Automated igneous rock identifiers for Mars Exploration

Details Automated igneous rock identifiers for Mars Exploration Automated igneous rock identifiers for Mars Exploration

Apr 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003eaeja....14314g&link_type=abstract

EGS - AGU - EUG Joint Assembly, Abstracts from the meeting held in Nice, France, 6 - 11 April 2003, abstract #14314

Astronomy and Astrophysics

Astronomy

Scientific paper

A key task for human or robotic explorers on the surface of Mars is choosing which particular rock or mineral samples should be selected for more intensive study. The usual challenges of such a task are compounded by the lack of sensory input available to a suited astronaut or the limited downlink bandwidth available to a rover. Additional challenges facing a human mission include limited surface time and the similarities in appearance of important minerals (e.g. carbonates, silicates, salts). Yet the choice of which sample to collect is critical. To address this challenge we are developing science analysis algorithms to interface with a Geologist's Field Assistant (GFA) device that will allow robotic or human remote explorers to better sense and explore their surroundings during limited surface excursions [1]. We aim for our algorithms to interpret spectral and imaging data obtained by various sensors. Our algorithms, for example, will identify key minerals, rocks, and sediments from mid-IR, Raman, and visible/near-IR spectra as well as from high-resolution and microscopic images to help interpret data and to provide high-level advice to the remote explorer. A top-level system will consider multiple inputs from raw sensor data output by imagers and spectrometers (visible/near-IR, mid-IR, and Raman) as well as human opinion to identify rock and mineral samples. Our prototype image analysis system identifies some igneous rocks from texture and color information. Spectral analysis algorithms have also been developed that successfully identify quartz, silica polymorphs, calcite, pyroxene, and jarosite from both visible/near-IR and mid-IR spectra. We have also developed spectral recognizers that identify high-iron pyroxenes and iron-bearing minerals using visible/near-IR spectra only. We are building a combined image and spectral database of rocks and minerals with which to continue development of our algorithms. Future plans include developing algorithms to identify key igneous, sedimentary, and some metamorphic rocks. In a preliminary test, our texture and color analysis algorithms were able to correctly distinguish 9 out of 10 granite samples from diorites and 5 out of 6 diorite samples from granite samples. Our texture algorithm was able to correctly identify plutonic rocks 88% of the time and volcanic rocks 91% of the time.

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