Distributed acoustic sensing (DAS) is a novel technology, which allows the seismic wavefield to be sampled densely in space and time. This makes it an ideal tool for retrieving surface waves, which are predominantly sensitive to the S-wave velocity structure of the subsurface. In this study, we evaluate the potential of DAS to image the near surface (top 50 m) using active-source surface waves recorded with straight fibers on a field in the province of Groningen, the Netherlands. Importantly, DAS is used here in conjunction with a Bayesian transdimensional inversion approach, making this the first application of such an algorithm to DAS-acquired strain-rate wavefields. First, we extract laterally varying surface wave phase velocities (i.e., “local” dispersion curves [DCs]) from the fundamental mode surface waves. Then, instead of inverting each local DC separately, we use a novel 2D transdimensional algorithm to estimate the subsurface’s S-wave velocity structure. We develop a few modifications to improve the performance of the 2D transdimensional approach. Specifically, we develop a new birth-and-death scheme for perturbing the dimension of the model space to improve the acceptance probability. In addition, we use a Gibbs sampler to infer the noise hyperparameters more rapidly. Finally, we introduce local prior information (e.g., S-wave logs) as a constraint to the inversion, which helps the algorithm to converge faster. We first validate our approach by successfully recovering the S-wave velocity in a synthetic experiment. Then, we apply the algorithm to the field DAS data, resulting in a smooth laterally varying S-wave velocity model. The posterior mean and uncertainty profiles identify a distinct layer interface at approximately 20 m depth with a sharp increase in velocity and uncertainty at that depth, aligning with borehole log data that indicate a similar velocity increase at the same depth.

Distributed acoustic sensing (DAS) that uses optical fibres as sensing units is attracting increasing interest for micro-seismic monitoring in geothermal projects. Standard optical fibres provide one-component measurements along the fibre and this pose challenges in determining certain characteristics of the source, such as its azimuth and its full moment tensor. Full source characteristics can be obtained via offset downhole measurements and/or measurements from horizontal well sections but these come with substantial extra costs. This paper proposes a single-well dual-cable DAS configuration to reduce the need for drilling additional wells or sections, where two DAS cables are assumed to be positioned within a single vertical well at opposite sides of the well. Synthetic DAS signals are generated by an open-source code that assumes plane-layered media and are used to study the feasibility of the dual-cable DAS for localising a seismic source and resolving its moment tensor. A localisation procedure is presented, and a sensitivity analysis of localisation accuracy is conducted with respect to source parameters and noise levels. In addition, an analysis is performed to assess the resolvability of the moment tensor components from the dual-cable DAS configuration. Results suggest the source location can be fully determined, yet low signal-to-noise ratio and azimuth close to 0∘ (North, aligned with the two cables) lead to a decrease in accuracy. The full moment tensor can be resolved only if the epicentral distance is 5 m or less, while non-double-couple components can be reliably resolved with an epicentral distance up to 20 m, showing improvement compared to installations with a single cable. Consequently, near-borehole failures, regardless of the source mechanisms, can be characterised within an epicentral distance of 5 m. With epicentral distance increasing, resolvability of the mix-mode failures is reduced first, followed by the resolvability of the pure shear or tensile failures, which depends on the azimuth. Overall, the results demonstrate that a single-well dual-cable configuration has the potential for monitoring and understanding near-borehole micro-seismic events induced during geothermal reinjection and stimulation operations.

