Aims and scope
Near Surface Geophysics is an international journal for the publication of research and development in geophysics applied to near surface. It places emphasis on geological, hydrogeological, geotechnical, environmental, engineering, mining, archaeological, agricultural and other applications of geophysics as well as physical soil and rock properties. Geophysical and geoscientific case histories with innovative use of geophysical techniques are welcome, which may include improvements on instrumentation, measurements, data acquisition and processing, modelling, inversion, interpretation, project management and multidisciplinary use. The papers should also be understandable to those who use geophysical data but are not necessarily geophysicists.
Impact Factor 2017: 1.186
Near Surface Geophysics is indexed/abstracted in the Current Contents/ Physical, Chemical & Earth Sciences , ISI Alerting Service and Science Citation Index Expanded .
Near Surface Geophysics is published 6 times a year.
Call for Papers Special Issue: Near-surface geophysics for geohazard assessment
Every year, natural hazards, such as earthquakes, landslides, or sinkholes, cause considerable damage to urban areas, civil structures, and critical infrastructure, while claiming thousands of lives worldwide. In light of a growing global population, requiring the extension of urban areas and an expansion of the infrastructure network, and increasing frequency of exceptional events due to climate change, assessing the risk of those hazards is becoming increasingly important. Near-surface geophysical methods can provide information critical to geohazard risk assessment and mitigation. This special issue will showcase the latest developments in near-surface geophysical methods as applied to geohazards. We invite papers that describe new developments in hazard characterization and monitoring using the broad range of geophysical sensing, and integration with geological, geotechnical, hydrological and environmental data. We encourage submissions that combine multiple approaches, including multi-disciplinary modeling or machine learning to effectively assess natural hazards.