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Grain size or grain size distributions (GSDs) play a major role in many fields of geoscience research. Paleopiezometry is based on the relation between grains size and flow stress. Environments of depositions have typical GSDs. Time temperature and grain size have characteristic relations during static grain growth. Fracture processes are associated with the fractal dimension of the GSD they produce, etc.. In all these cases, meaningful interpretations rest on the correct acquisition and quantification of grain size data.

The aim of this short course is to discuss with participants the following questions

1) when do we need grain size analysis ? what is it good for ? what are the limitations ?
2) how do we identify grains? what are the criteria for segmentation?
3) how do we define reliable measures for grain size ?
4) what do we mean by 'mean grain size' ?
5) how much data do we need ?
6) and what about errors ?

Handouts will be available in electronic form.

Please send email if you want to participate (renee.heilbronner@unibas.ch)

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Co-organized as CR3.14/EMRP1.92/GMPV7.19/TS13.1
Convener: Renée Heilbronner | Co-convener: Rüdiger Kilian
Thu, 11 Apr, 10:45–12:30
 
Room -2.31
Grain size or grain size distributions (GSDs) play a major role in many fields of geoscience research. Paleopiezometry is based on the relation between grains size and flow stress. Environments of depositions have typical GSDs. Time temperature and grain size have characteristic relations during static grain growth. Fracture processes are associated with the fractal dimension of the GSD they produce, etc.. In all these cases, meaningful interpretations rest on the correct acquisition and quantification of grain size data.

The aim of this short course is to discuss with participants the following questions

1) when do we need grain size analysis ? what is it good for ? what are the limitations ?
2) how do we identify grains? what are the criteria for segmentation?
3) how do we define reliable measures for grain size ?
4) what do we mean by 'mean grain size' ?
5) how much data do we need ?
6) and what about errors ?

Handouts will be available in electronic form.

Please send email if you want to participate (renee.heilbronner@unibas.ch)