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NASA NTRS1999

The Distribution of Titanium in Lunar Soils on the Basis of Sensor and In Situ Data Fusion

A variety of remote-sensing measurements have been used to map the distribution of elements on the Moon as a means of providing constraints on the processes from which its crust and major terranes originated. Discussed here is Ti, which is incorporated into refractory minerals such as ilmenite during the latter stages of differentiation, and is thus a most useful element for understanding mare basalt petrogenesis. One of the earliest Ti maps showed Ti variations in nearside maria on the basis of groundbased spectral reflectance measurements. A map of Ti derived from gamma-ray measurements on Apollo 15 and 16 was produced at about the same time, and was improved upon considerably by Davis and coworkers, who effectively removed sources of spurious variation from Fe and Al or REE (e.g., Th) interference, and calibrated Ti on the bases of landing-site soil averages. In recent years, spectral reflectance measurements from Clementine have been used by Lucey and coworkers to produce global Ti distribution maps as well. As we indicated previously, the Lucey and Davis maps agree to first order. Meanwhile, we are using the concept of sensor data fusion to combine measurements from the AGR (Apollo gamma-ray) and CSR (Clementine Spectral Reflectance) techniques with ground truth from lunar soils to utilize the differences between the two maps to understand the distribution of Ti within lunar soil components, as we have done with Fe. This technique should be verified and applied on Lunar Prospector gamma-ray measurements of Ti, as the calibrated data become available within the next couple of years. Lunar Ti is found principally in the mineral ilmenite, and is associated with certain components of lunar soil: crystalline Ilmenite mineral fragments and high Ti-bearing glass. All data indicate that Ti is associated with maria and mafic minerals. In AGR and CSR datasets, Ti is highest on the nearside and in the maria, particularly in southern Serenitatis/northern Tranquillitatis and northwestern Procellarum. Unlike CSR-derived values, AGR Ti values show modest increase (up to 0.7%) on the northern farside. Both techniques show a primarily unimodal distribution with a shoulder in the high Ti direction and a primary mode at approximately 0.2% (0.15% for CSR and 0.25% for AGR data), but the AGR Ti data have more structure in the shoulder, including an apparent minor mode at 1.2% Ti, representing non-highland areas. Recalibrating the two datasets on the basis of matching the peaks and range on the histograms would not account for this additional structure in the AGR data. Low Ti areas, which occur predominantly on the farside, are not represented well in the lunar sample collection, although several meteorites thought to be of lunar highland origin show Ti abundances averaging about 0.2%. (Error bars on AGR Ti values are about 0.5%.) Gamma-ray measurements reflect intrinsic Ti surface composition, regardless of the physical or chemical state of Ti. Lucey and coworkers have attempted to produce an equivalent bulk Ti map by normalizing the spectral feature at 415 nm to remove the effects of physical variations from soil to soil. The normalization is based on laboratory measurements of available samples, primarily nearside maria, and is undoubtedly optimized for the proportions in which Ti- components are found in these soils. In particular, mare soils tend to have a much higher proportion of opaque mineral grains, which appears to be well correlated with Ti abundance. The relationship between Ti abundance and proportion of Ti-bearing glass (weighted for Ti abundance) is not as direct. Also shows the relationship between landing site soil average, AGR, and CSR Ti abundances (with CSR filtered to match resolution and field of view of AGR data). AGR Ti values show the best agreement with Ti soil averages, CSR Ti values being somewhat higher in high-Ti soils, and lower in low-Ti soils. These results would tend to support the argument that the technique for deriving Ti from the CSR measurements is optimized for one of the primary Ti-bearing components, most likely opaque mineral grains, and is thus over-compensating and reporting higher Ti values in mare areas, and underreporting Ti in highland areas with much lower proportions of opaque mineral grains. More information is contained in original.