A geothermal doublet has been installed in a sedimentary reservoir for direct-use heating on the TU Delft campus, targeted to supply around 25 MW of thermal energy at peak conditions. This contribution presents the implementation and initial data collection from the doublet, including an initial evaluation of the logging and coring campaign. Nearly half of Netherlands natural gas consumption is allocated to heating, and the on-campus CO2 emissions from heating exceed 50%. The doublet has been designed with two primary aims of research and commercial heat supply, with the wells being completed in December 2023. The project will be operated by a commercial entity, and built into a larger thermal energy system including a high temperature underground storage system, with the first energy production planned in 2025. The research questions relate to field-scale geothermal operations, e.g. how reliable is the long-term energy production?, how do materials perform in the long-term? and how can geothermal projects be best monitored? The research programme involves the installation of a wide range of instruments alongside an extensive logging and coring program and monitoring network. The doublet has been cored, with substantial continuous samples from the heterogenous reservoir, alongside a large suite of open hole well logs in the reservoir and through casing logs in overlying geological units. A fiber-optic cable will monitor distributed pressure throughout the producer reservoir section, at approximately 2300m depth, which will be installed during commissioning. A local seismic monitoring network has been installed in the surrounding area with the aim of monitoring very low-magnitude natural or induced seismicity. The project is a key national research infrastructure and is being incorporated into the European EPOS (European Plate Observing System, https://www.epos-eu.org/), such that accessibility and data availability will be as wide as possible. All observations will be included in a digital-twin framework that will allow to make better decisions in future geothermal projects.

The TU Delft campus geothermal project has joint objectives of research and commercial thermal energy production. It has been developed and will be operated by the Geothermie Delft (GTD) consortium, a commercial cooperation between TU Delft, Aardyn, EBN and Shell Geothermal. This report gives an overview of the research activities that have been carried out during the implementation of the doublet drilling the wells DEL-GT-01 and DEL-GT-02, and the sidetracks DEL-GT-02-S1 and DEL-GT-02-S2 in the period June - December 2023. The research programme and related operations during the installation of the campus geothermal wells have been led by the scientific team of TU Delft department of Geoscience and Engineering. The project is part of the national research infrastructure for solid Earth science (https://epos-nl.nl/), and offers the possibility to do state of the art research on an operating geothermal system.
The main research activities that were carried out during the implementation of the geothermal wells included rock sampling in the form of a detailed drill cutting sampling set, full cores and sidewall cores of the caprock and the geothermal reservoir, open-hole logging of the reservoir formations and the installation of a fibre optic cable in the producer (still to be carried out).
Overall, the following samples and data were collected as part of the scientific programme:
- 15m of 4”core from the direct caprock of the producer reservoir section
- 71m of 4”core from the reservoir section of the producer
- 78 sidewall cores from the injector reservoir section
- 2400 cutting samples
- 3000m of open-hole and closed-hole logging data
Details of these activities can be found in the report and the related appendices. All data presented in this report have been published via TU Delft institutional data repository and can be found online as part of the data collection associated with the research programme of the project: Geothermal Project on TU Delft Campus Collection at https://doi.org/10.4121/85b3725b-80fa-4b0b-9db2-475bfd8f0265.

Monitoring temperature changes in geothermal applications is crucial to ensure sustainable heat production and storage operations. This work focuses on a geothermal project situated on the campus of Delft University of Technology in the Netherlands. In connection to deep low-enthalpy geothermal reservoir exploration, an aquifer thermal energy storage system for the purpose of seasonal shallow heat storage is planned. To enable monitoring changes in the electrical resistivity distribution due to heat injection and extraction operations in the shallow subsurface, a new 480 m deep borehole will be equipped with an electrode setup. Measuring the vertical component of the electric field in the borehole using a frequency-domain surface-to-borehole controlled-source electromagnetic setup is particular effective for monitoring reservoir changes. Due to corrosion effects, conventional electrodes have rather limited lifespans, which may not be sufficient for the multi-decade operational plan for this geothermal application.
We use a new approach integrating capacitive electrodes in composite borehole casings. Tests in shallow boreholes have shown comparable results to standard electrodes. Integrating the capacitive sensors in the composite borehole casing is rather time- and cost-intense requiring pre-drilling installation and specially designed electronics. Therefore, we want to optimise the electrode placement along the borehole trajectory. We simulate vertical electric fields at closely spaced receivers along the borehole trajectory for different subsurface scenarios originating from resistivity changes introduced by injecting and extracting hot fluids. Applying the Ramer–Douglas–Peucker algorithm, we determine which electrode locations and combinations are optimal to record resulting variations in the vertical electric field component. The proposed methodology for optimal electrode placement is promising to improve monitoring efficiency in geothermal applications, ensuring sustainable and effective operation over extended periods.

