In deep geothermal wells, conventional downhole data is commonly very limited, especially during long-term plant operation. Since 2021, permanently installed fibre optic cables in one production and one injection well at a hydrogeothermal site in Munich have enabled continuous DTS (Distributed Temperature Sensing) measurements along the entire borehole length (~4 km) as well as pressure monitoring at reservoir depth at 3000 m MD (measured depth) using Fabry–Pérot gauges. In addition, several DDSS (Distributed Dynamic Strain Sensing) campaigns were conducted during different operational stages of the geothermal plant. In this contribution, we present borehole and reservoir processes derived from this unique long-term fibre optic dataset and discuss their implications for reservoir characterization and geothermal field development.Different approaches were applied to resolve production and injection zones within the reservoir at high spatial resolution. These include energy and mass balance modelling of temperature profiles during production, thermal slug tracking to derive fluid velocities and flow contributions, and analysis of borehole warmback during shut-in periods. DTS-derived results provide rapid and robust characterization of flow zones and extend beyond the spatial and temporal limitations of conventional flowmeter logging. In addition, low-frequency DDSS (LF-DDSS) measurements reveal highly detailed flow dynamics and previously unresolved flow processes within the wells and reservoir. In the injection well, 78 % of the injection happens in the upper 120 m MD of the 1000 m long reservoir section. In the lower half, free convection cells dominate in the wellbore during steady injection and a 40 m thick localized hydraulic anomaly even shows 1 l/s of inflow into the wellbore from the formation.Furthermore, the temporal evolution of the production/injection temperature and flow zones indicates dynamic changes in reservoir properties during plant operation. Pressure data from fibre optic gauges enables repeated pressure transient analysis (PTA) of shut-in phases. These show that long-term operation has significantly increased the transmissivity of one of the wells and suggest an altered flow regime. The measurements further provide an important basis for calibration and validation of 3D thermo-hydraulic numerical models of the entire six-well system at the Munich plant.Overall, the presented results show how the applied methodical approaches can improve the geological and reservoir understanding of deep geothermal systems in the Bavarian Molasse Basin and will support future reservoir engineering, field development, and forecasting of long-term well performance.

In addition to permanent geophysical observation networks, temporary field measurements are an important component of solid Earth research. In seismology in particular, there has been a steady increase in the number of measuring devices used within one deployment. This is mainly due to the fact that dense networks (as opposed to individual stations with large distances between them) allow wave fields to be recorded in their entirety enabling new processing methods and higher-resolution subsurface imaging.Since the maintenance of such large numbers of devices required for dense networks is not a side issue, instrument pools are necessary to supply devices for the academic community. One of the largest instrument pools in Europe is the Geophysical Instrument Pool Potsdam (GIPP), which is operated by the GFZ (gipp.gfz.de). The GIPP provides geophysical and geodetic measurement technology (e.g., recorders and sensors) for temporary active seismic, passive seismological, electromagnetic, and GNSS experiments. The equipment is supplied for usage at universities and research institutes worldwide and free of charge for non-commercial experiments. We team up with our partners in Europe through the ORFEUS Mobile Pools Service Management Committee. Together we are working on improving the cooperation between the major European instrument pools and offering services that facilitate access to instruments, also within the EPOS-ON project.Over the past 30 years, we have supported more than 500 geophysical field experiments. The data of these experiments is archived at GFZ and is made available to the public, e.g., via the GEOFON repository. The majority of the seismological experiments consists of fewer than 50 stations, but the number of large networks (with up to 500 devices) is increasing. These large networks are realized either in the form of rolling arrays or as temporary installations (LARGE-N), sometimes in collaboration with other providers.In this presentation, we give an overview of the latest deployments, our data management approach, the challenges that we are facing due to the high demand of LARGE-N experiments, and technological advances in our equipment, particularly in robust node-type field recorders. Thereby, we want to discuss the challenges and potentials of such pools, making them best setup for future research.

