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NASA NTRS2019

Evolving Management Strategies to Improve NASA Flagship's Cost and Schedule Performance: LUVOIR as a Case Study

The LUVOIR study process has brought to fruition an extremely exciting scientific mission concept. The 3.5 year LUVOIR study duration enabled an unprecedented level of scientific, engineering, and technology thoroughness prior to the Astro2020 Decadal. This detail also shed light on many technical and programmatic challenges for efficiently developing a mission of this scale. While NASA's flagships perform exquisitely once on-orbit, there is understandable growing frustration in their development cost and schedule overruns. We felt it incumbent upon ourselves to ask how we could improve on delivering LUVOIR (or any of NASA's future flagships) on schedule and on budget, not just for the next mission, but for all NASA large strategic missions to come. We researched past and current NASA flagship's lessons learned publications and other large government projects that pointed to some systemic challenges that will only grow with larger and more complex strategic missions. Our findings pointed us to some ways that could potentially evolve NASA's current flagship management practices to help improve on their development cost and schedule performance despite their growing complexity.. This paper briefly comments on the science motivation for NASA's flagships and on the science motivation for a LUVOIR-like mission. We argue the motivation for improving NASA's flagships development cost and schedule performance. We review the specific challenges of NASA's flagships to acknowledge their specific issues. We then examine the most repeated systemic challenges we found from previous NASA flagship and other large government project lessons learned/observed. Lastly, we offer recommendations to tackle these repeated systemic challenges facing NASA's flagships. The recommendations culminate into a proactive integrated development and funding framework to enable improving the execution of NASA's future flagship's cost and schedule performance.

Crooke, Julie A.↗

DOE OSTI2026

Learning earthquake ground motions via conditional generative modeling

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose an artificial intelligence (AI) spectrogram generator, Conditional Generative Modeling for Ground Motion (CGM-GM). CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, when postprocessed with phase information, capturing spatially continuous Fourier amplitude spectra (FAS) as well as properties such as P and S arrivals, and waveform durations, without explicit physics constraints. This is achieved through a probabilistic autoencoder that extracts latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. Here, we report that CGM-GM demonstrates potential for complementing physics-based simulations and non-ergodic empirical ground motion models, as well as shows promise in seismology and beyond.

geophysics↗

DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗

DOE OSTI2020

Optimization of the Post-Operational Phase on Two Belgian Multi-Unit Nuclear Power Plants: the Case of the Non-Fissile Irradiated Core Items - 20156

The current legal framework in Belgium foresees the progressive phase out of nuclear power between October 2022 (Doel 3) and December 2025 (Doel 2). Upon its definitive shutdown, each unit of the Tihange and Doel sites will enter a Post-Operational Phase (POP) and be prepared for its Decontamination and Decommissioning (D and D). Prior to obtaining the D and D license, the Operator Electrabel is legally required to remove any non-fissile irradiated core items stored in the deactivation pools. The non-fissile irradiated core items consist essentially of control rods, poison rods and source thimbles as well as thimble plugs and foreign materials irradiated during operation: - Their significant content in highly radiant radionuclides (up to 6 TBq of Co-60 per kg of irradiated material) renders all existing operational waste management processes inadequate due to insufficient biological shielding; - Their high concentrations in long-lived radionuclides call for their disposal in a geological repository for which no final design nor waste acceptance criteria are expected prior to 2050. Uncertainties in the Belgian energy supply and security, however, require the Operator to be prepared for a partial nuclear phase out, where one or more units would benefit from lifetime extension while the remaining units would undergo decommissioning. The present paper aims at presenting how Electrabel, in partnership with Tractebel, addressed this challenge by maximizing the use of synergies within the respective sites as well as between both sites themselves, all the while accounting for site specificities. The most recent results and state of progress of the project will be detailed and the first lessons learned will be shared. The project has been split in multiple tasks and phased as follows: - An inventory phase aimed at mapping the contents, origin, composition and history of the non-fissile irradiated core items; - A pre-characterization phase based on neutron activation models; - A waste sorting phase aimed at separating waste forms for which an evacuation route exists from those for which such route does not exist; - A feasibility phase aimed at exploring all possible scenarios for the management of non-fissile irradiated core items and identifying the optimal feasible solution for each site; - A preparation phase (currently ongoing), developing further the optimal solution and ensuring that back-up solutions are available for any foreseeable change of context (licensing issue, modification in the nuclear phase-out program, etc.) and initiating early contacts with potential subcontractors for segmentation works and cask manufacturers, as well as the Belgian regulatory body and waste management agency. This phase also foresees the investigation of destructive and non-destructive radiological measurements to support the detailed characterization of the waste forms; - A realization phase (future work). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗

