Hasil untuk "Dynamic and structural geology"

M. De Lucia, S. Frigo, S. Frigo et al.

<p>Effective geothermal resource development requires sophisticated computational tools integrating different physical and chemical processes. This work describes a novel coupling of GOLEM, an open source simulator for thermal-hydraulic-mechanical modeling based on the MOOSE multiphysics framework, with the PHREEQC geochemical solver. GOLEM solves coupled partial differential equations governing subsurface fluid flow, heat transfer, conservative mass transport, and mechanical deformation in complex geological environments. PHREEQC is a proven and widely adopted geochemical simulator in the scientific community. The coupling to GOLEM is achieved leveraging an efficient and original wrapper based on the IPhreeqc module. The newly developed coupling of flow, transport and geochemical reactions is validated against standalone PHREEQC by means of 1D RTM benchmarks including both equilibrium and kinetic mineral reactions. A further demonstration of the capabilities of the GOLEM-PHREEQC coupling is shown, on a 2D domain with three distinct geochemical zones.</p>

Truong Xuan Khanh, Truong Quynh Hoa

Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many systems converge toward repetitive solution patterns rather than sustained growth. We argue that a central limitation of contemporary AI systems lies not in capability per se, but in the premature fixation of their performance frontier. To address this issue, we introduce the concept of a \emph{Dynamic Intelligence Ceiling} (DIC), defined as the highest level of effective intelligence attainable by a system at a given time under its current resources, internal intent, and structural configuration. To make this notion empirically tractable, we propose a trajectory-centric evaluation framework that measures intelligence as a moving frontier rather than a static snapshot. We operationalize DIC using two estimators: the \emph{Progressive Difficulty Ceiling} (PDC), which captures the maximal reliably solvable difficulty under constrained resources, and the \emph{Ceiling Drift Rate} (CDR), which quantifies the temporal evolution of this frontier. These estimators are instantiated through a procedurally generated benchmark that jointly evaluates long-horizon planning and structural creativity within a single controlled environment. Our results reveal a qualitative distinction between systems that deepen exploitation within a fixed solution manifold and those that sustain frontier expansion over time. Importantly, our framework does not posit unbounded intelligence, but reframes limits as dynamic and trajectory-dependent rather than static and prematurely fixed. \vspace{0.5em} \noindent\textbf{Keywords:} AI evaluation, planning and creativity, developmental intelligence, dynamic intelligence ceilings, complex adaptive systems

Ryotaro Sahashi, Po-Yen Chen, Teruyasu Mizoguchi

ScxAl1-xN has emerged as a promising wurtzite-type ferroelectric material, where increasing the Sc composition reduces both the coercive field (Ec) and remanent polarization (Pr). This composition-dependent behavior is physically attributed to two simultaneous changes: the increase in the internal structural parameter u (structural effect) and the weakening of bond strength (bonding effect). Because these factors are strongly coupled in experiments, their individual contributions to ferroelectric switching remain unclear. In this study, we systematically decoupled these effects using machine-learning force field-based molecular dynamics (MD) simulations under an applied electric field. By artificially tuning u via in-plane strain at a fixed composition, we demonstrated that Pr is determined exclusively by the structural effect, exhibiting a universal linear dependence regardless of the composition. In contrast, Ec deviated from this structural trend, implying an additional compositional contribution. To isolate this, we evaluated configurations with identical u but varying Sc compositions; Pr remained constant, whereas Ec systematically decreased due to bond weakening. Furthermore, static nudged elastic band (NEB) calculations revealed that the static switching barrier depends solely on u, failing to explicitly capture the bonding effect on Ec. These results establish that while Pr is governed strictly by the structural effect, Ec is determined by a superposition of structural and bonding effects. Our findings highlight the necessity of dynamic MD simulations for fully understanding ferroelectric switching in compositionally tunable materials.