Clark, P. E.↗
NASA NTRSDate not supplied

NASA Earth Systems Digital Twins (ESDT)

"Similarly to artificial intelligence, which is now revolutionizing many aspects of our daily lives, Earth system digital twin technologies have the potential to revolutionize the way Earth Science research will be conducted in the future, and how results and knowledge from this research will provide information to support decision making and yield impactful societal benefits. An Earth System Digital Twin or ESDT is a dynamic and interactive information system that first provides a digital replica of the past and current states of the Earth or Earth system as accurately and timely as possible; second, allows for computing forecasts of future states under nominal assumptions and based on the current replica; and third, offers the capability to investigate many hypothetical scenarios under varying impact assumptions. In other words, an ESDT provides the integrated What-Now, What-Next, and What-If pictures of the Earth or Earth system, by continuously ingesting newly observed data and by leveraging multiple interconnected models, machine learning as well advanced computing and visualization capabilities. Digital twins have been developed in engineering since 2002, but the interest in digital twins for the Earth domain is more recent and stems from the convergence of several developments: - The huge amount of diverse data that has now been collected continuously for more than 50 years, and that is becoming more and more difficult to access, understand, and utilize. - At the same time, because of climate change and its impacts the information produced by all of this data is becoming of interest to many new non-traditional users for analyzing and predicting various phenomena. - Because of advances in computational and visualization capabilities and the parallel unprecedented development of machine learning (ML), extracting relevant information from these large amounts of data and running complex models faster has become possible. As a result, it is becoming necessary and possible to build intuitive and interactive frameworks that will enable users with various skill levels and/or organizational hierarchy levels to easily access large amounts of targeted information along with the relevant tools and models (Earth system and human activity models), to support them in analyzing and visualizing this information, to help them understand interactions among models, to visualize the potential outcomes of various impacts, and to support decision or policy making. The full power of digital twins is that, through an integrated representation and standardized tools and software technologies, the same digital replica can address the needs of multiple users at various resolutions (spatial and temporal) and for various applications (science, economic, policy, etc.) – “from farmer to scientist”. With all these interests at stake, the challenges of building optimal digital twins are many and complex. The first challenge is to determine if a Digital Twin should be global or local, and multi-domain or thematic. For example, some domains such as Climate or Weather will require a global Digital Twin or Digital Twin capabilities while science areas such as Biodiversity might be more local. We can also envision that multiple thematic ESDTs, e.g., Air Quality, Wildfires, Hydrology could be federated or provide input to other ESDTs, either on a regional level or to a more global ESDT. Overall, we can imagine a future “web” of Digital Twins co-existing in a hierarchy or in a network, and capable of being connected or federated depending on the needs. This last point brings up the very important challenge of interoperability, including standards and protocols that will need to be built into these systems from the beginning. Each individual digital twin would have full flexibility in internal construction but would need standards-based interfaces (input and output) or hooks to make it compatible with others. Another challenge when building digital twins will be to decide how to organize each digital replica. Based on the applications targeted by the DT under implementation, various amounts and types of raw data, Analysis Ready Data (ARD) and information will need to be incorporated. Depending on the required latencies and needs of the users, various solutions can be considered, including Data Cubes, Data Lakes, pointers, or computing information on demand. We envision that each ESDT will choose a solution adapted to its specific objectives. Another important challenge is the type(s) of visualization that will be used, as well as the level of interactivity and refresh rate that will be required. Again, this will depend on the objectives of the ESDT, but also on the various users’ needs. In most cases, several types of visualizations and human interfaces will need to be offered depending on the projected users of that system. In parallel to the challenges highlighted above, there are also many tools and technologies that will need to be developed or improved for all types of digital twins. Among those are improved machine learning technologies, for example providing explainability, but also ML techniques for causality and providing a better integration of physics models. Additionally, reliable uncertainty quantification methods will be needed for all ESDT components, from validating data fusion and assimilation to assessing the accuracy of ML models and weighing the values of decisions supported by those systems. This presentation introduces the ESDT concept, presents several ESDT use cases, and a proposed ESDT architecture framework, as well as various technologies being developed by the Advanced Information Systems Technology (AIST) Program."

Earth Science Remote Sensing; Information Systems↗