Comprehensive characterisation of Mars requires global surface observations at high resolution. However, current exploration is conducted through large, infrequent, risky, and relatively high-cost space missions that gather highly localised data. For human missions in the upcoming decades, a new paradigm of exploration involving notable reductions in cost, risk, and timeframe is necessary.

The Tumbleweed Mission proposes a novel architecture, with a swarm of 90 spheroidal, autonomous, wind-driven, and solar-powered mobile impactors (rovers). Unfolding mid-air, they land near one of the poles and spread across the Martian surface for approximately 90 sols. Each impactor follows a different route, planned with consideration for the topography, wind conditions and sites of scientific interest. Once the desired spatial distribution is achieved, the rovers are arrested to a stationary phase for an undefined period. Rovers collect scientific data during both mobile and stationary phases. Each 5-meter diameter, 20 kg rover accommodates up to 5 kg of scientific payload.

The mission aims to produce (atmospheric) data over a multitude of spatial and temporal scales, corresponding to existing strategic knowledge gaps as outlined by NASA’s Mars Exploration Program Analysis Group (MEPAG). The mission would be able to characterise the dynamical and thermal state of the lower atmosphere and controlling processes on local to regional scales. Measure variations in the abundance of species such as water vapour, carbon dioxide and methane. As well as improve constraints to computational models and overall understanding of Martian climate and weather. In the stationary phase, Environmental Sensing Suites (ESS) will act as weather stations, providing frequent near-surface atmospheric data from up to 90 surface points of Mars to allow for the observation of changes at hourly, diurnal and seasonal time scales. Additionally, the ionising radiation environment at the surface would be characterised by unprecedented spatio-temporal resolution. The geomorphology and composition of previously inaccessible areas of Mars can now be constrained through imaging and spectroscopy. Also, the large network setup could provide Martian mantle properties through continuous measurements of nutation, precession, tidal deformation, and gravimetry. Identifying the abundance of carbon and other biologically important (CHNOPS) elements near the surface would provide contextual information concerning habitability and possibly, evidence of indigenous life. Surface measurements can be used to map future landing sites to mitigate the risks posed by hazardous terrain and radiation exposure.

The rover swarm will leverage a heterogeneous complement of analytical instruments during the mission and mostly employ legacy instruments. Currently, the integrated set of instrumentation is under investigation through an objective trade-off. A preliminary list includes a multispectral camera, environmental sensing suite, magnetometer, radio beacon, laser retroreflector, and miniaturised spectrometers. The next stage of development involves testing the proposed instrumentation in Mars analogous environments.

By providing large-scale data sets using rover swarms, the Tumbleweed Mission offers the opportunity to make deep space accessible for everyone. The presentation will provide an overview of the mission concept, review the most desirable science applications and their relevance to MEPAG goals, and discuss the instrument recommendations and main limitations.

How to cite: Kingsnorth, J., Pikulić, L., Shanbhag, A., Bonanno, L., de Pinto Balsemão, M., Rothenbuchner, J., and Mikulskytė, O.: The Tumbleweed Mission: A new paradigm of Martian exploration through swarm-based, wind-driven rovers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20149, https://doi.org/10.5194/egusphere-egu24-20149, 2024.

Italian Spring Accelerometer (ISA) is a scientific payload of the European Space Agency’s BepiColombo mission to Mercury. It aims to measure the Non Gravitational Perturbations acting on the MPO (Mercury Planetary Orbiter) spacecraft, allowing to consider it as a test-mass free falling in the planetary gravity field and hence disclosing the possibility to study the  Mercury's interior, surface, and environment, as well as to preform tests of Einstein's General Relativity theory.

ISA sensitivity to thermoelastic deformations of the spacecraft panel on which it is mounted on, is one of the limiting factors of the achievable acceleration measurements accuracy, whose target value is 10^-8 m/s² .

To address this challenge, a data analysis and reduction procedure is being developed; it is based on machine learning techniques and allows to compute an acceleration measurements correction signal, starting from the data provided by multiple supplementary sensors. Specifically, we employed the temperatures recorded by several thermometers and the information about power dissipated across the MPO in order to compute the correction signal to be applied to the ISA output. Indeed, these temperatures and dissipated power variations are responsible for the thermoelastic deformations of the mounting plate housing ISA.