B. Decharme, J. Colin

<p>The climate impacts of floodwater stored over large inundated areas and groundwater stored in large unconfined aquifers at the global scale are not yet well documented, despite their potential to affect the atmosphere through contributions to land surface evapotranspiration fluxes. To address these gaps in knowledge, the present study aims to assess the potential role of these processes in present-day climate using the CNRM-CM6-1 global climate model, the physical core of the Earth system model (ESM) used by the French National Center for Meteorological Research (CNRM) for climate projections. This model includes a dynamic river flooding scheme and a groundwater scheme, accounting for the world's 218 largest unconfined aquifer basins. The study consists of four experiments, each with five ensemble members driven by observed monthly sea surface temperature and sea ice cover for the 1980–2014 period. The experiments include configuration variations where both groundwater and floodplain processes were activated or deactivated and configurations where each process was individually activated. The various forcings used in CNRM-CM6-1 adhere to the CMIP6 recommendations. The false detection rate (FDR) test is employed to assess the significance of field differences. The simulated hydrological cycle is improved by representing floodplains and groundwater, thanks to an increased hydrological memory which allows us to better capture the seasonal cycle of the terrestrial water storage and river discharge. Additionally, the inclusion of groundwater and floodplains reduces precipitation and 2 m air temperature biases at the regional scale. Overall, the study highlights the importance of incorporating groundwater and floodplain processes into ESMs to improve the understanding of land surface–atmosphere interactions and the accuracy of climate simulations.</p>

J.-Y. Moon, J.-Y. Moon, J. Streffing et al.

<p>Earth's climate response to increasing greenhouse gas emissions occurs on a variety of spatial scales. To assess climate risks on regional scales and implement adaptation measures, policymakers and stakeholders often require climate change information on scales that are considerably smaller than the typical resolution of global climate models (<span class="inline-formula"><i>O</i></span>(100 km)). To close this important knowledge gap and consider the impact of small-scale processes on the global scale, we adopted a novel iterative global earth system modeling protocol. This protocol provides key information on earth's future climate and its variability on storm-resolving scales (less than<span id="page1104"/> 10 km). To this end we used the coupled earth system model OpenIFS–FESOM2 (AWI-CM3; Open Integrated Forecasting System – Finite volumE Sea ice–Ocean Model) with a 9 km atmospheric resolution (TCo1279) and a 4–25 km ocean resolution. We conducted a 20-year 1950 control simulation and four 10-year-long coupled transient simulations for the 2000s, 2030s, 2060s, and 2090s. These simulations were initialized from the trajectory of a coarser 31 km (TCo319) SSP5-8.5 transient greenhouse warming simulation of the coupled model with the same high-resolution ocean. Similar to the coarser-resolution TCo319 transient simulation, the high-resolution TCo1279 simulation with the SSP5-8.5 scenario exhibits a strong warming response relative to present-day conditions, reaching up to 6.5 °C by the end of the century at CO<span class="inline-formula">₂</span> levels of about 1100 ppm. The TCo1279 high-resolution simulations show a substantial increase in regional information and climate change granularity relative to the TCo319 experiment (or any other lower-resolution model), especially over topographically complex terrain. Examples of enhanced regional information include projected changes in temperature, rainfall, winds, extreme events, tropical cyclones, and the hydroclimate teleconnection patterns of the El Niño–Southern Oscillation and the North Atlantic Oscillation on scales of less than 1000 km. The novel iterative modeling protocol that facilitates coupled storm-resolving global climate simulations for future climate time slices offers major benefits over regional climate models. However, it also has some drawbacks, such as initialization shocks and resolution-dependent biases and climate sensitivities, which are further discussed.</p>

A. D. King, A. D. King, N. J. Abram et al.

<p>Climate changes under net zero emissions will take many centuries to play out, particularly in the Southern Hemisphere and in the ocean and cryosphere. New millennial-length Earth System Model simulations are required to better understand committed changes and their dependence on delays in reaching net zero emissions, especially with respect to local and regional extremes.</p>

Safonov, Dmitry A.

