Complex Fault Research Articles

Footwall geomorphology during necking domain evolution: A new model for the Frøya High, mid‐Norwegian rifted margin

AbstractObservations and modelling results from highly extended regions indicate that detachment fault systems recording displacements of 10 km or more become associated with footwall uplift and back‐rotation. This is commonly explained by the rolling hinge model, which predicts detachment fault back‐rotation and severe dip reduction (<20°) controlled by the amount of extension. Although detachment faults within necking domains at rifted margins often record displacements in orders consistent with those for the rolling hinge model, it is rarely invoked to explain the associated footwall configurations. Our study area encircles the necking domain of the mid‐Norwegian rifted margin, where the Middle Jurassic–Early Cretaceous Klakk Fault Complex (KFC) directly separates the Frøya High from the Rås Basin. The Frøya High represents the eroded footwall of the KFC detachment fault system which records displacements of 20–40 km. Seismic mapping and well correlation across the Frøya High reveal how three erosional unconformities correspond to three laterally extensive top basement segments which follow the strike of the sinuous KFC. The segments differ in terms of dip, basement geomorphology and the composition and age of the sediments that rest unconformably on the top of basement. We attribute the associated cross‐cutting basement unconformities across the Frøya High to footwall uplift and back‐rotation during fluctuating relative sea‐level and repeated subaerial exposure during Middle Jurassic–Early Cretaceous times. We herein introduce a revised tectono‐sedimentary model for the evolution of the Frøya High, with significant implications for sediment (re‐)routing across the high during rifting. The model indicates that spatio‐temporal sediment distribution was ultimately controlled by the process of necking and evolution of the KFC. Our findings indicate a rolling hinge‐type evolution for the KFC and further suggest that the associated mechanisms may be more common in the necking domains of rifted margins than previously assumed.

Faulty Feeder Detection Based on Grey Correlation Degree of Adaptive Frequency Band in Resonant Grounding Distribution System

Due to the particularity of their process, petrochemical enterprises have high requirements for the reliability of power supply. If a large-scale blackout occurs due to a grounding fault, it will pose a huge threat to safe production. When the resonant grounding system of petrochemical enterprises faults, due to the complex fault process and weak fault signal, it is difficult to accurately detect the faulty feeder by traditional methods. This paper presents a new method of grey correlation degree based on adaptive frequency band. Firstly, the transient zero-sequence current of each feeder is decomposed by coif5 wavelet, and the low frequency band a5 (power frequency component) and high frequency band d1, d2 (noise signal) are removed. By stacking all of the remaining frequency band signals to construct the wavelet area matrix, the faulty feeder detection characteristic scale and the first faulty feeder detection result are obtained. Secondly, based on the faulty feeder detection characteristic scale, the second faulty feeder detection result is obtained by the average grey correlation degree matrix, which detects the faulty feeder according to the waveform correlation degree. Finally, the final faulty feeder detection result is obtained by equal weight voting. In MATLAB/Simulink, the 10 kV resonant grounding system of petrochemical enterprises is modeled. A large number of simulation results show that the faulty feeder detection method is not affected by the initial phase angle (0°, 45° and 90°), transition resistance (10 Ω, 100 Ω and 1000 Ω), fault distance (1 km, 8 km and 15 km) and overcompensation degree (5%, 8% and 10%), and has good sensitivity.

Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review

In the modern age of digitalization, electronics are fundamental to any engineering system. With the current strong focus on the Internet of Things (IoT), autonomous vehicles and Industry 4.0, reliable electronics are gaining crucial importance. Predicting the health of complex systems is able to avoid catastrophic failures. Prognostic and Health Monitoring (PHM) approaches are an important step toward trustable and reliable electronics. Nowadays, Artificial Intelligence (AI) and machine learning (ML) algorithms are integrated into PHM approaches, enabling complex fault diagnosis. In this contribution, we provide an overview of the application of intelligent algorithms in PHM of electronics in a systematic manner. The challenges of prognostics in electronics are provided and a detailed overview of the available PHM precursors for various electronic components and the associated selection process is given. Based on the literature review conducted, the main research challenges with ML algorithms in PHM are discussed along with performances of each model.

