COMMON BEST PRACTICE PROCEDURES FOR SITE-EFFECTS CHARACTERIZATION: RESULTS FROM AN INTERNATIONAL QUESTIONNAIRE

Abstract

Setting-up standard practices, together with a clear evaluation of their quality, are becoming very important to reach high-level sitecharacterization metadata, useful for site effects studies, seismic microzonation, seismic hazard assessment and many other research fields. In the last years, several efforts have been done at national and international level to define standards and guidelines for seismic site characterization (e.g., Foti et al., Bull Earthquake Eng, 2017, doi:10.1007/s10518-017-0206-7; Geological Survey of Canada, Open File 7078, 2012, doi:10.4095/291753; Consortium of Organizations for Strong Motion Observation Systems, http://www.cosmos-eq.org; WP12- Deliverable D23.12, SESAME European research project, 2004). Within the 2017-2020 activities of the “Networking databases of site and station characterization” (WP7-NA5 of the SERA “Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe” Horizon 2020 Project), European teams are working on the best practice for site characterization and related quality assessment target to network operators and seismological and engineering communities. More specifically, the goals of this task are: (i) to evaluate the most relevant site effects indicators; (ii) to write practical guidelines for site effects characterization and related site condition parameters at rock sites (including topography effects) and soft soils (including non-linear and basin effects); (iii) to propose a quality metrics on the site characterization parameters. As a first step, we have prepared a Questionnaire for collecting existing bibliography and best practice schemes to compute indicators for site effects characterization. We sent the Questionnaire to selected research groups of different countries, both partners of the SERA project (ISTERRE-CNRS, France; ETH, Switzerland; INGV, Italy; AUTH, Greece) and several external groups involved in site characterization (Caltech-USGS, USA; AFAD, Turkey; Virginia Tech USA; GFZ, Germany; ITSAK, Greece; University of Potsdam, Germany; UoTUniversity of Texas, USA; INGV, Italy), and collected back the answers. Each team provided the list of site effects indicators, their importance for site effects assessment (based on expert judgment) and their preferred methods of analysis for retrieving the indicators. In the Questionnaire, the site-effect indicators were grouped into (i) Scalar (e.g. resonance frequency), (ii) Depthdependent (e.g. shear-wave Vs profile), (iii) Frequency-dependent (e.g. spectral ratio), (iv) Geological/Morphological (e.g. Surface geology/lithology unit) and (v) Advanced siteeffects (e.g. numerical 2D or 3D modeling) parameters. Each indicator is described through several fields, summarized into five main subsets: Importance - in-depth index related to the amount of knowledge on site-effects characterization supplied by each indicator, and it can assume three values (basic, intermediate and top), depending on the expert judgment; Feasibility - level of difficulty to measure the target indicator (easy, average and difficult); Data - type of data used to measure the proposed indicator; Analysis - method of analysis and suggested code to derive the indicator, including the value’s selection and uncertainty estimation; Bibliography - references and guidelines related to the best practice of measurement and analysis. A preliminary analysis of the Questionnaires reveals a consensus on several basic indicators, such as the resonance frequency, Vs30 or the 1D Vs profile, even if the teams use different data acquisition, analysis methods and metrics. Fewer teams indicated more advanced parameters, such as 2D-3D site goals of this task are: (i) to evaluate the most relevant site effects indicators; (ii) to write practical guidelines for site effects characterization and related site condition parameters at rock sites (including topography effects) and soft soils (including non-linear and basin effects); (iii) to propose a quality metrics on the site characterization parameters. As a first step, we have prepared a Questionnaire for collecting existing bibliography and best practice schemes to compute indicators for site effects characterization. We sent the Questionnaire to selected research groups of different countries, both partners of the SERA project (ISTERRE-CNRS, France; ETH, Switzerland; INGV, Italy; AUTH, Greece) and several external groups involved in site characterization (Caltech-USGS, USA; AFAD, Turkey; Virginia Tech USA; GFZ, Germany; ITSAK, Greece; University of Potsdam, Germany; UoTUniversity of Texas, USA; INGV, Italy), and collected back the answers. Each team provided the list of site effects indicators, their importance for site effects assessment (based on expert judgment) and their preferred methods of analysis for retrieving the indicators. In the Questionnaire, the site-effect indicators were grouped into (i) Scalar (e.g. resonance frequency), (ii) Depthdependent (e.g. shear-wave Vs profile), (iii) Frequency-dependent (e.g. spectral ratio), (iv) Geological/Morphological (e.g. Surface geology/lithology unit) and (v) Advanced siteeffects (e.g. numerical 2D or 3D modeling) parameters. Each indicator is described through several fields, summarized into five main subsets: Importance - in-depth index related to the amount of knowledge on site-effects characterization supplied by each indicator, and it can assume three values (basic, intermediate and top), depending on the expert judgment; Feasibility - level of difficulty to measure the target indicator (easy, average and difficult); Data - type of data used to measure the proposed indicator; Analysis - method of analysis and suggested code to derive the indicator, including the value’s selection and uncertainty estimation; Bibliography - references and guidelines related to the best practice of measurement and analysis. A preliminary analysis of the Questionnaires reveals a consensus on several basic indicators, such as the resonance frequency, Vs30 or the 1D Vs profile, even if the teams use different data acquisition, analysis methods and metrics. Fewer teams indicated more advanced parameters, such as 2D-3D site