Sub-ice-shelf bathymetry controls the delivery of warm water to the ice-shelf bottom in West Antarctica, making the bathymetry beneath ice shelves in the Amundsen Sea critical inputs to ice-sheet and ocean models. Previous estimates of the bathymetry have often used deterministic inversion frameworks or do not account for the non-uniqueness of the inverse problem, and ultimately lack robust uncertainty quantification. To provide more robust and reproducible bathymetry models, we implement a random walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) inversion approach, which iteratively generates model perturbations using random Gaussian fields and forward models the gravity disturbance of proposed bathymetry models. After convergence, our approach samples the posterior distribution allowing for estimation of the mean and variance of the bathymetry while providing realistic models of the sub-ice-shelf bathymetry. An ensemble of bathymetry models can then be used in ice-sheet and ocean simulations to propagate the uncertainty in bathymetry to dynamic ice processes, resulting in better uncertainty quantification of future sea-level rise. In addition to providing more robust bathymetry models, this work provides a step forward in the reproducibility of geophysical inversions by leveraging the growing open-access geoscientific computing ecosystem of Python.

How to cite: Field, M., MacKie, E., Wang, L., and Muto, A.: Gravity inversion of sub-ice shelf bathymetry in West Antarctica using a geostatistical Markov Chain Monte Carlo approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11380, https://doi.org/10.5194/egusphere-egu24-11380, 2024.

Because sedimentary basins may exert considerable control over ice sheet dynamics and basal heat flow, it is vital to constrain the extent, thickness, and level of consolidation of sediments throughout the continent and at local scales. Until recently, the South Pole Basin (SPB), situated between the Gamburtsev Subglacial Mountains, the Transantarctic Mountains, and Recovery Subglacial Highlands, has been one of Antarctica's least-explored regions. Previous studies based on seismic and machine learning models, including those by Baranov & Morelli (2023) and Li et al. (2022), have characterized SPB as a sedimentary basin with sediment thicknesses exceeding 1 km. Conversely, a seismic study conducted by Zhou et al. (2022) identifies SPB as a region with little to no sedimentary rock. A lack of dense geophysical data as well as the inherent difficulty of studying geology beneath the Antarctic Ice Sheet introduced a large amount of uncertainty into these assessments. Recent airborne radar, gravity, and magnetics data collected by the Center for Oldest Ice Exploration (COLDEX) has revealed two distinct geomorphological provinces within South Pole Basin: the southern portion of SPB which exhibits relatively smooth, reflective bedrock, while the northern SBP manifests as much rougher terrain. The abrupt boundary between Inner and Outer SPB is associated with the onset of subglacial melting, inferred from a rapid thinning of the basal layer, decreased ice sheet surface slope, and presence of subglacial lake-like features. In addition to surficial differences, these provinces are marked by distinct free-air, Bouguer, and isostatic gravity signatures. A large, arc-shaped magnetic high parallel to Recovery Subglacial Highlands cuts across SBP, facilitating a robust depth to basement analysis and providing constraints for gravity inversions. By integrating COLDEX data with previous airborne surveys and newly collected seismic data, we offer a revised geological interpretation of the South Pole Basin and discuss its tectonic history, potential for groundwater storage, and the preservation of ancient ice in this region.

How to cite: Kerr, M., Young, D., Shen, W., Ng, G., Singh, S., Buhl, D., Greenbaum, J., Yan, S., and Blankenship, D.: Are there thick sediments within South Pole Basin? Investigating the lithology of SPB using COLDEX airborne geophysics , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12510, https://doi.org/10.5194/egusphere-egu24-12510, 2024.