Timanian Fold-and-Thrust Belt and Caledonian Overprint in the Selis Ridge Imaged by New 3D Seismic Attributes and Spectral Decomposition

The present study is a detailed structural analysis of 3D seismic data in the Selis Ridge in the western Loppa High in the Norwegian Barents Sea, to which new seismic attributes and spectral decomposition were applied. The analysis reveals that pre-Devonian basement rocks are crosscut by a 40–50 kilometers wide, several kilometers thick, E–W- to WNW–ESE-striking fold-and-thrust belt, including a steep, kilometer-thick, top-SSW shear zone. The folds display dome- and trough-shaped geometries, the thrusts appear to have been reactivated dominantly as top-west structures, and the main shear zone warps over the top of the Selis Ridge. The fold-and-thrust belt is interpreted as part of the latest Neoproterozoic (ca. 650–550 Ma) Timanian Orogeny, which potentially reworked preexisting Neoproterozoic rift basins and highs, and was reworked during E–W Caledonian contraction in the Ordovician–Silurian. The results are analogous to recent findings in the northern Norwegian Barents Sea and Svalbard. The presented interpretation provides the basis for discussing Neoproterozoic–Paleozoic plate tectonics reconstructions, the influence of Precambrian–early Paleozoic structures on post-Caledonian fault complexes, the location of the Timanian and Caledonian suture zones, Neoproterozoic rifting, and the origin of the Seiland Igneous Province.

A new complex system fault detection method based on belief rule base for unreliable interval values

Failures to equipment such as milling machines and inertial navigation systems (INSs) can affect their normal operation, resulting in economic losses and personal injury in severe cases. Therefore, fault detection is of great importance. Belief rule base (BRB) is an expert system that plays an important role in fault detection. The traditional BRB has some problems in the explosion of the number of combination rules, the process of model inference, and the process of parameter optimization. To better deal with the above problems, this paper proposes a complex system fault detection method based on an interval-valued BRB fault detection interval-valued (FDIV) and provides the construction and inference process of the method. In the method construction, the form of interval value and disjunction rules are introduced to solve the problem of the number explosion of combination rules, the indicator reliability is added to improve the accuracy of the method, and a new calculation method of rule availability is proposed. In the inference process, twice fusions are made based on evidence reasoning (ER) analysis algorithm and ER rule algorithm respectively to deal with the interval uncertainties. Moreover, the proposed FDIV method is optimized by the projection covariance matrix adaptive evolutionary strategy algorithm projection covariance matrix adaptive evolutionary strategy (P-CMA-ES). Finally, the effectiveness of the proposed method was verified through the research on milling fault detection and the experimental verification of INS fault detection. The superiority of the model was also confirmed through comparative experiments.

The world-class carbonate-hosted Fankou Zn-Pb deposit in China. Part I. Structural analysis: Evolutionary three-order thrusting structures control on the localization of Zn-Pb orebodies

The Fankou giant deposit (with exceeding 10 Mt Zn + Pb metals, grading at 15 wt%) is a carbonate-hosted, stratabound Zn-Pb deposit in the northern Quren Basin, South China. Lenticular, stratiform and wedge-shaped orebodies are strictly hosted in specific layers of Upper Devonian to Lower Carboniferous carbonate sequences along a series of NE and NNE trending faults. Previous ore geological and geochemical studies proposed that the Fankou can be analogous to a Mississippi Valley-type (MVT) deposit, but how the evolution of complex fault system controls ore-forming fluids migration, metals accumulation and orebodies localization remains unclear. To address this question, we conducted a deposit-scale structural analysis based on the geological mapping, underground works, and diamond drills logging. We identified three-order structures gradually growing in the Fankou deposit, including: 1) The first-order fault represented by F203 is a NW-striking thrust fault penetrating basement and acts as a major conduit for ultimate hydrothermal fluids migrations from depth; 2) The second-order faults include a spectrum of NNW-striking faults F2, F3, F4, F4, F5, F6, F7, F8 and F9. They are intersected with F203 in depth, and soon before and synchronous activities with Zn-Pb mineralization; 3) The third-order faults contain a series of NNE-striking F100, F101 and F102 with limited scale, which is subordinate to second-order faults and control the terminal localization of Zn-Pb orebodies. Collectively, we conclude that the evolutionary three groups of deposit-scale faults, derived from the regional NE-striking Wuchuan-Sihui Fault, jointly control the ore-forming fluids migrations from deep to shallow and from SE to NW in space. It is noteworthy that the second- to third-order faults act as the direct metal precipitation space when the upwards metalliferous fluids replaced with the favorable lithological layers. Therefore, we highlight that the southeast part of the mining area, where the metalliferous fluids came from, is a promising exploration target area in future.

