Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues

Abstract

We advance an adaptable framework that couples statistical ecology with deep learning to recognize and predict biosignature spatial patterns in a polyextreme terrestrial environment. Drone flight imagery connected simulated HiRISE imagery to ground surveys, spectroscopy and biosignature mapping to reveal predictable distributions linked to environmental factors. AI/ML models successfully identified geologic features with high probabilities for containing biosignatures at spatial scales relevant to rover-based astrobiology exploration. Targeted approaches augmented by deep learning delivered 56.9-87.5% probabilities of biosignature detection versus <10% for random searches and reduced the physical search space by 85-97%. Libraries of biosignature distributions, detection probabilities, predictive models and search roadmaps for many terrestrial environments will standardize analog science research, enabling agnostic comparisons at all scales. This is vital to preparing, informing and optimizing biosignature quests on Mars, assisting high-stakes mission decisions between competing targets, and maximizing precise selection of high-priority samples. In extreme environments, the distribution of biosignatures is tightly controlled by a complex interdependency of geological, physicochemical, and biological interactions 1-5 . In such environments, microbial populations often occur in non-random spatial distributions closely tied to their

Gypsum‐lined degassing holes in tumuli

“…The latter, microbial gas domes are only centimetres to decimetres high with centimetre‐thick microbial mats above the domes that can rupture and collapse at triple‐junction cracks under high biogenic gas pressure. This is in contrast to the tumuli at SdP, which are up to 2 m in height and contain thin zones with endolithic microbial communities (Hinman et al, 2022; Warren‐Rhodes et al, 2023).…”

“…Our conceptual model is constrained by field observations, aided by aerial and satellite imagery and regional geologic, geomorphologic and hydrologic data. For method details, the readers are referred to three other works that present results about the morphology and composition of the surface features at SdP (Hinman et al, 2022), about the threshold and detection limits of the aerial surveys (Phillips, 2021) and on the distribution of microbial habitats within the study area (Warren‐Rhodes et al, 2023). Visual observations of the holes and the surface features in general were made during three field campaigns to SdP between October 2016 and October 2019.…”

“…Furthermore, at SdP, these tumuli are a preferred habitat for microbial communities and deciphering their origin on Earth might hold the key when searching for life on Mars and other planets (e.g. Warren‐Rhodes, 2023). Neither of these previous studies from SdP elaborated on small, circular holes, lined with a thin gypsum veneer that can be found closely associated with the gypsum tumuli.…”

Leveraging Machine Learning Approaches to Predict Organic Carbon Abundance in Mars‐Analog Hypersaline Lake Sediments

“…Machine learning excels at detecting patterns and structures within large and/or complex data sets (Peaple et al, 2021;Theiling et al, 2022;Warren-Rhodes et al, 2023). Such methods can be extremely valuable for using data that is using less resource-intensive means of data collection (such as non-destructive spectroscopy including X-ray Fluorescence (XRF)) to predict information that is resource intensive to gather (such as organic carbon (OC) or biosignature analysis; Warren-Rhodes et al, 2023;Jacq et al, 2019). For example, the Curiosity Rover can analyze an effectively unlimited number of samples using its remote laser-induced breakdown spectroscopy instrument, ChemCam (Maurice et al, 2012), but the Sample Analysis at Mars (SAM) instrument has a limited number of sample cups for analyzing powdered samples via gas chromatography mass spectroscopy (Mahaffy et al, 2012).…”

Salt Constructs in Paleo-Lake Basins as High-Priority Astrobiology Targets

“…Deltas, while effectively reducing the search space as indicated above, are nevertheless areally extensive features, and it is still not clear where, on or in a delta, is the best place to sample to yield the most conclusive evidence for past life. Are there environments that have a high potential to both host and preserve microbial organisms in situ, and that are specific targets, possibly identifiable from orbit, to which a rover could be driven, c.f., [17]? Here, we suggest that martian salt-encrusted paleo-lake basins that contain features constructed from salt may be environments that uniquely and affirmatively answer this question.…”