Applied Computing and Geosciences最新文献_第6页

Automated fault network extraction in complex tectonic regimes: A hybrid machine learning and structural attributes approach 复杂构造条件下断层网络的自动提取：一种混合机器学习和结构属性方法

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-07-17 DOI: 10.1016/j.acags.2025.100264

Muhammad Khan , Andy Anderson Bery , Yasir Bashir , Sya'rawi Muhammad Husni Sharoni , Syed Sadaqat Ali

Interpreting seismic faults is crucial for prospect generation, reservoir modeling, and CO₂ storage assessment. However, identifying faults in complex tectonic regimes remains challenging, particularly in regions that have experienced multiple phases of tectonic activity. Despite advancements in structural seismic attributes and machine learning, interpreters often still rely on manual methods to analyze intricate fault systems, such as those found in the Poseidon study area located in the Browse basin, Northwestern Australia, where the fault network is shaped by both extensional and compressional tectonic events. This paper introduces a hybrid approach that combines machine learning with seismic structural attributes to extract complex fault networks from 3D seismic data. The method begins by using pre-trained models to generate a fault probability cube, which is then refined through re-training with manually labeled data to incorporate local structural knowledge. To address false negatives, the model is further retrained using an ant-tracking volume generated from the fault probability cube of the manually trained model as automatically labeled data. The fault probability cube is regenerated from the automatically labeled trained model and further enhanced by post-processing techniques, such as ant-tracking, to improve fault connectivity and streamline the automated fault identification process. This hybrid approach effectively detects and extracts both major and minor discontinuities from 3D seismic data with high accuracy, significantly reducing the time and effort required for interpretation compared to traditional techniques.

地震断层的解释对于勘探区生成、储层建模和二氧化碳储量评估至关重要。然而，在复杂的构造体系中识别断层仍然具有挑战性，特别是在经历了多期构造活动的地区。尽管在构造地震属性和机器学习方面取得了进步，但解释人员通常仍然依赖于人工方法来分析复杂的断层系统，例如在澳大利亚西北部Browse盆地的Poseidon研究区发现的断层网络，其中断层网络由伸展和挤压构造事件形成。本文介绍了一种将机器学习与地震结构属性相结合的混合方法，用于从三维地震数据中提取复杂断层网。该方法首先使用预训练的模型生成故障概率立方体，然后通过手动标记数据的重新训练来细化该立方体，以纳入局部结构知识。为了解决假阴性问题，使用从手动训练模型的故障概率立方生成的反跟踪体作为自动标记数据进一步重新训练模型。故障概率立方体由自动标记的训练模型重新生成，并通过抗跟踪等后处理技术进一步增强，以提高故障连通性，简化故障自动识别过程。这种混合方法可以有效地从三维地震数据中检测和提取主要和次要的不连续面，并且精度很高，与传统技术相比，显著减少了解释所需的时间和精力。

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引用次数: 0

Assessing data reliability for AI-driven volcanic rock dating: A comparison of electron microprobe and laser ablation mass spectroscopy 评估人工智能驱动的火山岩测年数据可靠性：电子探针和激光烧蚀质谱的比较

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-07-05 DOI: 10.1016/j.acags.2025.100263

Ali Salimian , Megan Watfa , Ram Grung , Lorna Anguilano

This study explores the integrationof artificial intelligence (AI) and modern data analytics for accurately predicting and classifying three distinct periods of volcanic activity. By leveraging previously dated volcanic samples, we assess whether existing age and geochemical data can reliably group and predict volcanic episodes. Our study focuses on the Kula Volcanic Province (Turkey). We compare the effectiveness of two analytical techniques—Electron Microprobe Analysis (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)—in producing high-quality datasets for training deep learning models. While EPMA provides major and minor elemental compositions, LA-ICP-MS offers a broader range of trace elements, which may improve classification accuracy. Two experiments were conducted to evaluate the feasibility of AI-based volcanic rock age estimation. In the first experiment, an autoencoder and unsupervised clustering were applied to reduce dimensionality and group samples based on their elemental composition. The results revealed that EPMA data lacked sufficient detail to form well-defined clusters, whereas LA-ICP-MS data produced clusters that closely aligned with true age classes due to their higher sensitivity to trace elements. In the second experiment, a deep neural network (DNN) was trained to classify rock ages. The LA-ICP-MS-based model achieved a classification accuracy of 95 %, significantly outperforming the EPMA-based model (72 %). These findings underscore the importance of data quality and analytical technique selection in AI-powered geochronology, demonstrating that high-quality trace element data enhances AI model performance for volcanic rock age estimation.

