Hasil untuk "Geology"

Robert Blair Vocci

I. Gibson

K. C. Short, A. J. Stauble

R. Stallard, J. Edmond

Rushdi Said

Stretch-Pebble Metaconglomerate

A. Gansser

V. Guinn

R. L. Bates, J. Jackson

N. Obaje

J. Syvitski, J. Milliman

R. Cruickshank, K. Ko

D. Stead, A. Wolter

Yunping LIAO, Lixin WU, Guoji ZENG et al.

To address the challenge of achieving low cost, simplicity, and reliability in geological disaster monitoring and early warning, this study proposes a micro-vibration monitoring and pre-warning technology based on micro-electro-mechanical system (MEMS) sensors. Focusing on the common “dynamic” precursors of geological disasters, such as displacement, tilt, and vibration, and adhering to the comprehensive sensing principle of “hazard source–transmission path–affected body”, a pre-warning algorithm based on multi-point anomaly intensity factors was developed. Additionally, a micro-vibration monitoring and warning device with edge computing capabilities was designed. The effectiveness of the technology was systematically validated through in-situ tests on unstable rock masses and large-scale physical model experiments of landslides. Experimental results demonstrate that the monitoring device can effectively detect weak precursor signals prior to rockfall and landslide instability. Leveraging its edge computing capability, the warning device achieves pre-warning judgments within seconds, significantly enhancing the timeliness and accuracy of warnings. The micro-vibration monitoring and warning device is suitable for monitoring and pre-warning slopes and embankments in mountainous areas, particularly around residential areas. When integrated with traditional monitoring and warning technologies, it enables refined risk prevention and control of geological disasters.

S. Shirey, P. Cartigny, D. Frost et al.

Qiying Yu, Wenzhong Li, Yungang Bai et al.

Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° to 81.75° E, 34.75° to 36.25° N. Study Focus: To tackle the complexity of runoff prediction in high-altitude cold regions, alongside the limitations of existing machine learning approaches, where nonlinear relationships, long-term dependencies, and sparse observational data pose significant challenges, previous models have consistently struggled to account for these issues. In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies. In addition, the Bootstrap method is employed to merge point prediction outcomes for interval runoff forecasting, providing robust uncertainty estimates to address data limitations in these regions. New Hydrological Insights for the Region: The DBO-TCN-Transformer model consistently attains a Nash-Sutcliffe Efficiency (NSE) above 0.81, showcasing enhanced performance over traditional models. Across various forecast periods, the model’s NSE values are 6.9–26.9 % higher than those of the TCN and Transformer models, offering more reliable short-term and long-term predictions. Furthermore, the Bootstrap algorithm’s probabilistic approach provides valuable insights into forecast uncertainty, a crucial feature for managing water resources and mitigating flood risks in high-altitude cold regions with complex hydrological dynamics.

Guangyou Zhu, Xi Li, Bin Zhao et al.

Abstract The 10 000‐m ultradeep dolomite reservoir holds significant potential as a successor field for future oil and gas exploration in China's marine craton basin. However, major challenges such as the genesis of dolomite, the formation time of high‐quality reservoirs, and the preservation mechanism of reservoirs have always limited exploration decision‐making. This research systematically elaborates on the genesis and reservoir‐forming mechanisms of Sinian–Cambrian dolomite, discussing the ancient marine environment where microorganisms and dolomite develop, which controls the formation of large‐scale Precambrian–Cambrian dolomite. The periodic changes in Mg isotopes and sedimentary cycles show that the thick‐layered dolomite is the result of different dolomitization processes superimposed on a spatiotemporal scale. Lattice defects and dolomite embryos can promote dolomitization. By simulating the dissolution of typical calcite and dolomite crystal faces in different solution systems and calculating their molecular weights, the essence of heterogeneous dissolution and pore formation on typical calcite and dolomite crystal faces was revealed, and the mechanism of dolomitization was also demonstrated. The properties of calcite and dolomite (104)/(110) grain boundaries and their dissolution mechanism in carbonate solution were revealed, showing the limiting factors of the dolomitization process and the preservation mechanism of deep buried dolomite reservoirs. The in situ laser U‐Pb isotope dating technique has demonstrated the timing of dolomitization and pore formation in ancient carbonate rocks. This research also proposed that dolomitization occurred during the quasi‐contemporaneous or shallow‐burial periods within 50 Ma after deposition and pores formed during the quasi‐contemporaneous to the early diagenetic periods. And it was clear that the quasi‐contemporaneous dolomitization was the key period for reservoir formation. The systematic characterization of the spatial distribution of the deepest dolomite reservoirs in multiple sets of the Sinian and the Cambrian in the Chinese craton basins provides an important basis for the distribution prediction of large‐scale dolomite reservoirs. It clarifies the targets for oil and gas exploration at depths over 10 000 m. The research on dolomite in this study will greatly promote China's ultradeep oil and gas exploration and lead the Chinese petroleum industry into a new era of 10 000‐m deep oil exploration.