Topography and physical conditions at the base of the Antarctic ice sheet are critical inputs for studies of its present and future ice discharge, and of subglacial geology and hydrology. Airborne gravity and magnetic data, especially when interpreted jointly can help us to link the geology from outcrops towards the coastal areas to unknown subglacial regions further inland. Here we use airborne geophysical data obtained during the joint AWI-BGR campaign WEGAS/GEA between 2015 and 2017 in central Dronning Maud Land (DML) as input for a novel joint inversion scheme. With regard to Gondwana reconstruction, this region is critical because it hosts the ice-covered Forster Magnetic Anomaly, a prominent lineament crossing central DML for some 100s of kilometers south of the main mountain chain. This lineament, originally interpreted as the main pan-African suture of East and West Gondwana, likely represents the eastern margin of Kalahari and its boundary to the Tonian Oceanic Arc Super Terrane (TOAST). In the inversion using the software jif3D, sources of the gravity and magnetic field are combined through a coupling method which decreases the variation of information (VI), so data misfit and model dissimilarity are minimized simultaneously. The model results can be classified in geologically meaningful provinces by applying cluster analysis based on machine learning. Our joint inversion approach improves previous interpretations and sheds light on the crustal architecture of the study area, contributing to further studies on the interaction between the ice sheet and the underlying solid earth.

How to cite: Ginga, M., Ebbing, J., Ruppel, A. S., Läufer, A., and Eagles, G.: Joint inversion of airborne gravity and magnetic data for the crustal structure in central Dronning Maud Land, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2837, https://doi.org/10.5194/egusphere-egu24-2837, 2024.

Airborne gravimetry provides gravity observations of higher spatial resolution than what can be obtained from satellite gravity field measurements, and together with terrestrial measurements they augment the satellite observations to determine high-resolution geoid models. Satellite altimetry in coastal and ice-covered regions is known to have significant errors. We use modern strapdown gravimetry for the surveys and compare the indirect method, using Kalman filtering, and the direct filtering method for the processing. We present the result of strapdown gravimetry for two airborne campaigns conducted in Antarctica 2022 and Norway 2023. During both campaigns the sensors used were an iMAR navigation-grade inertial measurement unit together with a geodetic GNSS receiver.

The 2022 campaign covered part of the sea-ice covered Weddell Sea and was surveyed as a piggyback activity as part of the ESA CRYO2ICE and NERC DEFIANT 2022 Antractica campaign. The 2023 airborne campaign was carried out in the coastal region of Norway near Trondheim. In both areas the data were compared to satellite altimetry and other gravity data from ship or airborne surveys. Both campaigns show improvements in spatial resolution and accuracy of the new mGal-level airborne gravimetry data when compared to satellite altimetry and older marine gravity observations.

How to cite: Dale, B., Bjerregaard Simonsen, S., Christian Dahl Omang, O., Enzlberger Jensen, T., and Forsberg, R.: Satellite gravity validation by new airborne gravimetry in coastal regions of Antarctica and Norway, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10299, https://doi.org/10.5194/egusphere-egu24-10299, 2024.

The surface geology of Greenland is only known along the ice-free coast. The remaining 80% of Earth's largest island are covered by ice that masks the surface geology and makes direct observation nearly impossible. Interpolation of the known coastal geology over the inland ice, combined with expert knowledge, can provide a first, but not well-constrained picture. In contrast, the surface geology in Scandinavia is well-studied. The formerly adjacent northeastern part of Greenland belongs to the same Caledonian orogeny and is expected to be somewhat similar to Scandinavia. Therefore, we use Scandinavia as a case study to set up a workflow of joint inversion of potential field data to find physical relations for the known geological structure and apply this workflow to NE Greenland.

Results from individual inversion of potential field data are non-unique and have limited depth resolution. Combining gravity and magnetic data in a joint inversion can minimise the non-uniqueness and improve the depth resolution. The coupling furthermore creates comparable anomaly patterns for both inverted parameters. As coupling method, a variation of information (VI) constraint is used in the inversion. The VI creates representative parameter relationships where different branches reflect the numerous combinations of density and susceptibility for various rock types. Thus, the inverted parameter relationship can be used to map the surface geology.

Crucial parts in the workflow setup are how deeper sources are handled for the gravity data and at which resolution and height the magnetic data are required.  The simultaneous analysis of the well-studied surface geology in Scandinavia helps to verify the analysis, providing higher confidence in the resulting sub-ice geology for NE Greenland.

How to cite: Wansing, A. D., Ebbing, J., Moorkamp, M., and Heincke, B.: Joint inversion of potential field data to unmask sub-ice geology, from a case study in Scandinavia to application in NE Greenland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14771, https://doi.org/10.5194/egusphere-egu24-14771, 2024.

