Artificial Intelligence in Geosciences最新文献

英文中文

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2026-01-01

引用次数: 0

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2026-01-01

引用次数: 0

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2026-01-01

引用次数: 0

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2026-01-01

引用次数: 0

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2026-01-01

引用次数: 0

The Fossil Frontier: An answer to the 3-billion fossil question 化石前沿：30亿化石问题的答案

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-12-30 DOI: 10.1016/j.aiig.2025.100184

Iver Martinsen , Benjamin Ricaud , David Wade , Odd Kolbjørnsen , Fred Godtliebsen

Microfossil analysis is important in subsurface mapping, for example to match strata between wells. This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical samples with the aim of identifying informative microfossil species and genera. The current digitalization of large volumes of microfossil samples that is being conducted by the Norwegian Offshore Directorate, paired with AI development, opens up new opportunities for automating parts of the analysis to help the geologist in the analysis. Unsupervised representation learning is a research area in Artificial Intelligence (AI) that lies at the core of this challenge, as this way of learning can create useful image representations by utilizing large volumes of data without requiring labels. Previous work has presented good results for the classification of a limited number of classes, but there are still challenges related to classification in realistic settings where additional unknown species are present. In this paper, we connect unsupervised representation learning and uncertainty estimation and create a comprehensive tool to automate microfossil analysis. We present our methodology and results in three parts. In the first part, we train several AI models from scratch using state-of-the-art self-supervised learning methods, obtaining excellent results compared against state-of-the-art foundation models for image classification and content-based image retrieval. In the second part, we develop a method based on conformal prediction which enables our classifier to handle a large pool of images containing both in-distribution and out-of-distribution data, while at the same time allowing us to create error estimates to control the uncertainty of the prediction sets. In the third part, we use our method to create distribution charts of fossils for a range of genera in multiple wells.

微化石分析在地下测绘中很重要，例如在井间匹配地层。这种分析目前是由专业的地球科学家进行的，他们手动调查大量的物理样本，目的是识别信息丰富的微化石物种和属。目前，挪威海上管理局正在对大量微化石样本进行数字化处理，再加上人工智能的发展，为自动化部分分析提供了新的机会，从而帮助地质学家进行分析。无监督表示学习是人工智能（AI）的一个研究领域，也是这一挑战的核心，因为这种学习方式可以通过利用大量数据而不需要标签来创建有用的图像表示。以前的工作已经为有限数量的分类提供了良好的结果，但是在存在额外未知物种的现实环境中，分类仍然存在挑战。在本文中，我们将无监督表示学习和不确定性估计联系起来，并创建了一个自动化微化石分析的综合工具。我们分三部分介绍我们的方法和结果。在第一部分中，我们使用最先进的自监督学习方法从头开始训练几个AI模型，与最先进的图像分类和基于内容的图像检索基础模型相比，获得了出色的结果。在第二部分中，我们开发了一种基于保形预测的方法，该方法使我们的分类器能够处理包含分布内和分布外数据的大量图像，同时允许我们创建误差估计来控制预测集的不确定性。在第三部分中，我们使用我们的方法在多个井中创建了一系列属的化石分布图。

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引用次数: 0

Application of machine learning for permeability prediction in heterogeneous carbonate reservoirs 机器学习在非均质碳酸盐岩储层渗透率预测中的应用

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-12-27 DOI: 10.1016/j.aiig.2025.100183

Osama Massarweh , Abdul Salam Abd , Jens Schneider , Ahmad S. Abushaikha

Accurate prediction of reservoir permeability based on geostatistical modeling and history matching is often limited by spatial resolution and computational efficiency. To address this limitation, we developed a novel supervised machine learning (ML) approach employing feedforward neural networks (FFNNs) to predict spatial permeability distribution in heterogeneous carbonate reservoirs from production well rates. The ML model was trained on 25 black oil reservoir simulation cases derived from a geologically realistic representation of the Upper Kharaib Member in the United Arab Emirates. Input features for training included cell spatial coordinates

(x_{i}, y_{i}, z_{i})

, distances between cells and the

n

closest wells, and corresponding time-weighted oil production rates extracted from simulation outputs for each well. The target output was the permeability at each cell. The grid consisted of 22,739 structured cells, and training scenarios considered different closest well counts (

n =

1, 5, 10, and 20). The prediction performance of the trained model was evaluated across 12 unseen test cases. The model achieved higher accuracy with increased well input (

n

), demonstrating the potential of ML for efficient permeability estimation. This study highlights the effectiveness of integrating physical simulation outputs and spatial production patterns within a neural network structure for robust reservoir characterization.

