Social Network Users Create Seismic Intensity Maps: An Automatic Approach of the Methodology

Abstract

Main purpose of current article is to present current state-of-the-art, regarding a methodology for producing seismic intensity maps directly and exclusively from twitter data. The methodology had been presented previously, however, the vast majority of the processing procedures were based on manual work. In current state, an automatic approach is presented, aspiring thus to minimize the processing time needed which is quite enormous considering manual analytic processes. In specific, word patterns that had been identified, were used, to initially select tweet texts that contain macroseismic observations and for classifying them according to the corresponding values of the European macroseismic intensity scale (EMS98). As a next step machine learning was employed through the use of a long short term memory-recurrent neural network (LSTM-RNN) model, trained for removing ambiguity of potentially wrongly classified data. Sequentially, the classified information was geo-referenced by detecting geolocations contained within each tweet's text, while the precision of each georeference was indicated with a related value. Two georules improve the accuracy of the processed output, completing thus the extraction of macroseismic observations. Sequentially, the processed dataset was interpolated to create seismic intensity prediction surfaces at a raster format. The applied technique was the simple kriging, widely used for the creation of seismic intensity maps. As case study, the earthquake event of SW of Lesvos (M w = 6.3, 2017, Greece) was used. The output, was consisted of four seismic intensity maps, produced from macroseismic observations extracted from twitter, posted within 2, 6, 24 and total data period, respectively, since the earthquake event occurrence. The results were assessed through a comparison with official seismic intensity maps of the same earthquake published by seismological institutes and reality. Through the assessment, the main conclusions include significant steps towards the potential operational use of the methodology, as the produced output was close to reality while in some cases was able to be more precise than other approaches. Moreover, the time needed for data processing and for creating the final maps, has been limited drastically. Those facts emerge the potentials of integrating social network data approaches even in the most specialized scientific fields, like seismic intensity mapping.