Geotechnical and structural monitoring and inspection of existing linear infrastructures, with particular focus on tunnels, have gained a great prominence in recent years. In Italy, a new regulation for inspection, monitoring and maintenance of tunnels has been issued and a massive inspection plan is carried out to guarantee a constant maintenance and safe condition of existing tunnels. The relevance of geo-structural monitoring lies in the possibility of controlling the interaction between soil and structure and in prevention of damaging infrastructure systems because of deterioration. Monitoring also helps the prevention of natural disaster effects, according to the rising attention paid to hydrogeological risk in the country in the last decades. The Italian territory is vulnerable to earthquakes, floods, and landslides, which are the major hazards that can involve human settlements, constructions, and big infrastructures such as tunnels. An opportunity arose for the possibility of working on huge amounts of data and develop accurate methods of elaboration and visualisation of the most significant information for inspection and maintenance planning purposes. For this work, innovative methods and mobile survey technologies were used to get linear images and 3D point cloud data of some highway tunnels in central Italy, which are characterised by relevant structural deteriorations and cracks. High-resolution black and white images of tunnel linings were captured through a mobile system composed by line cameras, lamps for a correct illumination of the tunnel surface and positioning system. To represent the whole tunnel surface, 4 runs were performed: right and left wall and right and left ceiling. High-density point clouds of the tunnel were acquired by a mobile laser scanner mounted on a vehicle. The combination of 2D high-resolution images and 3D data can have a significant impact on the data visualisation and presentation in order to have a comprehensive representation of the actual state-of-the-art of the infrastructure. The 3D representation of the tunnel from 2D linear images is accomplished through the reconstruction of a 3D geometrical model of the tunnel section through a tool for the automatic elaboration and management of images. This automatic calculation algorithm provides the three-dimensional reconstruction of the infrastructure through 2D high-resolution images, in order to have the best representation and visualization of the elements inside the tunnel. The major advantage of the tool is the possibility of identifying and evaluating structural defects and cracks in the tunnel surface directly on the 3D model and of better understanding the effects on the infrastructure caused by deformation events in the geological context. It can also provide the comparison of subsequent surveys for monitoring. This work represents an innovative means affecting fundamental aspects of existing tunnels management and monitoring, e.g., the investigation of deformation phenomena, temporal evolution of deteriorations and cracks, causes identification/prevention, soil-structure interaction studies, geological hazard risk reduction.

How to cite: Dahanayaka, S. M., Riquelme Guill, A. J., Vecchietti, A., and Del Soldato, M.: Three-dimensional reconstruction of high-resolution images of existing tunnels for geo-structural monitoring and inspection purposes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8193, https://doi.org/10.5194/egusphere-egu24-8193, 2024.

The Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites (TRACERS) Small Explorers mission requires high-fidelity magnetic field measurements for its magnetic reconnection science objectives and for its technology demonstration payload MAGnetometers for Innovation and Capability (MAGIC). TRACERS needs to minimize the local magnetic noise through a magnetic cleanliness program such that the stray fields from the spacecraft and its instruments do not distort the local geophysical magnetic field of interest. Here we present an automated magnetic screening apparatus and procedure to enable technicians to routinely and efficiently measure the magnetic dipole moments of potential flight parts to determine whether they are suitable for spaceflight. This procedure is simple, replicable, and accurate down to a dipole moment of 1.59 × 10^-3 N m T^-1. It will be used to screen parts for the MAGIC instrument and other subsystems of the TRACERS satellite mission to help ensure magnetically clean measurements on-orbit.

How to cite: Dorman, C. J., Piker, C., and Miles, D. M.: Automated Static Magnetic Cleanliness Screening for the TRACERS Small-Satellite Mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-201, https://doi.org/10.5194/egusphere-egu24-201, 2024.

Current infrasound sensor designs have shortcomings inherent in their open-loop arrangement. Among them are the limited dynamic range, the lack of linearity of the response function over the desired frequency range and – may be most importantly – the fact that such sensors can only be calibrated in the laboratory and not under real conditions in the field.

Here we present a novel infrasound sensor design, which overcomes these and other shortcomings. At the core of our new sensor lies a feedback loop. It is based on a proven technology already applied in many sensor and control systems, particularly relevant for Earth science in the design and manufacturing of high fidelity broadband seismic sensors.