本研究探讨了人工智能（AI）与现代数据分析的结合，以准确预测和分类三个不同时期的火山活动。通过利用以前定年的火山样本，我们评估了现有的年龄和地球化学数据是否能够可靠地分组和预测火山事件。我们的研究重点是库拉火山省（土耳其）。我们比较了两种分析技术——电子显微探针分析（EPMA）和激光烧蚀电感耦合等离子体质谱（LA-ICP-MS）——在为训练深度学习模型生成高质量数据集方面的有效性。虽然EPMA提供了主要和次要元素组成，但LA-ICP-MS提供了更广泛的微量元素，这可能提高分类的准确性。通过两项实验对人工智能火山岩年龄估算的可行性进行了评价。在第一个实验中，采用自编码器和无监督聚类方法对样本进行降维，并根据元素组成对样本进行分组。结果表明，EPMA数据缺乏足够的细节来形成定义明确的簇，而LA-ICP-MS数据由于对微量元素的更高灵敏度而产生的簇与真实年龄类别密切相关。在第二个实验中，训练深度神经网络（DNN）对岩石年龄进行分类。基于la - icp - ms的模型实现了95%的分类准确率，显著优于基于epma的模型（72%）。这些发现强调了数据质量和分析技术选择在人工智能地质年代学中的重要性，表明高质量的微量元素数据提高了火山岩年龄估计的人工智能模型性能。

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引用次数: 0

Natural fracture network model using Gaussian simulation and machine learning algorithms 自然裂缝网络模型采用高斯仿真和机器学习算法

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-07-02 DOI: 10.1016/j.acags.2025.100258

Timur Merembayev, Yerlan Amanbek

In this paper, a fracture network model is proposed to enhance the understanding of subsurface fracture characterization. The model combines geostatistical methods such as sequential indicators and Gaussian simulations. The model uses data from natural faults in Kazakhstan to predict the segment, azimuth, and length of fractures in unknown areas. The model is validated by comparing the simulated fracture networks with the original fracture data and by hiding some regions within the fracture network. The results show that the geostatistical methods perform better than the machine learning algorithm for azimuth prediction, while the machine learning algorithm performs better for length prediction. In addition, the validation of the fracture network model is conducted by comparing the production curve profiles in the tracer test setting. They are in good agreement.

本文提出了一个裂缝网络模型，以增强对地下裂缝特征的理解。该模型结合了序贯指标和高斯模拟等地统计学方法。该模型使用哈萨克斯坦天然断层的数据来预测未知区域裂缝的分段、方位角和长度。通过将模拟裂缝网络与原始裂缝数据进行比较，并隐藏裂缝网络中的某些区域，验证了模型的有效性。结果表明，地统计学方法在方位预测方面优于机器学习算法，而机器学习算法在长度预测方面优于机器学习算法。此外，通过对比示踪剂测试设置中的生产曲线剖面，对裂缝网络模型进行了验证。他们意见很一致。

引用次数: 0

Sinkhole susceptibility analysis using machine learning for west central Florida 利用机器学习对佛罗里达州中西部进行地陷敏感性分析

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-26 DOI: 10.1016/j.acags.2025.100262

Olanrewaju Muili, Hassan A. Babaie

This study examined the feasibility and accuracy of applying machine learning for sinkhole classification and prediction and using the results in automated sinkhole susceptibility mapping for west central Florida. A two-stage processing pipeline was developed. In the first stage, we assessed the predictive power of five exemplary machine learning algorithms: random forest (RF), logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perceptron (MLP), and select the best-performing model. The top-performed model was then chosen to develop a sinkhole susceptibility map (SSM) in the second step of the process. Nine feature layers were derived from the collected geospatial data and utilized as conditional variables. Several statistical metrics and receiver operating characteristic curves were utilized to evaluate the accuracy of the models. The results showed that the RF model, with a ROC of 0.984, had the highest prediction capability in the research area.