Deformation and seismicity often precede and accompany volcanic eruptions. Models of magma emplacement and ground deformation associated with eruptions are obtained from GNSS and InSAR observations and associated seismic source mechanisms from seismometer observations. While satellite sensing techniques benefit from large spatial coverage with coarse temporal resolution and accuracy (mm range), seismometer networks acquire dense temporal data but are sparsely distributed and suffer from spatial aliasing. However, dynamic models of sources prior to the eruptive event are challenging to obtain, because they are in most cases too small or too slow to be observed accurately with conventional instrumentation. Here, we demonstrate that distributed fibre optic sensing with phase optical time domain reflectometry (Φ-OTDR) allows us to retrieve dynamic and static deformation processes associated to magma transfer from the reservoir below Svartsengi in SW Iceland, at depth and through diking events, prior to volcanic eruptions. Since November 2023, we are continuously monitoring an existing telecom fibre optic cable with a commercial iDAS interrogator, set-up on the western Reykjanes Peninsula. Reykjanes Peninsula is the onshore expression of the Mid-Atlantic oceanic ridge, where a series of magmatic intrusions and eruptions have occurred since 2020. Unlike previous studies, the used cable spans across locations from a large inflation/deflation area near dyke outbreaks at its eastern end, to a remote area where little signatures from eruptions are observed at its western end. In-situ down-sampled strain-rate data (1000 Hz to 200 Hz) are transferred continuously via internet to our computing centre at the GFZ in Germany. We further down-sample data to 2 minutes and perform time integration in order to analyse long period strain signals both spatially and temporally. We present resulting distributed dynamic strain (i.e., strain rate) observations and their source inversions associated with a series of eruptions and intrusions. Our inversions comprise a Mogi source and an Okada model, and we test several inversion methods. For each recorded eruption, we invert the distributed spatial strain taken every 2 minutes, allowing us to follow magma progression prior to each eruption with time. We investigate sizes and locations of the deflating reservoir and dykes with observed eruption locations. We also compare faults reactivated during the successive eruptions with the fibre optic cable records. These results show that distributed fibre optic sensing is capable of simultaneous seismological and geodetic observations in a volcanic context, opening the path for a better understanding and potentially improved real-time monitoring of volcanic processes.

Urban sustainable development and improved resilience to geohazards requires an exhaustive understanding of the geological structure, physical properties and dynamics of the shallow subsurface underneath urbanized areas at the sub-kilometer scale. Yet, our current understanding of the urban subsurface is limited by our ability to image its structure and temporal variations at high resolution using classical geophysical approaches. Recently, the application of conventional ambient noise interferometry analysis to dynamic strain data recorded using Distributed Acoustic Sensing (DAS) deployed on unused telecommunication fiber-optic cables (dark fibers) has emerged as an attractive alternative for cost-efficient, regional scale (10’s of km) seismic imaging and monitoring at high spatial and temporal resolution. Still, its application to urban environments remains vastly underutilized. One of the most significant hurdles is the lack of adequate and efficient data exploration and processing tools to address and harness the unique challenges associated with DAS-based urban seismic noise recordings, which include the complexity of the noise field, unconventional array geometries and non-uniform coupling conditions, and increasingly massive data volumes. The InDySE project (Interrogating the Dynamic Shallow Earth) aims at addressing these challenges to develop and validate the next-generation of subsurface imaging and monitoring platforms in urban areas based on the combination of existing fiber-optic networks and high-frequency infrastructure noise. The project comprises (1) developing high-performance computational tools, advanced processing workflows and machine learning approaches for efficient data exploration, selection and processing using existing datasets, (2) field experiments in target areas to retrieve high-resolution velocity models and monitor changes in seismic velocities, (3) integrating the resultant high-resolution seismic models with complementary datasets such as deformation maps derived from InSAR measurements. One of the selected study areas is the highly populated metropolitan area of Istanbul (Turkey), where understanding the structure, properties and dynamics of the shallow subsurface at high-resolution is critical for evaluating geohazard exposure. Among our objectives will be illuminating potential hidden faults underneath the city, obtaining high-resolution maps of geological materials and subsurface properties that can be translated into maps of local site response to large earthquakes, and tracking seismic velocity changes linked to hydrological dynamics that are known to be responsible for ground movements such as landsliding and subsidence. Ultimately, InDySE aims at developing efficient approaches for using dark fiber and ambient noise in urban subsurface investigations with implications in geohazard assessment.

