This study explores the geological complexity of the South Nicholson Region, an area spanning the Northern Territory and Queensland in Australia, from the newly drilled NDI Carrara 1 well, thus exposing the burial history of the Carrara sub-basin. Formed before the formation of the Nuna supercontinent, this region is positioned near resource-abundant basins and boasts a complex geological history. It has undergone significant tectonic shifts, orogenic activities, and the development of sedimentary basins over 1.6 billion of years while the world was developing as we see it in present. Despite its potential for mineral and petroleum resources, the South Nicholson Region was previously under-explored, lacking in-depth seismic, well, and geophysical data. Recently acquired data from the region includes five seismic lines and a new well, offering invaluable insights into the region's subsurface geology, including the identification of a new sub-basin, the Carrara Sub-basin. Characterised by a gravity low on its southeast side, the Carrara Sub-basin encompasses thick sequences of Proterozoic rocks from the Northern Lawn Hill Platform, Mount Isa Province and McArthur Basin. The primary objective of this study is to examine the burial history, tectonic subsidence and paleo-reconstruction of the South Nicholson region.

Our results indicate that the South Nicholson Region has undergone multiple cycles of sedimentation, tectonic uplift and erosion. Between ~1640 Ma and 1580 Ma, the region experienced increasing deposition rates. The presence of an unconformity obscures the sedimentation and tectonic history from 1600 to 500 Ma. However, by 500 Ma, significant subsidence had occurred, indicating that subsidence was the predominant geological force during this period. After this interval, an uplift event is evident, exhuming the layers until 400 Ma. From 400 Ma until today, little to no subsidence has been briefly interrupted by minor uplift events. Our calculated tectonic subsidence curve closely aligns with the regional deposition patterns, highlighting the intricate relationship between sediment deposition and tectonic activities, thereby providing valuable insights into the interplay between sedimentary and tectonic processes in the region.

How to cite: Nalinakumar, H. and Clark, S. R.: Basin evolution and Paleo reconstruction of the Mesoproterozoic South Nicholson Region, NE Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21753, https://doi.org/10.5194/egusphere-egu24-21753, 2024.

Tectonic processes influence numerous biogeochemical cycles. Accordingly, the evolution of the continental curst and changes in tectonic styles are inherently linked with secular chages in Earth’s surface environment. Here we present multiproxy mineralogical and geochronologic data to evaluate compositional changes in the upper crust along with variations in tectonic regimes and crustal recycling.  Our data indicate transitions from dominantly mafic to volumetrically extensive felsic upper crust occurred from the Archean into the Paleoproterozoic, which corresponds with evidence for enhanced crustal reworking. That later Paleoproterozoic through the Mesoproterozoic is characterized by a general reduction in crustal recycling and assimilatory tectonics with relatively limited active crustal thickening. Finally, the Neoproterozoic–Phanerozoic represents an interval with of increased juvenile magmatism and extensional tectonics, corresponding with deep and steep subduction and slab-rollback. This leads to enhanced island arc and back-arc basin formation, and subsequent arc collision.  These major shifts in composition and tectonic regimes that broadly bookended the Proterozoic have profound effects on numerous biogeochemical cycles particularly carbon, oxygen, and phosphorous cycles, and are thus likely linked to changes in the oxidative state and climate of Earth’s surface system observed during these times.

How to cite: McKenzie, N. R., Liu, H., Colleps, C., and Nordsvan, A.: Multiproxy investigation of secular changes in tectonic regimes and crustal recycling in Earth history, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14517, https://doi.org/10.5194/egusphere-egu24-14517, 2024.

