We develop a structural theory of chirality for inverse semigroups and show how it propagates canonically to étale groupoids and twisted groupoid $C^*$-algebras. Starting from inverse semigroup data equipped with admissible twist information, we construct a canonical twisted universal groupoid in the sense of Paterson and introduce a mirror correspondence encoding intrinsic asymmetry. Our main result identifies a structural obstruction to mirror self-duality at the level of twisted universal groupoids and shows that this obstruction descends to an obstruction for the associated reduced twisted groupoid $C^*$-algebra to be isomorphic to its opposite. The framework is representation-independent, yet compatible with concrete germ groupoid models, and provides a unified bridge between partial symmetries, groupoid structures, and analytic invariants in noncommutative operator algebras.
Real-time structural interpretation is essential for optimizing directional drilling operations, particularly in unconventional wells where gamma-ray (GR) measurements are often the only subsurface data available. Current GR-based geosteering workflows rely heavily on manual interpretation and engineering judgment, leading to inconsistent outcomes and delayed decisions. This study proposes a fully closed-loop system that integrates an automated GR-based structural interpretation engine (Auto-GR) with a directional drilling automation platform to enable real-time structural models that directly support steering decisions and demonstrate autonomous, formation-aware well placement in complex geological settings. At the core of this workflow is the Auto-GR system, which transforms one-dimensional GR data into three-dimensional structural models. Building upon the True Stratigraphic Thickness (TST) method, Auto-GR incorporates several advancements, including automated initialization and calibration using Dynamic Time Warping (DTW) to align the starting trajectory with reference well data, and dynamic selection of optimal pattern-matching window sizes to enhance model robustness. Azimuthal gamma ray (AGR) images are integrated as structural constraints, where directional patterns such as smile or frown shapes restrict possible hinging directions and reduce interpretation uncertainty. The platform further introduces lane detection and forward projection capabilities: lane detection identifies structurally consistent drilling corridors based on GR-derived stratigraphy and historical well paths, guiding the trajectory toward geologically favorable regions, while forward projection extends the current structural trend to forecast likely formations ahead of the bit, enabling proactive steering updates. Together, these features create a continuous feedback loop between real-time subsurface interpretation and directional control, supporting formation-aware autonomous drilling analogous to self-driving vehicles adapting to changing road conditions. The integrated Auto-GR and DD Advisor system was validated through multiple unconventional horizontal drilling case studies. The real-time structural models generated by Auto-GR closely matched expert interpretations, and the lane detection function consistently identified formation boundaries that aligned with known productive intervals. Forward projection allowed early recognition of structural deviations and timely trajectory adjustments, while the incorporation of AGR image data significantly reduced interpretation uncertainty. Ensemble analyses confirmed a substantial reduction in model variability when AGR constraints were applied, demonstrating that the integrated system improves the accuracy, consistency, and responsiveness of geosteering operation while reducing dependency on manual adjustments and accelerating decision-making in real time. This study introduces a real-time closed-loop geosteering framework that links AI-driven GR structural interpretation directly to directional drilling control. Unlike traditional workflows that separate interpretation from execution, this system unifies model construction and steering logic within a single automated feedback loop. The combination of automated initialization, adaptive pattern matching, AGR-informed constraints, and lane detection provides a robust and scalable solution for navigation in subsurface environments with limited data. By embedding high-frequency interpretation updates directly into the control logic, this approach moves the industry closer to fully autonomous well placement guided by real-time subsurface intelligence.
Accurate identification of isochronal surfaces is essential for interpreting stratigraphy, analyzing deformations, and advancing geological modeling. However, complex geological deformations over geological timescales challenge stratigraphic layer interpretation. Transforming deformed geological structures into flattened space simplifies their interpretation and enables the analysis of entire volumes of structures. Traditional single‐plane methods often fail to capture the fault complexities or avoid area distortion. We present a deep learning framework that restores structure from deformed to flattened states using a 3‐D lightweight neural network, which computes shifts to realign layers and conserve geometry. The predicted shifts are constrained by partial differential equations to confirm structural orientations, while dynamic time warping further enhances continuity across faults. Applied to several highly deformed field examples, our approach outperforms conventional methods, precisely aligning geological layers in complex faulting and folding regions. This framework integrates geological insights into restoration, offering fresh perspectives on 3‐D structural deformation mechanisms.