We present a development of capacitively coupled EM sensors integrated in non-corrosive casings for permanent CSEM monitoring in boreholes. Capacitive sensors are required to detect low-frequency (diffusive-field) signals where voltage measurements fail and ammeters need to be used. The permanent installation in boreholes necessitates surface placement of the electronic components to ensure their longevity and accessibility. An issue is that small current signals need to be transferred over a large distance via cables whose capacitances are larger than the ones from the sensors, so a circuit of a Zero-Resistance Ammeter with Integrator (ZRA-I) was developed for annihilating the cable-capacitance effect. Via modelling, lab and small-scale field testing, we were able to show that capacitive sensors with ZRA-I electronics worked well: although the desired signal is slightly decreased compared to the one from galvanically coupled sensors, the signal-to-noise ratios are comparable, for the frequencies used. So we show that capacitive sensors can successfully be integrated in composite casings and, with the proper sensor electronics, can well be used for permanent CSEM monitoring in boreholes.

Nearly half of the Netherlands’ natural gas consump tion is allocated to heating, with direct -use geothermal heating being one of the available low-carbon energy solutions. A geothermal well doublet, designed with the two primary aims of research and commercial heat supply, is currently being installed on the campus of Delft University of Technology. The project is a key national research infrastructure and is being incorporated into the European sustainable and distributed infrastructure (EPOS: European Plate Observing System, https://www.epos-eu.org/), such that accessibility and data availability will be as wide as possible. All observations will be included in a digital-twin framework, which will allow us to make better decisions in future geothermal projects. The project includes a comprehens ive research program, involving the installation of a wide range of instruments alongside an extensive logging and coring program and monitoring network. The doublet has been cored, with substantial continuous samples from the heterogeneous reservoir, alongside a large suite of well logs in both the reservoir and overlying geological units. Such investigation is rarely undertaken in geothermal projects. A fiber-optic cable will monitor the producer well all the way down to the reservoir section, at approximately 2300m depth, in the Lower Cretaceous Delft Sandstone that is used as a geothermal reservoir in a series of existing and planned doublets in the West Netherlands Basin. A local seismic monitoring network has been installed in the surrounding area with the aim of monitoring very low-magnitude natural or induced seismicity. A vertical observation well with electromagnetic sensors will be drilled in the near future between the injector and producer to monitor cold-front propagation. This paper presents the initial modeling for the project and steps towards the production of a digital twin. Two modeling examples in the paper will emp hasize current operational challenges relevant to the project.

A geothermal well doublet, designed with two primary aims; one of research and the second of commercial thermal energy supply, is currently being installed on the campus of Delft University of Technology, with the wells being drilled in the second half of 2023. The project includes a comprehensive research program, involving the installation of a wide range of instruments alongside an extensive logging and coring program and monitoring network. The doublet has been cored, with continuous samples from the heterogenous reservoir being complimented with more distributed side-wall cores, alongside a large suite of open-hole well logs in the reservoir section of both wells. Such investigation is rarely undertaken in geothermal projects. A fiber optic cable will monitor the production well, and will be installed all-the-way down to the reservoir section when the well completion is installed, at approximately 2300m depth. The reservoir is the fluvial Lower Cretaceous Delft Sandstone that is used as a geothermal reservoir in a series of existing and planned doublets in the West Netherlands Basin. A local seismic monitoring network has been installed in the surrounding area with the aim of monitoring very low-magnitude natural or induced seismicity. A vertical observation well with electromagnetic sensors will be drilled in a few y ears’ time between the injector and producer to monitor cold-front propagation. The total project is targeted to supply around 25 MW of thermal energy at peak conditions, next to this project a thermal energy storage system is planned to provide a seasonal buffer. The project is a key national research infrastructure and is being incorporated into the European infrastructure EPOS (European Plate Observing System, https://www.epos-eu.org/), such that accessibility and data availability will be as wide as possible. All observations will be included in a digital-twin framework that will allow better decisions to be made in future geothermal projects. This paper presents the implementation and initial data collection from the project, including an initial evaluation of the logging and coring campaigns.