The new infrasound sensor uses a precision bellow, which deflects in response to pressure variations or atmospheric infrasound waves. The movement of the bellow in single degree of deflection is measured with a differential capacitive displacement transducer. Its circuitry is a Blumlein bridge arrangement operating at a frequency of 45 KHz and a driver signal amplitude of 20 V. The transducer's output signal is then synchronously fed back to the regular linearised magnetic force transducer after passing through a Proportional Integral and Differential (PID) controller.

This design increases the bandwidth of the sensor to five decades, from 2.7 mHz to more than 200 Hz. At the same time the response of the sensor is essentially flat over the entire frequency range with only minor variations of less than +/- 0.1 dB. We measured the dynamic range of the sensor to be in excess of 155 dB, a significant increase compared to current open loop systems.

The infrasound sensors theoretical transfer function is compared to practical measurements providing sensors characteristics including its detection levels over the complete frequency response.

The system calibration is carried out analogously to the calibration of broadband seismic sensors. We inject a known calibration signal (either sinusoidal, square wave or broadband noise) directly into the feedback force transducer. This setup allows the calibration of the infrasound sensor in the laboratory as well as after deployment in a field station.

How to cite: Guralp, C., Minchinton, P., Rademacher, H., and McGowan, M.: A New Approach to Infrasound Sensor Design, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8243, https://doi.org/10.5194/egusphere-egu24-8243, 2024.

The Güralp Minimus broadband digitiser introduced innovative features to the market including easy network configuration; compact form-factor; extensive State of Health (SOH) monitoring; and low latency digitisation. Since it was launched in 2016, technological advances in semiconductors have significantly decreased their power requirements. The latest iteration of Minimus, Minimus₂, utilises modern microprocessors to reduce power consumption by over 50% whilst maintaining high levels of functionality. The resulting reduction in power consumption facilitates simplified field deployments for offline deployments.

The Minimus platform also provides a high level of functionality for online stations, including the industry unique option of sending State of Health (SOH) data via the SEEDlink protocol. As well as simplifying SOH monitoring for larger networks, this facility also allows for time-series analysis of SOH data. This means that operators have the data they need to proactively manage their station network and diagnose issues before they result in data loss. The Minimus platform interfaces with Discovery software which seamlessly integrates new stations into existing networks. The management of large numbers of real-time seismic stations is further enhanced with Guralp Data Centre (“GDC”) a cloud-based software package that is an optional add-on of the Discovery tool set.

The Minimus platform was built from the ground up to provide one of the lowest latency digitizers available with digitization latencies down to 40ms, making it well suited to Earthquake Early Warning applications. This is achieved with the use of causal decimation filters, high sample rates and Guralp’s proprietary GDI protocol. The Minimus platform is built as a modular digitizer platform that is available within a number of different packages to suit a range of applications, including as a stand-alone digitiser or built within broadband seismic instruments and force balance accelerometer systems. 

How to cite: Price, E., Watkiss, N., and Restelli, F.: The Minimus Digitizer Platform: a User-Friendly Ecosystem for Efficient Network Management and Seismic Station Configuration., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16231, https://doi.org/10.5194/egusphere-egu24-16231, 2024.

Fiber-optic gyroscopes, as rotational motion sensors, have emerged as powerful candidates for rotational seismology and Earth rotation observation due to their portability and high sensitivity. However, the stability of fiber-optic gyroscopes is degraded by environmental temperature variation that optical fibers are sensitive to. For the suppression of effects of temperature variation, conventional methods include the device level and post-processing level. However, the former has additional device requirements and higher costs, while the latter has a degradation of compensation for a more general environment. In this abstract, we propose a suppression method at the device level. We find that the effect of thermally induced phase fluctuations is significantly lower in the high-frequency band compared to the low-frequency band. Therefore, by upconverting the operating point of the fiber-optic gyroscope at a high-order harmonic of eigenfrequency, the effect of thermally induced phase fluctuations on the output is greatly suppressed. This method is easy to operate without requiring any additional optical or electrical components. To validate this method, we have conducted a time-varying temperature variation experiment using a portable fiber-optic gyroscope equipped with a 20 km long and 0.3 m diameter fiber-optic coil. The implementation of this upconverted frequency modulation technology resulted in a 32-fold reduction in temperature sensitivity for the fiber-optic gyroscope. The results demonstrate that the proposed technology enhances the temperature adaptability of fiber-optic gyroscopes, which is a critical aspect in practical geophysical applications. At the same time, the self-noise is reduced from 3×10^-8 rad/s/√Hz to 8×10^-9 rad/s/√Hz, further improving its sensitivity to observe geophysical rotation signals. Seismic records will be presented to demonstrate its utility in rotational seismology.