The technique is being developed during the mission's cruise towards Mercury, exploiting also the outcomes of the GAIN “Gravimetro Aereo INtelligente” project, which developed a similar methodology for airborne gravimetry.

The preliminary results related to measurement sessions during the cruise phase will be presented, and considerations on the implementation of such techniques for future space missions will be provided.

Indeed, despite ISA was not specifically designed for the use of the "GAIN method”, the preliminary results are promising, underscoring its potential and allowing to envisage that future space missions could benefit of a full implementation of such a method that should go through the development of purpose built and trained multi-sensor systems.

How to cite: Fusco, G., Lefevre, C., Iafolla, L., Magnafico, C., Chiappini, M., and Santoli, F.: Disturbances compensation in high accuracy spaceborne accelerometers using multi-sensors and machine learning approach., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12272, https://doi.org/10.5194/egusphere-egu24-12272, 2024.

With the rise of commercial constellation implementation in low earth orbit (LEO), the near-Earth space environment is becoming increasingly challenging to monitor and protect. As well as carefully considered policy frameworks, new observational techniques and instrumentation are needed to ensure that safe operations can be maintained by all space users. The Pervasive Sensing group at the University of Birmingham is exploring in-orbit conditional monitoring of satellites using inverse synthetic aperture radar (ISAR) as a technique for dedicated observation of high-value space-based assets. Our previous concept and design results for fixed-beam dual freqency ISAR observations in circular orbits have been extended to a variety of scenarios. I will discuss some of our recent results from both experiments and simulation. 

How to cite: Alconcel, L.-N., Jones, G., Coe, M., Gashinova, M., and Cherniakov, M.: Sub-Terahertz Inverse Synthetic Aperture Radar (ISAR) for monitoring of high-value space assets , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8635, https://doi.org/10.5194/egusphere-egu24-8635, 2024.

The use of multibeam MIMO-SONAR systems on marine vehicles (e.g. remotely operated vehicles, ROVs) enables the visual 3D reconstruction of the water-column by hydroacoustic ensonification of the surrounding environment in real-time. For underwater target detection and classification purposes, the processed SONAR data must be visualized for interpretation of the results by a human operator and to allow for a corresponding re-adjustment of the system parametrization during operation. Therefore, conventional 2D visualization approaches such as the plan position indicator (PPI) plot must be adapted to 3D. Challenges such as different signal-to-noise ratios, beamforming artifacts, and overlapping objects must be considered when choosing how to visualize the processed data to allow for the correct semantic interpretation of the scanned water-column.

In this presentation, an approach for the 3D voxel- and mesh-based visualization of real-time processed multibeam SONAR data is shown. The focus will be on how to consider the 3D beamforming and signal correlation processing, combined with data interpolation and filtering techniques, to allow for a visual reconstruction of the water-column from the SONAR data. An implementation of this approach in the C++ programming language using the Qt visualization framework will be shown in the Kiel Real-time Application Toolkit (KiRAT) for a virtual ocean environment.

How to cite: Kanarski, C., Kaulen, B., Kühne, F., Gussow, K., Röhrdanz, F., Driesen, M., Karatziotis, K., Greinert, J., and Schmidt, G.: Visualization of Real-time Processed Multibeam SONAR Data for 3D Water-column Reconstruction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10895, https://doi.org/10.5194/egusphere-egu24-10895, 2024.

A few powerful tools already exist for processing and investigating multibeam echosounder (MBES) data. They fulfill many industrial and scientific needs regarding quick visualization and bathymetric processing. However, none of these tools sufficiently target scientists that need to- develop new or custom MBES processing methods.

Projects focusing on investigating objects in the water column (e.g. gas bubble streams, suspended particulate matter, fish, …) formed the base for a new MEBS processing tool that is currently being developed within the framework of the TURBEAMS project. The aim is a tool that possesses the flexibility to execute custom processing routines, the transparency to understand the specific equations applied to the acoustic raw data, and the power to efficiently apply these customized routines to large amounts of MBES data gathered during scientific surveys.