NASA NTRSDate not supplied

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

The NASA Mars Science Laboratory (MSL) and Mars 2020 entry vehicles included temperature measurements inside and direct heat flux on the surface of the thermal protection system (TPS) material. The instrumentation was included to begin addressing predictive capabilities for aerodynamic heating and ablative TPS material response. The MSL aeroshell included temperature measurements at seven heatshield locations, from which total aerodynamic heating was reconstructed. The Mars 2020 instrumentation suite included eleven heatshield measurement locations and new backshell measurements at nine locations: six with temperature sensors, two with sensors to directly measure total heat flux, and one to measure radiative heat flux. Both instrumentation suites returned full sets of data, with all temperature sensors nearest to the surface surviving entry, indicating minimal material recession. Heatshield boundary layer transition (BLT) was observed for both vehicles prior to the time of peak heating, with transition starting near the shoulder and progressing towards the stagnation area. MSL’s higher speed on approach to Mars resulted in higher total heating compared to Mars 2020, with the maximum occurring near the heatshield’s leeside shoulder. The Mars 2020 backshell heating data indicate significant contributions from shock layer radiation. Heating from Navier-Stokes flowfield calculations are in family with reconstructed heating. The paper covers the instrumentation, BLT and reconstructed heating on both heatshields, backshell heating for Mars 2020, TPS performance, and predicted versus reconstructed heat fluxes. Opportunities for improved design approaches for future Mars missions are discussed, especially for the upcoming Mars Sample Return mission.

Mars Science Laboratory↗

NASA NTRSDate not supplied

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

Mars Science Laboratory↗

DOE OSTI2022

Automatic Detection of Defects in High-Reliability Components

Disastrous consequences can result from defects in manufactured parts—particularly the high consequence parts developed at Sandia. Identifying flaws in as-built parts can be done with nondestructive means, such as X-ray Computed Tomography (CT). However, due to artifacts and complex imagery, the task of analyzing the CT images falls to humans. Human analysis is inherently unreproducible, unscalable, and can easily miss subtle flaws. We hypothesized that deep learning methods could improve defect identification, increase the number of parts that can effectively be analyzed, and do it in a reproducible manner. We pursued two methods: 1) generating a defect-free version of a scan and looking for differences (PandaNet), and 2) using pre-trained models to develop a statistical model of normality (Feature-based Anomaly Detection System: FADS). Both PandaNet and FADS provide good results, are scalable, and can identify anomalies in imagery. In particular, FADS enables zero-shot (training-free) identification of defects for minimal computational cost and expert time. It significantly outperforms prior approaches in computational cost while achieving comparable results. FADS’ core concept has also shown utility beyond anomaly detection by providing feature extraction for downstream tasks.

47 OTHER INSTRUMENTATION↗

NASA NTRS2011

Temporary Urine and Brine Stowage System (TUBSS) Development

International Space Station (ISS) crew liquid human waste is treated with chromic and sulfuric acids to maintain stability prior to processing to recover water. This pre-treated urine (PTU) and its processed by-product, brine, are highly toxic fluids that require special containment for on-orbit stowage. The temporary urine and brine stowage syste m (TUBSS) is an assembly used to store and transfer pre-treated urine (PTU) and brine for processing or disposal at a later date. This paper describes the development of the TUBSS, including design for two-fault tolerance and materials selection to maintain a soft, collapsible container. In addition, this paper will provide results of testing as well as lessons learned from the project, culminating in the successful launch of the hardware.