We generated a susceptibility map using the RF model, and the study area was classified into high susceptibility (H) and low susceptibility (L) areas. Confusion Matrix (CM) and Matthews Correlation Coefficient (MCC) were used to confirm the results of the sinkhole susceptibility map's classification. We present a model that predicts sinkhole distribution in the study area, and the output of our model is consistent with the sinkhole hazard map that the Florida Division of Emergency Management had previously created. This work can assist the government, community, and land managers in creating plans for mitigating hazards and land degradation.

本研究考察了应用机器学习进行天坑分类和预测的可行性和准确性，并将结果用于佛罗里达州中西部的自动天坑敏感性测绘。开发了两阶段加工流水线。在第一阶段，我们评估了五种典型机器学习算法的预测能力：随机森林（RF）、逻辑回归（LR）、k近邻（KNN）、支持向量机（SVM）和多层感知器（MLP），并选择了表现最佳的模型。然后，在该过程的第二步中，选择表现最好的模型来开发天坑敏感性图（SSM）。从收集到的地理空间数据中得到9个特征层，并将其作为条件变量。利用一些统计指标和受试者工作特征曲线来评估模型的准确性。结果表明，该模型预测能力最强，ROC值为0.984。利用RF模型绘制了敏感性图，并将研究区划分为高敏感性区(H)和低敏感性区(L)。利用混淆矩阵（Confusion Matrix， CM）和Matthews相关系数（Matthews Correlation Coefficient， MCC）对地陷敏感性图的分类结果进行了验证。我们提出了一个预测研究区域天坑分布的模型，我们模型的输出与佛罗里达州应急管理部门先前创建的天坑危害图一致。这项工作可以帮助政府、社区和土地管理者制定减轻灾害和土地退化的计划。

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引用次数: 0

Advanced identification of geological discontinuities with deep learning 基于深度学习的地质不连续面高级识别

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-21 DOI: 10.1016/j.acags.2025.100256

Rushan Wang , Martin Ziegler , Michele Volpi , Andrea Manconi

Rock mass characterization is essential for various applications in geosciences. Traditional methods, such as manual mapping and interpretation, are labor-intensive and prone to inconsistencies. Although machine learning has advanced in many fields, its application in structural geology, especially for distinguishing different discontinuity types, remains limited. This study presents a deep learning-based approach for identifying geological discontinuities in borehole images, classifying features such as intact walls, induced cracks, and tectonic fault planes, among others. We evaluate deep learning architectures, including standard Convolutional Neural Networks and Transformer-based models, and optimize segmentation performance with multi-scale training, tiling strategies, and tailored loss functions. Our results demonstrate that the Transformer model, particularly SegFormer, outperforms U-Net in detecting complex geological features. The combined use of weighted cross-entropy and focal loss further improves model robustness, especially for underrepresented and challenging features. In addition, the choice of the tiling size significantly affects the classification performance of different geological features. This research establishes an efficient and accurate pipeline for automated geological interpretation, with significant implications for subsurface exploration and geotechnical engineering.

岩体表征在地球科学的各种应用中是必不可少的。传统的方法，如手工映射和解释，是劳动密集型的，并且容易产生不一致。尽管机器学习在许多领域都取得了进展，但它在构造地质学中的应用，特别是在区分不同的不连续类型方面的应用仍然有限。本研究提出了一种基于深度学习的方法，用于识别钻孔图像中的地质不连续性，对完整壁、诱导裂缝和构造断裂面等特征进行分类。我们评估了深度学习架构，包括标准卷积神经网络和基于transformer的模型，并通过多尺度训练、平铺策略和定制损失函数优化分割性能。我们的研究结果表明，Transformer模型，特别是SegFormer，在检测复杂地质特征方面优于U-Net。加权交叉熵和焦点损失的结合使用进一步提高了模型的鲁棒性，特别是对于代表性不足和具有挑战性的特征。此外，瓦片尺寸的选择显著影响不同地质特征的分类性能。本研究建立了一条高效、准确的自动化地质解释管道，对地下勘探和岩土工程具有重要意义。