Residues of industrial mining activities like rock waste, tailings, and stockpiles are amongst the largest human-made structures in both area and volume. In the case of tailings dams, the risk posed by failure is well documented and has led to the implementation of regulatory standards. As one of the measures to reduce potential harm to the environment and people, the Global Industry Standard on Tailings Managment (GISTM) implemented by UNEP in 2020 proposes the installation of "monitoring systems to manage risk at all phases of the facility lifecycle". This is one of the objectives of the EU-funded project MOSMIN (Multiscale observation services for mining-related deposits), which strives to develop holistic, full-site services for the geotechnical and environmental monitoring of mining-related deposits through the combination of Earth observation with in situ geophysical data. The integrated data sets should then be leveraged by using modern analysis approaches like machine-learning to characterize deformations and identify environmental hazards. In this work, we present the in situ geophysical campaigns conducted to acquire passive seismic data at two tailings dam facilities both related to copper mining, namely the tailings storage facilities of the FQM Trident mine in Kalumbila, Zambia, and the Codelco Chuquicamata mine near Calama, Chile. Both installations combine conventional seismic sensors with the deployment of a fibre-optic sensing array over targeted tailing dam sectors, for the continuous recording of ambient seismic noise. The main goal of this approach will be to both characterise the internal structure of the dams underneath the fibre-optic array and to monitor subsurface processes at different scales, resolutions and depths of investigation. We aim to apply several seismological methods for structural and material property characterization, the investigation of temporal changes in seismic properties and the evaluation of material contrasts in the body of the tailings dam. Suitable methods are ambient noise tomography and horizontal over vertical spectral ratios (H/V). Details of both setups like instrument deployment, fiber cable and trajectory, the procedure and construction of the layout, as well as preliminary results will be discussed.

Time lapse gravity measurements can give information on underground mass redistribution. Observations are especially valuable over the course of subsurface use, for instance during geothermal exploration of an area. To monitor the mass transfer of underground geothermal fluids associated with the harnessing of a hydrothermal system and to assess its long-term sustainability, we have performed long-term observations at Theistareykir (Icelandic North volcanic zone). In this study, we model the mass and fluid displacement through the use of the hybrid gravimetry technique. Hybrid gravimetry is a method which consists of the combination of several complementary gravity observations. At Theistareykir, the following experiments are collecting data since 2017:micro-gravity time lapse relative measurements are repeated yearly on a pre-designed network of points; relative gravity measurements are recorded continuously at several multi-parameter stations deployed within and outside the geothermal area. Each station is equipped with a superconducting or a spring gravimeter as well as a GNSS receiver, a broadband seismometer and hydrological and weather sensors. absolute gravity measurements are collected yearly, to constrain the instrumental drift of the relative gravimeters. Here, we present the complete time series recorded by two superconducting gravity meters at Theistareykir since 2017. Gravity changes associated with potential vertical displacements of the continuous gravity meters are obtained from GNSS data, and removed from the raw data. Similar reductions are performed for other contributions from the meteorological data (pressure, snow height). The reduced time series have been used to obtain an accurate local Earth tide model. Such model is subtracted from the continuous gravity records in order to obtain the gravity residual, sensitive to the geothermal activities (injection, extraction).From the analysis of the gravity time series we notice gravity decrease at the production site. This trend is also visible from the time lapse gravity changes maps, obtained by the integration of micro-gravity data with ground displacement data. Patterns from the spatial maps of gravity changes show gravity increase southwards of the injection area, suggesting drainage of the injected water along the Tjamaras fault. The modelling results are compared with mass changes estimated from the injection and production rates, provided by Landsvirkjun, the operating energy company, thereby constraining the interpretation. Ongoing work encompasses forward modelling approaches to quantify mass transfers (extraction, injection, recharge, atmospheric losses) within the geothermal system.

Achieving well integrity is mandatory for a geothermal well’s safe and sustainable operation. One of the most critical steps is the success of the primary cementing. Conventional monitoring only shows discrete snapshots after completion of the cement job. However, optical fiber sensors enable monitoring of the entire cementing process. Here, we investigate the cement placement and early hydration for a surface casing at a geothermal site in Munich, Germany. We show that distributed dynamic strain rate sensing (DDSS or DAS) allows for tracking rising fluid interfaces, determining the setting time of cement, and assessing the cement job’s success at each depth. We used DDSS and DTS (distributed temperature sensing) with a fiber optic cable permanently deployed behind the casing and combined the results with operational data, a model for the rise of fluids in the borehole, and laboratory experiments to estimate the cement setting phase. Our approach enables monitoring all phases of primary cementing, which can increase the success rate of achieving well integrity. Furthermore, it can reduce costs and improve society’s acceptance of deep geothermal wells in urban areas.