Collaborative Approach Solves Upstream R&D Challenges

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 211115, “IBTIKAR Digital Laboratory: A Collaborative Approach Toward Research and Development Challenges in Oil and Gas Upstream,” by Rauf Iqbal, Pranav Kumar, and Muhammad Aamir, ADNOC, et al. The paper has not been peer reviewed. _ The complete paper describes creation of an approach involving a research and development (R&D) environment that supports the business community. The operator’s Ibtikar laboratory is the result of an initiative to develop and provide in-house infrastructure (hardware and software) support within a national oil company’s operations. This synopsis is mostly devoted to a description of R&D challenges faced by the developers and the steps taken to overcome them. Efficient Seismic Attribute Extraction for Azimuthal or Angle Stacks An asset team acquired a few high-density broadband single-source, single-sensor seismic surveys characterized by an enormous amount of data. From acquisition to processing, multiple subcubes (90 GB/cube) were produced for the purpose of capturing subtle geological features. On a conventional machine, loading of the seismic cubes took 40 hours and realizing them took more than 7 days—and this was for only one seismic attribute. To fully use the data in the static and dynamic models of the respective reservoirs, the asset team collaborated with the team behind the described “digital laboratory” to provide a solution best suited for such a large processing requirement. The latter team analyzed the existing system, estimated requirements, and provided a high-performance computing (HPC) system that could handle the massive workload. Table 1 of the complete paper shows the results of a comparison of the conventional workstation with the HPC system. Computationally Intensive Fault-Likelihood Attribute Generation and Fault-Extraction Process In complex geological settings, fault identification and extraction of fault planes has always been a challenging task using traditional seismic-interpretation techniques. Conventional discontinuity-based seismic attributes often face limitations in delineating complex fault patterns, especially in areas of strike/slip and compressional-tectonics regimes. To address these limitations, geoscientists often use sophisticated seismic attributes such as fault likelihood, which follows a sequential multiattribute work flow to generate a fault-imaging volume used later to extract fault planes directly. The key challenge associated with running a computationally intensive attribute with a traditional workstation is that the process is time-consuming and performance is highly compromised because of hardware limitations. The fault-likelihood process itself generates six different seismic volumes during the automatic computation process. The task becomes even more challenging when multiple scenarios are required with varying parameters to assess and finalize the outcomes of fault-imaging volumes and extraction of fault planes. The digital laboratory was used to run this computing-intensive fault-likelihood process on a high-end machine with 800 GB random access memory and 96 cores with improved efficiency. It took only 10 days to run this multiattribute work flow on a seismic volume after running multiple scenarios using different attribute parameters to reduce fault uncertainty and automatically extract fault planes.

Multicharacteristics Arc Model and Autocorrelation-Algorithm Based Arc Fault Detector for DC Microgrid

DC microgrids contribute to the increasing penetration of renewable energies. However, a DC arc fault will lead to malfunctions and even fire hazards in a DC microgrid. To ensure the safe and efficient operation of a DC microgrid, the characteristics of DC arc faults should be determined, and a reliable arc-fault detection technique is required. Nevertheless, it is difficult to investigate the DC arc faults in a practical system. Thus, an accurate and reliable arc model for simulation research of arc faults is significant. Since a DC microgrid has numerous branches, the existing arc models cannot express the complex and random characteristics of potential arc faults. Besides, the detection points need to be determined for the appropriate installation of arc fault detectors. Furthermore, the arc features may be ambiguous and affected by the environmental noise and power electronics noise leading to nuisance tripping to arc detectors. In this paper, a multi-characteristics arc model is established based on the volt-ampere, current sag, and power spectral characteristics of arc faults. According to the frequency domain features of arc faults and interaction effects between different branches, the arc-detection-point selection principle is formed. A non-invasive arc fault detector based on magnetic-field sensing and autocorrelation algorithm is developed. It can avoid the effects of periodic environmental noise and power electronics noise by comparing the correlation between arc features in periods. The experimental results verify that the arc faults can be detected with high accuracy by installing the autocorrelation-algorithm-based arc detector at detection points selected by the arc-detection-point selection principle in a DC microgrid.