How to cite: Chen, Y., Zhu, L., Wang, W., Huang, H., Cao, X., and Li, Z.: Fiber-optic gyroscopes with enhanced temperature adaptability for geophysical rotational sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-246, https://doi.org/10.5194/egusphere-egu24-246, 2024.

Wee-g is a new gravimeter that utilises a micro-electromechanical system (MEMS) sensor. The use of MEMS-based sensors has benefits as a new gravimetry technology because the low cost of the base material silicon and accessibility of manufacturing facilities will enable increased availability of gravimeters. However, the challenge of working with silicon for a gravimetry device is its thermal sensitivity, which affects the Young's Modulus of the material. In the context of Wee-g's sensor design, when the flexures that support the proof mass become softer because of temperature changes, under gravity the proof mass change position. Changes to the ambient pressure can also result in changes to the proof mass position. Wee-g has a thermal control system that effectively controls the temperature at the sensor to within 1mK, but field observations indicate that large changes to ambient environmental conditions can be coupled to the sensor output. Here we present the results of environmental stability tests conducted on a Wee-g field prototype with implications for its performance in field environments that vary in temperature and pressure significantly.

How to cite: Passey, E., Prasad, A., Toland, K., Anastasiou, K., Paul, D., and Hammond, G.: Environmental stability of MEMS gravimeters., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19666, https://doi.org/10.5194/egusphere-egu24-19666, 2024.

Normalising flows is a novel generative neural network model, which can be applied to Bayesian parameter inference. When gravity inversion is reformulated as a probabilistic inference problem, stable results can be obtained that naturally incorporate the inherent uncertainties and noise from the source background and the instrument. As opposed to some standard methods, Bayesian gravity inversion does not default to a single solution in an ill-posed problem, but informs the user about all possibilities that are consistent with the gravimetry survey of interest. It has been demonstrated that the normalising flow method can provide accurate results for a simulated data set, even when applied to high-dimensional data. Once the network is trained, the results can be obtained within seconds and it can be reused, without retraining, for multiple gravimetry surveys that are consistent with the training data set. Here, improvements on the previous work are presented, where the method is applied to a more realistic and complex geophysical problem; the inversion of gravity measurements to infer parameters of geophysical faults. The normalising flow network is trained and tested for fault models with various complexities, and finally the method is applied to the inversion of airborne gravimetry data. 

How to cite: Rakoczi, H., Barnes, G., Prasad, A., Toland, K., Messenger, C., and Hammond, G.: Inversion of real gravity data from geological faults using a generative neural network model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16989, https://doi.org/10.5194/egusphere-egu24-16989, 2024.

The Direct Current Resistivity (DCR) method is one of the well-known geophysical methods used for a wide range of areas such as mining geophysics, hydrogeophysics, and archaeogeophysics investigations. DCR data is generally collected along profile using multi-electrode and multi-channel measurement systems and interpreted using ‘two-dimensional (2D) or three-dimensional (3D) inversion algorithms. The inverse problem of DCR data is ill-posed (nonlinear, nonunique, and unstable). Therefore, the generally smoothing regularization inversion method is used for DCR data inversion. Additionally, a homogenous resistivity model is used as the initial model in regularized inversion. Hence, we generally obtain a smooth resistivity model after 2D/3D inversion. However, some structures such as buried archaeological targets, cavities, and fault structures have sharp boundaries with their neighboring medium.

In this research, we propose enhancing 2D DCR data inversion results using a convolutional neural network (CNN), aiming for sharp boundaries. We developed a U-net-based CNN algorithm, named DCR2D_Net_Archeo. This method utilizes 2D inversion results as the input, with the real resistivity model serving as the output, streamlining geophysical data interpretation for archeological applications.  We tested the DCR2D_Net_Archeo algorithm by using synthetic and real data.  We showed that the developed resistivity model enhancement algorithm, DCR2D_Net_Archeo, improves smooth inversion results and buried archeological remains' size and position can be delineated from those enhanced models. 