: Rock collapse is a significant geological disaster that poses a serious threat to life and property in mountainous regions worldwide. Investigating the response of protective structures to rockfall impacts can provide valuable references for the design and placement of such structures. In this study, RocPro3D and ABAQUS were employed to comprehensively analyze rockfall movement trajectories and the structural response upon impact. The results indicate that when the impact velocity of rockfall at the protective structure reaches 20–30 m/sec, the corresponding bounce height ranges from 5 to 8 m, and most rockfall accumulates at the slope toe. The interface form of the structure significantly influences various impact response indicators, including impact force, penetration depth, contact area, concrete strain, and displacement of the slab’s lower surface. Furthermore, slabs equipped with a buffer layer experience substantially less damage compared to those without one.
This paper presents a proof of generic structural stability for Riemann solutions to $2 \times 2$ system of hyperbolic conservation laws in one spatial variable, without diffusive terms. This means that for almost every left and right state, shocks and rarefaction solutions of the same type are preserved via perturbations of the flux functions, the left state, and the right state. The main assumptions for this proof involve standard assumptions on strict hyperbolicity and genuine non-linearity, a technical assumption on directionality of rarefaction curves, and the regular manifold (submersion) assumption motivated by concepts in differential topology. We show that the structural stability of the Riemann solutions is related to the transversality of the Hugoniot loci and rarefaction curves in the state space. The regular manifold assumption is required to invoke a variant of a theorem from differential topology, Thom's parametric transversality theorem, to show the genericity of transversality of these curves. This in turn implies the genericity of structural stability. We then apply this theorem to two examples: the p-system and a $2 \times 2$ system governing the evolution of gravity-driven monodisperse particle-laden thin films. In particular, we illustrate how one can verify all the above assumptions for the former, and apply the theorem to different numerical and physical aspects of the system governing the latter.
<p>Large subduction earthquakes can rupture the shallow part of the megathrust with unusually large displacements and tsunamis. The long duration of the seismic source and high upper-plate compliance contribute to large and protracted long-period motions of the outer upper plate. The resulting shear stress at the sediment–water interface in, for example, the <span class="inline-formula"><i>M</i><sub>w</sub></span> 9.0 2011 Tohoku–Oki earthquake could account for surficial sediment remobilization on the outer margin. We test this hypothesis by simulating in physical tank experiments the combined effects of high- and low-frequency seismic motions on sediment of different properties (chemistry, grain size, water content, and salinity). Our results show that low-frequency motion during a 2011-like earthquake can entrain several centimeters of surficial sediment and that entrainment can be enhanced by high-frequency vertical oscillations. These experiments validate a new mechanism of co-seismic sediment entrainment in deep-water environments.</p>
With the rapid development of hyperspectral image classification (HSIC) technology, its applications in geological exploration and environmental monitoring have become increasingly prominent. Recently, Mamba has garnered significant attention owing to its outstanding performance in long-range sequence modeling and linear computational complexity. However, Mamba still exhibits significant limitations in HSIC: first, it does not fully consider the hierarchical spatial–contextual representation and nonlinear spectral interactions in hyperspectral images; second, its sequential processing approach leads to the loss of spatial structural information and feature redundancy. In response, this study proposes a structure-enhanced spatial–spectral dynamic gating Mamba (SEDGM) that leverages the collaborative design of spatial and spectral gating Mamba mechanisms to extract and exploit key regional features of hyperspectral data. The spatial branch employs hierarchical gating Mamba (HGM) to capture multidirectional pixel sequences and extract the hierarchical spatial features and their intrinsic relationships. In contrast, the spectral branch utilizes a random shuffled gating Mamba to disrupt the fixed order of traditional spectral sequences and capture higher order spectral couplings, effectively characterizing the cooperative variation patterns of spectral features. Both branches employ a dynamic gating mechanism that weights features based on sequence centrality, dynamically activating feature sequences. Additionally, shape-specific offset-aware attention (OAA) is incorporated into each branch to enhance the structured features that were lacking in the Mamba sequences. Finally, a spectral-oriented feature review module (SOFRM) is incorporated to achieve dynamic feature fusion and optimized refinement. Experiments were conducted on four large-scale benchmark hyperspectral imaging (HSI) datasets, with SEDGM achieving significant improvements in classification performance, validating the effectiveness of this approach in HSIC tasks. The code is available at https://github.com/shuai2023-hash/SEDGM
3D scene semantic segmentation is a key technology for intelligent perception in complex underground environments. Currently, issues such as multi-scale structural heterogeneity of targets and class imbalance due to long-tailed distributions persist in underground environments. In this study, we propose a semi-supervised semantic segmentation framework for underground environments, named U4SNet. To address the problem of multi-scale target representation, this framework employs a multi-level scene-adaptive perception mechanism. By hierarchically fusing low-dimensional local detail features with high-dimensional global topological features and combining them with a dynamic weight allocation strategy, it optimizes the capability of multi-scale target representation. To tackle the class imbalance issue, a class-balanced adaptive threshold algorithm is proposed. A differentiated threshold strategy is adopted—applying conservative thresholds to suppress noise in dominant classes and using lenient thresholds to retain effective information for minority classes. The thresholds are dynamically optimized through the combination of sample priors and model feedback. Validation based on a self-built coal mine dataset (MineSeg3D) and public datasets shows that the U4SNet model significantly outperforms existing methods in terms of mean Intersection over Union and Overall Accuracy metrics. It demonstrates particularly strong advantages in segmentation tasks for complex geological structures and small-scale targets, providing an effective technical solution for intelligent perception in underground spaces.