Geothermal productivity strongly depends on reservoir performance, which is regularly monitored. This work presents the results from Distributed Dynamic Strain Sensing (DDSS or DAS) measurements during the restart of injection and production in deep geothermal wells. This technology's high spatiotemporal resolution enables monitoring relative strain and temperature changes along the entire sensing cable. We monitored 3.7 km of a producer and approximately 4.1 km of an injector. Both cables were installed post-borehole completion and reached up to 1 km into the reservoir. Distributed sensing was achieved using a commercial DDSS acquisition system sampling the boreholes at 1 m spatial interval and 2000 Hz.Here, we focus on the low-frequency subsurface dynamics captured during the restart phase. We extracted the low-frequency content (

The construction of geodynamic and reservoir models requires - as many other applications - the knowledge of fault signatures and fracture systems. In general, structural images of the subsurface rely on sampling and experiment design, wavefield components retrieved, as well as coherence and focusing potential of the data recorded in different geological settings. Nonetheless, direct geophysical images of especially sub-/vertical or inactive faults are still hampered by fracture complexity and associated diffuse wavefields. Furthermore, back-tracing weak signals to their originating location remains one of the challenges for high-resolution imaging. While petrophysical and mechanical rock properties characterize the hosting material as such, they can provide at the same time assistance in fault or horizon tracking, respectively, and may allow pattern identification, for instance by machine learning tools.In the overview presented, we will discuss different examples from recent active and passive seismic surveys covering both sedimentary and hardrock environments using either dense or sparse seismic and fibre-optic arrays. These experiments are adapted to investigation depths between some km and only few 10s of metres scale, encompassing geodynamic, geothermal, hazard and critical zone investigations. Thereby, the wide applicability of seismic methods for imaging and characterizing distinct horizons, transitional zones, and fault systems is emphasized.

Applying ambient noise interferometry to distributed acoustic sensing (DAS) data recorded along telecommunication networks offers a promising way to image the urban subsurface with high resolution at reduced costs. However, extracting coherent seismic signals from DAS in urban environments can be challenging due to diverse, unevenly distributed noise sources, which can distort interferometry results and produce spurious signals, complicating analysis and interpretation. To address these complexities, we have developed a modified ambient noise interferometry workflow that allows for the efficient selection of high-quality data. We analyze 15 days of continuous passive DAS data collected from a pre-existing 11 km long dark fiber-optic cable running along a major urban road in Berlin, Germany. After retrieval of virtual shot gathers (VSGs) using the cross-correlation method, we develop a selection strategy to identify high-quality data through unsupervised clustering. Next, we stack the VSGs within each cluster and choose the highest quality, stacked VSG based on the quality of the corresponding dispersion spectra. We initially test the clustering method on synthetic VSGs to ensure its effectiveness. The clustering results identify distinct groups of VSGs that exhibit consistent patterns in both synthetic and real VSGs. These distinct groupings offer valuable insights into the temporal variations in human activities and allow a better interpretation and identification of viable ambient noise signals for further processing. Thereafter, multichannel analysis of surface waves is utilized to obtain 1D shearwave velocity models for consecutive array segments. A 2D subsurface velocity model is then constructed through merging the individual 1D velocity models derived from overlapping array subsections. This approach enables efficiently selecting highest-quality data within massive, noisy recordings, which ultimately results in enhanced dispersion measurements and thus improved images of the urban subsurface.

Volcanic environments are often characterized by frequent explosive activity and complex ground features. Explosions can couple into the ground, triggering ground response (GR) influenced by near-surface properties. While GR resulting from seismic input is well-studied, GR generated by air-to-ground coupling of volcanic explosions remains poorly understood. Investigating this phenomenon is crucial for understanding near-surface material dynamics and improving volcanic hazard assessments. To study explosion-induced GR, a multi-parametric network was deployed near Mt. Etna's summit craters in 2019, where GR had been previously observed. The network includes broadband seismometers, infrasound sensors, and a fibre optic cable for distributed dynamic strain sensing (DDSS). Over 65,000 explosions were recorded, with some triggering high-frequency GR signals (10-50 Hz) in the DDSS data. These high-frequency signals, embedded in low-frequency explosions (0.7-4 Hz), amplify upon coupling into the ground. We also classified the explosions using waveform similarity, and GR signals were analysed using an adapted approach incorporating temporal and spatial dimensions. Strain rate vs. pressure rate relationships derived from classified signals were interpreted in terms of either linear elastic or hyperelastic near-surface behaviour. Despite no clear consensus towards which mechanical model describes best the ground behaviour, we suggest a nonlinear site amplification driven by mechanical particle interactions rather than near-surface layer resonance.