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引用次数: 0

Integrating neuro-symbolic AI and knowledge graph for enhanced geochemical prediction in copper deposits 结合神经符号人工智能和知识图谱增强铜矿地球化学预测

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-18 DOI: 10.1016/j.acags.2025.100259

Weilin Chen, Jiyin Zhang, Wenjia Li, Xiang Que, Chenhao Li, Xiaogang Ma

The integration of machine learning (ML) and deep learning (DL) in geoscience has demonstrated great promise for mineral prediction. However, existing approaches are predominantly data-driven and often overlook expert geological knowledge, limiting their interpretability, accuracy, and practical applicability. This study introduces a new method that combines Large Language Models (LLMs), knowledge graphs (KGs), and Neuro-Symbolic AI (NSAI) models to predict mineralization systems in diverse copper deposits, significantly increasing the precision in prediction results. We utilize LLMs to generate KGs from geological literature, extracting symbolic rules that encode domain-specific insights about copper mineralization. These rules, derived dynamically from expert knowledge, are integrated into ML models as guidance during the training and prediction phases. By fusing symbolic reasoning with ML's computational power, our approach overcomes the limitations of black-box models, offering both improved accuracy and transparency in mineral prediction. To validate this method, we apply it to a comprehensive geochemical dataset of global copper deposits. The results show that rule-guided ML models achieve notable performance improvements, outperforming traditional ML methods in accuracy, precision, and robustness. Interpretability is further enhanced by using tools such as SHAP values, which explain the influence of individual geochemical features within the rule-based framework. This combination not only identifies critical geochemical elements like Cu, Fe, and S but also provides coherent, domain-aligned explanations for the predicted mineralization patterns. Our findings demonstrate the transformative potential of combining LLMs, KGs, and ML models for mineral prediction. This hybrid approach enables geoscientists to leverage both computational and expert knowledge, achieving a deeper understanding of mineralization systems.

地球科学中机器学习（ML）和深度学习（DL）的整合在矿物预测方面显示出巨大的前景。然而，现有的方法主要是数据驱动的，往往忽略了专家地质知识，限制了它们的可解释性、准确性和实际适用性。本研究提出了一种结合大语言模型（LLMs）、知识图（KGs）和神经符号人工智能（NSAI）模型的新方法，用于预测不同铜矿床的矿化系统，显著提高了预测结果的精度。我们利用llm从地质文献中生成KGs，提取编码有关铜矿化的特定领域见解的符号规则。这些从专家知识中动态导出的规则在训练和预测阶段被集成到机器学习模型中作为指导。通过将符号推理与机器学习的计算能力相融合，我们的方法克服了黑箱模型的局限性，提高了矿物预测的准确性和透明度。为了验证该方法的有效性，我们将其应用于全球铜矿床地球化学综合数据集。结果表明，规则导向的机器学习模型取得了显著的性能改进，在准确性、精密度和鲁棒性方面优于传统的机器学习方法。通过使用SHAP值等工具进一步提高了可解释性，这些工具可以在基于规则的框架内解释单个地球化学特征的影响。这种组合不仅确定了关键的地球化学元素，如Cu、Fe和S，而且还为预测的矿化模式提供了连贯的、域对齐的解释。我们的研究结果证明了结合llm、kg和ML模型进行矿物预测的变革潜力。这种混合方法使地球科学家能够利用计算和专家知识，对成矿系统有更深入的了解。

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引用次数: 0

Application of machine learning-based post-processing to improve crowd-sourced urban rainfall categorizations 基于机器学习的后处理应用于改进众包城市降雨分类