KEYWORDS: DC Resistivity, 2D, Inversion, Deep Learning, archaeo-geophysics.

ACKNOWLEDGEMENT: This study is part of the Ph.D. thesis of the first author and the manuscript about this study has been submitted to Pure and Applied Geophysics. This study is also made under the Ankara University Technopolis R&D projects (STBP code: 084286).  

How to cite: Över, D. and Candansayar, M. E.: Improving 2D resistivity model obtained from DC Resistivity Data Inversion by using Convolutional Neural Network Algorithm to Find Buried Archaeological Remains, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19337, https://doi.org/10.5194/egusphere-egu24-19337, 2024.

The age determination is crucial to unravel the evolution and to provide a framework of the geological, paleo-anthropological, and cultural conditions of a specific region of interest. The lack of chronological information makes difficult to correlate the site of interest with other temporal attributes provided by stratigraphy and paleoclimatology. Luminescence is a well-established dating technique for determining the absolute age of formation (or most recent reworking event) of geological deposits/sediments and archaeological finds on Earth, with a time range that spans from the last few decades up to one million years. The most common geological targets are dust, silt, sand, cobbles, and rock outcrops originating from different environments: aeolian, fluvial, alluvial, lacustrine, marine, glaciogenic, slope deposits, karstic, soils, tectonic activity. In the archaeological field, this technique is applied not only to the geological sediments in excavation sites but also directly to artifacts of interest, especially when they are made of pottery or stone.

A miniaturized luminescence dating prototype for in-situ examination has been designed by Alma Sistemi S.r.l., Guidonia, Italy, and validated by University of Sassari, Sassari, Italy, under European Union H2020-MSCA-RISE-2018 research programme (G.A. n.823934). The instrument is equipped with an infrared and blue optical stimulation subsystem to perform both optically and infrared-stimulated luminescence (OSL, IRSL) and is able to measure both paleo-dose and dose rate. An X-ray generator (XRG) irradiates the sample, while the response luminescence signal is obtained through a photon counting photomultiplier tube (PMT). A thermal subsystem consisting of a heater and air-cooling pumps allows the instrument to heat during SAR (single-aliquot regenerative-dose) procedure and to perform thermally stimulated luminescence. Remote control for data analysis application and a battery power supply are implemented on the instrument for usage on the field.

The development of this portable instrument is of great relevance since it finds practical use in geological and archaeological Earth's field applications. In fact, compared to current luminescence dating technologies and considering its reduced dimensions (11x11x18 cm excluding electronic box and cover) and weight (currently <5 kg), the presence of the air-cooling system Vs Nitrogen and the use of X-ray vs radiative source, qualify the instrument for direct use in the field. It is also provided with an advanced and user-friendly software tool, thus strongly reducing the need of a skilled operator. The prototype instrument has been validated using different samples and results compared with equivalent laboratory instrument (Risø TL/OSL Reader model TL/OSL-DA-20) at University of Sassari Luminescence laboratory.

At this stage, the instrument can perform a basic SAR protocol and accurately measure the response luminescence signal after different irradiations time and thus, measure the natural dose of the natural sediment sample. Here, the most recent results are presented.

How to cite: Di Iorio, A., Pascucci, V., Filippone, R., Di Iorio, G., Sechi, D., Andreucci, S., and Di Pietro, I.: A portable luminescence dating instrument: a new insight for in-situ Earth applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5984, https://doi.org/10.5194/egusphere-egu24-5984, 2024.

Observed meteorological data are essential for initialization, adjustment, assimilation, and prediction of numerical weather prediction (NWP) models and influence daily activities of people and society. Many key weather variables such as temperature, humidity and winds are well observed globally by combined surface weather stations and suborbital and orbital remote sensing platforms of current Earth Observing System (EOS). However, sea surface air pressure is a significant observational gap in the current EOS. There is no operational remote sensing method available for this crucial dynamic variable of the Earth’s climate and weather systems. Over open oceans, the pressure can only be observed by in-situ sensors of very limited buoys, ships, and oceanic platforms. Studies find that accurate sea level pressure (SLP) measurements can significantly improve not only dynamics but also thermodynamics, such as temperature fields of NWP models [1]. Weather forecasts, especially severe weather predictions including hurricanes, can also be improved considerably with pressure measurements.