The result of these specifications evolved into themachinethatgoesping (short: Ping), a new open-source python library (implemented in c++) for processing multi- and singlebeam echosounder data. Ping aims at simplifying the development and application of novel processing methods by providing a performant, pythonic interface to the acoustic raw data, together with commonly needed processing routines. A few examples:

• Extract configuration and navigation data.
• Extract quantitative meaningful backscatter data.
• Implement and test water column calibration routines.
• Create time series echograms or render water column images.
• Filter, georeference and grid acoustic samples in 2D and 3D space.

These functions – and the large amount of python data science libraries – form the base to implementing processing methods (or tools) that are shareable as comprehensive python scripts. Ping is still incomplete and e.g. currently limited to processing Kongsberg .all and Simrad EK80 .raw data files. But you can test it and follow the active development here: https://github.com/themachinethatgoesping

How to cite: Urban, P., Praet, N., Vandorpe, T., and Hermans, T.: Themachinethatgoesping: Pythonic(++) processing of MBES and SBES data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19250, https://doi.org/10.5194/egusphere-egu24-19250, 2024.

Climate warming increases glacial melt in polar environments, altering the pressure on extensive networks of nutrient-rich fluids and climate-changing gases below the surface and connecting from land to sea.

The increased transport of these fluids and gases to the marine environment has been observed in polar regions, but such processes remain difficult to detect and monitor. To that purpose, water-column acoustic measurements have proven extremely effective, allowing the detection, identification and quantification of fine changes in oceanography, stratified turbulence and mixing at large scales.

Here, we highlight recent visualisations of such anomalous acoustic features in polar regions collected on broadband split-beam systems ranging from 12 to 200 kHz. This allowed us to perform fine analysis of water masses and near-seafloor features. By coupling these acoustic with profiles of chemical properties of the water column and multi-disciplinary datasets, we interpret those, including meltwater, subglacial plumes, and seafloor seeps.

These observations show the potential of using water-column acoustics in the context of long-term monitoring changes in those regions, with the potential to capture short and long-term variations in sensitive areas to better understand those rapidly changing environments.

How to cite: Ladroit, Y., Seabrook, S., Weidner, E., and Loranger, S.: Tracking climate-driven changes of water masses and fluxes in polar regions using acoustics. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19013, https://doi.org/10.5194/egusphere-egu24-19013, 2024.

Sand extraction on the sand banks in the Belgian part of the North Sea has various impacts on the marine environment. The direct near-field effects in areas where extraction takes place, are regularly monitored for at least two decades and well understood. In contrast, several important questions remain regarding the far-field impact associated with the dispersion of suspended particle matter (SPM) plumes. On the longer term, these SPM plumes could significantly change the integrity of the seafloor and damage the ecological valuable habitats bordering the exploited sand banks. Therefore, the Continental Shelf Service of the SPF Economy, responsible for the management of the sand extraction, instigated a project to define the range and significance of the far field impact of this activity. In close partnership with RBINS, UGent and VLIZ, a number of controlled measurements were devised to validate models predicting far field dispersal and, if necessary, improve them. Based on these models, frequency and magnitude of disturbance on nearby marine protected areas can then be determined for all sand extraction sectors.

In order to characterize SPM plumes and to estimate their dispersion distance, several experimental setups were developed, combining continuous and point measurements with the use of a quasi-real-time dispersion model. The measurements were performed on board the RV Belgica during two campaigns in November 2022 and March 2023 following dredging vessels performing extraction operations.

During the experiments, the dispersion model mapped the estimated trajectory, extension, and the deposition of the SPM plumes in real time, using a combination of hydrodynamic, waves and wind data, estimated sediment properties and the position and activity of the dredging vessels. This real-time information allowed us to position the vessel in the ideal location to validate the presence of the plume and its properties using an experimental set-up of combined acoustic and in-situ measurements. Continuous acoustic measurements of the water column involving a Kongsberg EM2040 dual RX multibeam echosounder (MBES), a Simrad EK80 single beam echosounder (SBES) and a Teledyne Acoustic Doppler Current Profiler (ADCP) were carried out jointly to map the actual position, extent and density of the plumes. These continuous measurements were completed with in situ point measurements of the water column properties through the use of several acoustic (Aquascat 1000R) and optical (LISST-200X, OBS) sensors mounted on a carousel. Additionally, water samples were collected using Niskin bottles that were filtered on board for further analyses (SPM, particulate organic carbon and nitrogen, Chlorophyl a), and analysed using a Hach turbidimeter.  Samples of the extracted sediments were collected on the seafloor and onboard the extraction vessels for granulometric analysis.