This paper aims to enhance feature detection in geophysical seismic data by advancing Arthur E. Barnes’s 2002 shaded relief method. Our primary objective is to integrate a 3D gradient operator into the existing workflow, thereby improving the clarity of geological structures and enhancing interpretability across the entire seismic volume. Building upon Barnes’s method, which utilizes dip-azimuth attributes and directional illumination, our approach introduces several methodological improvements. We compute amplitude-derived gradients at the voxel level using a 3D gradient operator and incorporate these gradient vectors into the shaded relief algorithm. This integration enhances the visibility of subtle structural and stratigraphic features by improving illumination cues. Furthermore, we dynamically optimize "degree" and "azimuth" parameters, allowing for more adaptive illumination based on the characteristics of each slice. Finally, we merge the original seismic amplitude information with the shaded relief output, resulting in a more comprehensive and geologically interpretable volumetric representation. Our enhanced technique demonstrates superior effectiveness in highlighting both structural and stratigraphic features compared to Barnes’s original method. By integrating the 3D gradient operator, we reveal subtle variations in fault planes and reflectors, leading to improved clarity and interpretability. Applications on field datasets illustrate how dynamic parameter optimization—combined with gradient-based shading and subsequent data fusion—sharpens fault delineation and emphasizes complex geological details that were previously indistinct. This outcome underscores the value of tailoring illumination parameters to localized features, leading to a deeper understanding of subsurface morphology. Overall, the proposed framework provides a streamlined and robust means for advanced seismic volume interpretation. The equipment used in this research includes a high-performance computer with advanced graphics capabilities, seismic data processing software, and a 3D visualization system. These tools were essential for implementing the 3D gradient operator and dynamic parameter optimization. The high-performance computer handled the complex computations involved in processing seismic data, while the seismic data processing software facilitated the integration of the enhanced algorithm into the workflow. The 3D visualization system allowed for detailed analysis and interpretation of the seismic volume. Testing involved applying the method to various seismic datasets to evaluate its effectiveness in different geological settings. An unusual aspect of our approach was the dynamic optimization of illumination parameters, which required iterative testing and refinement to achieve optimal results. The equipment proved highly effective in handling the complex computations involved, demonstrating its suitability for advanced seismic interpretation tasks. This study contributes a novel gradient-based enhancement to Barnes’s shaded relief method, with dynamically optimized illumination parameters and a data-fusion approach. These innovations offer superior delineation of subsurface features, boosting the existing body of knowledge for fault detection and geological interpretation in the petroleum industry. The integration of advanced equipment and methodologies underscores the potential for continued advancements in seismic data analysis, ultimately contributing to more accurate and efficient energy exploration.
Application of lineament analysis in structural geology gained renewed interest when remote sensing data and technology became available through dedicated Earth observation satellites like Landsat in 1972. Lineament data have since been widely used in general structural investigations and resource and geohazard studies. The present contribution argues that lineament analysis remains a useful tool in structural geology research both at the regional and local scales. However, the traditional “lineament study” is only one of several methods. It is argued here that structural and lineament remote sensing studies can be separated into four distinct strategies or approaches. The general analyzing approach includes general structural analysis and identification of foliation patterns and composite structural units (mega-units). The general approach is routinely used by most geologists in preparation for field work, and it is argued that at least parts of this should be performed manually by staff who will participate in the field activity. We argue that this approach should be a cyclic process so that the lineament database is continuously revised by the integration of data acquired by field data and supplementary data sets, like geophysical geochronological data. To ensure that general geological (field) knowledge is not neglected, it is our experience that at least a part of this type of analysis should be performed manually. The statistical approach conforms with what most geologists would regard as “lineament analysis” and is based on statistical scrutiny of the available lineament data with the aim of identifying zones of an enhanced (or subdued) lineament density. It would commonly predict the general geometric characteristics and classification of individual lineaments or groups of lineaments. Due to efficiency, capacity, consistency of interpretation methods, interpretation and statistical handling, this interpretative approach may most conveniently be performed through the use of automatized methods, namely by applying algorithms for pattern recognition and machine learning. The focused and dynamic approaches focus on specified lineaments or faults and commonly include a full structural geological analysis and data acquired from field work. It is emphasized that geophysical (potential field) data should be utilized in lineament analysis wherever available in all approaches. Furthermore, great care should be taken in the construction of the database, which should be tailored for this kind of study. The database should have a 3D or even 4D capacity and be object-oriented and designed to absorb different (and even unforeseen) data types on all scales. It should also be designed to interface with shifting modeling tools and other databases. Studies of the Norwegian mainland have utilized most of these strategies in lineament studies on different scales. It is concluded that lineament studies have revealed fracture and fault systems and the geometric relations between them, which would have remained unknown without application of remote sensing data and lineament analysis.
