A log of the papers that the group read this quarter under the theme of ‘Inverse theory’:

Week 1/2: Monte-carlo methods with demonstrations by Daniel and David

Week 3: Brinkerhoff et al. (2016), Bayesian Inference of Subglacial Topography Using Mass Conservation. Frontiers in Earth Science.

Week 4/5: Tarantola (2005), Inverse problem theory and methods for model parameter estimation. Linked here.

Week 6: Steen-Larsen et al. (2010), Formulating an inverse problem to infer the accumulation-rate pattern from deep internal layering in an ice sheet using a Monte Carlo approach. Journal of Glaciology 56 (196).

Week 7: Liu, H. and Jezek, C. (2004) Automated Extraction of Coastline from Satellite Imagery by Integrating Canny Edge Detection and Locally Adaptive Thresholding Methods. International Journal of Remote Sensing, 25, 937-958.

Week 8: Ronneberger et al., U-Net: Convolutional Networks for Biomedical Image Segmentation. Linked here.

Mohajerani et al. (2018 preprint), Detection of glacier calving margins with convolutional neural networks: A case study.

A herd of sheep always seems fitting as an image for solving inverse problems…