基于地质统计建模和历史拟合的储层渗透率准确预测往往受到空间分辨率和计算效率的限制。为了解决这一限制，我们开发了一种新的监督机器学习（ML）方法，利用前馈神经网络（FFNNs）从生产井速率预测非均质碳酸盐岩储层的空间渗透率分布。ML模型在25个黑色油藏模拟案例中进行了训练，这些油藏模拟案例来自阿拉伯联合酋长国Upper Kharaib成员的地质现实代表。训练的输入特征包括单元空间坐标（xi,yi,zi），单元与最近的n口井之间的距离，以及从每口井的模拟输出中提取的相应的时间加权产油量。目标输出是每个细胞的渗透率。网格由22,739个结构化单元组成，训练场景考虑了不同的最近井数（n= 1,5,10和20）。经过训练的模型的预测性能在12个看不见的测试用例中进行评估。随着井输入(n)的增加，该模型获得了更高的精度，证明了ML在有效估计渗透率方面的潜力。该研究强调了在神经网络结构中整合物理模拟输出和空间生产模式的有效性，以实现稳健的油藏表征。

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引用次数: 0

Hierarchical machine learning for the automatic classification of surface deformation from SAR observations 基于分层机器学习的SAR地表形变自动分类

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-12-09 DOI: 10.1016/j.aiig.2025.100171

Jhonatan Rivera-Rivera , Héctor Aguilera , Marta Béjar-Pizarro , Carolina Guardiola-Albert , Pablo Ezquerro , Anna Barra

Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets. The database integrates InSAR measurement points (MPs) from 20 case studies in Spain, obtained from various institutional sources, and 32 geoenvironmental variables related to ground deformation, morphometry, geology, climate, and land use. The proposed classification strategy follows a hierarchical structure with two levels: first, distinguishing between landslides and subsidence; then, identifying the specific type within each main class (mining landslide, environmental landslide, constructive subsidence, mining subsidence, and piezometric subsidence). Several machine learning algorithms (Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting Machine, XGBoost, LightGBM, and CatBoost) and data configurations were tested, combining different spatial resolutions and class balancing techniques. The best performance (Cohen's Kappa = 0.78) was achieved with the hierarchical approach using the 200 m grid dataset, applying XGBoost for the parental and landslide models, and CatBoost for the subsidence model. Using this approach, 70 % de test sites achieved over 88 % correctly classified cells, 20 % had between 50 % and 83 %, and only one test case was entirely misclassified. The analysis of the most relevant variables indicates that annual mean precipitation, mining activity, buildings, landslide susceptibility, and slope are key factors. These results demonstrate the potential of the hierarchical approach to improve classification and lay the groundwork for future application at national and European scales, incorporating new training cases, process types, and continental data sources. In conclusion, this study presents, for the first time, a hierarchical machine learning model capable of accurately classifying ground deformation processes in Spain, with the aim of supporting territorial management and geohazard mitigation.

地面变形过程，如滑坡和下沉，会造成重大的社会、经济和环境影响。本研究旨在使用应用于基于insar的数据集的机器学习方法对西班牙的地面变形过程进行自动分类。该数据库整合了来自西班牙20个案例研究的InSAR测量点（MPs），这些数据来自不同的机构来源，以及与地面变形、地貌测量、地质、气候和土地利用相关的32个地球环境变量。提出的分类策略遵循两个层次的分层结构：第一，区分滑坡和沉降；然后，在每个主要类别中确定具体类型（采矿滑坡、环境滑坡、建设性沉陷、采矿沉陷和压力沉降）。结合不同的空间分辨率和类平衡技术，测试了几种机器学习算法（Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Extra Trees, Gradient Boosting machine, XGBoost， LightGBM和CatBoost）和数据配置。使用200米网格数据集的分层方法获得了最佳性能（Cohen’s Kappa = 0.78），对亲代和滑坡模型应用XGBoost，对沉降模型应用CatBoost。使用这种方法，70%的测试站点实现了超过88%的正确分类单元，20%的站点在50%到83%之间，并且只有一个测试用例完全被错误分类。对最相关变量的分析表明，年平均降水、采矿活动、建筑物、滑坡易感性和坡度是关键因素。这些结果显示了层次方法改进分类的潜力，并为将来在国家和欧洲范围内的应用奠定了基础，结合了新的培训案例、过程类型和大陆数据源。总之，本研究首次提出了一种分层机器学习模型，能够准确地对西班牙的地面变形过程进行分类，目的是支持领土管理和减轻地质灾害。

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引用次数: 0

Prediction of the soil–water retention curve of compacted clays using PSO–GA XGBoost 利用PSO-GA XGBoost预测压实粘土的土水保持曲线

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-12-03 DOI: 10.1016/j.aiig.2025.100173

Reza Taherdangkoo , Thomas Nagel , Vladimir Tyurin , Chaofan Chen , Faramarz Doulati Ardejani , Christoph Butscher

Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly laboratory testing. To offer a more efficient alternative, an extreme gradient boosting (XGBoost) model, optimized using a hybrid particle swarm optimization and genetic algorithm (PSO–GA), was developed. This hybrid model estimates the SWR across a broad suction range, accounting for both drying and wetting paths, along with key soil parameters. The performance of the model was evaluated through various statistical analyses and by comparing the predicted gravimetric water content with experimental data. A backward feature elimination method was employed to assess the impact of various input parameters on model accuracy and to offer a simplified model for scenarios with limited data availability. Additionally, Monte Carlo simulations were conducted to quantify the inherent uncertainties associated with the dataset, XGBoost hyperparameters, and model performance. The hybrid PSO–GA XGBoost model effectively estimates the water retention of clayey soils during both drying and wetting cycles, proving to be an alternative to traditional soil mechanics correlations.

