Individual Faults Research Articles

Rock strength and stress dependence of local flow-path connectivity within faults or fractures: a preliminary overview of virtual and in-situ hydraulic tests

Abstract Global flow path connectivity along faults or fractures depends on the degree of local flow path connectivity within each fault or fracture and is a key control of groundwater flow and solute transport. However, the mechanical controls on spatial variations in local flow path connectivity within individual faults or fractures are poorly understood. Local flow path connectivity is quantifiable by the laboratory-scale flow dimensions (n lab) within individual faults or fractures, with a lower n lab indicating lower local flow path connectivity. Virtual hydraulic tests were performed on modeled individual fractures to derive a relationship between n lab and a mappable indicator, the ductility index (DI), defined by the mean stress, groundwater pressure, and rock tensile strength. The derived relationship was verified with data obtained from in-situ hydraulic tests of natural faults in rocks with low matrix permeability, poor swelling capacity, and few fracture mineral fillings, also incorporating the effect of linkage among faults in the field. The test results demonstrated that local flow path connectivity within faults or fractures can be high (n lab > 1.5) when DI < 2 but is generally low (n lab < 1.5) when DI > 2, depending on the level of effective-normal-stress-dependent (DI-dependent) fracture-normal displacement. This relationship between n lab and DI is valid even when the value of DI is varied, or the faults are sheared. These findings can be used to help map spatial variations in local flow path connectivity within faults or fractures from limited borehole data.

The Limits of Applicability of the Gutenberg–Richter Law in the Problems of Seismic Hazard and Risk Assessment

The Gutenberg–Richter law establishes a log-linear relationship between the number of earthquakes that have occurred within some spatiotemporal volume and their magnitude. This similarity property presumably reflects fractal structure of the fault system in which earthquake sources are formed. The Gutenberg–Richter law plays a key role in the problems of seismic hazard and risk assessment. Using the Gutenberg–Richter relationship, we can estimate the average recurrence period of strong earthquakes from the recurrence rate of weaker earthquakes. Since the strongest earthquakes occur infrequently, with intervals of a few hundred years or more, it is not possible to directly assess their recurrence. From indirect geologic and paleoseismic estimates it often seems that strong earthquakes on individual faults occur more frequently than expected in accordance with the Gutenberg–Richter law. Such estimates underlie the hypothesis of the so called characteristic earthquakes. This hypothesis is in many cases additionally supported by the form of the magnitude–frequency distributions for individual faults, constructed from the data of modern earthquake catalogs. At the same time, an important factor affecting the form of the magnitude–frequency distribution is the choice of the spatial domain in which the distribution is constructed. This paper investigates the influence of this factor and determines the conditions under which the Gutenberg–Richter law is applicable for estimating the recurrence of strong earthquakes.

The certainty matrix for fault data and interpretations

This paper introduces an approach for expressing certainty in the analysis and interpretation of faults, using a modification of the risk matrix commonly used in risk assessment. The certainty matrix uses qualitative or semi-quantitative analyses of both the data used and an interpreted characteristic of individual faults or fault systems. These characteristics may include the existence of faults, the certainty of trace lengths, the age of faults, or the influence of faults on sub-surface fluid flow. This approach improves the ability to make justifiable interpretations and decisions about faults and fault-affected areas, including about issues relevant to geothermal energy exploration or the underground storage of radioactive waste. The use of this approach is illustrated using three faults from Somerset, UK.

