OpenHVSR - Processing toolkit: Enhanced HVSR processing of distributed microtremor measurements and spatial variation of their informative content

*Autore corrispondente

Abstract

The investigation of seismic ambient noise (microtremor) in spectral ratio form, known as the Horizontal-to-Vertical Spectral Ratio technique, is extremely popular nowadays both to investigate large areas in a reduced amount of time, and to leverage a wider choice of low cost equipment. In general, measurements at multiple locations are collected to generate multiple, individual spectral ratio curves. Recently, however, there has been an increasing interest in spatially correlating informative content from different locations. Accordingly, we introduce a new computer program, “OpenHVSR – Processing Toolkit”, developed in Matlab (R2015b), specifically engineered to enhance data processing with the purpose of spatially correlating different forms of informative data content, creation of maps, and display of the results in 2D and 3D. The interface is designed to be user friendly while tightly binding processing and visualization so that the effects of different processing choices can be immediately evaluated. Further, bedrock mapping capability, as introduced by Ibs-von Seht and Wohlenberg (1999) is included both through the computation of bedrock depth via a set of published regressions or by computing a customized regression based on the data at hand. The program aims at implementing the most effective and desirable processing tools present in other commercial and non-commercial alternatives, all in one bundle, freely available to the scientific community. In addition to incorporating and enhancing currently available state of the art tools, we have integrated several original features that are not present in any other program. The presented processing toolkit naturally integrates with our data inversion software, “OpenHVSR”, published in 2016. Together, they constitute a complete workflow for the Horizontal-to-Vertical Spectral Ratio method. We expect this first version to be of great use to researchers and hope it will constitute the basis for further collaborative development toward future releases oriented at exploring the potentials of this technique.

Received 30 December 2017, Revised 12 April 2018, Accepted 19 July 2018, Available online 20 July 2018.

https://doi.org/10.1016/j.cageo.2018.07.006

Trovalo in Computers & Geosciences, Vol. 120, pp. 10-20

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