Computers & Geosciences最新文献_第4页

Enhancing machine learning thermobarometry for clinopyroxene-bearing magmas 增强含烊辉石岩浆的机器学习热压测量法

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-31 DOI: 10.1016/j.cageo.2024.105707

{"title":"Enhancing machine learning thermobarometry for clinopyroxene-bearing magmas","authors":"","doi":"10.1016/j.cageo.2024.105707","DOIUrl":"10.1016/j.cageo.2024.105707","url":null,"abstract":"<div><p>In this study, we proposed a general workflow that aims to enhance the ML-based geothermobarometer modelling. Our workflow focuses on three key areas. Firstly, we developed a robust pre-processing pipeline that addresses data imbalance, feature engineering, and data augmentation. Secondly, we assessed modelling errors using a Monte Carlo approach to quantify the impact of analytical uncertainties on the final pressure and temperature estimates. Thirdly, we implemented a robust strategy to validate and test the ML models to avoid over- and under-fitting issues while correcting biases associated with the application of specific ML models (i.e., tree-based ensembles).</p><p>To facilitate the use of our workflow, we have developed a web app (<span><span>https://bit.ly/ml-pt-web</span><svg><path></path></svg></span>) and a Python module (<span><span>https://bit.ly/ml-pt-py</span><svg><path></path></svg></span>). The robustness of this strategy has been tested on two calibrations: clinopyroxene (cpx) and clinopyroxene-liquid (cpx-liq). Our results show a significant reduction in errors compared to the baseline model, as well as good generalization ability on an independent external dataset. The Root Mean Squared Errors are 57 °C and 2.5 kbar for the cpx calibration, and 36 °C and 2.1 kbar for the cpx-liq calibration. Finally, our models show improved outcomes on the external dataset compared to existing ML and classical cpx and cpx-liq thermobarometers.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001900/pdfft?md5=35a76aa189a72d9015dd976686c4e57f&pid=1-s2.0-S0098300424001900-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on microseismic signal identification through data fusion 通过数据融合识别微地震信号的研究

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-31 DOI: 10.1016/j.cageo.2024.105708

引用次数: 0

Massively parallel modeling of electromagnetic field in conductive media: An MPI-CUDA implementation on Multi-GPU computers 导电介质中电磁场的大规模并行建模：多 GPU 计算机上的 MPI-CUDA 实现

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-30 DOI: 10.1016/j.cageo.2024.105710

{"title":"Massively parallel modeling of electromagnetic field in conductive media: An MPI-CUDA implementation on Multi-GPU computers","authors":"","doi":"10.1016/j.cageo.2024.105710","DOIUrl":"10.1016/j.cageo.2024.105710","url":null,"abstract":"<div><p>Numerical modeling of electromagnetic (EM) fields in a conductive marine environment is crucial for marine EM data interpretation. During marine controlled-source electromagnetic (MCSEM) surveys, a variety of transmitter locations are used to introduce electric currents. The resulting electric and magnetic fields are then concurrently logged by a network of receivers. The forward simulation of MCSEM data for a subsea structure whose electrical properties vary in all three dimensions is computationally intensive. We demonstrate how such computations may be substantially accelerated by adapting algorithms to operate efficiently on modern GPUs with many core architectures. The algorithm we present features a hybrid MPI-CUDA programming model suitable for multi-GPU computers and consists of three levels of parallelism. We design the optimal kernels for different components to minimize redundant memory accesses. We have tested the algorithm on NVIDIA Kepler architecture and achieved up to 105 × speedup compared with the serial code version. We further showcased the algorithm's performance advantages through its application to a realistic marine model featuring complex geological structures. Our algorithm's significant efficiency increase opens the possibility of 3D MCSEM data interpretation based on probabilistic or machine learning approaches, which require tens of thousands of forward simulations for every survey.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining deep neural network and spatio-temporal clustering to automatically assess rockburst and seismic hazard – Case study from Marcel coal mine in Upper Silesian Basin, Poland 结合深度神经网络和时空聚类自动评估岩爆和地震危害--波兰上西里西亚盆地马塞尔煤矿案例研究

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-30 DOI: 10.1016/j.cageo.2024.105709

{"title":"Combining deep neural network and spatio-temporal clustering to automatically assess rockburst and seismic hazard – Case study from Marcel coal mine in Upper Silesian Basin, Poland","authors":"","doi":"10.1016/j.cageo.2024.105709","DOIUrl":"10.1016/j.cageo.2024.105709","url":null,"abstract":"<div><p>Mine induced seismic events are a major safety concern in mining and require careful monitoring and management to reduce their effects. Therefore, an essential step in assessing seismic and rock burst hazards is the analysis of mine seismicity. Recently, deep neural networks have been used to automatically determine seismic wave arrival times, surpassing human performance and allowing their use in seismic data analysis such as seismic event location and seismic energy calculation. In order to properly automate the rockburst and seismic hazard assessment deep neural network phase picker and a spatio-temporal clustering method were utilized. Seismic and rockburst hazards were statistically quantified using two-way contingency tables for two categorical variables: seismic energy level of mine tremors and number of clusters. Correlations between several spatio-temporal clusters and a statistical association between two categorical variables: seismic energy level and cluster number indicate an increase of seismic hazard in the Marcel hard coal mine in Poland. A new automated tool has been elaborated to automatically identify high-stress areas in mines in the form of spatio-temporal clusters.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001924/pdfft?md5=2bf4c7ce15a9d7979aa62ba8147334ed&pid=1-s2.0-S0098300424001924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DRRGlobal: Uncovering the weak phases from global seismograms using the damped rank-reduction method DRRGlobal：使用阻尼秩还原法从全球地震图中发现弱相