Paleogeographic maps illustrate the distribution of land and sea, as well as the topography of the Earth’s surface during different geological periods based on the compilation of a wide range of geological and geophysical datasets. These maps provide boundary conditions for various models of the Earth’s systems, including climate, mantle convection, and land surface evolution. A number of software programs and computer algorithms have been developed in the past three decades to reconstruct either the past positions of continents and oceans or their elevation and depth. We recently developed the open-source and user friendly "Terra Antiqua", allowing users to create digital paleogeographic maps in a Geographic Information System (GIS) environment (QGIS), using various tools that are easy to operate in combination with Gplates, a widely used software for plate tectonic reconstructions. The next step is to develop a comprehensive and integrated solution easily accessible on the web that can automate most of the steps involved in reconstructing past plate configurations and topography. We present here a web application ("Terra Antiqua online") that we are developing for the creation of digital paleogeographic maps. The web application has two parts: (1) The front-end uses CesiumJS, an open-source JavaScript library for making 3D globes and maps, to visualize the databases and let the users interact with it.  (2) The back-end uses Python algorithms and libraries such as GDAL and pyGPlates to process the data and perform tectonic and hypsometric reconstructions.  Terra Antiqua online uses pyGplates API to access existing tectonic models and apply them to rotate plate positions and datasets to their past position. New developments are allowing it to estimate the elevation, depth and distribution of the land and sea by automatically processing various geological proxy data (e.g. paleofacies maps, paleo-elevation proxies, fossils databases etc…) according to physically based algorithms. The project further aims to incorporate web-based landscape modeling tools and develop a community around a geological database and paleogeographic reconstruction methods and standards.

How to cite: Aminov, J., Dupont-Nivet, G., Tirandozova, N., Poblete, F., Rakhimjanov, I., Amonbekov, L., and Rikamov, R.: A web-based and data-driven approach to paleogeographic reconstructions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15087, https://doi.org/10.5194/egusphere-egu24-15087, 2024.

The Panalesis model (Vérard, 2019) was developed in a preliminary version according to concepts, methods and tools that follow the work carried out for more than 20 years at the University of Lausanne (Stampfli & Borel, 2002; Hochard, 2008). Although the techniques are relevant, development under ArcGIS® does not allow visibility and easy accessibility of the model to the scientific community.

A major effort is therefore underway to migrate the entire model to an open source version using a FAIR approach for research software (Chue Hong et al., 2021). This migration concerns both the plate tectonic maps covering all the world over the entire Phanerozoic and part of the Neoproterozoic, but also the creation of paleoDEMs (global quantified topographies).

The Panalesis model and its entire architecture is therefore currently migrated to QGIS (a free and open source geographic information system). TopographyMaker, the software designed to convert polylines from the reconstruction map into a points grid with elevation values is now working as a plugin on QGIS. The output palaeoDEMS will also be published according to the FAIR principles for scientific data management and stewardship (Wilkinson et al., 2016).

The development and future refinements of TopographyMaker will enhance the Earth system modelling, especially coupling between models of different shells of the Earth such as atmospheric circulation, climatic evolution, and mantle dynamics. The topography is, for instance, considered a first order controlling factor for CO₂ evolution over geological timescales, through silicate weathering (MacDonald et al., 2019).

How to cite: Franziskakis, F., Vérard, C., and Giuliani, G.: Reconstructing the Earth in Deep-Time: A New and Open Framework for the PANALESIS Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7977, https://doi.org/10.5194/egusphere-egu24-7977, 2024.

Earth’s climate shows a remarkable variability on geological timescales, ranging from widespread glaciation to ice-free greenhouse conditions over the course of the Phanerozoic, i.e. the last 540 million years. Earth system modelling allows us to better understand and constrain the drivers of these changes and provides valuable reference data for other paleoclimate disciplines (e.g., chemistry, geology, hydrology). However, the sheer volume and complexity of these datasets often prevents direct access and use by non-modellers, limiting their benefits for large parts of our community.