Dries, Kevin↗

NASA NTRS2012

Magnetic Testing, and Modeling, Simulation and Analysis for Space Applications

The Aerospace Corporation (Aerospace) and Lockheed Martin Space Systems (LMSS) participated with Jet Propulsion Laboratory (JPL) in the implementation of a magnetic cleanliness program of the NASA/JPL JUNO mission. The magnetic cleanliness program was applied from early flight system development up through system level environmental testing. The JUNO magnetic cleanliness program required setting-up a specialized magnetic test facility at Lockheed Martin Space Systems for testing the flight system and a testing program with facility for testing system parts and subsystems at JPL. The magnetic modeling, simulation and analysis capability was set up and performed by Aerospace to provide qualitative and quantitative magnetic assessments of the magnetic parts, components, and subsystems prior to or in lieu of magnetic tests. Because of the sensitive nature of the fields and particles scientific measurements being conducted by the JUNO space mission to Jupiter, the imposition of stringent magnetic control specifications required a magnetic control program to ensure that the spacecraft's science magnetometers and plasma wave search coil were not magnetically contaminated by flight system magnetic interferences. With Aerospace's magnetic modeling, simulation and analysis and JPL's system modeling and testing approach, and LMSS's test support, the project achieved a cost effective approach to achieving a magnetically clean spacecraft. This paper presents lessons learned from the JUNO magnetic testing approach and Aerospace's modeling, simulation and analysis activities used to solve problems such as remnant magnetization, performance of hard and soft magnetic materials within the targeted space system in applied external magnetic fields.

magnetics↗

DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗

NASA NTRS1991

An Intelligent Computer-aided Training System (CAT) for Diagnosing Adult Illiterates: Integrating NASA Technology into Workplace Literacy

An important part of NASA's mission involves the secondary application of its technologies in the public and private sectors. One current application being developed is The Adult Literacy Evaluator, a simulation-based diagnostic tool designed to assess the operant literacy abilities of adults having difficulties in learning to read and write. Using Intelligent Computer-Aided Training (ICAT) system technology in addition to speech recognition, closed-captioned television (CCTV), live video and other state-of-the-art graphics and storage capabilities, this project attempts to overcome the negative effects of adult literacy assessment by allowing the client to interact with an intelligent computer system which simulates real-life literacy activities and materials and which measures literacy performance in the actual context of its use. The specific objectives of the project are as follows: (1) to develop a simulation-based diagnostic tool to assess adults' prior knowledge about reading and writing processes in actual contexts of application; (2) to provide a profile of readers' strengths and weaknesses; and (3) to suggest instructional strategies and materials which can be used as a beginning point for remediation. In the first and development phase of the project, descriptions of literacy events and environments are being written and functional literacy documents analyzed for their components. From these descriptions, scripts are being generated which define the interaction between the student, an on-screen guide and the simulated literacy environment.

Yaden, David B., Jr.↗

DOE OSTI2023

$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Alloy theory↗

DOE OSTI2020

Retrieval of full angular- and energy-dependent complex transition dipoles in the molecular frame from laser-induced high-order harmonic signals with aligned molecules

High-order harmonic signals generated in molecules are the consequence of coherent summation of complex laser-induced transition dipoles $\textit{d}(θ, ω)$ with each fixed-in-space molecule; here $\theta$ is the angle of the molecular axis with respect to the laser polarization axis and $\omega$ is the harmonic energy. In the so-called rotational coherent spectroscopy, it is proposed to extract the fixed-in-space $\textit{d}(θ, ω)$ in the molecular frame by measuring harmonics generated by a probing laser from the rotational molecular wave packets that have been prepared by a prior pump laser. By varying the time delay between the two lasers, methods have been utilized to extract the $\theta$ dependence of both the amplitude and phase of each individual harmonic, but the relative phase between harmonics cannot be retrieved. Here we report that this limitation can be removed. It requires the additional measurement of harmonic spectra versus the pump-probe angles at one fixed time delay. The two-dimensional input harmonic data (time-delay and pump-probe angle) are then used to retrieve the full complex transition dipole $\textit{d}(θ, ω)$ using a retrieval method based on machine learning algorithms. Finally, we demonstrate this method on N 2 and CO 2 molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗

DOE OSTI2020

Jefferson Laboratory C100 Superconducting Radio-Frequency Cavity Fault Data, 2020

