Computers & Geosciences最新文献

{"title":"Automatic variogram calculation and modeling","authors":"Luis Davila Saavedra ,&nbsp;Clayton V. Deutsch","doi":"10.1016/j.cageo.2024.105774","DOIUrl":"10.1016/j.cageo.2024.105774","url":null,"abstract":"<div><div>The variogram is one of the most used tools in geostatistics. It represents a key step for the results of estimation and simulation. This paper presents a methodology of the experimental variogram points calculation and subsequent modeling, including some practical considerations. The proposed methodology infers the variogram parameters directly from the dataset to require minimum user input. Autovar is a program that implements the described methodology, giving an initial variogram model for disseminated and tabular deposits.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105774"},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707098","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}

{"title":"SaltFormer: A hybrid CNN-Transformer network for automatic salt dome detection","authors":"Yang Li ,&nbsp;Suping Peng ,&nbsp;Dengke He","doi":"10.1016/j.cageo.2024.105772","DOIUrl":"10.1016/j.cageo.2024.105772","url":null,"abstract":"<div><div>Salt dome interpretation of seismic data is a crucial task in the exploration and development of oil and gas. Conventional techniques, such as multi-attribute analysis, are laborious, time-consuming, and susceptible to subjective biases in their results. To achieve a more automated and precise identification of salt dome, we developed a hybrid network for salt dome detection. In order to optimally exploit both local and global features, a hierarchical Vision Transformer is employed as an encoder for feature extraction. Concurrently, the concurrent spatial and channel squeeze &amp; excitation attention module is utilized to improve detection accuracy in the decoder. Furthermore, we leveraged the complementarity of information between multiple tasks to enhance the model’s generalization performance. Using the competition data from the Kaggle platform provided by TGS-NOPEC Geophysics Company, automatic segmentation of salt domes was completed with a detection accuracy of 85.20%. A series of experiments were conducted using state-of-the-art models and the SaltFormer model, which was found to have higher detection accuracy compared to other networks. Finally, the test conducted with seismic field data from the Netherlands offshore F3 block in the North Sea demonstrate that the novel method is highly effective in detecting salt domes in seismic data.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105772"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707100","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}

{"title":"MagTFs: A tool for estimating multiple magnetic transfer functions to constrain Earth’s electrical conductivity structure","authors":"Zhengyong Ren ,&nbsp;Zijun Zuo ,&nbsp;Hongbo Yao ,&nbsp;Chaojian Chen ,&nbsp;Linan Xu ,&nbsp;Jingtian Tang ,&nbsp;Keke Zhang","doi":"10.1016/j.cageo.2024.105769","DOIUrl":"10.1016/j.cageo.2024.105769","url":null,"abstract":"<div><div>Time-varying magnetic signals measured by geomagnetic observatories and satellites carry information about the Earth’s deep electrical conductivity structure and external current sources in the ionosphere and magnetosphere. Estimating magnetic transfer functions (TFs), which reflect the Earth’s internal conductivity structure, is a primary task in interpreting geomagnetic data from observatories and satellites. However, available TFs estimation tools either focus on a single source (ionosphere currents or magnetosphere currents) or are not publicly accessible. Therefore, we developed a flexible TFs estimation tool, named MagTFs, to achieve robust and precise estimation of magnetic TFs from the time series of magnetic field data acquired through land or satellite-based observations. This tool can handle magnetic data originating from time-varying currents in both the ionosphere and magnetosphere. We tested its performance on four kinds of data sets, and the good agreements with published results underscore the tool’s maturity and versatility in accurately estimating multi-source TFs. As a contribution to the scientific community, we have released MagTFs as an open-source tool, facilitating broader utilization and collaborative advancements.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105769"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707101","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}

{"title":"An identification for channel mislabel of strong motion records based on Siamese neural network","authors":"Baofeng Zhou ,&nbsp;Bo Liu ,&nbsp;Xiaomin Wang ,&nbsp;Yefei Ren ,&nbsp;Maosheng Gong","doi":"10.1016/j.cageo.2024.105780","DOIUrl":"10.1016/j.cageo.2024.105780","url":null,"abstract":"<div><div>Strong motion records are first-hand data for studying the seismic response of sites or engineering structures, and their objectivity is crucial for the credibility of the results in earthquake engineering and engineering seismology. However, domestic and international earthquake data may be mislabeled between horizontal and vertical channels. This issue is typically addressed by manually comparing the similarity between the three components of strong motion records, which is inherently subjective and inefficient in identification. To achieve the intelligent recognition of massive records, this study used 14,983 sets of ground motion records with significant differences between horizontal and vertical components from the NGA-West2 database. A Siamese neural network preliminarily distinguished the similarity between the acceleration waveform and the three components of the Fourier amplitude spectrum (FAS) of ground motion records. Combined with manual identification, an efficient and accurate method for identifying vertical components in ground motion records was proposed, and applied to verify the channel directions of the strong motion records in Strong Motion Network in China. It was found that 308 sets of records from 170 stations were suspected of mislabeling vertical and horizontal components. This advancement significantly enhances the objectivity of strong motion records. This proposed method holds potential for remote maintenance of strong motion stations, verifying the channels of strong motion instruments, and mitigating the negative impact of channel confusion on research results.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105780"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707136","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}