We present the online platform “climatearchive.org” to break down these barriers and provide intuitive access to paleoclimate data for everyone. More than 100 global coupled climate model simulations covering the entire Phanerozoic at the stage level build the backbone of the web application. Key climate variables (e.g. temperature, precipitation, vegetation and circulation) are displayed on a virtual globe in an intuitive three-dimensional environment and on a continuous time axis throughout the Phanerozoic. The software runs in any web browser — including smartphones — and promotes visual data exploration, streamlines model-data comparisons, and supports public outreach efforts. We discuss the current proof of concept and outline the future integration of new sources of model and geochemical proxy data to streamline and advance interdisciplinary paleoclimate research.

We also present ongoing efforts for an integrated model-data synthesis to quantify changes in meridional and zonal temperature gradients throughout the Phanerozoic and to address the relative roles of individual forcings (greenhouse gases, solar, geography). While substantial effort has been made to quantify the evolution of global mean temperatures over the last 540 million years, changes in the large-scale temperature gradients and their causes are comparably less constrained. As a fundamental property of the climate system, changes in the spatial patterns of surface temperature play a critical role in controlling large-scale atmospheric and ocean circulation and influence hydrological, ecological, and land surface processes. The resulting best estimate product of meridional and zonal temperature gradients over the last 540 million years will represent a step change in our understanding of the drivers and consequences of past temperature gradient changes and will provide the community with a valuable resource for future climatological, geological, and ecological research.

How to cite: Steinig, S., Johnson, H., Robinson, S., Valdes, P. J., and Lunt, D. J.: Towards a community platform for paleoclimate data and temperature gradients over the last 540 million years , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18486, https://doi.org/10.5194/egusphere-egu24-18486, 2024.

Geological models can be constructed with a variety of mathematical methods. Generally, we can describe the modeling process in a formal way as a functional relationship between input parameters (geological observations, orientations, interpolation parameters) and an output in space (lithology, stratigraphy, rock property, etc.). However, in order to obtain a suitable implementation in geophysical inverse frameworks, we have to consider specific requirements. In recent years, a substantial amount of work focused on low-dimensional parameterizations and efficient automation of geological modeling methods, as well as their combination with suitable geophysical forward simulations. In this contribution, we focus on differential geomodelling approaches, which allow for an integration of geological modeling methods into gradient-based inverse approaches.

In this work, we emphasize differential geomodelling approaches. These approaches seamlessly integrate geological modeling methods into gradient-based inverse approaches. To achieve this integration, we actively employ modern machine learning frameworks, specifically TensorFlow and PyTorch. We then incorporate these geometric geological modeling methods into a Stein Variational Gradient Descent (SVGD) algorithm. SVGD is adept at addressing the challenges of multimodality in probabilistic inversion. Moreover, we demonstrate the implementation of these methods in a Hamiltonian Monte Carlo approach.

Our results are promising, showing that treating geological modeling as a differentiable approach unlocks new possibilities. This method facilitates novel applications in the integration of geological modeling with geophysical inversion, paving the way for advanced research in this field.

How to cite: Wellmann, F., de la Varga, M., and Liang, Z.: Differentiable Geomodeling: towards a tighter implementation of structural geological models into geophysical inverse frameworks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20508, https://doi.org/10.5194/egusphere-egu24-20508, 2024.

How to cite: Bobe, C., von Harten, J., Chudalla, N., and Wellmann, F.: GeoBUS - A Probabilistic Workflow Combining ERT Inverse Modeling and Implicit Geological Modeling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15712, https://doi.org/10.5194/egusphere-egu24-15712, 2024.

The Earth's interior structure must be inferred from geophysical observations collected at the surface. Compared to just a few decades ago, the amount of geophysical data available today is voluminous and growing exponentially. Dense seismic networks like USArray, AlpArray, and AusArray now enable joint inversions of various geophysical data types to maximise subsurface resolution at scales ranging from local to continental. However, the practical application of joint inversions faces several challenges:

• Various geophysical techniques typically probe different scales and depths, complicating the choice of an appropriate discretisation for the Earth's interior.
• Different geophysical observables may respond to physical properties that are not directly related (e.g., density and electrical conductivity), making the construction of self-consistent parameterisations a non-trivial task.
• Without a comprehensive understanding of noise characteristics, standard methods require assigning weights to different data sets, yet robust choices remain elusive.

