Generalized inversion techniques (GITs) have become popular for determining seismological parameters (e.g., source, attenuation, and site response), particularly in low-to-moderate seismicity regions. Indeed, GITs can potentially provide reliable site-response estimates when a minimum number of recordings is available, as well as valuable information about source parameters and regional attenuation characteristics. Significant advances have been made on GITs in which different approaches and hypotheses were investigated, such as the application of “nonparametric” and “parametric” inversion schemes. In this context, several scientific questions have arisen that depend on the final scope of the GITs: What is the optimal inversion strategy for a given dataset configuration? What is the impact of the different choices, assumptions, and implementations on the reliability of the results? Is it possible to quantify the associated epistemic uncertainties? Here, we have considered and compared the different approaches of GITs to improve the understanding of each for use in different applications. A methodological benchmark that includes different GIT methods and dataset configurations is set up to fulfill the objective, using a simple synthetic dataset, a French regional sparse dataset, and an Italian national dense dataset. The benchmark is developed in two phases: (1) phase I: a free phase with no common constraints; and (2) phase II: a constrained phase with unified reference conditions. Despite unifying the reference conditions in the different inversions, the variability was not reduced. Discrepancies are observed between different terms of GITs. Site responses appear to be the most robust estimates, compared to source and attenuation terms. The way that stress drops of earthquakes and quality factors for crustal attenuation are parameterized appears to lead to significant variability between different approaches. Finally, uncertainties are addressed by quantification of the inter-method variability for the different terms and parameters.

For ground-motion analyses, seismic hazard assessment and earthquake engineering applications, high-quality data sets of earthquakes recordings over a wide frequency range (ideally 0.1–100 Hz to cover the frequency band of interest for engineering applications) are needed. Although in the past the implementation of such ground-motion data sets was mostly concerning active seismic regions, in recent years, interest for it has also grown in low-to-moderate seismicity areas, where epistemic uncertainties associated with seismic hazard estimates were largest, due to lack of data. In this article, we provide an updated version of the Traversa et al. (2020) ground-motion data set for mainland France, now extended from 1996 to 2021. This updated data set contains more than 16,000 high-quality waveforms recorded by 622 accelerometers and broadband seismometers of the Epos-France network (formerly RESIF: Réseau Sismologique et Géodésique Français), related to more than 600 earthquakes (ML≅2−5.6) that occurred in France and neighboring countries. The updating procedure includes (1) efficient download and basic processing (instrument-response correction, baseline correction) of the available waveforms, using the stream2segment package (Zaccarelli et al., 2019); (2) mixed manual and automated waveform inspection and phase-arrival picking stage, based on the Stockwell transform (Stockwell et al., 1996); (3) waveform processing leading to the main ground-motion parameters computation; (4) verification and validation procedure implemented by Traversa et al. (2020), based on residual analyses. All these steps are applied to the entire data set, including the content of the previous version, resulting in a completely homogeneous data set. The updating procedure, the main features, and the characteristics of the updated Epos-France data set are discussed in this article, and the data and metadata are distributed through associated flatfiles and ASCII files.

Nepal is one of the most seismically active regions in the world, as highlighted by the recent devastating 2015, M_w~7.8 Gorkha earthquake, and a robust assessment of seismic hazard is paramount for the design of earthquake-resistant structures. In this study, we present a new probabilistic seismic hazard assessment (PSHA) for Nepal. We considered data and findings from recent scientific publications, which allowed us to develop a unified magnitude homogenized seismicity catalog and propose alternative seismic source characterization (SSC) models including up-to-date parameters of major thrust faults like main frontal thrust (MFT) and main boundary thrust (MBT), while also considering existing SSC models and various seismic hazard modeling strategies within a logic tree framework. The sensitivity analyses show the seismic hazard levels are generally higher for SSC models integrating the major thrust faults, followed by homogenous volume sources and smoothed seismicity approach. The seismic hazard maps covering the entirety of Nepal are presented as well as the uniform hazard spectra (UHS) for five selected locations (Kathmandu, Pokhara, Biratnagar, Nepalganj, and Dipayal) at return periods of 475- and 2475-years considering Vs,30 = 760 m/s. The results obtained are generally consistent with most recent studies. However, a notable variability in seismic hazard levels and several discrepancies with respect to the Nepal Building Building Code NBC105: 2020 and global hazard model, GEM are noted, and possible causes are discussed.

Site‐specific seismic hazard assessment involves the prior knowledge of (1) the input ground motion at the local bedrock and (2) the site response. In this article and its companion, we address the deconvolution approach to obtain a reference ground‐motion model, which consists of removing site effects from surface ground‐motion recordings. Laurendeau et al. (2018) applied this approach on the KiK‐net network with site response from 1D SH‐wave (1DSH) simulations, calculated using the $VS$ profiles available for most sites. Indeed, this approach presents several limitations with 1DSH site response if it is considered to be applied to other networks, especially in the European context. First, the approach requires identification of sites with dominant 1D effects. Second, it needs the presence of accurately measured $VS$ profiles. In this context, we propose to derive deconvolved ground‐motion models using site response from generalized inversion techniques () for two main reasons. The first reason is that the GIT delivers empirical site response for all types of sites, conditioned by the presence of sufficient amount of data, and the second is that it reduces the need for $VS$ profiles. We focus on the estimation of site effects from different approaches and present a methodology to obtain reliable site terms from GIT based on the experience from previous studies. We also introduce and detail the difference between absolute and relative site response, which mainly depends on the chosen reference. We estimate and compare site response for the KiK‐net stations with different methods, that is, empirical and theoretical 1DSH. We also conclude a list of 1D sites based on comparisons between theoretical and empirical estimates of site response. The results obtained in this article will be the primary input for the deconvolution approach applied in the companion article.

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.