G. Longo

Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples

Using more than 1000 thin section photos of ancient (Phanerozoic) carbonates from different marine environments (pelagic to shallow-water) a new numerical methodology, based on digitized images of thin sections, is proposed here. In accordance with the Dunham classification, it allows the user to automatically identify carbonate textures unaffected by post-depositional modifications (recrystallization, dolomitization, meteoric dissolution and so on). The methodology uses, as input, 256 grey-tone digital image and by image processing gives, as output, a set of 23 values of numerical features measured on the whole image including the white areas (calcite cement). A multi-layer perceptron neural network takes as input this features and gives, as output, the estimated class. We used 532 images of thin sections to train the neural network, whereas to test the methodology we used 268 images taken from the same photo collection and 215 images from San Lorenzello carbonate sequence (Matese Mountains, southern Italy), Early Cretaceous in age. This technique has shown 93.3% and 93.5% of accuracy to classify automatically textures of carbonate rocks using digitized images on the 268 and 215 test sets, respectively. Therefore, the proposed methodology is a further promising application to the geosciences allowing carbonate textures of many thin sections to be identified in a rapid and accurate way. A MATLAB-based computer code has been developed for the processing and display of images.

The GAP-TPC

Several experiments have been conducted worldwide, with the goal of observing low-energy nuclear recoils induced by WIMPs scattering off target nuclei in ultra-sensitive, low-background detectors. In the last few decades noble liquid detectors designed to search for dark matter in the form of WIMPs have been extremely successful in improving their sensitivities and setting the best limits. One of the crucial problems to be faced for the development of large size (multi ton-scale) liquid argon experiments is the lack of reliable and low background cryogenic PMTs: their intrinsic radioactivity, cost, and borderline performance at 87 K rule them out as a possible candidate for photosensors. We propose a brand new concept of liquid argon-based detector for direct dark matter search: the Geiger-mode Avalanche Photodiode Time Projection Chamber (GAP-TPC) optimized in terms of residual radioactivity of the photosensors, energy and spatial resolution, light and charge collection efficiency