Due to methodological changes in the work of the Sakhalin Branch of the Federal Research Center “Geophysical Survey of the Russian Academy of Sciences” (SB FRC GS RAS), it became necessary to clarify the transition relationships between the energy characteristics of earthquakes in the Sakhalin region used for the magnitude unification of the catalog. To obtain the transition relationships, a sample for the period from 2017 to October 2024 was used from the database of the “Yuzhno-Sakhalinsk” regional information processing center, which is a part of the SB FRC GS RAS. Using the generalized orthogonal regression method, the relationships linking the magnitude of crustal (h less then 40 km ) earthquakes ML and the energy classes KР and KC were calculated, as well as the magnitudes ML and MPVA separately for crustal and deep-focus (h = 250–600 km) earthquakes in the region. The relationship between ML and the magnitude of the Japan Meteorological Agency Mj was also obtained. It was revealed that for shallow Sakhalin earthquakes Mj ≈ ML; for deep-focus earthquakes, an underestimation of the magnitude ML relative to Mj was noted. As the observational data accumulates, it is assumed that the obtained relationships will be refined.

Jackson Carrion, Joseph H. Davis

Cryo-electron tomography (cryo-ET) has emerged as a powerful tool for studying the structural heterogeneity of proteins and their complexes, offering insights into macromolecular dynamics directly within cells. Driven by recent computational advances, including powerful machine learning frameworks, researchers can now resolve both discrete structural states and continuous conformational changes from 3D subtomograms and stacks of 2D particle-images acquired across tilt-series. In this review, we survey recent innovations in particle classification and heterogeneous 3D reconstruction methods, focusing specifically on the relative merits of workflows that operate on reconstructed 3D subtomogram volumes compared to those using extracted 2D particle-images. We additionally highlight how these methods have provided specific biological insights into the organization, dynamics, and structural variability of cellular components. Finally, we advocate for the development of benchmarking datasets collected in vitro and in situ to enable a more objective comparison of existent and emerging methods for particle classification and heterogeneous 3D reconstruction.

Sophia Lin, Shenen Chen, Ryan A. Rasanen et al.

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions.

O. Branch, L. Jach, T. Schwitalla et al.

<p>Potential for regional climate engineering is gaining interest as a means of solving regional environmental problems like water scarcity and high temperatures. In the hyper-arid United Arab Emirates (UAE), water scarcity is reaching a crisis point due to high consumption and over-extraction and is being exacerbated by climate change. To counteract this problem, the UAE has conducted cloud-seeding operations and intensive desalination for many years but is now considering other means of increasing water resources. Very large “artificial black surfaces” (ABSs), made of black mesh, black-painted, or solar photovoltaic (PV) panels have been proposed as a means of enhancing convective precipitation via surface heating and amplification of vertical motion. Under the influence of the daily UAE sea breeze, this can lead to convection initiation under the right conditions. Currently it is not known how strong this rainfall enhancement would be or what scale of black surface would need to be employed. This study simulates the impacts at different ABS scales using the WRF-Noah-MP model chain and investigates impacts on precipitation quantities and underlying convective processes. Simulations of five square ABSs of 10, 20, 30, 40, and 50 km sizes were made on four 1 d cases, each for a period of 24 h. These were compared with a Control model run, with no land use change, to quantify impacts. The ABSs themselves were simulated by altering land cover static data and prescribing a unique set of land surface parameters like albedo and roughness length.</p> <p>On all 4 d, rainfall is enhanced by low-albedo surfaces of 20 km or larger, primarily through a reduction of convection inhibition and production of convergence lines and buoyant updrafts. The 10 km square ABS had very little impact. From 20 km upwards there is a strong scale dependency, with ABS size influencing the strength of convective processes and volume of rainfall. In terms of rainfall increases, 20 km produces a mean rainfall increase over the Control simulation of 571 616 m<span class="inline-formula">³</span> d<span class="inline-formula">⁻¹</span>, with the other sizes as follows: 30 km (<span class="inline-formula">∼</span> 1 million m<span class="inline-formula">³</span> d<span class="inline-formula">⁻¹</span>), 40 km (<span class="inline-formula">∼</span> 1.5 million m<span class="inline-formula">³</span> d<span class="inline-formula">⁻¹</span>), and 50 km (<span class="inline-formula">∼</span> 2.3 million m<span class="inline-formula">³</span> d<span class="inline-formula">⁻¹</span>). If we assume that such rainfall events happen only on 10 d in a year, this would equate to respective annual water supplies for <span class="inline-formula"><i>&gt;</i></span> 31 000, <span class="inline-formula"><i>&gt;</i></span> 50 000, <span class="inline-formula"><i>&gt;</i></span> 79 000, and <span class="inline-formula"><i>&gt;</i></span> 125 000 extra people yr<span class="inline-formula">⁻¹</span> at UAE per capita consumption rates. Thus, artificial heat islands made from black panels or solar PV offer a means of enhancing rainfall in arid regions like the UAE and should be made a high priority for further research.</p>