This chapter describes fiber optic sensing methodologies and their applications for understanding volcanic structure and processes. We assess their benefits for volcano monitoring and offer possible solutions to address their challenges. The physical principles at the basis of fiber optic sensing technologies have been known for several decades. These principles are related to various processes involving electro-magnetic interactions of light sent by a laser within glass. Intrinsic physical properties of glass within the optical fiber, when engineered appropriately and interrogated with an adequate light source, enable us to access a number of environmental parameters, such as temperature, strain and rotation over a large frequency range. However, only recently developed instruments have been able to sense these parameters efficiently, either at a point or densely in a distributed way for geophysical and volcanological applications. Rotational sensors allow us to measure the rotational components of the seismic wave field, which have been discarded in the past. Distributed fiber optic sensing provides access to quasi-continuous measurements of temperature, strain and strain rate along km-long fibers with a high spatial resolution (meter) and sampling rate (kHz). We show examples on volcanoes both on land and in submarine environments. We demonstrate that data from optical strainmeters, rotational sensors, distributed fiber optic strain and/or temperature sensing can reveal unknown structural features and processes in volcanoes. These examples testify that fiber optic sensing methodologies owe to be implemented as additional tools for improved volcano monitoring and for volcanic crisis management.

Ambient noise tomography Derived from Distributed Acoustic Sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at local to regional scales and high resolution effectively with a small footprint. This capability can contribute to the assessment of the urban subsurface's potential for sustainable and safe utilization in countless applications, such as geothermal development of an area. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments remains a challenge. One obstacle is the presence of complex noise sources in urban environments, which may not be homogeneously distributed. Consequently, long-duration recordings are required to calculate high-quality virtual shot gathers, which entails significant time and computational cost. In this study, we present the analysis of 15 days of passive DAS data recorded on a pre-existing fiber optic cable (dark fibers) running along an 11~km long major road in urban Berlin (Germany). We identify anthropogenic activities, mainly traffic noise from vehicles and trains, as the dominant seismic source and use it for ambient noise interferometry. To retrieve Virtual Shot Gathers (VSGs), we apply interferometric analysis based on the cross-correlation approach. Before stacking, we designed a selection scheme to carefully identify high-quality VSGs, which optimizes the resultant stacked VSG . Moreover, we modify the conventional ambient noise interferometry workflow by incorporating a coherence-based enhancement approach designed for wavefield data recorded with large-N arrays. We then conduct Multichannel Analysis of Surface Waves (MASW) to retrieve 1D shear-wave velocity models of the subsurface along consecutive portions of the array and validate them against local lithologic models. Finally, a 2D velocity model of the subsurface is obtained by concatenation of individual 1D velocity models from overlapping array subsections. The expansion into 2D requires an automatic identification of high-quality VSGs based on unsupervised learning, such as clustering, to exclude transient incoherent noise in the process of selective stacking. The clustering results reveal distinct groups of VSGs that exhibit similar patterns. These distinct groups provide valuable insights into the temporal variations in human activities and allow a better understanding and interpretation of the recorded DAS ambient noise data. We find that recordings obtained predominantly during rush hour are viable for further processing and improve the accuracy of dispersion measurements, in particular for traffic-induced noise data. Moreover, the resulting 1D velocity models correspond well with available lithographic information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (< 1 Hz) of the signal. This improvement leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these enhanced results were achieved using significantly less data than required using conventional processing schemes, thus opening the opportunity for reduced acquisition times and efforts.

Monitoring of seismic activity around volcanoes has been conventionally performed using data from continuous seismic and deformation networks, which give real-time information on the status of a volcano at any time. In case of a volcanic crisis, the number of earthquakes often increases with time and conventional networks are completed by deployment of additional sensors, which allow for a better hazard assessment, e.g., by lowering the detection threshold and improving earthquake locations. The deployment of such additional sensors is labour intensive and may be dangerous due to increased volcanic hazard.Existing fibre optic telecommunication cables can be used with distributed dynamic strain sensing interrogators to density and complement the monitoring network. It has been demonstrated that the usage of fibre optic sensing allows for a rapid response and the acquisition of crucial data describing a developing crisis (e.g., at Vulcano, Italy). However, fibre optic interrogators are rarely deployed as permanent interrogating systems, despite the capability of such systems for long-term monitoring as demonstrated during a 7 months continuous recording on the Reykjanes Peninsula, Iceland, in 2020. Instead, interrogators are usually deployed for limited time periods when the activity occurs, ideally before new activity starts. For example, we connected an iDAS interrogator on the telecommunication 16-km long cable running between the Reykjanes and Svartsengi (“Blue Lagoon”) geothermal power plants in 2015 for an initial test of 10 days, in 2020 for 7 months (GFZ rapid response to the seismic crisis and precursory activity to the 2021 and 2022 eruptions of Fagradalsfjall volcano), and in November 2023 (recording still on 10.01.2024) as a GFZ rapid response before the 18 december 2023 eruption.In this work, we investigate the possibility to use repetitive campaign-based measurements of dynamic strain sensing performed in the course of multiple years on the Reykjanes Peninsula (2015; 2020; 2023-2024) and at Etna volcano (2018; 2021; 2022; 2023-2024). Analysing earthquakes and ambient noise, we search for differences and similarities in the strain-rate response between the different and disjunct recording periods. We report preliminary results.

