Direct current electrical resistivity tomography (ERT) is a widely used geophysical method for near-surface investigations, offering high-resolution imaging for geological, engineering and environmental applications. While traditional ERT surveys typically target depths of 0-200 m, technological advancements have enabled deeper investigations, commonly referred to as Deep ERT. In this study, we explore the practical challenges and methodological improvements associated with Deep ERT, particularly when combined with induced polarization (IP) measurements. Rather than other electromagnetic methods, ERT offers a more straightforward framework for analysing IP effects, which can potentially correlate with the volume fraction of ore minerals. Nevertheless, deep IP investigations are often challenged by weak signal strength and various sources of electromagnetic interference. To address these challenges, we evaluated key strategies including survey planning, high-power current injection, unconventional electrode configurations and advanced signal processing techniques. The adoption of nodal geophysical recording systems eliminates the logistical constraints of cabled multi-electrode setups, improving flexibility and data acquisition efficiency. Additionally, continuous full time-series recording allows for enhanced noise filtering and signal stacking, ultimately increasing the signal-to-noise ratio and extending the effective exploration depth. We demonstrate this methodology through a comprehensive case study conducted at the Koillismaa Linear Intrusion Complex in Finland, where a 3-D Deep ERT-IP survey successfully delineated conductive and chargeable anomalies at depths exceeding 1.5 km. These anomalies closely align with independent gravity and borehole logging data, consistent with the mafic-ultramafic intrusion structures. Our results emphasize the importance of balancing data quality, survey efficiency and spatial resolution in survey design. This work not only provides a robust workflow for the implementation of Deep ERT-IP surveys but also represents the first documented successful acquisition of high-quality IP data at these substantial depths, significantly advancing the state of deep geoelectrical exploration.

In this study, we conducted an extensive geophysical survey to explore the potential of electrical resistivity methods in delineating deep ore deposits within between Koillismaa Intrusion and Näränkävaara intrusion, northeastern Finland. Preliminary investigations in 2022, including magnetic, gravity and audio-magnetotelluric (AMT) methods, along with drilling, uncovered significant anomalous structures in the survey area. Subsequent drilling of an exploration well provided positive lithological indications of a ultramafic igneous rock at more than 1.5 km depth, which are very likely of the same age as the layered intrusions in the area. Borehole data indeed revealed that the Archaean basement gneiss extends down to approximately 510 m, underlain by a granite dyke with interspersed thin layers of pyroxenite and peridotite. Notably, peridotite layers around 1500 m depth exhibited distinct magnetic and IP responses in core data.We employed electrical methods at the site, including electrical resistivity tomography (ERT) and induced polarization (IP). To cover a large-scale area, 25 transmitter dipoles, each 1 km long and using three different transmitter systems, were deployed and data were recorded at 119 receiver stations. This work presents the acquisition and preliminary results from the ERT-IP surveys. During the processing of ERT and IP data, we utilized full time-series data recorded across the four lowest main frequencies (from 0.0625 Hz to 8Hz) to capture voltage data in a steady state. Apparent resistivity data were derived from the stacked voltage data, while IP data were initially extracted from these decay curves of these stacked voltage data and subsequently processed in the frequency domain (outphasing). Analysis of the resistivity and IP responses revealed notable IP signals at depths exceeding 1.5 km. Meanwhile, the resistivity data indicated generally very high values, around 10,000 ohm-m, with complex variations observed near the surface. This study demonstrates the efficacy of ERT and IP methods in delineating deep-seated mineral deposits, with the deep-depths IP responses being particularly noteworthy.