Active Deformation Across the Western Anatolian Extensional Province (Türkiye) From Sentinel‐1 InSAR

AbstractQuantifying interseismic deformation of fault networks which are predominantly deforming in a north‐south direction is challenging, because GNSS networks are usually not dense enough to resolve deformation at the level of individual faults. The alternative, interferometric synthetic aperture radar (InSAR), provides high spatial resolution but is limited by a low sensitivity to N‐S motion. We study the active normal fault network of Western Türkiye, which is undergoing rapid N‐S extension, using InSAR. Since most faults in the study region are normal faults, we overcome the low N‐S sensitivity by focusing on the vertical deformation component, which presents its own challenges. Sediment‐filled grabens show rapid anthropogenically induced subsidence, whereas urban areas tend toward erroneous uplift signals. Additionally, the morphological relief results in topographic and atmospheric disturbances of the InSAR signal. Our solution to these challenges is a systematic analysis of the high‐resolution vertical velocity field to deduce insights into regional deformation patterns, combined with detailed investigations of deformation along individual faults in the Western Anatolian Extensional Province. We show that tectonic deformation in the large graben systems is not restricted to the main faults. Smaller and seemingly less active faults are accommodating strain, favoring a continuum model of deformation over block models. We also observe a potential correlation between recent seismicity and active interseismic surface deformation. Observed deformation rates provide an estimate of current activity for many faults in the region. We discuss the potential and limitations of InSAR time series analysis for extensional regimes.

Large-scale variability in style and magnitude of footwall rift-related unconformities, northern Carnarvon Basin, offshore NW Australia

Fault-scarp degradation complexes record rift-related erosion of normal fault scarps. At the scale of an individual fault segment, the magnitude and distribution of erosion are related to the total and along-strike variability in fault throw. However, previous studies on degradation complexes focused on one type of rift-related erosional unconformity, with the factors controlling the development, magnitude and style of footwall erosion at a far larger scale (i.e. several fault blocks) being poorly constrained. This study uses high-resolution subsurface data to constrain the timing, magnitude and style of footwall erosion across the NW Shelf of Australia. By doing so, we test conceptual and numerical model predictions for the development of rift-related unconformities. We show that development of footwall erosion complexes is dependent on the occurrence and duration of footwall exposure and that fault throw and footwall uplift control the magnitude of erosion. Furthermore, the style of footwall erosion depends on the driving process(es), with gravitational instabilities resulting in cuspate footwall crest erosional surfaces, whereas peneplain-style surfaces develop in response to wave-driven erosion dictated by the base level. The results of this study can help refine rift-related fault evolution and the effects of sea-level changes in tectonostratigraphic models of rift basins.

Preservation of Cultural Heritage Buildings in Central Plains via Novel Detection Approach

Modern nations recognize cultural heritage as an expression of culture and variety. Conserving and repurposing historic buildings has only been more popular in the last decades. Nonetheless, a considerable portion of cultural legacy is afflicted by structural concerns that endanger the safety of buildings and people. Challenges include a scarcity of resources, whether financial, human, or material. This initiative aims to employ deep learning (DL) approaches to preserve cultural heritage buildings, particularly in poor nations where these buildings are still being maintained. To overcome these issues this study proposed a novel Flower Pollination improved Resnet (FP-IResNet) to detect preservation of cultural heritage buildings. The image data were collected from China's cultural heritage buildings. The data is preprocessed using normalization. Histogram of Oriented Gradients (HOG) using extract the features for preprocessed data. The proposed method is implemented using Python software. The findings reveal that the suggested obtained greater performance in the detection of cultural heritage buildings than other traditional algorithms. The suggested concept enables the computerized preservation of cultural heritage buildings, leading to improved accuracy and reduced individual fault. Performance measures show that the model is successful in correctly categorizing and detecting heritage buildings that require preservation, with high accuracy (96.82%), precision (97.21%), recall (97.58%), and an F1 score (93.58%). The research emphasizes how computerized techniques could enhance the precision and effectiveness of CH conservation initiatives

Multi-fault diagnosis of lithium battery packs based on comprehensive analysis of locally weighted Manhattan distance and voltage ratio

