ESMpro: A Proposal for Improved Data Management for the Engineering Strong-Motion Database (ESM)

Abstract

AbstractThe strategy for data processing in the Engineering Strong-Motion Database (ESM) is to disseminate only manually revised data to ensure the highest quality. However, manual processing is no longer sustainable, due to the ever-increasing rate of digital earthquake records, from global, regional, and national seismic networks, and a new framework for strong-motion data processing is required, so that records are automatically processed and the human revision is restricted to selected significant records. To this end, we present ESMpro—a modular Python software for a renewed processing framework of ESM. The software is available in a stand-alone beta version to facilitate testing and sharing among the scientific community. ESMpro provides automatic settings for waveform trimming and filtering, along with the automatic recognition of poor-quality data and multiple events. ESMpro allows classifying each record in different quality classes to reduce manual revision on a subset of the incoming data. ESMpro also allows handling different processing techniques in a modular and flexible structure to facilitate the implementation of new or alternative algorithms and file formats. The testing performed on the ESM database results in a good correspondence between the automatic and manual data processing, supporting the migration toward fully automatic procedures for massive data processing.