This study presents the SLP retrieval with emphasis on the evaluation of potential impacts of instrumental and environmental uncertainties on the retrievals for measurements of V-band O₂ differential absorption radar systems operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz). This study finds that precise knowledge on instrument attitude in current design will result in negligible retrieval errors. The spectral control of the instrument and the knowledge on frequency changes will provide accurate information for forward radiative transfer calculations and then, SLP retrieval. Furthermore, the retrieval algorithm combining all three channels, i.e., the 3-channel approach, can effectively mitigate major atmospheric (e.g., water vapor and cloud) and sea surface influences on sea surface air pressure retrieval.

The major uncertainty for sea surface pressure retrieval is caused by the noise in radar power returns for the current design. Analysis demonstrates the potential of global SLP observation with error similar to that of marine in-situ measurements (about 1 ~ 2 mb), which is urgently needed for improvement of NWP models. Currently, NASA is developing an airborne system for demonstration of space applications.  Our presentation will provide more details on the system, SLP retrieval and their applications.

Reference

[1] Prive, N., M. Mclinden, B. Lin, I. Moradi, M. Sienkiewicz, G. Heymsfield, and W. McCarty, “Impacts of marine surface pressure observations from a spaceborne differential absorption radar investigated with an observing system simulation experiment”, J. Atmos. Oceanic Tech., 40, 897 – 918, 2023.

How to cite: Lin, B., Mclinden, M. W., Cai, X., Heymsfield, G. M., Privé, N., Harrah, S., and Li, L.: Global sea surface air pressure observations with V-band O2 differential absorption radar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1499, https://doi.org/10.5194/egusphere-egu24-1499, 2024.

La Palma Island (708 km²), situated in the northwest of the Canarian Archipelago, stands as one of the youngest (~2.0 My) islands. A new volcanic eruption took place at the Cumbre Vieja volcanic system, located in the southwest flank of the island, on September 19, 2021. Cumbre Vieja is renowned as the most active basaltic volcano in the Canaries. The eruptive event, which lasted for 85 days, featured various volcanic activities, including lava effusion, strombolian activity, lava fountaining, ash venting, and gas jetting, and concluded on December 13, 2021.

Regular surface geochemical studies have been conducted focusing on hydrogen (H₂) emissions along Cumbre Vieja. H₂, being one of the most abundant trace species in volcano-hydrothermal systems, plays a pivotal role in numerous redox reactions occurring in the hydrothermal reservoir gas. This comprehensive study of H₂ emissions has been ongoing since 2001, encompassing continuous monitoring of soil gas samples collected at a depth of approximately 40 cm across 600 sites during each survey. H₂ concentrations have been meticulously analyzed using a micro-gas chromatograph (Agilent 490 microGC).

Spatial distribution maps have been generated using sequential Gaussian simulation (sGs) techniques to quantify the diffuse H₂ emissions from the study area. The time series data of the diffuse H₂ emissions indicate significant increases before and during the occurrence of seismic swarms observed between 2017 and 2021. Furthermore, during the eruptive phase, substantial spikes in the diffuse H₂ emissions were observed, closely correlating with the volcanic tremor escalation. These fluctuations in diffuse H₂ emissions were observed preceding the peak of diffuse CO₂ emissions, aligning with the anticipated behavior of these gases. Over the last two years following the eruption, the values have reverted to levels like those observed during periods of volcanic calm, reinstating the stability in the diffuse H₂ emissions.

The absence of visible volcanic gas emissions before the eruption, such as fumaroles or hot springs, on the surface of Cumbre Vieja underscores the importance of such studies in serving as a critical tool for continuous volcanic surveillance and monitoring purposes. This update represents ongoing efforts to comprehensively study and understand the behavior of hydrogen emissions within the volcanic system, providing essential insights into volcanic activity and potential precursor signals for enhanced monitoring and risk assessment.

How to cite: Expósito, M., Ioli, S., Santangelo, I., Melián, G. V., Asensio-Ramos, M., Arencibia, M., Cartaya, S., Méndez, C., Pérez, N. M., Padrón, E., Hernández, P. A., Rodríguez, F., Padilla, G. D., and Álvarez, A. J.: Soil H2 degassing studies: a useful geochemical tool for monitoring Cumbre Vieja volcano, La Palma, Canary Islands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19870, https://doi.org/10.5194/egusphere-egu24-19870, 2024.