Joint inversion can utilize multiple geophysical datasets to supplement or enhance the information on subsurface structures, improve the resolution and certainty of recovered subsurface structures, and provide broad prospects for various geophysical application scenarios. Model coupling is crucial in joint inversion, and there are two main coupling methods based on structural similarity and petrophysical information. These two coupling methods have their own advantages and disadvantages. The structural similarity-based coupling method can obtain structurally similar models without prior information, but the assumption of structural similarity between models is not always valid. The petrophysics-based coupling method provides finer constraints on physical property values, and its difficulty lies in acquiring petrophysical information, which is usually imprecise and incomplete in the inversion region. Joint inversion using a single model coupling approach is insufficient to face complex joint inversion situations. Combining the two coupling methods can complement the structural similarity of the model in the inversion of incomplete petrophysical information.

We develop a novel joint inversion method based on the extended alternating direction method of multipliers (eADMM), which is compatible with multiple model coupling methods and reduces non-uniqueness and uncertainty more effectively. Multiple model coupling methods are contained in an indicator function, which requires the model to satisfy specific mathematical sets, allowing the various models to satisfy arbitrary relationships and ranges. The inequality constraints and linear and nonlinear relational equations extracted from the petrophysical information are expressed directly in mathematical sets, and the structural similarity coupling is implemented by a constraint set that requires a cross-gradient of zero between models. The solution of the indicator function in the eADMM framework is converted into a projection function, and we develop corresponding projection algorithms for multiple constraint sets of both model coupling strategies. The constraint sets are also spatially flexible. Regions with complete petrophysical information and regions requiring increased structural similarity can be constrained by the corresponding sets, respectively.

We apply the method to gravity and magnetic data to test its performance. We compare the performance of our method with that of the joint inversion using a single coupling method for incomplete petrophysical information, including petrophysical information for partial regions and partial geologic units. Synthetic examples show that regions and geologic units with known petrophysical information are recovered with accurate geometric boundaries and physical property values closer to the true values, and structural similarity coupling provides structural information for unknown regions or geologic units, recovers more accurate geometric structures and reduces model uncertainty. The new joint inversion method provides higher resolution models than the traditional joint inversion method, and the inversion results are closer to the true model.

How to cite: Wang, K. and Yang, D.: joint inversion of gravity and magnetic data with petrophysical and structural coupling constraints using indicator functions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14351, https://doi.org/10.5194/egusphere-egu24-14351, 2024.

Precise determination of Moho topography holds paramount importance in advancing our comprehension of Earth's structural characteristics, geodynamic phenomena, and the exploration of resources. This study introduces an innovative methodology employing conditional Generative Adversarial Networks (cGAN) to unveil Moho topographies from observed gravity anomalies. To address the scarcity of real Moho datasets for training the cGAN model, we meticulously generated a comprehensive set of quasi-realistic synthetic training data using the FFT filtering technique. The forward estimation of gravity anomalies, arising from synthetic Moho topographies, was assessed through spherical prism-based gravity modeling. These estimated anomalies served as input data for the training of the cGAN model. For evaluating the efficacy of our developed cGAN algorithm in deriving Moho architecture, we conducted a comparative analysis against a conventional inversion scheme. This assessment utilized various synthetic datasets and a real case study in Southern Peninsular India, renowned for its geological diversity and ancient continental tectonic blocks. The established Bott's inversion scheme was employed as a benchmark to validate the Moho surface estimation obtained through the Deep Learning approach. To mitigate the impact of diverse factors such as topography, bathymetry, sediments, crustal and mantle heterogeneities, observed gravity anomalies underwent meticulous corrections using spherical prism-based forward gravity modeling for real case studies. The gravity contribution exclusively associated with the pure Moho was subsequently inverted using both the cGAN and traditional Bott's inversion schemes. Crucial hyperparameters, including the mean Moho depth and density contrast between the crust and mantle, were determined by utilizing seismic constraints. Our results underscore the potential of the cGAN and spherical prism-based gravity modeling approach in accurately predicting Moho topography. This study provides valuable insights into high-resolution Earth's Moho architecture and contributes to advancing our understanding of geodynamic processes, facilitating resource exploration endeavours with reduced computational demands.