Tracking temperature changes by measuring the resulting resistivity changes inside low-enthalpy reservoirs is crucial to avoid early thermal breakthroughs and maintain sustainable energy production. The controlled-source electromagnetic method (CSEM) allows for the estimation of sub-surface resistivity. However, it has not yet been proven that the CSEM can monitor the subtle resistivity changes typical of low-enthalpy reservoirs. In this paper, we present a feasibility study considering the CSEM monitoring of 4–8 Ω·m resistivity changes in a deep low-enthalpy reservoir model, as part of the Delft University of Technology (TU Delft) campus geothermal project. We consider the use of a surface-to-borehole CSEM for the detection of resistivity changes in a simplified model of the TU Delft campus reservoir. We investigate the sensitivity of CSEM data to disk-shaped resistivity changes with a radius of 300, 600, 900, or 1200 m at return temperatures equal to 25, 30, …, 50 °C. We test the robustness of CSEM monitoring against various undesired effects, such as random noise, survey repeatability errors, and steel-cased wells. The modelled differences in the electric field suggest that they are sufficient for the successful CSEM detection of resistivity changes in the low-enthalpy reservoir. The difference in monitoring data increases when increasing the resistivity change radius from 300 to 1200 m or from 4 to 8 Ω·m. Furthermore, all considered changes lead to differences that would be detectable in CSEM data impacted by undesired effects. The obtained results indicate that the CSEM could be a promising geophysical tool for the monitoring of small resistivity changes in low-enthalpy reservoirs, which would be beneficial for geothermal energy production.

Distributed acoustic sensing has been limited in its use for surface-seismic reflection measurements due to the fiber’s decreased broadside sensitivity when the fiber is deployed horizontally. Deploying the fiber in a helically wound fashion has the promise of being more sensitive to broadside waves (e.g., P-wave reflections) and less sensitive to surface waves than a straight fiber (SF). We examine such claims and compare the responses of SFs and helically wound fibers (HWFs) with different wrapping angles, using standard and engineered fibers. These fibers have been buried in a 2 m deep trench in a farmland in the province of Groningen in The Netherlands, where we performed an active-source survey. We observe in our field data that using HWF has a destructive effect on the surface-wave amplitudes. Our data confirms the effect of the wrapping angle on the polarity of the surface-wave arrival and the dampening effect of the helical winding, behaving in quite a predictable fashion. Apart from the effect of the wrapping angle, the different design choices, e.g., cable filling and material type, do not show a significant effect on the amplitude of the signals. As for P-wave reflections, we observe that engineered SF and HWF provide reflection images comparable with those obtained from simultaneously deployed geophones at the surface despite the SF’s decreased broadside sensitivity. A polarity reversal and an amplitude difference between the SF and HWFs are observed. Finally, we demonstrate that the combined use of SF and HWF proved to be useful because SF showed better sensitivity in the shallower part and HWF in the deeper part.

A field experiment was conducted in Zuidbroek, the Netherlands to compare the performance of a DAS and horizontal-geophone system for shear-wave (SV) reflection surveying. The data were subjected to processing for reflection imaging, including conversion of the geophone data to strain-rate data, to enable such a comparison on migrated-section level. Our findings indicate that DAS straight-fibre data shows a lower-frequency information content, but achieves better reflector continuity than the geophone data due to the more continuous and denser sampling with the DAS system.