The dataset was created to train machine learning models for the task of identifying the (1) cavity and (2) fault type from C100-type cryomodules at the Thomas Jefferson National Accelerator Facility (Jefferson Lab), thereby replacing the time-consuming efforts of a subject matter expert. Superconducting radio-frequency (SRF) cavity trips represent a significant source of accelerator downtime. Real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type would give control room operators valuable feedback for corrective action planning. The anticipated benefit is increased beam-on-target time for users and provides performance metrics that can be used to improve future cavity designs. A series of 17 RF signals are recorded for each of the 8 cavities in a C100 cryomodule every time a cavity trips. These time-series signals are written to file using a specially designed data acquisition system. The dataset represents fault events recorded during Continuous Electron Beam Accelerator Facility (CEBAF) beam operations between January 18, 2019 and March 9, 2020. The following filtering steps were applied to collected data; (1) only 4 of the 17 signals per cavity are retained (GMES, GASK, CRFP, DETA2) (2) only events with data from each of the eight cavities in the cryomodule are kept, (3) only events that were sampled at 5 kHz were kept, (4) events from cryomodule 0L04 were neglected, (5) events occurring between February 4, 3PM and February 5, 12PM were neglected. As a result of preprocessing, the dataset is comprised of 2,375 unique events. The full dataset is comprised of three files: features.csv, cavity_labels.csv, fault_labels.csv. Each instance in faults.csv includes a timestamp (“date_time”), a label for the cryomodule which experienced the trip (“zone_label”), and 192 features (“feature_1”, “feature_2”... “feature_192”). The features correspond to 6 autoregressive features for each of 4 signals per cavity for each of the 8 cavities (6 × 4 signals/cavity × 8 cavities/cryomodule = 192). To deal with the large variation of signal amplitudes, time-series standardization via the z-score (standard score) function was applied prior to computing the features. For each instance, there is an associated label for the (1) cavity which faulted first (cavity_labels.csv) and (2) the type of fault that caused the trip (fault_labels.csv). The cavity identification can take values of [0, 1, 2, 3, 4, 5, 6, 7, 8] and the fault type can take values of [‘Microphonics’, ‘Quench_100ms’, ‘Controls_Fault’, ‘E_Quench’, ‘Quench_3ms’, ‘Single_Cav_Turn_Off’ , ‘Heat_Riser_Choke’, ‘Multi_Cav_Turn_Off’].

43 PARTICLE ACCELERATORS↗

NASA NTRS2003

Martian Crustal Magnetism: What Have We Learned After Approximately 6 Years of MGS Observations?

The MAG/ER investigation aboard the Mars Global Surveyor (MGS) has established conclusively that an internal, dynamo-generated field does not currently exist at Mars and discovered, unexpectedly, strong magnetization in the crust. An estimate of the upper limit of the current Mars dipole moment derived from the MGS data yields M < ~2 x 10(exp 17) A-m2, which corresponds to a surface equatorial field strength of < 0.5 nT. The intense magnetization of the crust is closely associated with the ancient, heavily cratered high terrain, which lies south of Mars dichotomy boundary. The correlation of magnetization with the old terrain and the role of impacts, which have modified the magnetic properties of the crust, constitute a new and powerful diagnostic tool that is providing a unique view into the early thermal history of the planet, which was almost totally unknown prior to the arrival of MGS. Data from the Lunar Prospector mission complement contemporary analyses and interpretation of crustal magnetism in planetary system bodies that do not currently possess core dynamos. The observation of magnetic lineations over Terra Sirenum (Sirenum Fossae) and Terra Cimmeria, are suggestive of tectonic processes observed at Earth in association with sea-floor spreading and geomagnetic field reversals. If this association is correct, it would indicate the possible existence of plate tectonics and magnetic field reversals in Mars' early history. Alternative models involving fault/graben formation associated with the fracturing of a thin, magnetized crustal layer by tectonic or volcanism-induced stresses, yield equally valid interpretations. To date, no reliable correlation between topography, geology and crustal magnetism has been established and the origin of these remarkable Martian magnetic anomalies remains a mystery.

M H Acuna↗