Mt. Etna, the largest volcano in Europe, is known for its almost persistent activity and complex seismic wavefield, making it an attractive location for examining volcanic explosions and testing new instrumentation in seismology (e.g., rotational sensors, strain-meters, fiber optic sensing). In 2018, a study was conducted at the Pizzi Deneri (PDN) observatory, situated near Mt. Etna’s summit to understand new instrumentation responses to the local seismo-acoustic wavefield. During volcanic explosions the released energy is mainly partitioned into seismic waves traveling through the ground, and sound waves traveling through the atmosphere. To capture this phenomenon, a temporary multi-parameter network comprised of infrasound sensors, broad-band seismometers (BB) and a fiber optic cable buried within the local loosed granular medium (scoria layer). The fiber optic cable was connected to a Distributed Dynamic Strain Sensing (DDSS) interrogator. At Etna, volcanic explosions were observed at co-located BB, infrasound and DDSS virtual sensors. A notable example(visible in both BB and DDSS data) is the successive occurrence of a 1-2 Hz seismic signal with a duration of ~4 seconds, followed by a ~2 Hz acoustic signal originating from the explosion, recorded at infrasound sensors. Unusually, simultaneous to the arrival of the acoustic signal observed at the infrasound sensors, DDSS and BB sensors record a signal with a frequency content of 15-20 Hz with a duration of ~2 seconds. We hypothesize that the 15-20 Hz signal is resulting from a non-linear ground response due to the air-to-ground coupling of the air pressure wave. In order to better characterize this phenomenon, a second experiment was conducted in 2019 at PDN with a similar instrumentation as in 2018, but with a different spatial arragenment. During three months the infrasound sensors recorded each about 65000 volcanic explosions. In this work we analyze the respective ground responses of volcanic explosions observed in the DDSS records of the 2019 campaign. We observe similar phenomenon as in 2018 (non-linear ground response), nevertheless, not all explosion can trigger this response. We first characterize the explosion events from both infrasound and DDSS records, and then classify them using their waveform similarity. The preliminary results provide a broad characterization of the non-linear ground response phenomenon and an insight into the physical properties and processes that are necessary for a pressure wave to trigger a non-linear ground response. The outcomes of this work provide a better understanding of acoustic-to-ground energy coupling in volcanic environments and their potential to trigger other hazards.

