Integration of Geological, Geophysical and Seismological Data for Seismic Hazard Assessment Using Spatial Matching Index

Abstract

Probabilistic seismic hazard assessment (PSHA) takes into account as much data as possible for defining the initial seismic source zone model. In response to this, an algorithm has been developed for integration of geological, geophysical and seismological data through a spatial index showing the presence or absence of a potential seismic source feature in the input data. The spatial matching index (SMI) is calculated to define the coincidence of independent data showing any indications for existence of a fault structure. It is applied for hazard assessment of Bulgaria through quantification of the seismic potential of 416 square blocks, 20 × 20 km in size covering the entire territory of Bulgaria and extended by 20 km outside of the country borders. All operations are carried out in GIS environment using its capabilities to work with different types of georeferenced spatial data. Results show that the highest seismic potential (largest SMI) is observed in 56 block elements (13% of the territory) clearly delineating cores of the source zones. Partial match is registered in 98 block elements when one of the features is missing. Not any evidence for earthquake occurrence is predicted by our calculation in 117 elements, comprising 28% of the examined area. The quantitative parameter for spatial data integration which is obtained in the present research may be used to analyze information regardless of its type and purpose.

Potential Locations of Strong Earthquakes in Bulgaria and the Neighbouring Regions

“…Over the past decades, many seismic hazard assessment studies have been carried out for the Bulgarian region [3][4][5][6][7][8][9]. Most of these works implemented the probabilistic seismic assessment approach (PSHA), the reliability of which has come under considerable criticism in recent years [10,11].…”

Identification of patterns for space-time event networks

“…In this line, Salje et al (2016) analyze distances between sequential cases in a transmission chain and characterize transmission distances. Trifonova et al (2019) use the juxtaposition of seismogenic features based on geological, geophysical and seismological data, and outline the areas with the highest earthquake capacity. Elliott and Wartenberg (2004) considered that spatially organized socio-economic effects can have important influence on the disease rates observed in small areas and on areas where some people are exposed to the effects of pollution.…”