Цифровой репозиторий ГГМ РАН
Репозиторий переведен на новую версию - DSpace 9.0. В репозитории содержится 46 500 публикации с тематикой "Науки о земле".
Последние отправления
listelement.badge.dso-typeЭлемент, Metasomatic Mineral Systems with IOA, IOCG, and Affiliated Critical and Precious Metal Deposits: A Review from a Field Geology Perspective(2025) Corriveau L.; Montreuil J.-F.; Huot-Vézina G.; Blein O.Worldwide, a growing list of critical (Bi, Co, Cu, F, Fe, Mo, Ni, P, PGE, REE, W, U, and Zn) and precious metal (Ag and Au) resources have been identified in mineral systems forming Fe-oxide-copper-gold (IOCG) deposits; Fe-oxide-apatite (IOA); Fe-sulfide Cu-Au (ISCG); and affiliated W skarn; Fe-rich Au-Co-Bi or Ni; albitite-hosted U or Au ± Co; and five-element (Ag, As, Co, Ni, and U) vein deposits. This paper frames the genesis of this metallogenic diversity by defining the Metasomatic Iron and Alkali-Calcic (MIAC) mineral system and classifying its spectrum of Fe-rich-to-Fe-poor and alkali-calcic deposits. The metasomatic footprint of MIAC systems consists of six main alteration facies, each recording a distinct stage of mineralization as systems have evolved. The fluid flow pathways and the thermal and chemical gradients inferred from the space–time distribution of the alteration facies within a system are best explained by the ascent and lateral propagation of a voluminous hypersaline fluid plume. The primary fluid plume evolves, chemically and physically, as metasomatism progresses and through periodic ingresses of secondary fluids into the plume. Exploration strategies can take advantage of the predictability and the expanded range of exploration targets that the MIAC system framework offers, the building blocks of which are the alteration facies as mappable prospectivity criteria for the facies-specific critical and precious metal deposits the systems generate. Global case studies demonstrate that these criteria are applicable to MIAC systems worldwide.listelement.badge.dso-typeЭлемент, cigChannel: A massive-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation(2024) Wang G.; Wu X.; Zhang W.Identifying buried channels in 3D seismic volumes is essential for characterizing hydrocarbon reservoirs and offering insights into paleoclimate conditions, yet it remains a labor-intensive and time-consuming task. The data-driven deep learning methods are highly promising to automate the seismic channel interpretation with high efficiency and accuracy, as they have already achieved significant success in similar image segmentation tasks within the field of computer vision (CV). However, unlike the CV domain, the field of seismic exploration lacks a comprehensive benchmark dataset for channels, severely limiting the development, application, and evaluation of deep learning approaches in seismic channel interpretation. Manually labeling 3D channels in field seismic volumes can be a tedious and subjective work and most importantly, many field seismic volumes are proprietary and not accessible to most of the researchers. To overcome these limitations, we propose a comprehensive workflow of geological channel simulation and geophysical forward modeling to create a massive-scale synthetic seismic dataset containing 1,200 256×256×256 seismic volumes with labels of more than 10,000 diverse channels and their associated sedimentary facies. It is by far the most comprehensive dataset for channel identification, providing realistic and geologically reasonable seismic volumes with meandering, distributary, and submarine channels. Trained with this synthetic dataset, a convolutional neural network (simplified from the U-Net) model performs well in identifying various types of channels in field seismic volumes, which indicates the diversity and representativeness of the dataset. We have made the dataset, codes generating the data, and trained model publicly available for facilitating further research and validation of deep learning approaches for seismic channel interpretation.listelement.badge.dso-typeЭлемент, Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping(2026) Dou Y.; Wu X.; Gao H.; Bi Z.Relative Geologic Time (RGT) estimation from seismic data is a cornerstone of subsurface structural modeling, depositional evolution analysis, and reservoir characterization, supporting horizon correlation and depositional system reconstruction. Yet accurate RGT estimation remains challenging: RGT is intrinsically a topologically constrained continuous field, in which local errors readily propagate globally and distort the overall result. Conventional methods rely heavily on priors, attribute extraction, and manual interaction, leading to cumbersome workflows. Existing deep-learning approaches mostly use a regression formulation with pixel-wise MSE/MAE losses, which struggle to capture thin horizons and fail to model the stratigraphic semantics of the RGT field, yielding limited generalization and unstable ordering across diverse structural and depositional settings. We propose RGT-Est, a deep-learning framework that transfers the optimization target from the topologically constrained continuous field into a differentiable sinusoidal space, which explicitly