Retrieving accurate microseismic source locations induced by hydraulic-fracturing operations is an important step to gain insights into the hydraulically stimulated reservoir volume. Recently, deep neural networks have been proposed that directly recover source locations from the seismic waveforms. The optimal performance of the proposed deep neural networks usually requires large training sets. The need for a large training set can be circumvented if a previously trained deep neural network can be used to start the training process with its weights instead of randomly initialized weights. These weights can then be fine-tuned using a smaller training set, which is also known as transfer learning. In this work, we implement a transfer learning workflow to update the weights of a deep neural network that was initially trained on a large synthetic dataset to localize microseismic events. We present two methods of processing, namely one post-monitoring mode and one continuous mode where the processing takes place during the monitoring period. We apply the methods to field data from a hydraulic fracturing site in Texas, USA. In the first scenario, a subset of the field data from the entire monitoring period is used to update the weights of the deep neural network, which is then applied to the remaining data resulting in mean and median distances of 227 and 182 m, respectively, compared to the results of a good localization method. In the second scenario, the deep neural network is updated daily with previously detected and located events and applied to the events detected the following day. Since the observed data used for training generally do not cover a wide range of source locations, we enrich the training set with synthetic data. The addition of synthetics for transfer learning ensures that the updated deep neural network provides accurate source locations for events with locations far from locations used during transfer learning. Transfer learning combining synthetic and real data performs significantly better (more consistent) locations than transfer learning without synthetics.

In a surface-seismic setting, Distributed Acoustic Sensing (DAS) is still not a widely adopted method for near-surface characterisation, especially for reflection seismic imaging, despite the dense spatial sampling it provides over long distances. This is mainly due to the decreased broadside sensitivity that DAS suffers from when buried horizontally in the ground (that is when the upgoing wavefield (e.g. reflected wavefield) is perpendicular to the optical fibre). This is unlike borehole settings (e.g. zero-offset Vertical Seismic Profiling), where DAS has been widely adopted for many monitoring applications. Advancements in the field, like shaping the fibre to a helix, commonly known as helically wound fibre, allow better sensitivity for the reflections. The promise of spatially dense seismic data over long distances is an attractive prospect for retrieving the local variations of near-surface properties. This is particularly valuable for areas impacted by induced seismicity, as is the case in the Groningen Province in the north of The Netherlands, where near-surface properties, mostly composed of clays and peats, play an essential role on the amount of damage on the very near-surface and the structures built on it. Installing fibre-optic cables for passive and active measurements is valuable in this situation. We installed multiple cables containing different fibre configurations of straight and helically wound fibres, buried in a 2-m deep trench. The combination of the different fibre configurations allows us to obtain multi-component information. We observe differences in the amplitude and phase information, suggesting that these differences can be used for separating the different components of the wave motion. We also see that using enhanced backscatter fibres, reflection images can be obtained for the helically wound fibre as well as the straight fibre, despite the decreased broadside sensitivity for the latter.

The overall conditions under which geophysical data are being acquired have changed over the past five years due to the global economy combined with an increased emphasis on low environmental impact sustainability and safety. For land seismic acquisition, minimizing land disturbance, reducing CO2 emissions and increasing crew safety are key motivators to use innovations that drastically change conventional land seismic acquisition methods. One of the sources proven to do this is the eVibe developed by Seismic Mechatronics B V. They were recently contracted to undertake an urban seismic program utilizing their proprietary eVibe source in combination with Stryde Nodes. The seismic survey was acquired in one of the largest cities in the Netherlands, without the need for permits. Being able to minimize environmental impact, to reach a high safety standard and to acquire high-quality data in a noisy urban environment with the used technology made this project a success. This paper compares the results achieved by the Storm10 eVibe in combination with Stryde nodes to results previously obtained by an explosive survey. We show that the results are technically superior, with the eVibe and the Stryde Nodes proving far better suited to acquiring seismic data within this challenging and restrictive urban environment.

