  Segmentation and analysis of individual pores and grains of mudrocks from
scanning electron microscope images is non-trivial because of noise, imaging
artifacts, variation in pixel grayscale values across images, and overlaps in
grayscale values among different physical features such as silt grains, clay
grains, and pores in an image, which make their identification difficult.
Moreover, because grains and pores often have overlapping grayscale values,
direct application of threshold-based segmentation techniques is not
sufficient. Recent advances in the field of computer vision have made it easier
and faster to segment images and identify multiple occurrences of such features
in an image, provided that ground-truth data for training the algorithm is
available. Here, we propose a deep learning SEM image segmentation model,
MudrockNet based on Google's DeepLab-v3+ architecture implemented with the
TensorFlow library. The ground-truth data was obtained from an image-processing
workflow applied to scanning electron microscope images of uncemented muds from
the Kumano Basin offshore Japan at depths < 1.1 km. The trained deep learning
model obtained a pixel-accuracy about 90%, and predictions for the test data
obtained a mean intersection over union (IoU) of 0.6591 for silt grains and
0.6642 for pores. We also compared our model with the random forest classifier
using trainable Weka segmentation in ImageJ, and it was observed that
MudrockNet gave better predictions for both silt grains and pores. The size,
concentration, and spatial arrangement of the silt and clay grains can affect
the petrophysical properties of a mudrock, and an automated method to
accurately identify the different grains and pores in mudrocks can help improve
reservoir and seal characterization for petroleum exploration and anthropogenic
waste sequestration.