Over the past decade or so, laser spectrometric instruments have revolutionized the field of isotope analysis of water samples. These instruments do not require complex lab facilities, are easy to use and can provide hydrogen and oxygen isotope data at high precision and high throughput.

One well-known shortcoming of these laser spectrometric analyzers is that individual measurements display significant sample-to-sample memory effects. Particularly at larger isotopic differences between samples, isotopic contamination by the previous sample can off-set the following measurements even after multiple injections. Therefore, it is common in many laboratories to run 7 or more replicate analyses of each sample, and discard the first 4 or so, to come to an accurate isotope value of that sample.

Because the single-shot precision of these instruments is rather good, the sample replication is not so much necessary for obtaining better precision, but indeed mostly needed to flush out the memory effect on the isotope values. Therefore, any technical adaptation that decreases the memory effect of these analyzers, and thus reduces the number of replicate analyses required to come to an accurate isotope ratio, would greatly improve the sample throughput of these instruments.

We here present an adapted injection interface system, coupled to a Picarro L2140i analyzer, that practically removes sample to sample memory effects. This effectively leads to accurate and high-precision isotope analysis of single-shot sample injections, even at large sample-to-sample isotope differences. Key to the removal of the memory effect is that the analyzer runs on a moisturized carrier gas, providing a constant water background upon which the injected samples are analyzed (De Graaf et al., 2021). We will present results of series of standard waters and natural samples (including seawaters) and discuss protocols that we developed for data calculation and quality control.

Reference:

de Graaf, S., Vonhof, H.B., Levy, E.J., Markowska, M., Haug, G.H., 2021. Isotope ratio infrared spectroscopy analysis of water samples without memory effects. Rapid Communications in Mass Spectrometry 35.

How to cite: Vonhof, H., de Graaf, S., Levy, E., and Schroeder, J.: Removing the memory effect from water stable isotope analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12932, https://doi.org/10.5194/egusphere-egu24-12932, 2024.

Automated mineralogical analysis (quantitative scanning electron microscopy) is a powerful tool that has been used extensively to understand the occurrence and deportment of precious and base metals and critical minerals and is used to optimize the design of extractive metallurgy methodologies. However, this data-rich product can also be used in a predictive context and as the basis for data integration across the range of scales. Automated mineralogical data include quantitative mineral abundance and textural data that can form the basis for machine learning algorithms to improve statistical subsampling strategies, data integration, mineralogical upscaling, and to increase the value of x-ray fluorescence- and hyperspectral data.   

The Mineral and Materials Characterization Facility in the Department of Geology and Geological Engineering at the Colorado School of Mines in Golden, USA, houses two scanning electron microscopy-based automated mineralogy systems. These systems are used to conduct research over a broad range of disciplines including all stages of the mine life cycle, energy and petroleum resources investigations, provenance and climate studies, and environmental and biological studies.   

During this presentation, we will explore examples of how automated mineralogy can play a crucial role across the mine life cycle that spans from mineral exploration, mine planning and mining, extractive metallurgy, proactive waste rock and tailings management, to reclamation. The example use-inspired research projects, conducted through the Center to Advance the Science of Exploration to Reclamation in Mining (CASERM) using the Advanced Mineral Analysis and Characterization System (AMICS) from Bruker based on a field-emission scanning electron microscope from Hitachi, focus on the integration of diverse geoscience data types to accelerate and improve decision making across the mine life cycle.   

Quantitative scanning electron microscopy provides important mineralogical and textural data that can inform statistical, thermodynamic, and kinetic models. These data improve not only our understanding of the subsurface in the context of hard-rock mining, but can inform other disciplines such as geothermal energy exploration and extraction and understanding the carbonation potential, helping move the world towards a greener future. 

How to cite: Pfaff, K.: Automated Mineralogy – A Valuable and Data-Rich Product to Advance the Green Energy Transition  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20638, https://doi.org/10.5194/egusphere-egu24-20638, 2024.