The diagnosis of faults in lithium-ion battery packs is pivotal to ensuring the operational safety of electric vehicles. A fault diagnosis method is introduced to address the lack of a discharge phase and the existence of a single fault type in traditional diagnostic methods. This method relies on a comprehensive assessment involving locally weighted Manhattan distance and voltage ratio anomalies. The KD-Tree-MLLE dimensionality reduction technique is utilized for fast data processing, which improves the computational efficiency by 1.3 % compared to the original MLLE algorithm. The fault detection method utilizing locally weighted Manhattan distance can meet the same threshold evaluation criteria as the charging phase. This approach resolves the constraint of a single phase and accomplishes comprehensive fault detection. The precision and efficacy of the methodology are confirmed through F1-score evaluation. Accuracy and recall rates are compared under varying thresholds during the charging and discharging phases to determine the optimal threshold value. The ratio between the voltage data of the defective battery and the average voltage data of the normal batteries is calculated, and the comprehensive anomaly analysis method based on the voltage ratio and temperature is proposed. The use of comprehensive data such as voltage ratio and temperature to judge the type of fault can simultaneously achieve the judgment of a single fault and mixed fault types. The applicability of locally weighted Manhattan distance under dynamic circumstances and the efficacy of the voltage ratio analysis method across charging and discharging phases are confirmed through the establishment of an experimental and simulation platform dedicated to lithium-ion batteries. Findings indicate that all faults in both phases can be accurately located when the threshold is set to 0.17. This affirms the superior accuracy of the proposed method in detecting and localizing individual faults and combinations of faults quickly and reliably.

A novel multi-label classification deep learning method for hybrid fault diagnosis in complex industrial processes

Hybrid Fault Detection and Diagnosis, encompassing both individual and simultaneous faults, is an important solution needed in chemical process safety practice. We systematically examine the two perspectives of simultaneous faults in previous studies: multi-class and multi-label classification, and highlight the limitations of the former while demonstrating the efficacy of the latter. Then, a novel multi-label classification Hybrid Fault Transformer (mcHFT) model was put forward to address hybrid faults. Our model is capable of learning not only intrinsic features of individual faults but also their coupled relationships. Importantly, this work constitutes the first comprehensive evaluation of Hybrid FDD on the Tennessee Eastman (TE) process to our knowledge. The mcHFT model significantly enhances key performance indicators over existing models and introduces an adaptive strategy to reduce false positives. The dataset developed for this research is made available under an MIT license, contributing a valuable resource for future exploration in this field.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

Full‐waveform inversion as a tool to predict fault zone acoustic properties

AbstractUnderstanding the physical properties of fault zones is essential for various subsurface applications, including carbon capture and geologic storage, geothermal energy and seismic hazard assessment. Although three‐dimensional seismic reflection data can image the geometries of faults in the sub‐surface, it does not provide any direct information on the physical properties of fault zones. We currently cannot use seismic reflection data to infer directly which faults may be leaking or sealing and are reliant instead on shale‐gauge ratio type calculations, which are fraught with uncertainties. In this paper, we propose that full‐waveform inversion P‐wave velocity models can be used to extract information on fault zone acoustic properties directly, which may be a proxy for subsurface fault transmissibility. In this study, we use high‐quality post‐stack depth–migrated seismic reflection and full‐waveform inversion velocity data to investigate the characteristics of fault zones in the Samson Dome in the SW Barents Sea. We analyse the variance attribute of the post‐stack depth migrated and full‐waveform inversion volumes, revealing linear features that consistently appear in both datasets. These features correspond to locations of rapid velocity changes and seismic trace distortions, which we interpret as faults. These observations demonstrate the capability of full‐waveform inversion to recover fault zone velocity structures. Our findings also reveal the natural heterogeneity and complexity of fault zones, with varying P‐wave velocity anomalies within the studied fault network and along individual faults. Our results indicate a correlation between P‐wave velocity anomalies within fault zones and the modern‐day stress orientation. Faults with high P‐wave velocity are the ones that are perpendicular to the present‐day maximum horizontal stress orientation and are likely under compression. Faults with lower P‐wave velocity are the ones more parallel to the present‐day maximum horizontal stress orientation and are likely in extension. We propose that these P‐wave velocity anomalies may indicate differences in how ‘open’ and fluid filled the fault zones are (i.e. faults in extension are more open, more fluid filled and have lower VP) and therefore may provide a promising proxy for fault transmissibility.