How to cite: Roy, A., Sharma, R. K., Jash, D., and Kallukalam, T. J.: Innovative Insights into Earth's Interior: Moho Topography Estimation using Conditional Generative Adversarial Networks from Observed Gravity Anomalies , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1405, https://doi.org/10.5194/egusphere-egu24-1405, 2024.

The Moho interface is an important parameter for describing the structure and morphology of the Earth's crust, and it is of significant importance in the study of the formation and evolution of the crust and mantle, as well as deep-seated dynamic processes (Stern et al., 2018). Existing Moho models derived from seismic data often suffer from inaccuracies due to irregular distribution and regional imbalances of seismic data. However, with the development of gravity satellite technology, high-precision satellite gravity data has injected new vitality into the study of lithospheric tectonic features and crustal evolution. In this study, constrained by seismic data (Li et al., 2013; Zhang et al., 2021), we utilized an improved regularized Bott method (Uieda et al., 2017) to invert high-precision satellite gravity data and obtained a high-precision unified Moho depth model for the East Asian region, encompassing both land and sea areas. The research results show that the Moho depth model exhibits a continuous increase in depth from east to west, and its overall distribution in the horizontal direction is non-uniform, displaying distinct regional block features. This paper provides a high-resolution and high-precision Moho model for studying the evolution of the East Asian continental tectonics and plate interactions, and further discusses the macrostructural framework and geological implications of East Asia.

References

Li Y Gao M, Wu Q. Crustal Thickness Map of the Chinese Mainland from Teleseismic Receiver Functions [J]. Tectonophysics, 2013, 611.

Stern, Robert, J, et al. Continental crust of China: A brief guide for the perplexed [J]. Earth Science Reviews the International Geological Journal Bridging the Gap Between Research Articles & Textbooks, 2018.

Uieda L, Barbosa V. Fast nonlinear gravity inversion in spherical coordinates with application to the South American Moho [J]. Geophysical Journal International, 2016.

Zhang J , Yang G , Tan H , et al. Mapping the Moho depth and ocean-continent transition in the South China Sea using gravity inversion [J]. Journal of Asian Earth Sciences, 2021, 218(3–4):104864.

How to cite: Xue, Z., Guo, D., Fang, J., and He, H.: Moho Depth Model and Structural Characteristics of China and Adjacent Regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9258, https://doi.org/10.5194/egusphere-egu24-9258, 2024.

This study employs gravity modelling to investigate the subsurface geometry of a pull-apart basin located in Elazığ, Turkey. The study area is situated along the East Anatolian Fault Zone—a major active system that recently produced two devastating M7.0+ earthquakes at Kahramanmaraş in February 6, 2023.

Recently collected gravity data, comprising approximately 600 data points from Sivrice and Gezin provinces, form the basis of our investigation. Preliminary examinations show that the gravity anomalies in Gezin are notably lower than those in Sivrice, suggesting a deeper basement for the former. We aimed to estimate the subsurface model using a proprietary computer program. Given the known different geological units with constant density contrasts, the program was employed to deduce their geometry up to a maximum depth of 350 meters in 2D. A total of 16 sections were modeled—8 each for Sivrice and Gezin provinces—yielding RMS values consistently below 0.1 mGals. Next, quasi-3D and 3D models were prepared for Talwani models at Sivrice and Gezin. We assumed the geometry beneath Lake Hazar to be similar to the bathymetry of the lake, assigning sediment thickness to estimate the basement in this part. The individual models were then integrated into a full 3D representation of the geometry of the basin.

Our findings suggest that the pull-apart basin situated here is in its extinction phase, with pull-apart tectonics no longer active, and only strike-slip movement along main East Anatolian Fault is observed. Notably, slips along the main fault have impacted the basement geometry. This study contributes valuable insights into the current state of the basin, emphasizing the importance of 3D modelling in unraveling the complexities of pull-apart basins.

How to cite: Aydın, N. G. and İşseven, T.: Gravity Modelling of a Pull-Apart Basin in Elazığ, Turkey: Unraveling the 3D Basement Geometry (Preliminary Results), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-397, https://doi.org/10.5194/egusphere-egu24-397, 2024.