Imaging the Lower Cretaceous complex fault systems using common reflection angle migration, Abu Gharadiq Basin, Western Desert, Egypt

Imaging the Lower Cretaceous complex fault systems using common reflection angle migration, Abu Gharadiq Basin, Western Desert, Egypt

Faulty Feeder Detection Under High Impedance Faults for Resonant Grounding Distribution Systems

The problem of high impedance fault (HIF) detection in resonant grounding distribution systems has not been well solved. In practical applications, the existing detection methods either have complex calculation processes and low accuracy or have a weak ability to adapt to complex fault conditions, which limits the adaptability of the existing methods. Based on a theoretical analysis, a novel faulty feeder detection method based on the inner product transformation of extreme values is proposed in this paper. First, to eliminate the interference of high-frequency components, the low-frequency characteristic modes of zero-sequence current of each feeder are extracted by the variational mode decomposition (VMD) algorithm. On this basis, the low-frequency intrinsic mode functions (IMFs) are processed by the first-order difference to obtain the current waveform after the difference. Second, the zero crossing point of the differential current waveform is obtained, and then the extreme value point of the original zero-sequence current is calibrated, including the maximum and minimum. Third, at the extreme value point, the comprehensive inner product values (CIPV) of each feeder are calculated. Then, when the values are positive, it is judged as a bus fault, when the value has at least one negative value, it is judged as a feeder fault, and the feeder with the minimum value is judged as a faulty feeder. A large number of simulations and field tests show that this method has better fault detection accuracy in practical applications and is suitable for strong noise interference and asynchronous sampling.

A data-driven approach to fault modeling and diagnosis of brake-by-wire system

E-Booster plays an important role in braking safety as the pressure source for vehicle hydraulic systems. According to the design feature of its series controller, the signals on key nodes, which can be directly measured, can be used as the data source for fault diagnosis. In this paper, a deep learning technique of residual 1-D CNN equipped with parallel structure is proposed for fault recognition and classification in a dynamic process. With the help of wavelet transform and probabilistic heat map, it is found that the phase current fault feature is distinct in the frequency domain. In contrast, the uniform demagnetization fault and pressure recession fault are more obvious in the time domain. Therefore, the parallel network structure with a wide and narrow convolutional kernel is used, which can handle multiple complex fault features simultaneously. Simulation models based on both data-driven and mathematical formulations are also established to better fit the actual nonlinear conditions. Finally, the proposed network structure can reach 92.1% classification accuracy compared with the commonly used lightweight 2-D CNN model. It can be concluded that 1-D CNN achieves similar classification results as 2-D CNN with less computational resource consumption.

Effective Water Injection Development Test for Rolling Outward Expansion of Low-grade Reservoir

The development of ultra-low permeability reservoirs in the oilfield mainly involves a large proportion of ultra-low permeability and it is difficult to recover reserves because of scattered distribution, low reserve abundance, poor reservoir physical property, and great development difficulty as well as low production intensity, rapid production decline, and low stable production of oil wells. In order to speed up the development of undeveloped reserves and explore the effective technical approach to solve problems in low permeability and difficulty in recovering reservoirs, we carried out the effective water injection development test in the B1 test area. In this paper, in-depth research has been carried out in reservoir geological understanding, development well pattern design optimization, and advanced water injection technology research. Finally, a set of effective production technologies for low abundance reserves in complex geological conditions are formed, effective production in B1 Block that was hard to recover reserves is realized, the drilling success rate is improved greatly, and well pattern design optimization is achieved, advanced water injection efficient development technologies for low abundance reserves blocks are also developed. The demand for rolling production can be gradually extended to other oilfields, which has certain reference significance for the development practice of peripheral oilfields and similar small complex fault block oilfields.