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100255

Mohammad Ashar Hussain , Venkatesh Budamala , Rajarshi Das Bhowmik

In recent years, citizen science has gained significant attention in the hydrometeorological sciences as an alternative to traditional monitoring systems while also raising awareness of natural processes. Crowd participation in reporting rainfall, known as crowdsourcing rainfall, has the potential to provide insights into the spatio-temporal variability of urban rainfall. However, crowdsourcing often suffers from inaccuracies in rainfall classification due to inadequately trained participants. This study investigates whether machine learning models can reduce misclassification in crowd-sourced rainfall reports under a synthetic framework. A state-of-the-art stochastic rainfall generator is deployed to simulate high-resolution rainfall over Bangalore, India, traditionally monitored by only two rain gauge stations. The study assumes that the 'synthetic' crowd reports qualitative descriptions of two rainfall characteristics—intensity and duration—based on which a categorization of a rainfall event (normal/moderate/severe) is issued. Ten scenarios are introduced to represent varying degrees of misclassification in the crowd reports. Two machine learning models, random forest and logistic regression, are employed to address these misclassifications and improve the resulting rainfall categorization. The findings indicate that while the random forest model outperforms logistic regression, its performance declines as misclassification rates increase. Moreover, the study highlights that increasing the number of participants significantly enhances the post-processing performance, emphasizing the importance of properly training the crowd for accurate reporting.

近年来，公民科学作为传统监测系统的替代方案，在水文气象科学领域获得了极大的关注，同时也提高了人们对自然过程的认识。群众参与降雨报告，被称为众包降雨，有可能提供对城市降雨时空变化的见解。然而，由于参与者训练不足，众包在降雨分类方面经常存在不准确的问题。本研究探讨了在合成框架下，机器学习模型是否可以减少众包降雨报告中的错误分类。部署了最先进的随机降雨发生器来模拟印度班加罗尔的高分辨率降雨，传统上只有两个雨量站监测。研究假设“合成”人群报告两种降雨特征（强度和持续时间）的定性描述，并以此为基础发布降雨事件的分类（正常/中等/严重）。引入了十个场景来表示人群报告中不同程度的错误分类。两种机器学习模型，随机森林和逻辑回归，被用来解决这些错误分类，并改进最终的降雨分类。研究结果表明，虽然随机森林模型优于逻辑回归，但其性能随着误分类率的增加而下降。此外，该研究强调，增加参与者的数量显著提高后处理性能，强调了正确训练人群准确报告的重要性。

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引用次数: 0

Comparison of ETAS parameter estimates across different time windows within the North and East Anatolian Fault Zones, Turkey 土耳其北安纳托利亚断裂带和东安纳托利亚断裂带不同时间窗内ETAS参数估计的比较

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100253

Suchanun Piriyasatit , Ercan Engin Kuruoglu , Mehmet Sinan Ozeren

Located at the intersection of major lithospheric plates, Turkey is characterized by significant seismic activity, particularly along the North Anatolian Fault (NAF) and East Anatolian Fault (EAF). This paper employs the Epidemic-Type Aftershock Sequence (ETAS) model, fitted using the BFGS quasi-Newton method, to study earthquake triggering processes along these faults from 1990 to 2023. Our findings show distinct temporal variations in seismicity parameters along these faults. Along the NAF, the ETAS model highlighted a lower background seismicity rate (

μ

) and aftershock productivity (

K_{0}

) compared to the EAF. In contrast, the EAF exhibits lower magnitude sensitivity (

α

), indicating that smaller earthquakes are more likely to trigger aftershocks, due to weaker dependence on mainshock magnitude. The aftershock decay rate (

p

) is notably faster in the NAF, suggesting quicker post-event stabilization. Our analysis across different time windows reveals significant non-stationarities in ETAS parameters, indicating that seismic behaviors along these faults do not strictly follow historical patterns. This temporal variability highlights the challenges in short-term seismic forecasting using historical data alone. A detailed comparison of ETAS parameters across time frames showcases the necessity for incorporating dynamic modeling approaches to improve earthquake forecasting in seismically active regions.