encodes the periodic stratigraphic semantics of RGT and alleviates over-smoothing of fine horizons. Pointwise, perceptual, and adversarial losses are jointly imposed in this space to enforce local fidelity, inter-layer consistency, and global structural plausibility, providing both fine-horizon discrimination and global stratigraphic awareness. An optional horizon-guidance module further accepts sparse 2D or 3D horizons as priors. Trained on synthetic data and evaluated on field surveys with densely faulted zones, large unconformities, steeply dipping strata, folded deformations, and clinoforms, RGT-Est achieves state-of-the-art performance among AI-based methods without horizon constraints, and attains substantially higher horizon-correlation accuracy and global topological consistency once sparse priors are incorporated.listelement.badge.dso-typeЭлемент, LFD (v1.0): Latent-Compression-Free Generative Diffusion with Geological Priors and Geophysical Regularization for Implicit Structural Modeling(2026) Guo Z.; Wu X.; Dou Y.; Gao H.; Caumon G.Diffusion models provide a promising way to model the distribution of implicit structural models, potentially improving generalization across surveys. However, existing diffusion Transformer pipelines scale poorly to high-dimensional geophysical data because noise- or velocity-prediction objectives are often unstable at large patch sizes, forcing the use of small patches that lead to long token sequences and high computational cost. To reduce computation, most approaches rely on Variational autoencoders (VAEs) and latent diffusion, but robust pretrained VAEs are scarce in geophysics, and enforcing geological priors in latent space is difficult. To address these scalability bottlenecks and the difficulty of enforcing geological priors in latent space, we propose Latent-Compression-Free Generative Diffusion (LFD) with Geological Priors and Geophysical Regularization for implicit structural modeling. Built on flow matching, LFD generates implicit structural models directly in the data space, enabling efficient large-patch Vision Transformer (ViT) inference and allowing fault/horizon constraints and geophysical regularization to be applied explicitly during generation. To strengthen structural conditioning, we design a structure-enhanced Transformer that injects horizon and fault embeddings at multiple layers. We further introduce two prior-guided losses: a horizon loss to match the generated models to the input horizons, and a fault-aware bending-energy term that regularizes smoothness while ignoring stencils across faults. By enforcing these priors directly in the data space, the model is effectively constrained to generate geologically reasonable structures. Experiments on both synthetic data and real surveys validate the effectiveness of LFD for prior-guided implicit structural modeling. Benefiting from large-patch inference, LFD generates a 512x512 model in 1.56 s on an NVIDIA H20 GPU. With relative positional encoding, LFD can be extended to higher resolutions via simple adaptation without retraining. Overall, LFD offers new insights into deploying diffusion models for high-dimensional geophysical data, enabling efficient generation with interpretable, prior-guided constraints.listelement.badge.dso-typeЭлемент, Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding(2025) Dou Y.; Wu X.; Bangs N.L.; Sethi H.S.; Li J.; Gao H.; Guo Z.Understanding Earth's subsurface is critical for energy transition, natural hazard mitigation, and planetary science. Yet subsurface analysis remains fragmented, with separate models required for structural interpretation, stratigraphic analysis, geobody segmentation, and property modeling-each tightly coupled to specific data distributions and task formulations. We introduce the Geological Everything Model 3D (GEM), a unified generative architecture that reformulates all these tasks as prompt-conditioned inference along latent structural frameworks derived from subsurface imaging. This formulation moves beyond task-specific models by enabling a shared inference mechanism, where GEM propagates human-provided prompts-such as well logs, masks, or structural sketches-along inferred structural frameworks to produce geologically coherent outputs. Through this mechanism, GEM achieves zero-shot generalization across tasks with heterogeneous prompt types, without retraining for new tasks or data sources. This capability emerges from a two-stage training process that combines self-supervised representation learning on large-scale field seismic data with adversarial fine-tuning using mixed prompts and labels across diverse subsurface tasks. GEM demonstrates broad applicability across surveys and tasks, including Martian radar stratigraphy analysis, structural interpretation in subduction zones, full seismic stratigraphic interpretation, geobody delineation, and property modeling. By bridging expert knowledge with generative reasoning in a structurally aware manner, GEM lays the foundation for scalable, human-in-the-loop geophysical AI-transitioning from fragmented pipelines to a vertically integrated, promptable reasoning system. Project page: https://douyimin.github.io/GEM