Capable of overcoming these recognised challenges and allowing estimates of model uncertainty, probabilistic inversions have grown in popularity in the geosciences over the last few decades, and have been successfully applied to specific modelling problems. Here, we present BayesBridge, a user-friendly Python package for generalised transdimensional and hierarchical Bayesian inference. Computationally optimised through Cython, our software offers multi-processing capabilities and runs smoothly on both standard computers and computer clusters. As opposed to existing software libraries, BayesBridge provides high-level functionalities to define complex parameterisations, with prior probabilities (defined by uniform, Gaussian, or custom density functions) that may or may not be dependent on depth and/or geographic coordinates. By default, BayesBridge employs reversible-jump Markov chain Monte Carlo for sampling the posterior probability, with the option of parallel tempering, but its low-level features enable effortless implementations of arbitrary sampling criteria. Utilising object-oriented programming principles, BayesBridge ensures that each component of the inversion -- such as the discretisation, the physical properties to be inferred, and the data noise -- is a self-contained unit. This design facilitates the seamless integration of various forward solvers and data sets, promoting the use of multiple data types in geophysical inversions.

How to cite: Magrini, F., He, J., and Sambridge, M.: Streamlining Multi-Data Geophysical Inference with BayesBridge, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6992, https://doi.org/10.5194/egusphere-egu24-6992, 2024.

Over the past twenty years, Trans-dimensional Bayesian Inference has become a popular approach for Bayesian sampling. It has been applied widely in the geosciences when the best class of model representation, e.g. of the subsurface, is not obvious in advance, or the number of free variables undecided. Making arbitrary choices in these areas may result in sub-optimal inferences from data. In trans-D, one typically defines a finite number of model states, with differing numbers of unknowns, over which Bayesian Inference is to be performed using the data.

A key attraction of Trans-D Bayesian Inference is that it is designed to let the data decide which state, as well as which configurations of parameters within each state, are preferred by the data, in a probabilistic manner. Trans-D algorithms may hence be viewed as a combination of fixed dimensional within-state sampling and simultaneous between-state sampling where Markov chains visit each state in proportion to their support from the data.

In theory, each state may be completely independent, involving different classes of model parameterization, with different numbers of unknowns, data noise levels, and even different assumptions about the data-model relationship. Practical considerations, such as convergence of the finite length Markov chains between states, usually mean that each state must be closely related to each other, e.g. differing by a single layer in a 1-D seismic Earth model. In addition, since the form of the necessary Metropolis-Hastings balance condition depends on the mathematical relationship between the unknowns in each state, then implementations are often bespoke to each class of model parameterization and data type. To our knowledge there exists no automatic trans-D sampler where one can define arbitrary independent states, together with a prior and Likelihood, and simply pass to a generalised sampling algorithm, as is common with many fixed dimensional MCMC algorithms and software packages. 

A second limitation in trans-D sampling is that since implementations are bespoke within a class of model parameterizations, within-state sampling is typically performed with simplistic and often dated algorithms, e.g. Metropolis-Hastings or Gibbs samplers, thereby limiting convergence rates. Over the past 30 years fixed dimensional sampling has advanced considerably with numerous efficient algorithms available and many conveniently translated into user friendly software packages, almost all of which have not been used within a trans-D framework due to a lack of a way to conveniently deploy them in a trans-D setting.

In this presentation we will address all of these issues by describing the theory under-pinning an ‘Independent State’ (IS) Trans-D sampler, together with some illustrative examples. In this algorithm class, sampling may be performed across states that are completely independent, containing arbitrary numbers of unknowns and parameter classes. In addition, the IS-sampler can conveniently take advantage of any fixed dimensional sampler without the need to derive and re-code bespoke Markov chain balance conditions, or specify mechanisms for transitions between model parameters within different states. In this sense it represents a general purpose automatic trans-D sampler.