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-23 DOI: 10.1016/j.cageo.2024.105687

{"title":"DRRGlobal: Uncovering the weak phases from global seismograms using the damped rank-reduction method","authors":"","doi":"10.1016/j.cageo.2024.105687","DOIUrl":"10.1016/j.cageo.2024.105687","url":null,"abstract":"<div><p>Some target seismic signals in the earthquake data can be very weak compared with interfering phases, and are thus difficult to detect, which further hinders the effective usage of these weak phases for subsequent high-resolution imaging of earth interiors. The strong ambient noise makes this situation even more troublesome since the weak signals can be mostly buried in the noise. Here, we present an open-source package for uncovering the weak phases from global seismograms. We adopt a two-step scheme to reconstruct and denoise array data. The first step is weighted average interpolation which puts the data into irregular grids. The second step adopts the weighted projection-onto-convex sets based on damped rank-reduction to further interpolate and denoise for the binned data. Taking the complexity of the weak signal into consideration, we adopt the automatic strategy to select an appropriate rank in different localized windows. We conduct several synthetic tests to carefully investigate the performance regarding effectiveness, robustness, and efficiency, and compare the algorithm with the frequency–wavenumber-domain projection onto convex sets method that is already used in the global seismology literature. Finally, the proposed framework is validated via a recorded array data set of the 1995 May 5 Philippines earthquake.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forward modeling of single-sided magnetic resonance and evaluation of T2 fitting error based on geometric analytical method 基于几何分析法的单侧磁共振前向建模和 T2 拟合误差评估

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-22 DOI: 10.1016/j.cageo.2024.105705

{"title":"Forward modeling of single-sided magnetic resonance and evaluation of T2 fitting error based on geometric analytical method","authors":"","doi":"10.1016/j.cageo.2024.105705","DOIUrl":"10.1016/j.cageo.2024.105705","url":null,"abstract":"<div><p>Single-sided magnetic resonance (SSMR) offers advantages of portability and noninvasive measurement for water detection, with significant potential applications in groundwater exploration, petroleum well logging, and soil moisture monitoring. However, the inherent highly inhomogeneous static magnetic field and radiofrequency (RF) field in SSMR necessitate the utilization of the Carr–Purcell–Meiboom–Gill (CPMG) sequence measurement scheme. To accelerate forward modeling during pulse excitation, we introduce a Geometric Analysis Method (GAM) and assess <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> error using its primary parameters. The GAM involves applying spatial geometric rotations on the magnetization vector, leading to an analytical solution to the Bloch equation that disregards relaxation effects. Compared with the rotation matrix (RM) method, the GAM demonstrates high accuracy and reduces computational time by approximately 20.9%. By analyzing the primary parameters governing the magnetization vector in the analytical formula, we evaluated their impact on the transverse relaxation time (<span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>) obtained through fitting the SE signal. Ultimately, the forward modeling results of the CPMG sequence within the region of interest (ROI) of a single-sided Halbach magnet array are validated. The <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> fitting error increases as the primary parameters deviate from the ideal values, highlighting their significant role in the <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> fitting results. This study provides a theoretical foundation for optimizing the design of SSMR magnets and RF coils.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced taxonomic identification of fusulinid fossils through image–text integration using transformer 利用转换器进行图像-文本整合，加强对燧石化石的分类鉴定

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-17 DOI: 10.1016/j.cageo.2024.105701

{"title":"Enhanced taxonomic identification of fusulinid fossils through image–text integration using transformer","authors":"","doi":"10.1016/j.cageo.2024.105701","DOIUrl":"10.1016/j.cageo.2024.105701","url":null,"abstract":"<div><p>The accurate taxonomic identification of fusulinid fossils holds significant scientific value in palaeontology, paleoecology, and palaeogeography. However, imbalanced image samples lead to the model preferring to learn features from categories with many samples while ignoring fewer sample categories, greatly reducing the prediction accuracy of fusulinid fossil identification. Moreover, the textual description of fusulinid fossils contains rich feature information. We collected and created an order fusulinid multimodal (OFM) dataset for research. We proposed a transformer-based multimodal integration framework (TMIF) using deep learning for fusulinid fossil identification. Compared to traditional neural networks, the transformer can create global dependencies between features at different locations. TMIF incorporates image and text branches dedicated to extracting features for both modalities, and a pivotal cross-modal integration module that allows visual features to learn textual semantic features sufficiently to obtain a more comprehensive feature representation. Experimental evaluation using the OFM dataset shows that TMIF achieves a prediction accuracy of 81.7%, which is a 2.8% improvement over the only image-based method. Further comparative analyses across multiple networks affirm that the TMIF performs optimally in addressing the taxonomic identification of fusulinid fossils with imbalanced samples.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic segmentation of coastal aerial/satellite images using deep learning techniques: An application to coastline detection 利用深度学习技术对海岸航空/卫星图像进行语义分割：海岸线探测应用