Developing accurate 3D geological models of the subsurface is crucial, as they provide the foundations for multiple uses (e.g., resource exploration and exploitation, geohazard assessment, and environmental geoscience). The construction of these models is an intrinsically integrative task, which jointly takes into account all available data and information from multiple sources, i.e. structural geology, stratigraphy, petrophysics, geophysics. Despite the progress made in automating the integration, in particular with recent advances in artificial intelligence, human interpretation remains essential. Consequently, the performance and limitations of human geological interpretation need to be carefully assessed particularly when subsurface data are incomplete, sparse and imprecise. In this context, the French geological survey – BRGM – has set up a blind interpretation exercise that enables the geo-interpreters to test their ability to answer two main operational questions when jointly analyzing geological and multi-source geophysical datasets (seismic, gravimetric, electric/magneto-telluric): (q1) Is it possible to detect and characterize structural traps and potential migration pathways at several kilometers depth? (q2) Do the errors associated with each of the different datasets influence / affect / bias the geological interpretation? If so, how?To this end, the following procedure was applied: (1) a simplified 3D geological model was constructed using a real exploration project dedicated to the characterization of helium reservoirs in a deep Permian sedimentary basin; (2) two cross-sections were extracted from the model with realistic petrophysical properties to constrain geophysical forward models, i.e. gravimetric, magneto-telluric, and seismic; (3) these geophysical "truths" were intentionally degraded to reflect measurement errors and realistic processing. During the 6-hour exercise, the degraded geophysical datasets along with geological data from one borehole and from the 1:1,000,000 scale geological map were provided to three teams of interpreters - each consisting of a geologist and a geophysicist, with the aim of interpreting the two cross-sections.This communication summarizes the main lessons learned from this exercise by discussing the interaction between data resolution, quality and reliability, and cognitive biases. It points out the value of fostering recurrent exchanges with data producers during the geological interpretation process. Finally, we propose recommendations for improving the links between data-centric and human-centric inversion procedures.

Summary Direct Current Electrical Resistivity Tomography (ERT) is a widely used geophysical method for near-surface investigations, offering high-resolution imaging for geological, engineering, and environmental applications. While traditional ERT surveys typically target depths of 0–200 m, technological advancements have enabled deeper investigations, commonly referred to as Deep ERT. In this study, we explore the practical challenges and methodological improvements associated with Deep ERT, particularly when combined with Induced Polarization (IP) measurements. Rather than other electromagnetic methods, ERT offers a more straightforward framework for analyzing IP effects, which can potentially correlate with the volume fraction of ore minerals. Nevertheless, deep IP investigations are often challenged by weak signal strength and various sources of electromagnetic interference. To address these challenges, we evaluated key strategies including survey planning, high-power current injection, unconventional electrode configurations, and advanced signal processing techniques. The adoption of nodal geophysical recording systems eliminates the logistical constraints of cabled multi-electrode setups, improving flexibility and data acquisition efficiency. Additionally, continuous full time-series recording allows for enhanced noise filtering and signal stacking, ultimately increasing the signal-to-noise ratio and extending the effective exploration depth. We demonstrate this methodology through a comprehensive case study conducted at the Koillismaa Linear Intrusion Complex in Finland, where a 3D Deep ERT-IP survey successfully delineated conductive and chargeable anomalies at depths exceeding 1.5 km. These anomalies closely align with independent gravity and borehole logging data, consistent with the mafic-ultramafic intrusion structures. Our results emphasize the importance of balancing data quality, survey efficiency, and spatial resolution in survey design. This work not only provides a robust workflow for the implementation of Deep ERT-IP surveys but also represents the first documented successful acquisition of high-quality IP data at these substantial depths, significantly advancing the state of deep geoelectrical exploration.

The transition towards carbon neutral transportation and energy sources increases the global demand for mineral raw materials while easy-to-find near-surface ( 200 m) ore deposits are unlikely discovered in well-explored areas such as Europe. In order to increase the mineral exploration success rate, the project SEEMS DEEP (SEismic and ElectroMagnetic methodS for DEEP mineral exploration) develops geophysical deep exploration workflow capable of imaging the bedrock from the surface down to several kilometres depth. In this paper, we present first results from ground electrical and electromagnetic surveys conducted at the SEEM DEEP geological test site, namely the Koillismaa Layered Intrusion Complex in north-eastern Finland. Here, a 1.7 km long hole drilled by GTK intersected mafic-ultramafic rocks with anomalous electrical and chargeability properties at  1400 m depth, making it an interesting case study to test the ability of such technologies for imaging resistivity and chargeability contrasts at several kilometre depth.