The Drilling the Ivrea-Verbano zonE (DIVE) project focuses on the continental lower crust from petrological, geophysical, fluid and gas, as well as microbiological perspectives in the framework of ICDP expedition 5071. Two scientific boreholes of DIVE phase 1 have cored 578.5 and 909.5 metres of lower crustal rocks in Val d’Ossola, Italy, and preparations for DIVE phase 2 have already started. The primary goals are to continuously sample the crust–mantle transition, and to test the suitability of a natural peridotite body for serpentinization and hydrogen production.The structural characterization of the drilling target and the assessment of the subsurface physical properties has been ongoing for several years, and at various spatial scales. Up to date, three active seismic campaigns, one passive seismic profile, regional and local gravity campaigns, and drone-based photogrammetry (digital outcrop model based fracture network analysis) have been undertaken under the umbrella of, or in connection with, project DIVE. Furthermore, aeromagnetic data is available over the region, and geological mapping is being refined in the area planned for drilling. This contribution will present the results reached so far, the differences between them as a function of spatial resolution, models of the Balmuccia peridotite body and related questions, as well as the currently ongoing efforts of geophysical imaging and modelling to reduce the uncertainties. Ultimately, we present the current drilling strategy of the 5071_2 borehole(s). ReferencesHetényi G, Baron L, Scarponi M, et al. (2024) Report on an open dataset to constrain the Balmuccia peridotite body (Ivrea-Verbano Zone, Italy) through a participative gravity-modelling challenge. Swiss J Geosci 117:2. doi:10.1186/s00015-023-00450-3Liu Y, Greenwood A, Hetényi G, Baron L, Holliger K (2021) High-resolution seismic reflection surveys crossing the Insubric Line into the Ivrea-Verbano Zone: Novel approaches for interpreting the seismic response of steeply dipping structures. Tectonophys 816:229035. doi:10.1016/j.tecto.2021.229035Menegoni N, Panara Y, Greenwood A, Mariani D, Zanetti A, Hetényi G (2024) Fracture network characterisation of the Balmuccia peridotite using drone-based photogrammetry, implications for active-seismic site survey for scientific drilling. J Rock Mech Geotech 16:3961-3981. doi:10.1016/j.jrmge.2024.03.012Pasiecznik D, Greenwood A, Bleibinhaus F, Hetényi G (2024) Seismic structure of the Balmuccia Peridotite from a high-resolution refraction and reflection survey. Geophys J Int 238:1612-1625. doi:10.1093/gji/ggae239Ryberg T, Haberland C, Wawerzinek B, Stiller M, Bauer K, Zanetti A, Ziberna L, Hetényi G, Müntener O, Weber M, Krawczyk CM (2023) 3-D imaging of the Balmuccia peridotite body (Ivrea–Verbano zone, NW-Italy) using controlled source seismic data. Geophys J Int 234:1985-1998. doi:10.1093/gji/ggad182Scarponi M, Hetényi G, Berthet T, Baron L, et al. (2020) New gravity data and 3D density model constraints on the Ivrea Geophysical Body (Western Alps). Geophys J Int 222:1977-1991. doi:10.1093/gji/ggaa263Scarponi M, Hetényi G, Plomerová J, Solarino S, Baron L, Petri, B (2021) Joint seismic and gravity data inversion to image intra-crustal structures: the Ivrea Geophysical Body along the Val Sesia profile (Piedmont, Italy). Front Earth Sci 9:671412. doi:10.3389/feart.2021.671412Scarponi M, Kvapil J, Plomerová J, Solarino S, Hetényi G (2024) New constraints on the shear-wave velocity structure of the Ivrea geophysical body from seismic ambient noise tomography (Ivrea-Verbano Zone, Alps). Geophys J Int 236:1089-1105. doi:10.1093/gji/ggad47

At the geothermal research platform Gross Scho center dot nebeck (NE German Basin), we analysed 3-D seismic reflection data to determine the degree and direction of azimuthal velocity anisotropy which is interpreted as the effect of sub-vertical fracturing. Above the Zechstein salt, the observed anisotropy roughly correlates to fault structures formed by an upwelling salt pillow. Below the salt, faults are not obvious and the direction of less pronounced anisotropy and interpreted fracturing follows the trend of the regional stress field. The fracturing in an extensional setting above salt pillows may cause higher permeability and better conditions for geothermal exploitation.

Geothermal reservoirs require reliable well-completion techniques to reach well integrity. Well, integrity means there are no flow paths behind the casing at all. While constructing the well, the drilling mud must be fully displaced by uncontaminated cement, measured in displacement efficiency. Conventional real-time measurements show average pumping parameters to control the cement job's success. However, studies show issues with well integrity worldwide. We aim to improve well integrity by closing an information gap within the construction phase using continuous distributed fiber-optic sensing with dense spatial sampling. This study investigated the primary cementing of an 874 m surface casing in Munich, Germany. A fiber optic cable deployed behind the casing enabled the measurement of distributed dynamic strain rate (DDSS or DAS) and distributed temperature (DTS) during cement placement. We used field data from the cementing service, developed a fluid displacement model, and compared the results with those of the fiber optics. While we can trace only the rise of a cold front in the temperature data, the combined interpretation of DAS data shows features that allow comprehensive insights into the subsurface displacement process. We observed two rising velocities at a constant pumping rate. We were able to correlate them to the rise of different fluid interfaces. We conclude that the freshwater spacer does not displace the drilling mud but the first arrival of cement. Once the breakouts are filled, the succeeding cement seeks the path of least resistance without displacing cement in the breakouts again. Our findings suggest the possibility of tracking the rise of different fluids and the stability of their interfaces in real-time with distributed dynamic strain sensing. Having sensors along the borehole to track the displacement efficiency enables on-site reactions to ensure the cement jobs' success.

