TY  - JOUR
A1  - Chen, Yen-Shin
A1  - Weatherill, Graeme
A1  - Pagani, Marco
A1  - Cotton, Fabrice Pierre
T1  - A transparent and data-driven global tectonic regionalization model for seismic hazard assessment
JF  - Geophysical journal international
N2  - A key concept that is common to many assumptions inherent within seismic hazard assessment is that of tectonic similarity. This recognizes that certain regions of the globe may display similar geophysical characteristics, such as in the attenuation of seismic waves, the magnitude scaling properties of seismogenic sources or the seismic coupling of the lithosphere. Previous attempts at tectonic regionalization, particularly within a seismic hazard assessment context, have often been based on expert judgements; in most of these cases, the process for delineating tectonic regions is neither reproducible nor consistent from location to location. In this work, the regionalization process is implemented in a scheme that is reproducible, comprehensible from a geophysical rationale, and revisable when new relevant data are published. A spatial classification-scheme is developed based on fuzzy logic, enabling the quantification of concepts that are approximate rather than precise. Using the proposed methodology, we obtain a transparent and data-driven global tectonic regionalization model for seismic hazard applications as well as the subjective probabilities (e.g. degree of being active/degree of being cratonic) that indicate the degree to which a site belongs in a tectonic category.
KW  - Fuzzy logic
KW  - Earthquake hazards
KW  - Seismicity and tectonic
Y1  - 2018
U6  - https://doi.org/10.1093/gji/ggy005
SN  - 0956-540X
SN  - 1365-246X
VL  - 213
IS  - 2
SP  - 1263
EP  - 1280
PB  - Oxford Univ. Press
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Ebert, Thomas
A1  - Trauth, Martin H.
T1  - Semi-automated detection of annual laminae (varves) in lake sediments using a fuzzy logic algorithm
JF  - Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences
N2  - Annual laminae (varves) in lake sediments are typically visually identified, measured and counted, although numerous attempts have been made to automate this process. The reason for the failure of most of these automated algorithms for varve counting is the complexity of the seasonal laminations, typically rich in lateral fades variations and internal heterogeneities. In the manual counting of varves, the investigator acquired and interpreted flexible numbers of complex decision criteria to understand whether a particular simple lamination is a varve or not. Fuzzy systems simulate the flexible decision making process in a computer by introducing a smooth transition between true varve and false varve. In our investigation, we use an adaptive neuro fuzzy inference system (ANFIS) to detect varves on the basis of a digital image of the sediment. The results of the application of the ANFIS to laminated sediments from the Meerfelder Maar (Eifel, Germany) and from a landslide-dammed lake in the Quebrada de Cafayate of Argentina are compared with manual varve counts and possible reasons for the differences are discussed. (C) 2015 Elsevier B.V. All rights reserved.
KW  - Varve
KW  - Lake sediment
KW  - Image processing
KW  - Fuzzy logic
KW  - MATLAB
KW  - Statistics
Y1  - 2015
U6  - https://doi.org/10.1016/j.palaeo.2015.05.024
SN  - 0031-0182
SN  - 1872-616X
VL  - 435
SP  - 272
EP  - 282
PB  - Elsevier
CY  - Amsterdam
ER  -