In this study, we conducted a comprehensive geophysical survey near Kuusamo (Finland) to assess the potential of electrical resistivity methods in delineating mineral deposits at depths greater than 1 km. Preliminary investigations, including magnetic and gravity methods as well as drilling, revealed significant anomalous structures in the survey area. We employed multiple electrical and electromagnetic methods at the site, comprising controlled-source electromagnetic (CSEM), magnetotelluric (MT), electrical resistivity tomography (ERT), and induced polarization (IP). To obtain the geophysical data in very large-scale area, we used a total of 25 transmitter dipoles with 1km long using three distinct transmitter systems and recorded data at 119 receiver stations. In this paper, we present the acquisition and preliminary results from ERT-IP. Analysis of the resistivity and IP responses revealed notable IP signals at depths exceeding 1.5 km. Meanwhile, the resistivity data indicated generally very high values, around 10,000 ohm-m, with complex variations observed near the surface.

Accurately determining the mineralogical composition of rocks is essential for precise assessments of key petrophysical properties like effective porosity, water saturation, clay volume, and permeability. Mineral volume inversion is particularly critical in geological contexts characterized by heterogeneity, such as in the Upper Rhine Graben (URG), where both carbonate and siliciclastic formations are prevalent. The estimation of mineral volumes poses challenges that involve both linear and nonlinear relationships associated with geophysical data. To address this complexity, our methodology strategically integrates the robust insights from standard statistical approaches with three machine learning (ML) algorithms: multi‐layer perceptron, random forest regression, and gradient boosting regression. Furthermore, we propose a new hybrid ensemble model that incorporates a weighted average of multiple ML approaches to predict mineral composition within the Muschelkalk and Buntsandstein formations of the URG. ML techniques for mineral composition prediction in these formations exhibit robust predictive performance. The predicted mineral volumes align closely with quantitative estimates derived from X‐ray diffraction analysis. Additionally, they are in good qualitative agreement with mineral descriptions obtained from cores and cuttings of the Muschelkalk and Buntsandstein formations.

A wide range of geophysical methods is used for the exploration of deep geothermal resources. It aimsat characterizing the deep fractured network and its capacity for fluid/heat extraction. This relieshowever on the capacity of geophysical techniques to 1) image the geometry of the fractured networkbut also 2) characterize the petro-physical properties of the fracture network and matrix. The challengeis however that the geophysical inverse problem is ill-posed and multi-scale.To overcome these challenges, we propose here to take a multi-physics and multi-scale approach of thegeophysical/petro-physical inverse problem. To do so, we are developing iteratively petro-physical andgeophysical models that can explain the observables at the different scales. In this paper, we report outthe results of the first iteration phase of this research project that consists in building an initial petrophysical model from well logs and prior geological knowledge, and single-domain inversion ofgeophysical data. We applied this methodology to the Upper Rhine Graben where a wealth ofknowledge and dataset on deep fractured formations are available.

Three-dimensional geophysical exploration is essential in identifying favorable areas and derisking high-temperature geothermal projects. In particular, electrical resistivity imaging plays a key role in this exploration due to its sensitivity to the presence of alteration products, geothermal fluid circulation, and temperature. As 3D seismic exploration is rarely effective in volcanic environments, resistivity methods such as magnetotellurics (MTs) are often used to extract deep structural information. However, deep electromagnetic (EM) imaging in coastal areas of volcanic islands with MT can be challenging due to anthropogenic noise induced by urbanized areas concentrated around the coast. The use of active EM sources, such as airborne EM (AEM) and controlled-source EM (CSEM), as a complement to MT is a solution for overcoming anthropogenic noise. However, applying these methods in this context is still challenging due to the proximity to the sea/land interface, large variations in topography and nearshore bathymetry, and the heterogeneity of the near surface. Our approach outlines the challenges of acquiring, processing, and inverting nearshore and land 3D CSEM data in such complex environments. The CSEM data are part of a multimethod geothermal exploration on the island of Martinique in the French West Indies, and its 3D inversion result is compared with the previous regional 3D MT and AEM inversion results. In addition to MT, CSEM may highlight the location of a potential high-temperature paleo-reservoir at shallow depths under the urbanized area of Petite-Anse covered by a paleo-clay cap ranging from a few meters to several hundred meters in thickness. We conclude by proposing possible improvements for CSEM and multimethod methodologies to obtain more reliable exploratory models.