土壤保水是理解非饱和粘土水力学特性的基础。土壤-水保持曲线通常是通过广泛和昂贵的实验室测试获得的。为了提供更有效的替代方案，开发了一种使用混合粒子群优化和遗传算法（PSO-GA）进行优化的极端梯度增强（XGBoost）模型。该混合模型估计了在广泛的吸力范围内的SWR，考虑了干燥和湿润路径，以及关键的土壤参数。通过各种统计分析，并将预测的重力含水率与实验数据进行比较，对模型的性能进行了评价。采用反向特征消去法评估各种输入参数对模型精度的影响，并为数据可用性有限的场景提供简化模型。此外，还进行了蒙特卡罗模拟，以量化与数据集、XGBoost超参数和模型性能相关的固有不确定性。混合PSO-GA XGBoost模型有效地估计了粘土在干湿循环中的保水性，证明是传统土壤力学相关性的替代方法。

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引用次数: 0

GeoNeXt: Efficient landslide mapping using a pre-trained ConvNeXt V2 encoder with a PSA-ASPP decoder GeoNeXt：使用预训练的ConvNeXt V2编码器和PSA-ASPP解码器进行有效的滑坡测绘

IF 4.2

Artificial Intelligence in Geosciences

Pub Date : 2025-12-01 DOI: 10.1016/j.aiig.2025.100172

Rodrigo Uribe-Ventura , Willem Viveen , Ferdinand Pineda-Ancco , César Beltrán-Castañon

Landslides constitute one of the most destructive geological hazards worldwide, causing thousands of casualties and billions in economic losses annually. To mitigate these risks, accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance. While recent advances in deep learning, particularly with transformer architectures and large pre-trained models like the Segment Anything Model (SAM), have shown promise, their application to landslide mapping is often hindered by high computational costs, prompt dependency, and challenges with data imbalance. To address these limitations, we propose GeoNeXt, a novel semantic segmentation architecture for intelligent landslide recognition. It combines a scalable, pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention (PSA) and Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. Through domain adaptation on the large-scale CAS landslide dataset, we refined the encoder's general pre-trained features to learn robust, landslide-specific features. GeoNeXt exhibited zero-shot transferability, achieving 74–78 % F1 and 64–66 % mIoU across three distinct test datasets from diverse regions, which were entirely excluded from the training process. Ablation studies on decoder variants validated the PSA-ASPP synergy, achieving a superior F1 of 90.39 % and mIoU of 83.18 % on the CAS dataset. Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods, achieving F1 scores of 94.25 %, 86.43 %, and 92.27 % (mIoU: 89.51 %, 78.21 %, 86.02 %) on the Bijie, Landslide4Sense, and GVLM datasets, respectively, with 10× fewer parameters than SAM-based methods and lower computational demands. We showed that modernized convolutions, paired with strategic training, were a viable alternative to resource-intensive transformers. This efficiency facilitated their use in operational intelligent landslide recognition and geohazard monitoring systems.

滑坡是世界上最具破坏性的地质灾害之一，每年造成数千人伤亡和数十亿美元的经济损失。为了减轻这些风险，准确有效的滑坡像素映射自动语义分割是至关重要的。虽然深度学习的最新进展，特别是变压器架构和大型预训练模型，如分段任意模型（SAM），已经显示出前景，但它们在滑坡测绘中的应用往往受到高计算成本、快速依赖和数据不平衡挑战的阻碍。为了解决这些限制，我们提出了GeoNeXt，一种用于智能滑坡识别的新型语义分割架构。它结合了一个可扩展的、预训练的ConvNeXt V2编码器和一个利用金字塔挤压注意（PSA）和阿特鲁斯空间金字塔池（ASPP）来捕获多尺度特征的解码器。通过对大规模CAS滑坡数据集的域自适应，我们改进了编码器的一般预训练特征，以学习鲁棒的滑坡特定特征。GeoNeXt表现出零射击可转移性，在来自不同地区的三个不同的测试数据集上实现了74 - 78%的F1和64 - 66%的mIoU，这些数据集完全排除在训练过程之外。对解码器变体的消融研究验证了PSA-ASPP的协同作用，在CAS数据集上实现了90.39%的F1和83.18%的mIoU。对比分析证实，GeoNeXt优于基于sam的方法，在Bijie、Landslide4Sense和GVLM数据集上的F1得分分别为94.25%、86.43%和92.27% (mIoU分别为89.51%、78.21%和86.02%)，参数比基于sam的方法少10倍，计算需求更低。我们展示了现代化的卷积，加上战略训练，是资源密集型变压器的可行替代方案。这种效率促进了它们在智能滑坡识别和地质灾害监测系统中的应用。

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