Delft geothermal project (DAPwell) is a planned geothermal well doublet, where relatively cold water is going to be injected through one well into a low enthalpy geothermal reservoir to produce hot water from the other well. The volume of the cold water around the injection well will increase over time and, in the end, result in a thermal breakthrough. Thus, it is essential to trace the time-lapse change in the volume of the cold water to monitor the DAPwell efficiently. The invaded reservoir volume by the cold water is associated with a decrease in the pore fluid temperature and salinity. This increases the electrical resistivity of the geothermal reservoir, where the cold front is located. Hence, estimating the time-lapse change in the electrical resistivity of the geothermal reservoir can be used to identify the distribution of the cold water. From a theoretical point of view, the controlled-source electromagnetic (CSEM) method can be used to get information about the change in the electrical resistivity within the geothermal reservoir. In this study, we investigate the feasibility of monitoring a geoelectric model of the DAPwell using land CSEM forward modelling. The optimal survey design is investigated as well as the influence of cold water volumetric changes on the time-lapse electric field response. The impact of measurements undesired effects on time-lapse CSEM response is analysed and then synthesized. A subsurface model of the DAPwell is illuminated by a horizontal electric dipole source, which emits a sinusoidal field with several frequencies. Based on the numerical experiments, surface measurements do not pick up sufficient time-lapse signal to use them for field applications. On the other hand, the difference in the z-component of the electric field, determined along a depth section, allows for a successful detection of the electrical resistivity changes within the geothermal reservoir. The correlation between the spatial distribution of the cold water and the difference in time-lapse electric field responses is clarified. Finally, it is noticed that the difference in time-lapse signal is measurable in the presence of the different sources of noise.

The multi-purpose research borehole at the Delftse Hout is the third of four seismic monitoring locations of the seismic monitoring network for the geothermal research project on the TU Delft campus (Geothermal Delft GTD, also known as DAPwell, https://geothermiedelft.nl/). For the geothermal research project, two deep wells (“a doublet” consisting of an injector and a producer) for geothermal energy extraction will be installed on the TU Delft campus next to the combined heat and power plant (“warmtekrachtcentrale - WKC”). The system will produce geothermal heat to supply the campus of TU Delft and part of the city of Delft.
The herein presented borehole describes the installation of a multi-purpose research borehole (called DAPGEO-02), which was installed in the period February - May 2022. DAPGEO-02 is part of a seismic monitoring system for the shallow and deeper subsurface in the vicinity of the planned geothermal doublet. The locations of all four stations are given in Figure 1. The monitoring network and the related research gathers knowledge about the current status of the subsurface on the basis of periodic data measurements, and possible seasonal effects.
Within the seismic monitoring network, three seismic monitoring stations have already been installed, respectively DAPGEO-01 on the proposed location of the geothermal project near the Leeghwaterstraat in Delft, DAPGEO-03 on the Kerkpolderweg in Delft, and ZH03 in on the Ackersdijkseweg in Pijnacker-Nootdorp (installed and equipped by KNMI).

Hydraulic fracturing plays an important role when it comes to the extraction of resources in unconventional reservoirs. The microseismic activity arising during hydraulic fracturing operations needs to be monitored to both improve productivity and to make decisions about mitigation measures. Recently, deep learning methods have been investigated to localize earthquakes given field-data waveforms as input. For optimal results, these methods require large field data sets that cover the entire region of interest. In practice, such data sets are often scarce. To overcome this shortcoming, we propose initially to use a (large) synthetic data set with full waveforms to train a U-Net that reconstructs the source location as a 3D Gaussian distribution. As field data set for our study we use data recorded during hydraulic fracturing operations in Texas. Synthetic waveforms were modelled using a velocity model from the site that was also used for a conventional diffraction-stacking (DS) approach. To increase the U-Nets ability to localize seismic events, we augmented the synthetic data with different techniques, including the addition of field noise. We select the best performing U-Net using 22 events that have previously been identified to be confidently localized by DS and apply that U-Net to all 1245 events. We compare our predicted locations to DS and the DS locations refined by a relative location (DSRL) method. The U-Net based locations are better constrained in depth compared to DS and the mean hypocenter difference with respect to DSRL locations is 163 meters. This shows potential for the use of synthetic data to complement or replace field data for training. Furthermore, after training, the method returns the source locations in near real-time given the full waveforms, alleviating the need to pick arrival times.