Oluwaseun Fadugba, Valerie Sahakian, Diego Melgar et al.

Accurately modeling time-dependent coseismic crustal deformation as observed on high-rate Global Navigation Satellite System (HR-GNSS) lends insight into earthquake source processes and improves local earthquake and tsunami early warning algorithms. Currently, time-dependent crustal deformation modeling relies most frequently on simplified 1D radially symmetric Earth models. However, for shallow subduction zone earthquakes, even low-frequency shaking is likely affected by the many strongly heterogeneous structures such as the subducting slab, mantle wedge, and the overlying crustal structure. We demonstrate that including 3D structure improves the estimation of key features of coseismic HR-GNSS time series, such as the peak ground displacement (PGD), the time to PGD (tPGD), static displacements (SD), and waveform cross-correlation values. We computed synthetic 1D and 3D, 0.25 Hz and 0.5 Hz waveforms at HR-GNSS stations for four M7.3+ earthquakes in Japan using MudPy and SW4, respectively. From these synthetics, we computed intensity-measure residuals between the synthetic and observed GNSS waveforms. Comparing 1D and 3D residuals, we observed that the 3D simulations show better fits to the PGD and SD in the observed waveforms than the 1D simulations for both 0.25 Hz and 0.5 Hz simulations. We find that the reduction in PGD residuals in the 3D simulations is a combined effect of both shallow and deep 3D structures; hence incorporating only the upper 30 km of 3D structure will still improve the fit to the observed PGD values. Our results demonstrate that 3D simulations significantly improve models of GNSS waveform characteristics and will not only help understand the underlying processes, but also improve local tsunami warning.

Haonan Yuan, Qingyun Sun, Zhaonan Wang et al.

Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers a promising way to optimize graph structures. However, aside from encountering unacceptable quadratic complexity, it overly relies on heuristic priors, making it hard to discover underlying predictive patterns. How to efficiently refine the dynamic structures, capture intrinsic dependencies, and learn robust representations, remains under-explored. In this work, we propose the novel DG-Mamba, a robust and efficient Dynamic Graph structure learning framework with the Selective State Space Models (Mamba). To accelerate the spatio-temporal structure learning, we propose a kernelized dynamic message-passing operator that reduces the quadratic time complexity to linear. To capture global intrinsic dynamics, we establish the dynamic graph as a self-contained system with State Space Model. By discretizing the system states with the cross-snapshot graph adjacency, we enable the long-distance dependencies capturing with the selective snapshot scan. To endow learned dynamic structures more expressive with informativeness, we propose the self-supervised Principle of Relevant Information for DGSL to regularize the most relevant yet least redundant information, enhancing global robustness. Extensive experiments demonstrate the superiority of the robustness and efficiency of our DG-Mamba compared with the state-of-the-art baselines against adversarial attacks.