During 2018, a study was conducted to understand the response of new seismic instrumentation to the complex seismo-acoustic wavefield of Mt. Etna. The study consisted on deploying a multi-instrumental network at Pizzi Deneri (PDN) observatory, near the main craters of Mt. Etna. The multi-instrumental network comprised infrasound sensors, broad-band seismometers (BB) and a fiber optic cable buried within the local loosed scoria surface. The cable was connected to a Distributed Dynamic Strain (DDSS) interrogator. Part of the collected data reveals, what is believed to be, a case of a non-linear ground response from an air-to-ground coupling from an acoustic wave. An infrasound sensor registered the arrival of a signal from a volcanic explosion with a main frequency of ~2 Hz. Immediately, a BB and fibre optic virtual sensor (DDSS channel), co-located with the infrasound sensor, registered a signal linked to the acoustic arrival. However, the dominant frequencies captured by the BB and DDSS range between 15 and 20 Hz. To further study this phenomenon, a second experiment was conducted in 2019 in the same place (PDN) and using the same type of instrumentation, but in a different spatial arrangement. In a total of three months, we obtained more than 65000 examples of acoustic signals linked to volcanic explosions. Embedded in the examples, there are cases of non-linear ground response. However, the dataset also contains cases with no ground response triggered by acoustic signals. To understand which acoustic inputs could trigger the ground response, we performed a classification of the acoustic signals based on waveform similarity. In addition, to understand the resulted ground response, we extended the waveform similarity classification to the DDSS records to achieve spatial-temporal characterization of the phenomenon in study. The outcomes of this method allows us to understand the spatial effect of acoustic signals on the ground, monitor temporal variations, and discriminate between reliable data and DDSS signal artifacts such as saturation.

The application of ambient noise interferometry to distributed acoustic sensing (DAS) data recorded on existing telecommunication networks provides a promising opportunity for effectively imaging the urban subsurface with high resolution at local and regional scales. This approach holds significant potential for various applications, including assessing the suitability of the urban subsurface for safe utilization, such as in geothermal development, and evaluating risks associated with subsurface activities, particularly concerning geological hazards like subsidence and sinkholes. Such capabilities are essential for developing resilience strategies and mitigating potential impacts in urban environments. However, extracting coherent seismic signals from the ambient wavefield recorded by DAS in urban settings remains a challenge. One obstacle is the presence of diverse and complex noise sources, which are often unevenly distributed. These localized sources can introduce deviation into the result of ambient noise interferometry and generate nonphysical arrivals, complicating the analysis and interpretation of the results. In this study, we present the analysis of 15 days of continuous passive DAS data recorded on a pre-existing fiber optic cable (dark fiber) spanning 11 km along a major urban road in Berlin, Germany. Our investigation reveals anthropogenic activities, predominantly traffic noise from vehicles and trains, as the primary seismic source. To retrieve Virtual Shot Gathers (VSGs), we apply interferometric analysis based on the cross-correlation approach. Before stacking, we design a selection scheme to identify high-quality VSGs, thereby optimizing the resulting stacked VSG. Then, Multichannel Analysis of Surface Waves (MASW) is applied to derive 1D shear-wave velocity models across successive array segments. We construct a 2D velocity model of the subsurface through the concatenation of individual 1D velocity models obtained from overlapping array subsections. This expansion into 2D necessitates automatically identifying high-quality VSGs, achieved through unsupervised learning methods such as clustering. This process is crucial for excluding transient incoherent and localized noise sources during selective stacking. To implement clustering, we initially reduce the dimensionality of the VSGs using principal component analysis. We then cluster the features within this reduced-dimensional space. Finally, we stack the VSGs in each cluster and select the best-stacked VSGs. We initially test the clustering algorithm on synthetic VSGs before applying it to DAS ambient noise field data to ensure its reliability and effectiveness in real-world scenarios. The clustering results reveal distinct groups of VSGs that demonstrate consistent patterns across synthetic and field DAS datasets. These distinct groupings offer valuable insights into the temporal variations in human activities and allow a better understanding and interpretation of the recorded DAS ambient noise data, enabling the identification of viable ambient noise signals for further processing. Ultimately, this approach enhances the accuracy of dispersion measurements, enabling improved subsurface imaging in urban areas.