Floating photovoltaics (PVs) are progressively constructed in the ocean sea; therefore, the effect that freak waves have on their structural design needs to be considered. This paper developed a dedicated numerical model coupling the floating PV platform and mooring line structures to investigate their dynamic responses under freak waves. A feasible superposition approach is presented to generate freak wave sequences via the combination of transient waves and random waves. A large floating PV platform moored by twenty lines for a water depth of 45 m was designed in detail according to the actually measured ocean environmental and geological conditions. The global time domain analyses of the floating PV mooring structures were implemented to obtain dynamic responses, including PV platform motions and the mooring line configuration and tension under freak waves. A comparison of the response results with those caused by random waves was conducted to illustrate the intuitive evidence of the freak wave effects, which offer a significant reference for the preliminary design of the floating PV platform and mooring line structures.
Malan loess possesses unfavourable engineering mechanical properties that may vary depending on the geological context in which it exists. In the context of roadbed loading, the structural characteristics of the loess roadbed often result in uneven settlement, which significantly impacts transportation safety. To investigate the dynamic behaviour of loess under the influence of vehicle loading, groups of dynamic rebound modulus tests were conducted using a dynamic triaxial apparatus. Three key aspects are highlighted: compaction degree, moisture content and stress state. The results reveal that the dynamic rebound modulus of loess tends to increase with higher compaction degrees, decrease with increased moisture content and rise under greater confining pressure. For Maran loess, the water content has the greatest influence on its physical and mechanical properties. Under conditions of a confining pressure of 60 kPa and a deviatoric stress of 30 kPa, as the moisture content increased from w = 9% to w = 18%, the minimum dynamic rebound modulus decreased by 63%.
Linus C. Erhard, Christoph Otzen, Jochen Rohrer
et al.
Characteristic shock effects in quartz serve as a key indicator of historic impacts at geologic sites. Despite this geologic significance, atomistic details of structural transformations of quartz under high pressure and shock compression remain poorly understood. This ambiguity is evidenced by conflicting experimental observations of both amorphization and transitions to crystalline polymorphs. Utilizing a newly developed machine-learning interatomic potential, we examine the response of α-quartz to shock compression with a peak pressure of 56 GPa over nanosecond timescales. We observe initial amorphization of quartz before crystallization into a d-NiAs-structured silica phase with disorder on the silicon sublattice, accompanied by the formation of domains with partial order of silicon. Investigating a variety of strain conditions of quartz enables us to identify non-hydrostatic stress and strain states that allow for direct diffusionless transformation to rosiaite-structured silica.
The high-speed train (HST), as a special kind of seismic source, can be simplified as a mobile combination source. The HST-induced seismic data (also known as HST seismic data) contains rich geological structural information. However, HST seismic data interfere highly in space and time and their spectra have narrow-band separation spectral characteristics in frequency domain. It is very difficult to effectively extract the geological structural information contained in HST seismic data. In this work, we try to use the elastic wave equation traveltime inversion (EWTI) method to explore the subsurface velocity information using the seismic data induced by HSTs running on viaducts, which is conducive to providing a more reliable initial model to serve full waveform inversion (FWI). However, as mentioned previously, HST seismic data are highly complex, which makes the EWTI method based on first-break picking no longer applicable to them. Therefore, we apply dynamic time warping (DTW) to obtain the time shifts between observed HST-induced seismic data and synthetic HST-induced seismic data. Based on the time shifts obtained by DTW, the subsurface velocity model can be updated by establishing the misfit function and calculating the gradient. Numerical experiments using the simple velocity anomaly model and the Marmousi2 model show that the DTW-based EWTI method is able to obtain the large-scale background model and provide a reliable initial model to serve FWI. The results in this work also demonstrate the considerable potential of underground exploration using HST-induced seismic data.