Development of Machine Learning Algorithms to Segment and Study Images of Astromaterial Samples

Introduction: Micrometer-scale chemical analyses
of chondritic meteorites and mission-returned
asteroid samples can reveal details of the physical and
chemical processes operating in the early solar system,
including processes that gave rise to planets, moons,
and minor bodies. These primitive astromaterials are
comprised of chondrules, calcium- and aluminum-rich
inclusions (CAI), and many other silicates, oxides,
metals, sulfides, and fine-grained materials. The chemical
and mineralogical complexity of these samples,
vast populations of different components, and heterogeneity
across mm to km scales, all limit our understanding
of the origin and evolution of these materials.
Here, we describe recent efforts to use machine
learning techniques to automate the segmentation of
chemical maps of chondritic meteorites, designed to
aid studies of asteroid samples returned by spacecraft.
By automating the task of segmentation it will become
possible to rapidly analyze and interpret the sizes,
shapes, mineralogy, chemistry, and other properties of
every chondrule, calcium- and aluminum-rich inclusion
(CAI) and other clast within and between asteroid
samples. Sample return missions significantly accelerate
and heighten the need to develop such new data
analysis techniques, and associated data repositories.
Techniques: Neural networks require abundant
training data, i.e. images which have been segmented
by a human user. We have manually segmented data
available from previous petrologic and chemical work
at NASA Johnson Space Center and the American Museum
of Natural History [1-4]. These data were derived
from energy- and wavelength-dispersive X-ray spectroscopy
(EDS, WDS) mapping of samples from many
chondrite groups.
The Deeplabv3+ [5] neural network architecture
was trained on human-labeled masks and used to create
machine-labeled masks. Several different algorithms
were investigated, with inputs ranging from
common RGB image formats through to hyperspectral
datasets, with raw data comprising greyscale maps of
Mg, Ca, and Al, with or without Si, Fe, Ti for both
EDS and WDS data, and extending to other elements
in EDS only. Each greyscale image was paired with a
binary mask for each labelled particle type.
Results: The trained algorithms can segment (Fig
1), classify, and measure the dimensions of thousands
of particles in chemical maps of a standard 1-inch
round petrographic section in seconds to minutes, rather
than many hours needed by a human. Accuracy of
the algorithms varied from chondrite to chondrite and
across particle types. Further results and details of the
algorithms will be presented at the workshop.
Future directions: Machine learning has the potential
to revolutionize our understanding of complex
particle populations contained within primitive astromaterial,
with segmentation being a critical first step.
Example applications include better understanding of
particle transport, nebular reservoirs, parent body accretion,
and a deeper understanding of the relationships
between particle populations and bulk rock elemental
and isotopic compositions.
In addition to benefits that machine learning can
bring to individual researchers, building a community
data repository of thousands to millions of particles
across hundreds of samples will open up many other
possibilities. For example, with a large enough dataset
it will be possible to search for exceptionally closely
matching particles across disparate samples. Such a
capability would enable a single CAI from OSIRISREx
or Hayabusa/II samples to be matched to chondritic
CAIs that exhibit near-identical size, texture, and
mineralogy, down to the level of similar core phenocrysts,
zonation, and rim sequences. Such comparative
analyses will help to disentangle precursor chemistry,
chronology, gas/dust reservoirs during heating, and
accretion. Such an endeavor would be impossible
without machine learning and a large community data
repository of astromaterial chemical/mineralogic maps.