses, estimating the earthquake losses, conducting programs for reducing the vulnerability of existing structures, enforcing building codes, planning for emergency response and long-term recovery, and designing and implementing phases of new constructions.

Microtremor study for evaluating the site response characteristics in the Surat City of western India

The Surat City, which is the second most populated city in the state of Gujarat in western India, warrants site-specific seismic hazard assessment due to its rapid urbanization and proximity to major seismogenic zones. This study reports results of microtremor investigations at 72 single stations and 4 arrays in an area of 325 km2 spanning the city. The resonant frequencies, associated peak amplification values and liquefaction vulnerability indices were deduced from the horizontal to vertical spectral ratios. Ground amplification (AHVSR) in the range of 3.0–5.0 was observed in the 2.0–4.0-Hz frequency band at most of the sites. A secondary AHVSR between 2.0 and 3.0 is also observed in the 6.0–7.0-Hz frequency band at a few sites. Locales that are most susceptible to liquefaction are identified based on their vulnerability index (K g) exceeding the value of 10. The shear wave velocities (V s) ≥ 500 m/s inferred from array measurements occur at 38 m depth in the western part and ~16 m depth in the eastern part of city. The response spectra estimated from strong motion data recorded at an accelerograph site in Surat from three earthquakes of M w ≥ 3.2 that occurred in Kachchh, Saurashtra and Narmada regions are in accordance with our inferences of characteristic site frequencies and amplification. Our results, in agreement with the damage scenario during the 2001 Bhuj earthquake, provide valuable inputs for site-specific seismic hazard evaluation of the Surat City.

Challenges Ahead for Nuclear Facility Site-Specific Seismic Hazard Assessment in France: The Alternative Energies and the Atomic Energy Commission (CEA) Vision

Seismic analysis in the context of nuclear safety in France is currently guided by a pure deterministic approach based on Basic Safety Rule (Regle Fondamentale de Surete) RFS 2001-01 for seismic hazard assessment, and on the ASN/2/01 Guide that provides design rules for nuclear civil engineering structures. After the 2011 Tohohu earthquake, nuclear operators worldwide were asked to estimate the ability of their facilities to sustain extreme seismic loads. The French licensees then defined the ‘hard core seismic levels’, which are higher than those considered for design or re-assessment of the safety of a facility. These were initially established on a deterministic basis, and they have been finally justified through state-of-the-art probabilistic seismic hazard assessments. The appreciation and propagation of uncertainties when assessing seismic hazard in France have changed considerably over the past 15 years. This evolution provided the motivation for the present article, the objectives of which are threefold: (1) to provide a description of the current practices in France to assess seismic hazard in terms of nuclear safety; (2) to discuss and highlight the sources of uncertainties and their treatment; and (3) to use a specific case study to illustrate how extended source modeling can help to constrain the key assumptions or parameters that impact upon seismic hazard assessment. This article discusses in particular seismic source characterization, strong ground motion prediction, and maximal magnitude constraints, according to the practice of the French Atomic Energy Commission. Due to increases in strong motion databases in terms of the number and quality of the records in their metadata and the uncertainty characterization, several recently published empirical ground motion prediction models are eligible for seismic hazard assessment in France. We show that propagation of epistemic and aleatory uncertainties is feasible in a deterministic approach, as in a probabilistic way. Assessment of seismic hazard in France in the framework of the safety of nuclear facilities should consider these recent advances. In this sense, the opening of discussions with all of the stakeholders in France to update the reference documents (i.e., RFS 2001-01; ASN/2/01 Guide) appears appropriate in the short term.

A new site classification approach based on neural networks

Site classification is an important procedure for a reliable site-specific seismic hazard assessment. On the other hand, the site conditions at strong-motion stations are essential for accurate interpretation and analysis of the recorded ground motion data obtained from different regions of the world. For some countries with insufficient data on the subsurface geological settings, the required site condition information is not available. This paper presents a new and efficient approach for site classification based on artificial neural networks (ANN) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves for four site classes. The nonlinear nature of ANN and their ability to learn in a complex environment make it highly suitable for function approximation and solving complicated engineering problems. Two types of radial basis function (RBF) neural networks, namely, probabilistic neural networks (PNN) and generalized regression neural networks (GRNN) were chosen in this study, as no separate training phase is required, rendering them particularly suitable for site classification. The proposed approach has been tested using data of the Chi-Chi, Taiwan, earthquake ( Mw=7.6) recorded from 87 stations at which the site conditions are known. Analyses show that both the PNN and the GRNN perform very well with similar accuracy in estimating site conditions, with successful rates of 78% and 75%, respectively.

Scaling of intraplate earthquake recurrence interval with fault length and implications for seismic hazard assessment

Consensus indicates that faults follow power‐law scaling, although significant uncertainty remains about the values of important parameters. Combining these scaling relationships with power‐law scaling relationships for earthquakes suggests that intraplate earthquake recurrence interval scales with fault length. Regional scaling data may be locally calibrated to yield a site‐specific seismic hazard assessment tool. Scaling data from small faults (those that do not span the seismogenic layer) suggest that recurrence interval varies as a negative power of fault length. Due to uncertainties regarding the recently recognized changes in scaling for large earthquakes, it is unclear whether recurrence interval varies as a negative or positive power of fault length for large faults (those that span the seismogenic layer). This question is of critical importance for seismic hazard assessment.