Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/39889

Title: Soil Classification Maps for the Lower Tagus Valley Area, Portugal, Using Seismic, Geological, and Remote Sensing Data
Authors: Carvalho, João
Dias, Ruben
Borges, José
Quental, Lidia
Caldeira, Bento
Keywords: seismic hazard
Lower Tagus Valley
VS30
soil classification
seismic refraction
seismic noise
multispectral images
machine learning
Issue Date: 11-Apr-2025
Publisher: Remote Sensing
Citation: Carvalho, J.; Dias, R.; Borges, J.; Quental, L.; Caldeira, B. Soil Classification Maps for the Lower Tagus Valley Area, Portugal, Using Seismic, Geological, and Remote Sensing Data. Remote Sens. 2025, 17, 1376. https://doi.org/10.3390/ rs17081376
Abstract: The Lower Tagus Valley (LTV) region has the highest population density in Portugal, with over 3.7 million people living in the region. It has been struck in the past by several historical earthquakes, which caused significant economic and human losses. For a proper seismic hazard evaluation, the area needs detailed Vs30 and soil classification maps. Previously available maps are based on proxies, or an insufficient number of velocity measurements followed by coarse geological generalizations. The focus of this work is to significantly improve the available maps. For this purpose, more than 90 new S-wave seismic velocities measurements obtained from seismic refraction and seismic noise measurements, doubling the number used in previously available maps, are used to update available Vs30 and soil classification maps. The data points are also generalized to the available geological maps using local lithostratigraphic studies and, for the first time, satellite images of this area. The results indicate that lithological and thickness changes within each geological formation prevent a simple generalization of geophysical data interpretation based solely on geological mapping. The maps presented here are the first attempt to produce maps at a scale larger than 1:1,000,000 in Portugal, with direct shear wave velocity measurements. A tentative approach to produce more detailed maps using machine learning was also carried out, presenting promising results. This approach may be used in the future to reduce the number of shear wave measurements necessary to produce detailed maps at a finer scale.
URI: http://hdl.handle.net/10174/39889
Type: article
Appears in Collections:CREATE - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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