Database for the Fault Displacement Hazard Initiative Project

New predictive models for fault displacement and surface rupture hazard analysis developed through the Fault Displacement Hazard Initiative (FDHI) research program require a high-quality empirical database to apply advanced statistical methods and improve hazard estimates. This article discusses the development and contents of the FDHI Project database. We systematically collected, reviewed, and organized fault displacement measurements, surface rupture maps, and supporting information from the scientific literature. A framework was developed and implemented to classify principal and distributed faulting. Best-estimate net displacement amplitudes were calculated from slip component measurements and quality codes were assigned to all net displacement values. The database contains 75 historical, surface-rupturing crustal earthquakes ranging from M 4.9 to 8.0. Thirty-five earthquakes have a strike-slip faulting mechanism, while 25 and 15 events are reverse/reverse-oblique and normal/normal-oblique, respectively. Although most of the earthquakes are from Western North America, Japan, and other active tectonic regions, there are nine reverse faulting events from the stable continental region of Australia. The database contains over 40,000 individual fault displacement measurements for various slip components from roughly 28,000 observation sites. Geographic coordinates are included for all data, and event-specific coordinate systems are provided for each earthquake that transform data into an along-strike dimension. Our new database provides a standardized collection of surface rupture and fault displacement data and metadata that is the result of a comprehensive effort to create a reliable and stable product for the FDHI model development teams and the geoscience community.

Implications of Activity Rate Characterization and Fault-Based Partitioning on Probabilistic Seismic Hazard Assessment: A Case Study of Extensional Tectonic Regime in Western Anatolia, Türkiye

ABSTRACT Complex fault geometries with multiple sets of inclined active faults pose a challenge to the accurate representation of fault-based seismic sources in probabilistic seismic hazard assessment (PSHA). In the absence of slip rates associated with fault segments and the presence of sparse seismic and geodetic data, the estimation of segment-specific activity rate includes significant uncertainty. This study proposes a comprehensive procedure for defining the segment-specific activity rates and associated uncertainties in fault-based PSHA for extensional tectonic regimes and applies the procedure in the northern margin of Western Anatolian Extensional Province. The seismic sources are modeled as rupture systems with individual fault segments using the connections between available active fault traces, first-order geological complexities, earthquake catalog, and focal mechanism solutions. Alternatives to estimate and partition segment-specific activity rates based on annual slip rate, seismicity rate, and moment rate are explored. Each alternative is implemented in PSHA, and the results are compared in terms of peak ground acceleration (PGA) maps for a 475-year return period. A comparison of the 475 yr PGA maps showed that the activity rates based on annual slip rates translate into higher hazard estimates and a more uniform distribution of PGA; whereas, the activity rates based on seismicity and moment rate result in a PGA distribution that is more sensitive to the occurrence and location of previous large-magnitude events. The approach utilized to partition activity rates among parallel segments has a noticeable effect in the areas where highly asymmetric fault activity is inferred from morphology. Hence, alternative approaches for estimation and partition of activity rates are combined to model the epistemic uncertainty in segment-specific activity rates, and a repeatable procedure is developed to build fault-based seismic source models in the presence of complex fault geometries.

An exploration of potentially reversible controls on millennial-scale variations in the slip rate of seismogenic faults: Linking structural observations with variable earthquake recurrence patterns

Paleoseismic studies show that faults within a fault system may trade off slip over time, with mechanically complementary faults displaying alternating fast- and slow periods. Each of these periods spans multiple seismic cycles, and typically involves ~20-25m of slip. This suggests that the relative strength (or tendency to slip) of individual faults varies, over time and displacement scales larger than those of individual seismic cycles. The mechanisms responsible for these strength variations must: affect rocks in the strongest portion of the fault (the brittle-ductile transition) as this likely controls the overall slip rate of the fault; be reversible (or able to be counteracted) on a cyclical basis; provide a negative feedback that operates to change the fault from its current state; and have a measurable effect on fault strength over a time or length scale that corresponds to the observed fast and slow periods of fault slip. In this paper, we systematically explore 19 potentially weakening and 11 potential strengthening mechanisms and evaluate them in light of these criteria. This analysis reveals a relatively small subset of mechanisms that could account for the observed behavior, leading us to suggest a possible model for fault strength evolution.