How to cite: Sambridge, M., Valentine, A., and Hauser, J.: An Independent State sampler for Trans-dimensional Bayesian Inference, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16036, https://doi.org/10.5194/egusphere-egu24-16036, 2024.

Full waveform inversion (FWI) has become a commonly used tool to obtain high resolution subsurface images from seismic waveform data. Typically, FWI is solved using a local optimisation method which finds one model that best fits observed data. Due to the high non-linearity and non-uniqueness of FWI problems, finding globally best-fitting solutions is not necessarily desirable since they fit noise in the data, and quantifying uncertainties in the solution is challenging. In principle, Bayesian FWI calculates a posterior probability distribution function (pdf), which describes all possible model solutions and their probabilities. However, characterising the posterior pdf by sampling alone is often intractably expensive due to the high dimensionality of FWI problems and the computational expense of their forward functions. Alternatively, variational inference solves Bayesian FWI problems efficiently by minimising the difference between a predefined (variational) family of distributions and the true posterior distribution, requiring optimisation rather than random sampling. We propose a new variational methodology called physically structured variational inference (PSVI), in which a physics-based structure is imposed on the variational family. In a simple example motivated by prior information from past FWI solutions, we include parameter correlations between pairs of spatial locations within a dominant wavelength of each other, and set other correlations to zero. This makes the method far more efficient in terms of both memory requirements and computational cost. We demonstrate the proposed method with a 2D acoustic FWI scenario, and compare the results with those obtained using three other variational methods. This verifies that the method can produce accurate statistical information of the posterior distribution with significantly improved efficiency.

How to cite: Zhao, X. and Curtis, A.: Physically Structured Variational Inference for Bayesian Full Waveform Inversion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8215, https://doi.org/10.5194/egusphere-egu24-8215, 2024.

It is common knowledge that climate variability and change have a profound impact on crop production. From the principle that “it is green and it grows” to the assessment of the actual impacts of major weather drivers and their extremes on crop growth through the adoption of agro-management strategies informed by tailored and effective climate services, there is a well documented scientific and operational gap. This work focuses on the development, implementation and testing of an AI-based methodology that aims to reproduce a crop growth model informing on grain maize yield in the European domain. A surrogate AI model based on Bayesian deep learning and inference is compared for its efficiency against the process-based deterministic ECroPS model developed by the Joint Research Centre of the European Commission. The rationale behind this effort is that such mechanistic crop models rely on multiple input meteorological variables and are relatively costly in terms of computing resources and time, crucial aspects for a scalable and widely adopted solution. Such approaches make it possible to run very large ensembles of simulations based, for instance, on ensembles of climate predictions and projections and/or a perturbed parametrization (e.g. on the atmospheric CO2 concentration effects). Our surrogate crop model relies on three weather input variables: daily minimum and maximum temperatures and daily precipitation, where the training was performed with the ECMWF-ERA5 reanalysis. 

How to cite: Vlachopoulos, O., Luther, N., Ceglar, A., Toreti, A., and Xoplaki, E.: Impact modeling with Bayesian inference for crop yield assessment and prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17157, https://doi.org/10.5194/egusphere-egu24-17157, 2024.

This research introduces a novel approach to seismic exploration on the Moon and Mars, employing autonomous robotic swarms equipped with seismic sensing and processing hardware. By relying on probabilistic inference methods, we aim to survey large surface areas to both autonomously identify and map subsurface features such as lava tubes and ice deposits. These are crucial for future human habitats and potential in-situ resource utilization.

This endeavor presents unique challenges due to the communication limitations and uncertainties inherent in remote, autonomous operations. To address these challenges, we adopt a distributed approach with robotic swarms, where each rover processes seismic data and shares the results with other rovers in its vicinity, contending with imperfect communication links. Thus, the swarm is used as a distributed computing network. The decisions made within the network are based on probabilistic modeling of the underlying seismic inference problem. A key innovation in this respect is the use of factor graphs to integrate uncertainties and manage inter-rover communications. This framework enables each rover to generate a localized subsurface map and autonomously decide on strategic changes in the seismic network topology, either exploring new areas or repositioning to enhance measurement accuracy of targeted underground regions.