Summary Exploitation of deep hydrothermal fluids for heat, electricity and lithium production relies on our knowledge and prediction capacity of hydrothermal fluid property, mainly temperature, flow rate and chemistry. Most of hydrothermal fluid properties are obtained from post-drilling phases, whereas proxy information is hard to access in an economically viable way. The main challenge is then to develop methods to access these properties at the early stage of the exploration phase. Deep fractured reservoirs are usually characterized by non-invasive geophysical methods (mainly seismic). However, these techniques have a low sensitivity to geothermal fluids and so, do not predict accurately the geothermal resource, and more particularly reservoir permeability, before drilling operation. Due to their sensitivity to fluids and particularly brine water in rocks, electromagnetic (EM) techniques have been traditionally used to investigate the subsurface conductivity. EM methods have shown to be effective to characterize geothermal reservoir geometry in volcanic areas. In this paper, we show that EM methods can also provide valuable information for de-risking targets with high geothermal and lithium potential in deep fractured reservoirs, despite the presence of a high man-made noise. We illustrate that with actual data acquired recently with a CSEM survey performed in the Upper Rhine Graben.

Exploiting high temperatures geothermal resources in sedimentary and basement rocks, rifts or flexural basins to produce electricity is now possible because of the development of binary geothermal power plant technology. However it remains challenging because the presence of fluid and permeability at 4-5 km depth is necessary. A multi-scale and multi-disciplinary approach to increase our knowledge of the transition zone between the sedimentary cover and the basement have been undertaken to provide fundamental knowledge for the assessment of its geothermal potential. In this paper, we report out the results of a study performed on an exhumed transition zone in the Ringelbach area in the Vosges Mountain, on the flank of the Rhine graben. In this analogue of a deeply buried transition zone of the Rhine Graben, Triassic sandstones are still present on the top of the fractured and altered granitic basement providing the unique opportunity to study in-situ the physical properties of this transition zone. We focused here on electrical and seismic properties of the transition zone as they are the main physical parameters usually assessed with the help of geophysical methods during the exploration phase of a geothermal project.We show that altered porous and potentially permeable granite targeted in deep geothermal exploration has a clear signature on both electrical conductivity and seismic measurements that can be measured at core scale, borehole scale, and are still visible with surface geophysical methods such as refraction and reflection seismic and Controlled Source Electromagnetism at a few hundred meter depth. The results suggest that best discrimination between permeable and non permeable rocks may be provided by the joint interpretation of both resistivity and seismic velocities.

Summary Within the framework of the development of the exploration of geothermal energy resources in coastal areas, we tested the importance of nearshore magnetotelluric (MT) prospection at the land-sea interface (less than 1 km from the coast). Indeed, the current exploration methods in geothermal, in general exclusively terrestrial, do not allow to extend the knowledge of the environments for the evaluation of the energy resource, the coast being the physical limit of the methods used. The objective of these explorations is to better understand the volumetric extent of the resource and in particular its possible extension beyond the coast to optimize the positioning of geothermal drilling and increase the production of these renewable energies. In all coastal geothermal regions (seas but also lakes), without information beyond the terrestrial domain, subsoil models are poorly constrained and the resource poorly evaluated. We deployed new marine MT systems designed for shallow water and light deployments in Mayotte (Comoros Archipelago) and Guadeloupe (French West Indies) to investigate the impact of offshore MT sites on the resistivity distribution in depth.

Summary The use of seismic inversion for reservoir characterization, widely developed in the oil and gas industry, remains a challenge for geothermal quantitative interpretation due to a lack of reliable well and seismic data stemming from limited budgets and restrictions when operating in populated areas. Yet, the benefits of seismic data to de-risk geothermal activity have been demonstrated in the past (Mougenot, 1999). The recent development of advanced deep neural networks (DNNs) has opened the door to a new viable approach for directly estimating reservoir properties from seismic data. Although this kind of neural networks requires a large amount of labelled data to be trained, only a limited amount of real well data is required as synthetic data can be used to augment the training set. Recently introduced theory-guided techniques based on rock physics models can help generate catalogues of pseudo-logs representative of geologic variations. DNNs trained with synthetic data only are applied to a geothermal carbonate reservoir located in the Dogger formation north-east of Paris, France. The goal of the study is to characterize the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.