On the effect of background seismicity in physics-based earthquake simulations

Physics-based simulations have been extensively employed to generate synthetic earthquake catalogs over the past decade. In this kind of simulation, primary known faults are typically modeled, while smaller and intermediate faults are often neglected. As a result, the location of earthquakes in the simulation is confined to the modeled faults. Furthermore, due to the elimination of off-fault seismicity, a complete match of the frequency-magnitude distribution of the synthetic catalog with observed data is not achieved. This paper presents an approach to include off-fault seismicity (or background seismicity) within the simulation. First, background earthquakes are separated from the primary faults’ earthquakes, and a fault section is assigned to each event. By employing a scaling relationship, the dimensions of these fault sections are determined according to the magnitude of the corresponding event. The fault section’s slip rates are estimated based on the observed frequency-magnitude distribution. Combining the background fault model with the primary fault model results in a comprehensive model that accounts for all stress interactions between fault elements within the simulator in actual locations. As a result, a broader range of frequency-magnitude distribution in the synthetic catalog can be matched to the observations. A case study using one of the available simulators, Virtual Quake, on a sample area is presented. The results demonstrate that eliminating off-fault seismicity could lead to an underestimated assessment of the earthquake probabilities for the whole region and a biased estimation of earthquake rates in individual faults.

Middle Eastern carbonate reservoirs – the critical impact of discrete zones of elevated permeability on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organized DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay and earlier-than-expected water or gas breakthrough to production wells. Optimization of production and ultimate recovery rely on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterization process to permit their inclusion in static and dynamic reservoir models.

Strain Partitioning and Fault Kinematics in the Northern Qilian Shan (NE Tibet) Determined From Bayesian Inference of Geodetic Data

AbstractOblique convergence across the northern Qilian Shan is accommodated by sub‐parallel strike‐slip and thrust faults that ruptured simultaneously in the Mw 8 Gulang earthquake in 1927. We investigate the kinematics of fault loading in the northern Qilian Shan and provide insights into the conditions necessary for generating multi‐fault earthquakes. We perform Bayesian inversions for the geometry and creep rate on the fault network. We infer that all of the thrust faults are locked north of the Qilian‐Haiyuan strike‐slip fault and are accumulating elastic strain. Multi‐fault earthquakes may occurr in this fault system because the faults are simultaneously loaded by the same source of deformation and are linked together by locked fault segments. The interseismic velocity field alone can not contain the location or activity of individual faults visible in the geomorphology, therefore the short‐term geodetic measurements may not reliably indicate the long‐term behavior of the fault system.

Transformer encoder based self-supervised learning for HVAC fault detection with unlabeled data

Data driven methods are the most studied fault detection and diagnostics (FDD) type in buildings HVAC systems. However, most studies rely on labeled data for specific faults which are hard to find and collect for real systems. While the fault-free data is easier to collect, it is still time consuming to label for large systems operation. Moreover, most of the studies rely on the usage of supervised learning algorithms which do not generalize well beyond the training data making unseen faults hard to detect. In this paper, we define a methodology to use a self-supervised learning method for HVAC systems' FDD using a Transformer encoder, moreover, we tested it on a real case study. By strategically masking portions of the multivariate time-series data using Markov chain approach with two states. The model is trained by predicting these concealed segments. This approach, independent of labeled data, offers a scalable solution for practical HVAC applications. Anomalies are labeled using the Peak Over Threshold (POT) method, which dynamically determines thresholds by fitting reconstruction errors to a generalized Pareto distribution. Subsequent fault diagnostics emphasize features with pronounced reconstruction errors, pinpointing potential HVAC malfunctions. This methodology reduces dependence on labeled datasets and augments the model's generalization, facilitating detection of unobserved faults. This approach was applied to data from a real building. As a results multiple faults were detected mainly due to the malfunctioning of the monitoring system. The model demonstrates the ability to detect both sequential and individual faults. The period from October 19th to December 23rd was detected as a fault period due to the change in the trend of the data because of the monitoring system.