P. Asaridis, D. Molinari

<p>Flood damage assessment is a critical aspect in any decision-making process on flood risk management. For this reason, reliable tools for flood damage estimation are required for all the categories of exposed elements. Despite infrastructures can suffer high economic losses in case of flood, compared to other exposed sectors, their flood damage modelling is still a challenging task. This is due, on the one hand, to the structural and dynamic complexity of infrastructure networks, and, on the other hand, to the lack of knowledge and data to investigate damage mechanisms and to calibrate and validate damage models. Grounding on the investigation of the state-of-the-art, this paper presents a conceptualization of flood damage to power grids and reviews the methodologies in the field for an in-depth understanding of the existing modelling approaches, challenges, and limitations. The conceptual model highlights: (i) the different kinds of damage (i.e., direct, indirect, and systemic) the network can suffer, (ii) the hazard, exposure, and vulnerability parameters on which they depend, (iii) the spatial and temporal scales required for their assessment, (iv) the interconnections among power grids and economic activities, and (v) the different recipients of economic losses. The development of the model stresses the importance of dividing the damage assessment into two steps: the estimation of damage in physical units and the consequent economic losses in monetary terms. The variety of damage mechanisms and cascading effects shaping the final damage figure arises, asking for an interdisciplinary and multi-scale evaluation approach. The ultimate objective of the conceptual model is to be an operative tool in support of more comprehensive and reliable flood damage assessments to power grids.</p>

Li Yang, Abdallah Shami

With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges, specifically, effective model selection, design/tuning, and updating, which have brought massive demand for experienced data scientists. Additionally, the dynamic nature of IoT data may introduce concept drift issues, causing model performance degradation. To reduce human efforts, Automated Machine Learning (AutoML) has become a popular field that aims to automatically select, construct, tune, and update machine learning models to achieve the best performance on specified tasks. In this paper, we conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML in order to identify and summarize the optimal solutions for every step of applying ML algorithms to IoT data analytics. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted in this work. Lastly, we discuss and classify the challenges and research directions for this domain.

T. Bösinger

<p>After a preface, we will first try to depict the history of the Geophysical Observatory in Sodankylä (SGO) by referring to the personalities who have run and have shaped the observatory. Thereafter, we describe the history from a technical point of view, i.e., what the measurements were, and which instruments were primarily used at the observatory. We will also refer to present operational forms and techniques.</p> <p>We start with the very first systematic meteorological and geophysical observations made in Finland and end by referring to the involvement in ongoing international scientific programs.</p>

M Shajahan G Razul, Gurpreet S Matharoo, Balakrishnan Viswanathan

We use the isoconfigurational (IC) ensemble to show the connection between emerging heterogeneities in the tetrahedral order parameter and the dynamic propensity in a mildly undercooled glass-forming liquid. We observe that spatially correlated tetrahedrally-(dis)ordered clusters of molecules are observable on the time scale of structural relaxation. The heterogeneities of tetrahedrally-(dis)ordered clusters correlate with dynamical heterogeneities (DH) and these correlations reach peaks at similar time scales. We discover that the angular component of the tetrahedral order parameter is strongly correlated to the dynamics compared to the radial component. Moreover, these correlations between the dynamics and tetrahedrally-(dis)ordered regions enormously influence the system, with spatial correlations being observable for a prolonged period beyond the peaks of maximum DH. Further, we discover that the crystalline particle environments in our water model (as identified by the IC ensemble) may be the origin for slow dynamics of dynamical heterogeneity in our undercooled model water system.

Armin Feist, Gero Storeck, Sascha Schäfer et al.

Ultrafast measurement technology provides essential contributions to our microscopic understanding of the properties and functions of solids and nanostructures. Atomic-scale vistas with ever-growing spatial and temporal resolution are offered by methods based on short pulses of x-rays and electrons. Time-resolved electron diffraction and microscopy are among the most powerful approaches to investigate non-equilibrium structural dynamics in excited matter. In this article, we discuss recent advances in ultrafast electron imaging enabled by significant improvements in the coherence of pulsed electron beams. Specifically, we review the development and first application of Ultrafast Low-Energy Electron Diffraction (ULEED) for the study of structural dynamics at surfaces, and discuss novel opportunities of Ultrafast Transmission Electron Microscopy (UTEM) facilitated by laser-triggered field emission sources. These and further developments will render coherent electron beams an essential component in the future of ultrafast nanoscale imaging.