YU Kecheng, SUN Shengsi, DONG Yunpeng et al.

Objective  A typical granulite–migmatite–gneiss dome developed in the Foping area of the central Qinling orogenic belt. This area is key to studying the metamorphic deformation of continental crust and the Mesozoic tectonic evolution of Qinling. The Yangtianba–Shimudi ductile shear zone along the dome's southern margin records information on middle–deep structural deformation during the late Triassic compressional–extensional transition, offering crucial constraints on the exhumation mechanism of the Foping dome.   Methods  A detailed investigation of representative metamorphic and deformed rock samples from the shear zone was conducted using structural analysis, mineral geochemistry, crystallographic preferred orientation (CPO), and geochronology. Field observations and kinematic vorticity analysis show that this shear zone developed under right-lateral ductile shear deformation controlled by pure shear.   Results  In the felsic mylonite, quartz primarily shows prism <a> and prism <c> slip systems, suggesting deformation occurred under amphibolite facies conditions at approximately 550–650 °C. The characteristics of the metamorphic mineral assemblages and the results of garnet–biotite–plagioclase thermobarometry indicate a clockwise P–T path, with peak metamorphic conditions of 568–611 °C/5.2–5.3 kbar and 630–654 °C/7.1–7.9 kbar. The isothermal decompression stage M2 recorded conditions of 590–616 °C/3.5–4.5 kbar. Zircon U–Pb dating of the leucosomes in the migmatites within the shear zone yielded an age of 180.8 ± 3.8 Ma, representing the lower limit of the ductile shear deformation.   Conclusion  Integrated with regional geological data, the metamorphic and deformational evolution of the study area can be reconstructed as follows: Prior to ~210 Ma, the central segment of the South Qinling tectonic belt was dominated by collisional orogenesis, leading to crustal thickening and the development of progressive metamorphism (M1) in the Foping area. During 210–200 Ma, the Foping region transitioned into post-collisional extension. This transitional phase was characterized by a bidirectional stress regime combining horizontal shortening and vertical collapse, which triggered ductile shear deformation (D1) in the Yangtianba-Shimudi area and initiated the isothermal decompression metamorphic event (M2). The region entered a phase of post-collisional extension at about 180 million years. Continued extension resulted in the formation of partial melts in the northern part of the study area. During the subsequent exhumation of the ductile shear zone, the mylonitic foliation was reformed by late fold deformation. [Significance] The findings provide a reference for discussing the detailed process of metamorphic deformation response in the process of Late Triassic–Early Jurassic tectonic transformation in the south of Foping dome.

Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta et al.

The real-time process of directional changes while drilling, known as geosteering, is crucial for hydrocarbon extraction and emerging directional drilling applications such as geothermal energy, civil infrastructure, and CO2 storage. The geo-energy industry seeks an automatic geosteering workflow that continually updates the subsurface uncertainties and captures the latest geological understanding given the most recent observations in real-time. We propose "DISTINGUISH": a real-time, AI-driven workflow designed to transform geosteering by integrating Generative Adversarial Networks (GANs) for geological parameterization, ensemble methods for model updating, and global discrete dynamic programming (DDP) optimization for complex decision-making during directional drilling operations. The DISTINGUISH framework relies on offline training of a GAN model to reproduce relevant geology realizations and a Forward Neural Network (FNN) to model Logging-While-Drilling (LWD) tools' response for a given geomodel. This paper introduces a first-of-its-kind workflow that progressively reduces GAN-geomodel uncertainty around and ahead of the drilling bit and adjusts the well plan accordingly. The workflow automatically integrates real-time LWD data with a DDP-based decision support system, enhancing predictive models of geology ahead of drilling and leading to better steering decisions. We present a simple yet representative benchmark case and document the performance target achieved by the DISTINGUISH workflow prototype. This benchmark will be a foundation for future methodological advancements and workflow refinements.

Yangyu Huang, Tianyi Gao, Haoran Xu et al.

Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispensable in various fields, including disaster detection, resource exploration, and civil engineering. Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding. This gap is primarily due to the challenging nature of cartographic generalization, which involves handling high-resolution map, managing multiple associated components, and requiring domain-specific knowledge. To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding, which assesses the full-scale abilities in extracting, referring, grounding, reasoning, and analyzing. To bridge this gap, we introduce GeoMap-Agent, the inaugural agent designed for geologic map understanding, which features three modules: Hierarchical Information Extraction (HIE), Domain Knowledge Injection (DKI), and Prompt-enhanced Question Answering (PEQA). Inspired by the interdisciplinary collaboration among human scientists, an AI expert group acts as consultants, utilizing a diverse tool pool to comprehensively analyze questions. Through comprehensive experiments, GeoMap-Agent achieves an overall score of 0.811 on GeoMap-Bench, significantly outperforming 0.369 of GPT-4o. Our work, emPowering gEologic mAp holistiC undErstanding (PEACE) with MLLMs, paves the way for advanced AI applications in geology, enhancing the efficiency and accuracy of geological investigations.