The first analysis of the November 2022 and March 2023 experiments indicate the good performance of the used dispersion model and the excellent concordance between the continuous acoustic detection of the sediment plumes with the MBES, SBES and ADCP. Additionally, our results show the importance of a profound knowledge of the spatial configuration of the involved instruments and the impact of the research vessel itself on the water column.

How to cite: Van Roozendael, B., Degrendele, K., Barette, F., Vandenreyken, H., Piette, A.-S., Van Lancker, V., Kint, L., Baetens, K., Denis, P., Urban, P., Praet, N., and Roche, M.: Multidisciplinary approach to assess the far-field effects of sand extraction in the Belgian part of the North Sea. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7810, https://doi.org/10.5194/egusphere-egu24-7810, 2024.

Monitoring suspended particulate matter (SPM) in coastal areas is essential for research, management and protection of coastal ecosystems. In the Belgian part of the North Sea, the dynamic nature of SPM variability and the increasing human activities (offshore windmill parks, dredging and dumping) call for 3D monitoring of these natural and human-induced SPM changes.

Multibeam echosounders (MBES) provide, in addition to bathymetry and seafloor backscatter data, a 3D dataset of acoustic measurements in the water column, which can be used to monitor SPM in coastal waters. Although MBES water column data are commonly used by fisheries and gas seepage research, only a handful of studies focus on the quantification of SPM in the water column.

During the Timbers project, we developed a novel methodology to convert MBES water column data into 3D SPM maps. In contrast to most studies that deploy the MBES from stations, we quantified SPM using MBES from a sailing vessel. Simultaneous optical and acoustic measurements were collected during ship transects to yield an empirical relation using linear regression modeling. This relationship was then used to convert the acoustic measurements into a 3D grid that displays the mass concentration of SPM. The large spatial coverage of these SPM maps allows us to observe phenomena in the water column that otherwise would be missed by traditional monitoring approaches. Furthermore, several valuable lessons were learned. In particular, the interpretation of the acoustic signal is not straightforward, which makes it difficult to distinguish between different types of scatterers (sediment, plankton, flocs, bubbles, fish, etc.) captured by the MBES. Hence, additional research efforts focusing on discriminating scatterers in the water column are needed to unlock the full monitoring potential of MBES water column data.

In the ongoing Turbeams project, we are exploring multi-frequency approaches to differentiate between various scatterers and their wide spectrum of sizes. Additionally, we are applying imaging tools on collected water samples and we are using underwater cameras that capture particles in their natural environment. These improvements will help to move towards operational use of MBES as a common tool for SPM monitoring in the future.

How to cite: Praet, N., Urban, P., Roche, M., Mortelmans, J., Lagaisse, R., and Vandorpe, T.: Towards 3D SPM monitoring in the North Sea using multibeam sonar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1359, https://doi.org/10.5194/egusphere-egu24-1359, 2024.

Fluid emissions from the seafloor affect ocean chemistry and are involved in the geological processes taking place along active and passive continental margins. These emissions are linked to geological hazards, such as earthquakes, sedimentary instabilities, and extensive methane release. Detecting and locating the sources of fluid emissions is therefore of paramount importance. Hydrographic MultiBeam EchoSounders (MBES) designed for seafloor mapping can record the acoustic backscatter of the water column. Due to the impedance contrast between gas and seawater, gas bubbles form "acoustic plumes" in the echograms. Acoustic data assists in guiding the exploration of seeps and their associated geological structures. However, processing the vast amount of data generated by these sounders is a significant undertaking.

The present study is based on the data collected from two surveys, GAZCOGNE1 (Bay of Biscay, Aquitaine Basin) and GHASS2 (Black Sea) during which data were collected using a Kongsberg EM302 MBES (30 kHz transmit frequency) and a Reason Seabat 7150 MBES (24 kHz transmit frequency) respectively. These sounders have proven to be very effective in identifying fluid emissions.