土耳其位于主要岩石圈板块的交汇处，地震活动频繁，特别是沿北安纳托利亚断层（NAF）和东安纳托利亚断层（EAF）。本文采用流行型余震序列（ETAS）模型，采用BFGS准牛顿方法拟合，研究了1990 - 2023年沿这些断裂的地震触发过程。我们的发现表明沿这些断层的地震活动性参数有明显的时间变化。在NAF上，ETAS模型显示背景地震活动性（μ）和余震生产力（K0）比EAF低。相比之下，东震场表现出较低的震级敏感性（α），表明由于对主震震级的依赖性较弱，较小的地震更有可能引发余震。余震衰减率(p)在NAF中明显更快，表明震后稳定更快。我们对不同时间窗的分析揭示了ETAS参数的显著非平稳性，表明沿这些断层的地震行为并不严格遵循历史模式。这种时间变异性突出了仅使用历史数据进行短期地震预报的挑战。ETAS参数跨时间框架的详细比较显示了采用动态建模方法来改进地震活跃地区地震预报的必要性。

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引用次数: 0

3D clay microstructure synthesis using Denoising Diffusion Probabilistic Models 基于去噪扩散概率模型的三维粘土微观结构综合

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100248

Ali Aouf , Eric Laloy , Bart Rogiers , Christophe De Vleeschouwer

This work is concerned with the challenging task of generating 3D-consistent binary microstructures of heterogeneous clay materials. We leverage denoising diffusion probabilistic models (DDPMs) to do so and show that DDPMs outperform two classical generative adversarial networks (GANs) for a 2D generation task. Next, our experiments demonstrate that our DDPMs can produce high-quality, diverse realizations that well capture the spatial statistics of two distinct clay microstructures. Moreover, we show that DDPMs can be implicitly trained to generate porosity-conditioned samples. To the best of our knowledge, this is the first study that addresses clay microstructure generation with DDPMs.

这项工作涉及到产生三维一致的非均质粘土材料二元微结构的挑战性任务。我们利用去噪扩散概率模型（ddpm）来做到这一点，并表明ddpm在2D生成任务中优于两个经典的生成对抗网络（gan）。接下来，我们的实验表明，我们的ddpm可以产生高质量的，多样化的实现，很好地捕获两种不同粘土微观结构的空间统计。此外，我们表明ddpm可以隐式训练来生成孔隙率条件的样本。据我们所知，这是第一个用ddpm处理粘土微观结构生成的研究。

引用次数: 0

Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall 加强印度夏季风预测：深度学习方法用于熟练的长期降雨预测

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Applied Computing and Geosciences

Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100257

Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

The prediction of the Indian summer monsoon rainfall (ISMR) in the June–September (JJAS) season at long-lead times is challenging. The state-of-the-art dynamical models often fail to capture the sign and amplitude of the rainfall anomalies in the extreme rainfall seasons, limiting the overall skill of the models. We attempted to address this issue using a deep learning model based on convolutional neural networks (CNN). An ensemble of JJAS rainfall predictions using the CNN model with a unique custom function showed high skills in predicting ISMR at a long-lead time of 12 months. The predictions had an anomaly correlation coefficient (ACC) exceeding 0.5 at all the lead times from 2 to 17 months. The CNN model predictions could capture the sign and phase of the extreme rainfall events in the study period realistically. Analysis of saliency-based heatmaps indicated the high skill to be due to the model capturing the leading modes of climate variability, such as the Indian Ocean Dipole and El Niño-Southern Oscillation, realistically. The ensemble of CNN ISMR predictions can supplement the predictions of the forecasting centers.

6 - 9月（JJAS）季节的印度夏季季风降雨（ISMR）的长期预测是具有挑战性的。最先进的动力模式往往不能捕捉极端降雨季节降雨异常的信号和幅度，限制了模式的整体技能。我们尝试使用基于卷积神经网络（CNN）的深度学习模型来解决这个问题。使用CNN模型和独特的自定义函数的JJAS降雨预测集合显示出在12个月的长提前期预测ISMR的高技能。2 ~ 17个月的预测异常相关系数（ACC）均大于0.5。CNN模型预测能够真实地捕捉研究时段极端降雨事件的信号和阶段。对基于显著性的热图的分析表明，高技能是由于该模式实际捕获了气候变率的主要模式，如印度洋偶极子和El Niño-Southern振荡。CNN ISMR预测集合可以补充预报中心的预测。

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引用次数: 0