The Geologist's Field Assistant: Developing an Innovative Science Analysis System for Exploring the Surface of Mars

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. Our algorithms will interpret spectral and imaging data obtained by various sensors. The 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 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. A top-level system allows 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 and metamorphic rocks from texture and color information. Spectral analysis algorithms have also been developed that successfully identifiy 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.