Deep learning has become increasingly popular in marine science over the last few decades due to the use of Graphical Processing Units and large amounts of labelled data. This method has proven to be particularly robust in accurately analysing large datasets and identifying complex patterns. We have devised a deep-learning approach that allows us to: 1) Detect fluid-related echoes in multibeam echograms. 2) Conduct near real-time fluid detection and tracking during the acquisition surveys and provide accurate positioning of the fluid outlet beneath the seafloor based on acoustic and spatial attributes. 3) Discriminate between fluid-related echoes emanating from the primary lobe of the multibeam directivity and those originating from the side lobes, in order to accurately locate the fluid outlet. This last approach results from antenna modelling and multibeam survey simulation. The technique for echo discrimination using antenna modelling was produced with the open-source toolbox published by Urban et al 2023 (https://doi.org/10.1002/lom3.10552).

Detection on the multibeam echograms is performed by adapting the open-source You-Only-Look-Once algorithm (version 5). Training on Ifremer datasets showed that the results surpass those of a state-of-the-art method regardless of the MBES used for training and testing. Hence, this method can be applied to diverse MBES data, acoustic acquisition parameters and environmental conditions. The algorithm can detect signals throughout the entire water column, even in areas affected by acoustic artefacts such as specular side lobes and different emission sectors. We have developed methods to improve neural network learning using training sets when limited labelled MBES data are available. The method was tested during an oceanographic expedition in the summer of 2022 (MAYOBS23), demonstrating its ability to operate in near real-time with excellent performance.

The marine expedition GAZCOGNE1, part of the PAMELA project, was co-funded by TotalEnergies and IFREMER. The expedition GHASS2 was co-founded by the Agence Nationale de la Recherche for BLAck sea MEthane (BLAME) project and IFREMER. MAYOBS23 was funded by the Mayotte volcanological and seismological monitoring network REVOSIMA.

How to cite: Perret, T., Le Chenadec, G., Gaillot, A., Ladroit, Y., and Dupré, S.: Detection and localisation of fluid emissions in water column data using Deep Learning with acoustic and spatial information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-275, https://doi.org/10.5194/egusphere-egu24-275, 2024.

Seepage of gases from natural sources of subsurface hydrocarbon accumulations escaping through the seafloor to the water column is recorded in many parts of the world’s oceans. These occurrences can be acoustically and visually observed using various sensors onboard various platforms. This is particularly common when carrying out bathymetric surveys of large areas using multibeam echosounder systems with the capability of recording the whole water column acoustic backscattering. The so-called “water-column data” that these systems produce can then be inspected for acoustic anomalies that are characteristic of gas seepages (i.e. acoustic “gas flares”), and those instances and their attributes (e.g. strength, confidence, height, etc.) can be recorded in a database. The Geological Survey of Norway has been building such a database for the Norwegian offshore since 2010. To date, this database includes over 5,000 flares of varying magnitudes and sizes, detected in an area of >140,000 km². The water-column data used for this task mainly originates from the many multibeam surveys carried out since 2005 over large areas of the Norwegian offshore for the MAREANO program, which is aimed at mapping habitats, but also from datasets acquired in associated projects and sources.

We present the results from these comprehensive surveys and discuss the various challenges faced in making such a database. Our main challenges are the very large size of the datasets and our reliance on visual interpretation, which necessitate dedicated software, high-performance processing systems, storage solutions of very large capacity and fast access, considerable interpretation time, and procedures of cross-validation between different interpreters. Another challenge is the variety of the data and its quality due to various acquisition parameters, weather conditions, and water depths, but also from the use of various systems, models, generations, and frequencies. This variety impacts the visual aspect of acoustic gas flares and thus affects the ability of the interpreters to consistently estimate flare magnitude and size. However, this variety also presents research opportunities. For example, we possess several instances of acoustic gas flares that were imaged with a range of frequencies, allowing for frequency dependence analysis. Finally, we will discuss future possibilities for interpreting water-column data in more time-efficient and interpreter-independent manners.

How to cite: Chand, S., Schimel, A. C. G., Thorsnes, T., and Bellec, V.: Mapping gas seeps with multibeam water column data in the Norwegian offshore – Challenges and way forward, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5925, https://doi.org/10.5194/egusphere-egu24-5925, 2024.