Since few years, a lot of companies and industries are developing battery recycling processes for the recovery of critical elements such as Li, Ni and Co. Before hydrometallurgical processing, mechanical and/or thermal treatment are applied in order to produce a black powder, also called blackmass. This high-value powder contains these above mentioned critical elements as well as graphite, rare earth metals and impurities such as solvents, plastics, aluminium and copper. The only data currently used to determine the quality of blackmass are chemical analyses of the major elements. However, micro-textures, liberation of elements and phases, as well as the amount of impurities in various phases are important parameters for the efficiency and performance of a hydrometallurgical process.

In order to evaluate the suitability for hydrometallurgical recycling process, it is essential to analyse the blackmass not only chemically but also with respect to size, shape and composition of particles. This presentation shows how these data can be acquired by using a refined QEMSCAN database. This database was created based on billions of point analyses on a total of some million particles. The results show that:

• Particles can be micro-texturally characterized and classified with respect to chemical element contents.
• Important textural and chemical particle variations exist in the blackmass of different origins showing different qualities.
• Elements deleterious to hydrometallurgical processing (i.g. Si, Mg, K, Ca, Fe, Al, Cu and others) can be present in specific and well liberated particles.
• Cathode active material compositions (different types of NMC as well as LCO, NCA, LFP, NiMH, etc) that are specific for each battery type can be distinguished.
• Digital simulation of additional physical mineral processing can optimize blackmass quality with respect to valuable elements.
• Special attention must be given to potential health risks during recycling and the processing of blackmass as elements like Cd and Co can be present in ultrafine particles.

How to cite: Dadé, M.: The automated mineralogy: an important tool for geometallurgy studies of battery recycling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9867, https://doi.org/10.5194/egusphere-egu24-9867, 2024.

The increasing demand for critical raw materials (CRMs) linked to the energy transition, Europe’s reliance on a few third countries (incl. China) for the supply of these combined with increased ESG issues calls for a new mining paradigm: i.e., responsible, zero-waste, multi-metal/mineral mining. Li-hard rock deposits (pegmatites and rare-metal granites) are perfect candidates for such an approach, where, besides lithium, numerous by-products including industrial minerals (quartz, feldspar, micas) and CRMs (Nb, Ta, and so forth) could be potentially extracted. To assess whether these by-products can be recovered during Li production requires a mineral-centric, integrated geometallurgical approach. Automated mineralogy is a key technology for such an approach. Determining how to utilize the secondary material streams and recovery of the by-products relies on the knowledge of the material, its chemical composition, particle size, crystal structure and texture to grain size, liberation grade and mineral associations. In this study, four European lithium mine projects, two pegmatite projects (Keliber, Finland and Savannah, Portugal) as well as two rare-metal granite (RMG) projects (Beauvoir, France and St Austell, UK), are investigated. Different ore types as well as process samples (concentrates, residues, and tailings) were investigated to assess the by-product potentials of industrial minerals, CRM’s as well as the status and behavior of potentially harmful elements (PHEs) throughout the processing flowsheet. Gathering of all this information is started by the Extended BSE Liberation Analysis (XBSE_STD) and Grain-Based X-ray Mapping (GXMAP) measurements with the FEI Quanta 650F, an automated Scanning Electron Microscope equipped with a field emission gun electron source, two Energy Dispersive X-ray spectrometers (EDX) (Bruker X-Flash 6130), and FEI’s Mineral Liberation Analyzer (MLA) quantitative mineralogy software v. 3.1.4. Additional data is collected with Micro-XRF Bruker M4 Tornado plus with AMICS. Details are further studied with additional methods, such as X-ray powder diffraction, Inductively Coupled Plasma Optical Emission spectroscopy, Electron Probe Micro-Analyses, Laser ablation ICP-MS, and X-ray Fluorescence measurements.  

To define how the ore properties and the PHE/CRM deportment affect the options for usability, a comprehensive geometallurgical assessment will be conducted starting from collecting the basic mineralogical data to creating process flowsheet options and predicting theoretical process performance. These results are then to be tested at the lab and pilot scale according to the produced process protocols to be validated.  

How to cite: Lavikko, S., Dehaine, Q., Prado Araujo, F., and Muchez, P.: Automated mineralogy as a key technology toward zero-waste mining – The EXCEED project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7394, https://doi.org/10.5194/egusphere-egu24-7394, 2024.