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-15 DOI: 10.1016/j.cageo.2024.105704

{"title":"Semantic segmentation of coastal aerial/satellite images using deep learning techniques: An application to coastline detection","authors":"","doi":"10.1016/j.cageo.2024.105704","DOIUrl":"10.1016/j.cageo.2024.105704","url":null,"abstract":"<div><p>A new CNN based approach supported by semantic segmentation, was proposed. This approach is frequently used to carry out regional-scale studies. The core of our method revolves around a CNN model, based on the famous U-Net architecture. Its purpose is to identify different classes of pixels on satellite images and later to automatically detect the coastline. The recently launched Coast Train dataset was used to train the CNN model. Traditional coastline detection was improved (“water/land” segmentation) by means of two new aspects the use of the Sobel-edge loss function and the segmentation of the satellite images into several categories like built-up areas, vegetation and land besides beach/sand and water classes. The approach used ensures a more precise coastline extraction, distinguishing water pixels from all other categories. Our model adeptly identifies features, such as cliff vegetation or coastal roads, that some models might overlook. In this way, coastline localization and its drawing for regional scale study, have minor uncertainties. The performance of the CNN-based method, achieving 85% accuracy and 80% IoU (Intersection over Union) in the segmentation process. The ability of the model to extract the coastline was validated on a Sicilian case study, notably the San Leone beach (Agrigento). The model's results align closely with the ground truth, moreover, its reliability was further confirmed when it was tested on other Sicilian coastal regions.</p><p>Beyond robustness, the model offers a promising avenue for enhanced coastal analysis potentially applicable to coastal planning and management.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAIPy: A Python package for single-station earthquake monitoring using deep learning SAIPy：利用深度学习进行单站地震监测的 Python 软件包

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-14 DOI: 10.1016/j.cageo.2024.105686

{"title":"SAIPy: A Python package for single-station earthquake monitoring using deep learning","authors":"","doi":"10.1016/j.cageo.2024.105686","DOIUrl":"10.1016/j.cageo.2024.105686","url":null,"abstract":"<div><p>Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open-source Python package specifically developed for fast seismic data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake signal detection, seismic phase picking, first motion polarity identification and magnitude estimation. We introduce upgraded versions of previously published models such as CREIME_RT capable of identifying earthquakes with an accuracy above 99.8% and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state-of-the-art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIME_RT, DynaPicker_v2, and PolarCAP, along with benchmark datasets. It also, to the best of our knowledge, introduces the first fully automated deep learning based pipeline to process continuous waveforms. The package has the potential to be used for real-time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to further enhance SAIPy’s performance and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem. A detailed description of all functions is available in a supplementary document.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0098300424001699/pdfft?md5=181c42ee7372a7ceb6bfb0f6134f713e&pid=1-s2.0-S0098300424001699-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning Geo-Hgan：通过潜在空间学习对地球化学数据进行无监督异常检测

IF 4.2 2区地球科学

Computers & Geosciences Pub Date : 2024-08-14 DOI: 10.1016/j.cageo.2024.105703

{"title":"Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning","authors":"","doi":"10.1016/j.cageo.2024.105703","DOIUrl":"10.1016/j.cageo.2024.105703","url":null,"abstract":"<div><p>Reconstructing geochemical data for anomaly detection using Generative Adversarial Networks (GANs) has become a prevalent method in identifying geochemical anomalies. However, injecting random noise into GANs can induce model instability. To mitigate this issue, we propose a novel anomaly detection model, Geo-Hgan, which integrates a dual adversarial network architecture with a Latent Space Adversarial Module (LSAM) to learn the distribution of latent variables from arbitrary data and optimize the sample reconstruction process, thereby alleviating instability during GAN training. Additionally, an encoder guided by the LSAM-pretrained GAN is employed to extract variational features, facilitating rapid and effective sample mapping into the latent space defined by LSAM. Experimental results demonstrate that under unsupervised conditions, Geo-Hgan achieves an Area Under the Curve (AUC) score of 85% across three geochemical datasets, outperforming similar models in accuracy and reconstruction capabilities. To assess its versatility and generalization ability, we extend Geo-Hgan to anomaly detection tasks in computer vision, where it achieves an average AUC score of 98.7% on the MvtecAD dataset, setting a new state-of-the-art performance in the domain. Furthermore, we propose AnomFilter, a method for setting anomaly thresholds based on the clustering hypothesis. AnomFilter identifies high-confidence anomaly samples identified by Geo-Hgan in the source domain and iteratively transfers them to the target domain. These high-confidence anomaly samples, combined with a small number of known positive samples in the target domain, enhance the accuracy of supervised geochemical anomaly detection in the target domain, which achieved an AUC score of 94%. The utilization of anomaly detection models for sample transfer learning offers a novel perspective for future work.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0