The vision is to implement this approach on a distributed factor graph, allowing for a coordinated, probabilistic analysis of seismic data across the swarm. This strategy represents a significant departure from traditional static seismic sensor arrays, offering a dynamic and adaptable solution for planetary exploration. The first step towards realizing this vision involves implementing a Kalman filter for the one-dimensional linear heterogeneous wave equation. This has been achieved by reformulating finite difference schemes for wave propagation simulation into a state-space description. The resulting linear continuous n-th order system can be explicitly solved and rewritten into a discrete state space model that can be used in the standard Kalman filter recursion. However, the standard Kalman filter is limited due to its assumption that both model and process noise are Gaussian. With factor graphs, this limitation can be overcome, enabling a more robust and versatile analysis. Several simulation results will be shown to demonstrate the performance of these approaches.

We intend to extend the approach to higher-dimensional problems, implementing distributed versions of the Kalman filter and factor graph with simulated, non-perfect communication links. Eventually, the seismic inverse problems will be solved in these frameworks. Successfully achieving these objectives could greatly enhance our capabilities in extraterrestrial exploration, paving the way for more informed and efficient future space missions.

How to cite: Nierula, K., Shutin, D., Shin, B.-S., Igel, H., Keil, S., Bernauer, F., Reiss, P., Sesko, R., and Lindner, F.: Probabilistic Approach toward Seismic Exploration with Autonomous Robotic Swarms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15385, https://doi.org/10.5194/egusphere-egu24-15385, 2024.

Earthquakes occur in a depth range where the physical conditions allow rocks to behave as brittle and to deform in a stick-slip fashion. This range is limited by the so-called upper and lower seismogenic depths, which are input parameters for bounding seismogenic ruptures in models of seismic hazard assessment.

Usually, such limits are estimated from the observed depth distribution of hypocenters. An exact estimation is not possible, because earthquake locations (and particularly hypocentral depths) are uncertain. Also, the sample of observed earthquakes is finite, and shallower or deeper earthquakes than those so far observed at a site could eventually happen. For these reasons, the extreme values of the distribution (the shallowest and the deepest earthquakes in the sample) are weak estimators, especially if a small sample (with few earthquakes) is used.

A common, more statistically robust, proxy to those limits is a given percentile of the distribution of earthquake depths. For example, the 90%, 95% or 99% percentiles (named D90, D95 or D99, respectively) are frequently used as proxies to the lower seismogenic depth. But the actual uncertainties of such estimates are, so far, not properly assessed.

Here I present a method for calculating such percentiles with an unbiased estimator and quantifying their uncertainties in detail.

Earthquakes are more easily missed (more difficult to detect) the deeper they are. So earthquake catalogues preferentially contain shallow events. To avoid this bias, only those events with magnitude at least equal to the magnitude of completeness of the sample are regarded.

A mapping procedure is used in order to highlight spatial variations of seismogenic depths, considering, for each point in the map, the subsample of its closest earthquakes. Uncertainties arising from the finite sample size are dealt with by using bootstrap.

Each hypocentral location is randomized in space in a Monte Carlo simulation, to take into account the reported location uncertainties. Also, crustal earthquakes can be considered separately from deeper ones, by truncating the hypocentral depth distribution with a Moho model for which the uncertainty can also be taken into account.

This procedure allows calculating statistically robust maps of the seismogenic depths with a realistic treatment of their uncertainties, as exemplified with the analysis of a regional seismic catalogue.

How to cite: González, Á.: Robust estimation of seismogenic depths and their uncertainties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6149, https://doi.org/10.5194/egusphere-egu24-6149, 2024.