Difficulties in detecting and characterising shallow objects close the surface with seismic shear waves are often problematic because of dominant surface waves. By sequencing a specific combination of two data driven processing steps followed by diffraction tomography can overcome these problems. Small scattering objects become visible in the final image that can have importance of the understanding of subsurface locations, such as areas of archaeological interest. On the other hand, deep changes in the electric resistivity on land are often problematic to detect and especially to monitor time-lapse change over long periods of time. The usual electrodes slowly erode and vanish. Geothermal heat production environments often lead to changes in the resistivity between in-situ water-filled formations and cooler injected water-filled formations of less than one order of magnitude. A dedicated set of capacitively coupled electrode could overcome to erosion problem. When placed in a well with composite casing, these could be used in measurements of much enhanced detectability. In that case it is necessary to have electrodes in a zone from below to above the target layer. By changing the source offset at the surface, optimal measurements can be done to detect the small and deep changes in resistivity.

Seismic imaging characteristics of a prototype electrically driven, linear synchronous motor-based, vertical-force seismic vibrator ("e-vib") were evaluated at a site in the Netherlands. The system weighs 1.65 t and excites seismic signals with a peak force of 6.7 kN. Data were recorded along two collocated geophone-based nodal and landstreamer microelectromechanical system - MEMS-based sensor 2D seismic profiles. To obtain a broad bandwidth data set, the e-vib operated with a 1-200 Hz linear sweep. Shot gathers of the merged nodal-landstreamer data set indicated good-quality seismic data of a broadband nature. The processed merged data set demonstrates high-resolution reflections of the stratigraphic members from approximately 200 m to 2 km, with visible reflections as deep as 2.5-2.9 km. As a reference, we also processed a legacy 3D microspread data set acquired at the same site with a magnitude stronger (14.1 t, 67.5 kN) hydraulic vibrator. Comparison of our nodal-landstreamer seismic section versus 2D slices extracted from the processed microspread seismic volume suggested similar signal penetration depth and the same key marker horizons seen in both. Analysis of the reaction mass and base-plate accelerometer signals recorded with the e-vib source operating on grass and on asphalt surfaces indicates that the e-vib has low total harmonic distortion. The results obtained indicate that, although relatively small, the e-vib is capable of generating high-quality broadband seismic data.

A main challenge in microseismic monitoring is that the seismic signals recorded at the Earth's surface are weak and thus localization of those microseismic earthquakes becomes challenging. Diffraction stacking is a traditional method used to localize weak earthquakes, which involves stacking the waveforms along precomputed travel-time curves from different locations, where the maximum is used to determine the source location. In this work we aim to recover the source location of weak microseismic earthquakes using a deep neural network (DNN) that resembles the U-Net but uses fewer skip connections. However, the size of the field data is too small to train the DNN from scratch. Thus, we propose to pretrain a DNN using synthetic data that resembles the field data and that learns to map the source location in terms of a 3D Gaussian distribution directly from the seismic signals. This pretrained DNN is capable of localizing the higher magnitude earthquakes in the field data, but fails for the weaker earthquakes. To be able to localize the weaker magnitude earthquakes we therefore, fine tune the pretrained DNN using the higher magnitude field-data earthquakes. We observe that the updated model is able to extrapolate the information learned during the fine tuning step from higher magnitude earthquake data to lower magnitude earthquake data.