Numerical Simulation of Oil Displacement in Complex Fault Block Reservoir

Numerical Simulation of Oil Displacement in Complex Fault Block Reservoir

Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems

Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software.

Complexity of initiation and evolution of the 2013 Yunlong earthquake swarm

3D fault geometry has recently been shown to play a key role in controlling the kinematics of earthquake swarms. With the aid of a temporary dense seismic array, machine-learning-based detection, and accurate relocation, we outline a complex 3D fault zone accommodating a small earthquake swarm near Yunlong city, western Yunnan, China from February to May 2013. Our results show that the swarm initiated from a compressive stepover zone and subsequently activated a complex fault zone including six planar fault segments. The migration front of the swarm can be well-modeled by fluid diffusion, indicating the swarm was primarily driven by pressurized fluid. A nearby GPS site exhibits a slight displacement that started immediately before the swarm, then gradually decayed in the following months, implying the stress fluctuations caused by a possible aseismic slip likely initiated the swarm and, along with fluid diffusion, drove the migration of the swarm. Within the stepover zone, complex and dense fractures act as conduits connecting the reservoir and fault zone, facilitating fluid flow. Meanwhile, the stress in the stepover zone tends to increase in response to the compression of the two boundary faults, which not only makes the stepover zone more susceptible to be triggered by those transient stresses but also forms a fluid pumping mechanism that drives fluids from the stepover zone into the complex fault zone. We suggest the complexity of the initiation and evolution of the 2013 Yunlong swarm is strongly controlled by 3D fault geometry.

Implications of Geological Conditions on Gas Contents: A Case Study in the Pingdingshan Coalfield

Coalbed methane occurrence and its geological control factors are important for coal and gas co-mining. In recent years, more than 50% of the outburst accidents occurred near the faults and the thickening strips in Chinese mines. Therefore, studying the gas occurrence and its controlling factors is vital to prevent gas disasters in outburst mines with complex geology. In this paper, the distributions of faults, coal thickness, and gas content were characterized and analyzed based on fractal theory and geostatistics. Then, finer coal thickness inverted by the cross-layer boreholes for gas extraction was analyzed at a single working face. The geological control characteristics of gas contents were finally studied. The results show that coal thickness, burial depth, and fault complexity are the geological factors controlling gas contents from a regional to local scale. The sedimentary environment determines the regional coal thickness distribution and even stratification, which leads to higher gas content in the eastern thick-coal region than in the west. And the gas content varies linearly with burial depth in the same geological zonation. The fault fractal dimension is a quantitative representation of fault complexity. The dense distribution of minor faults is why the sizable fractal dimension in the local region is crucial in controlling the uneven enrichment of gas content. Therefore, more refined geological detection and characterization is the way to gas precision control in the future.

Adaptive diagnosis and non-fragile predictive control for HFV engine with non-Gaussian uncertain output

This study presents an adaptive fault estimation and model predictive fault-tolerant control (FTC) for the hypersonic vehicle engine with non-Gaussian uncertain output jet plume. The non-Gaussian probability density function (PDF) describing the plume velocity in a stochastic system is approximated by Type II fuzzy radial basis functions. In the fault observer, the novel prey adaptive estimation method is designed for the valve faults with different amplitudes. Ultimately, this fuzzy-prey fusion adaptive observer shields perturbation and accurately estimates complex faults including incipient and intermittent faults. A predictive tolerant controller compensates for all the faults effects and non-fragile compensation factors of FTC eliminate the perturbation interference, thence the output PDF matches with the expected value. Finally, the more refined internal structure of plume output distribution replaces the local and rough valve position variables, helping this study realizes the hypersonic vehicle engine integrated refine control. Simulation result verifies the effectiveness of the proposed approaches and the superiority compared with the existing method.

Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections.

Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures and offer high-resolution images of the complex subsurface. Nevertheless, there are some limitations to the chosen initial model, aperture illumination and computation efficiency. RTM has a strong dependency on the initial velocity model. The RTM result image will perform poorly if the input background velocity model is inaccurate. One solution is to apply least-squares reverse-time migration (LSRTM), which updates the reflectivity and suppresses artifacts through iterations. However, the output resolution still depends heavily on the input and accuracy of the velocity model, even more than for standard RTM. For the aperture limitation, RTM with multiple reflections (RTMM) is instrumental in improving the illumination but will generate crosstalks because of the interference between different orders of multiples. We proposed a method based on a convolutional neural network (CNN) that behaves like a filter applying the inverse of the Hessian. This approach can learn patterns representing the relation between the reflectivity obtained through RTMM and the true reflectivity obtained from velocity models through a residual U-Net with an identity mapping. Once trained, this neural network can be used to enhance the quality of RTMM images. Numerical experiments show that RTMM-CNN can recover major structures and thin layers with higher resolution and improved accuracy compared with the RTM-CNN method. Additionally, the proposed method demonstrates a significant degree of generalizability across diverse geology models, encompassing complex thin layers, salt bodies, folds, and faults. Moreover, The computational efficiency of the method is demonstrated by its lower computational cost compared with LSRTM.

Interpretation of Sand Body Architecture in Complex Fault Block Area of Craton Basin: Case Study of TIII in Sangtamu Area, Tarim Basin

The complex fault block oilfields in the craton basin contain vast reserves of oil and gas resources. During the development of an oilfield, the flow of oil, gas, and water, is controlled by faults and configuration boundaries. The distribution of remaining oil and gas depends on the interpretation of the reservoir’s architecture. However, recognizing the faults and the architecture boundary remains a challenge, hindering the efficient development of these resources. This study proposes a new idea for interpreting the configuration of thick sand bodies. This study was conducted in order to interpret the fine architecture of thick sand bodies in the Sangtamu area, using core samples, well logging, and production data, guided by sedimentation patterns from ancient to modern times. Results indicate that the Sangtamu area is a braided river delta front sedimentary system, dominated by the backbone underwater distributary channel and branch-type underwater distributary channels. The backbone channel is larger in scale, with a relatively large rock grain size and a box-shaped logging curve, whereas the smaller-scale branch channels have a bell-shaped logging curve resulting from the gradual weakening of water energy. Sandstone bodies from different types of underwater distributary channels are spatially overlapped, forming thick plate-like sandstones. The architecture interface between channels can be used as the fluid seepage boundary and can help prevent bottom water intrusion to a certain extent. The remaining oil is primarily concentrated in the architecture boundary area, which presents the next potential tapping area.

Structural evolution of the reactivated Møre–Trøndelag Fault Complex, Fosen Peninsula, Norway

The ENE–WSW-trending Møre–Trøndelag Fault Complex (MTFC) in Central Norway is a 10–50 km-wide, steeply dipping reactivated fault zone. Onshore, it transects Devonian sedimentary rocks and a series of east to SE transported metamorphic nappes, which were emplaced during the Scandian (Silurian–Devonian) Orogeny. Offshore, the MTFC defines the southern margin of the Møre Basin and the northern margin of the Viking Graben, meaning that the fault complex played a major role in controlling the architecture of these Mesozoic basins. Onshore, the MTFC has had a prolonged and heterogeneous kinematic history. The complex comprises two major fault strands: the Hitra–Snåsa Fault (HSF) and the Verran Fault (VF). These two faults seem to have broadly initiated as part of a single system of sinistral ductile shear zones during the early Devonian ( c. 410 Ma). Sinistral transtensional reactivation (Permo-Carboniferous; 290 Ma) of the ENE–WSW-trending HSF and VF led to the development of cataclasites and pseudotachylites together with the formation of north–south-trending faults establishing the present-day brittle fault geometry of the MTFC. Later phases of Mesozoic reactivation focused along the Verran Fault Zone (VFZ) and north–south-linking structures were probably related to mid- to late Jurassic to early Cretaceous rifting and late Cretaceous to early Cenozoic opening of the North Atlantic. Supplementary material: Supplementary datasets are available at https://doi.org/10.6084/m9.figshare.c.6390518 Thematic collection: This article is part of the Caledonian Wilson cycle collection available at: https://www.lyellcollection.org/topic/collections/the-caledonian-wilson-cycle