The transition towards cleaner energy and the manufacture of associated new technologies will require extraction of mineral resources at volumes much greater than at present.  Consequently, identification of new deposits is both economically and strategically important, driving a boom in mineral exploration coupled with a need to lower the environmental impact through more efficient mining capabilities.  Crucial is an understanding of mineralogy and texture across scales, and thus improving knowledge at each stage of the mining cycle – from exploration through to production and ultimately to waste handling.  A key tool in this understanding is Automated Mineralogy.     

Automated Mineralogy has been integral to process mineralogy for more than two decades, with SEM being the traditional analytical platform.  However, the extension of Automated Mineralogy using scanning micro-XRF instruments allows the technique to be implemented across broader spatial scales.  In practice, the same logical workflow can be applied from the scale of large cut or split (minimally prepared) drill-core samples,through to polished thin sections or block mounts of various sample types (fragments or crushed plant material).  At the most detailed level, the information obtained can be at the sub-micron scale of mineral classification, or even zonation withing single grains. 

An example of Automated Mineralogy as applied to Au-Co exploration is presented that highlights the benefit of analysis across scales, integrating information collected using the AMICS platform on drill core measured using scanning micro-XRF, and thin sections measured by SEM.  The example will also demonstrate the ability to use the same Automated Mineralogy approach to define and quantify sub-micron information within individual mineral grains.   

How to cite: Menzies, A., Butcher, A. R., and Kelly, N. M.: Automated Mineralogy: from drill cores to sub-micron information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22519, https://doi.org/10.5194/egusphere-egu24-22519, 2024.

Automated mineralogy systems are widely used for the mineralogical characterization of powder samples for mineral processing purposes. This characterization method requires the mineral powder to be embedded in a resin (polished block (PB) preparation). Very quickly, some problems linked to polished block preparation arose. This particularly involves the mineral settlement in the liquid resin due to differences in mineral size and density. Several authors have suggested solutions to overcome the error results due to the PB preparation method, such as vertical section, the addition of sized graphite, dynamic hardening, and the addition of black carbon (BC) to increase resin viscosity avoiding mineral settlement. Only the BC method resolved all the errors associated with the PB preparation. Indeed, it eliminated the mineral settlement and provided excellent spatial dispersion of particles on the observation surface, ensuring better mineral quantification and liberation/association estimation, except for the graphite-bearing samples. In fact, as graphite shows no contrast with resin under back-scattered electron-based based images, it cannot be characterized by the automated mineralogy systems. few studies have addressed this problem by the addition of carnauba wax or iodoform to contrast resin and graphite. The iodoform was easy to use and provided better contrast compared to carnauba wax. This work presents an innovative polished block preparation method that combines CB and iodoform to prevent both particle settlement and to contrast the resin and graphite which was very challenging. The obtained results are highly satisfying and comparable to those of standard characterization techniques such as XRD and chemical assay. This new preparation method is highly useful for graphite-bearing black mass (obtained from battery recycling) characterization by automated mineralogy systems.

How to cite: Bouzahzah, H., Lenoir, L., pirard, E., and Mermillod-Blondin, R.: Accurate characterization of graphite in ores and black mass using an innovative sample preparation method for automated mineralogy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15996, https://doi.org/10.5194/egusphere-egu24-15996, 2024.

Mine optimisation and anticipation of ore behaviour in the mineral processing and separation circuits are major economic drivers for all mining operation. Recent methodological developments with the inception of geometallurgy across multiple commodities has highlighted the importance of mineralogy in addition to grades. Since several decades, many quantitative tools have been developed, mostly SEM-based such as QEMSCAN®, to provide quantitative mineralogical composition and textural properties of ore and gangue samples. We aim to compare the more established SEM-based techniques to the Solsa combined XRD-XRF analyser to highlight their respective potential and limitations depending on the minerals and goals of the mining operators. The combined XRF-XRD of the SOLSA analytical solution brings a new methodology able to produce quantitative mineralogical and geochemical data at a speed compatible with routine data collection, from exploration to quality control on the different streams of minerals in a processing plant.

How to cite: Herbelin, M., Delchini, S., Pillière, H., Lutterotti, L., Nicco, M., Dia, M., and Riegler, T.: The pros or cons of X-ray diffraction vs electron beam techniques in the assessment of mineral assemblages: improvements of Solsa combined XRD-XRF analyses applied to the Grande Cote Operation Ti-Zr mine, Senegal., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17031, https://doi.org/10.5194/egusphere-egu24-17031, 2024.