De-yun Zhong, Liguan Wang, Mingtao Jia et al.

In this paper, we present an improved approach to the surface reconstruction of orebody from sets of interpreted cross sections that allows for shape control with geometry constraints. The soft and hard constraint rules based on adaptive sampling are proposed. As only the internal and external position relations of sections are calculated, it is unnecessary to estimate the normal directions of sections. Our key contribution is proposing an iterative closest point correction algorithm. It can be used for iterative correction of the distance field based on the constraint rules and the internal and external position relations of the model. We develop a rich variety of geometry constraints to dynamically control the shape trend of orebody for structural geologists. As both of the processes of interpolation and iso-surface extraction are improved, the performance of this method is excellent. Combined with the interactive tools of constraint rules, our approach is shown to be effective on non-trivial sparse sections. We show the reconstruction results with real geological datasets and compare the method with the existing reconstruction methods.

M. Correia, J. C. H. Filho, D. Schiozer

Abstract The significant world oil and gas reserves related to naturally fractured carbonate reservoirs adds new frontiers to the development of upscaling and numerical simulation procedures for reducing simulation time. This work aims to accurately represent fractured reservoirs in reservoir simulators within a shorter simulation time when compared to dual porosity models, based on special connections between matrix and fracture mediums, both modeled in different grid domains of a single porosity flow model. For the definition of special connection fracture model (SCFM), four stages are necessary: (a) construction of a single porosity model with two symmetric structural grids, (b) geomodelling of fracture and matrix properties for the corresponding grid domain, (c) application of special connections through the conventional reservoir simulator to represent the fluid transfer between matrix and fracture medium, (d) calculation of the fracture-matrix fluid-transfer. For a proper validation, we apply our methodology in a fractured reservoir type II (tight matrix with flow controlled by fractures) and consider a probabilistic framework regarding geological and dynamic uncertainties. The probabilistic approach of SCFM under several static uncertainties revealed a good dynamic matching with DP. Under three rock-wettability scenarios (water-wet, oil-wet and intermediate-wet) the dynamic matching with DP is preserved. Furthermore, SCFM did not present convergence issues, considering all probabilistic realizations. The results revealed that the new method can be applied to commercial flow simulators in fractured reservoirs and it presents itself as a solution to reduce simulation time without disregarding the upscaling and dynamic representation of dual porosity flow models.

Peixing Zhang, Zhen Huang, Shuai Liu et al.

Along with the need for western economic development, the number of long tunnel projects which go through mountains is constantly on the rise. In the process of construction, various disaster-causing structures are frequently exposed, which leads to many geological disasters. The traditional idea is that the reverse fault is not easily developed for an underground river, which means that the tunnel elevation design is not considered adequately. When some tunnels cross the bottom of the river, the fractures near the fault between the underground river and the excavation space may be activated and then evolve into channels, causing serious water inrush accidents during construction and operation processes. Taking the Qiyueshan Tunnel site as an example, on the premise of the anatomy of the control mechanism of the reverse fault on the development of the underground river, based on the multiperiod typical structural traces of the tunnel and surface outcrop, it was found that stratifications, dip joints, transverse joints, and tension joints of good aperture grade are important control factors. The cut block easily loses its stability and provides space for karst development, while intermittent uplifting of regional structures provides hydrodynamic conditions for the development of the underground river, causing the hydraulic gradient to be inconsistent in the overall underground river. Finally, the rainwater dynamic monitoring and tracer connectivity are data that can be fully utilized to demonstrate that a reverse fracture can control the development of the underground river. The authors further considered the effect of the vertical zoning of the fault structure and the excavation disturbance, and, drawing on the experience of the relative location of the same site in the same field, put forward the suggestion that the construction of the follow-up tunnel in the study area should be slightly higher than the elevation of the underground river. The research results can provide useful reference for similar engineering problems in the future.