Compound fault diagnosis of diesel engines by combining generative adversarial networks and transfer learning

In order to solve the problem of compound fault diagnosis of diesel engine fuel injection system under the condition of few samples, a comprehensive diagnosis method based on generative adversarial networks and transfer learning based multi label classification models is proposed in this paper, which is based on convolutional attention networks. The generative adversarial networks are used to enhance the original data, and the enhanced data is used as the source data for transfer learning to ensure its effectiveness. Design multi-dimensional labels for compound fault diagnosis to enable the model to diagnose compound faults and their individual faults. Pre train the convolutional attention network with enhanced data generated from adversarial networks. Subsequently, the original data is used to integrate the feature information in the network and perform secondary training on the classification module. Finally, a compound fault diagnosis is implemented based on the model after secondary training. The effectiveness of the comprehensive diagnostic method proposed in the article is verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. The effectiveness of the comprehensive diagnostic method proposed in the article was verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. Compared to the method of directly training convolutional attention networks using raw data and the method of directly training convolutional attention networks without transfer learning using GAN enhanced datasets, the accuracy is improved by 16% and 8%, respectively.

Strain partitioning dominated growth of strike-slip fault: Insight from the Tan-Lu fault in Eastern China

The dynamic variations of the strain field and associated tectonic deformation during fault evolution can provide useful insights into the fault growth mechanisms. As one of the longest strike-slip fault system in the world, the ∼3000-km length Tan–Lu fault zone (TLFZ) in eastern China, is ideal for studying the mechanisms of fault propagation and linkage. This study investigated the Cenozoic Liaodong Bay subbasin, which hosts main structures of four faults in the central TLFZ, using structural analysis and strain calculations based on abundant seismic data. Our new results reveal three stages of fault growth (D1, D2, and D3) during the Cenozoic. Strain partitioning occurred during D1, with non-coaxial deformation within the high-strain regions leading to the growth of individual fault segments. D2 involved the redistribution of strain partitioning, with localized strain overlapping along fault tips to produce both left- and right-stepping fault sets. The locally increased simple-shear component favored the development of right-stepping fault arrays. Fault linkage appears to be achieved in two ways: 1) left-stepping, dextral faults were linked by folds dominated by pure shear along overlapping fault tips, and 2) fault segments were linked via fractures developed in simple-shear-dominated right-stepping overlap zones. D3 was characterized by the continued accumulation of simple shear, which resulted in fault linkage through fractures and formed continuous faults with high strain. Therefore, we propose a tectonic model in which strain partitioning results in linkage of segmented faults along fault tips. This model takes into account the spatial and temporal variations in fault geometry and strain type during the growth of the strike-slip fault, provides theoretical support for the structural patterns observed in natural faults worldwide.

“Overgrown Children” and Where to Find Them: Film Portrayals of Coresiding and Residentially Independent Siblings’ Developmental Maturity

Though young adult coresidence (in which individuals aged 18–35 reside within the family home) is stigmatized in mass media, research has not explored how such depictions relate to understandings of development. We used qualitative content analysis to explore how contemporary Canadian and American films depicted coresiding young adults and their similarly aged, residentially independent siblings with respect to various markers of adulthood. We found that coresiding characters were largely portrayed as developmentally immature both socially (e.g., full-time work) and characterologically (e.g., relational competence). In contrast, residentially independent siblings were overwhelmingly cast as developmentally on time. We argue that these depictions and contrasts reinforce a stigmatized coresider trope, framing the traits and actions of coresiders in terms of atypical development and attributing this living arrangement to individual faults. Implications for social attitudes and the wellbeing of emerging adults are discussed.