{"title":"ReUNet: Efficient deep learning for precise ore segmentation in mineral processing","authors":"Chanjuan Wang ,&nbsp;Huilan Luo ,&nbsp;Jiyuan Wang ,&nbsp;Daniel Groom","doi":"10.1016/j.cageo.2024.105773","DOIUrl":"10.1016/j.cageo.2024.105773","url":null,"abstract":"<div><div>Efficient ore segmentation plays a pivotal role in advancing mineral processing technologies. With the rise of computer vision, deep learning models like UNet have increasingly outperformed traditional methods in automatic segmentation tasks. Despite these advancements, the substantial computational demands of such models have hindered their widespread adoption in practical production environments. To overcome this limitation, we developed ReUNet, a lightweight and efficient model tailored for mineral image segmentation. ReUNet optimizes computational efficiency by selectively focusing on critical spatial and channel information, boasting only 1.7 million parameters and 24.88 GFLOPS. It delivers superior segmentation performance across three public datasets (CuV1, FeMV1, and Pellets) and achieves the most accurate average particle size estimation, closely matching the true values. Our findings underscore ReUNet’s potential as a highly effective tool for mineral image analysis, offering both precision and efficiency in processing mineral images.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105773"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707099","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}

{"title":"Spectral whitening based seismic data preprocessing technique to improve the quality of surface wave's velocity spectra","authors":"Tarun Naskar ,&nbsp;Mrinal Bhaumik ,&nbsp;Sayan Mukherjee ,&nbsp;Sai Vivek Adari","doi":"10.1016/j.cageo.2024.105784","DOIUrl":"10.1016/j.cageo.2024.105784","url":null,"abstract":"<div><div>A high-quality surface wave velocity spectrum, also known as a dispersion image, is paramount for any MASW survey to accurately predict subsurface earth properties. The presence of diversified noise during field acquisition and dissimilar attenuation due to mechanical and radial damping makes it challenging for any wavefield transformation technique to produce a detailed and precise velocity spectrum. Standard surface wave data preprocessing techniques, such as trace normalization and bandpass filtering, along with postprocessing techniques like frequency-wise amplitude normalization, fail to address all these issues appropriately. In this paper, we present a spectral whitening-based data preprocessing technique that can adequately eradicate most of the shortcomings associated with different wavefield transformation techniques. Instead of normalizing each trace, it normalizes the amplitude of every frequency present in the seismogram. The spectral whitening can regain the relative amplitude losses due to both radial and mechanical damping, thus improving the signal-to-noise ratio. Along with diversified field data including Love and Rayleigh wave surveys, a synthetic dataset is used to demonstrate the efficacy of the proposed technique. Furthermore, field noise is added to random traces to test the ability of the proposed technique to filter asymmetric noise. Overall, the spectral whitening procedure significantly improves the quality of the velocity spectrum and produces a sharper dispersion image with well-separated modes. The work presented here enhances our ability to interpret surface wave velocity spectra precisely and helps explore accurate properties of the subsurface earth. It can help avoid the need for repeated field tests in cases of extremely noisy data, thereby significantly reducing costs and saving time.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105784"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707092","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}

{"title":"Heterogeneous layer effects on mining-induced dynamic ruptures","authors":"Yatao Li","doi":"10.1016/j.cageo.2024.105776","DOIUrl":"10.1016/j.cageo.2024.105776","url":null,"abstract":"<div><div>The risk of dynamic disasters increases with the trend toward deeper mining, highlighting an urgent need to better understand induced seismicity. To address this need, we developed custom code to implement the open-source software PyLith in the study of induced seismicity for the first time. We examined the effects of heterogeneous geological conditions on dynamic ruptures induced by deep mining operations. Our focus was on the dynamic ruptures and their effects on the nearby working face, analyzing parameters such as peak slip rates and rupture velocities. Our results show that rupture duration ranges from 255 ms to 676 ms and peak slip rates vary between 1.3 m/s and 5.0 m/s, with rupture velocities decreasing from 1.29 km/s to 0.17 km/s as the critical slip distance (<em>D</em>_c) increases. The relationship between peak slip rate and rupture velocity is consistent with Bizzarri's (2012) findings. A linear relationship between the times of peak slip rate (T_pv) and breakdown time (T_b) was observed, with a ratio of 1.0. In examining the induced seismic waves at the working face, we found that heterogeneous models exhibited more irregular slip distributions and higher peak particle acceleration (PPA) and peak particle velocity (PPV) compared to homogeneous models, indicating amplified seismic responses due to material heterogeneity. The study also identified potential risks to the working face's structural integrity, with more pronounced effects observed in hanging wall mining compared to footwall mining. These findings underscore the importance of considering geological heterogeneity in seismic hazard assessments and support the development of more accurate predictive models for mining-induced seismic events. It is important to note that our comparison of heterogeneous and homogeneous modeling is based on the assumption of identical initial traction, focusing on the effects of heterogeneous layers.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105776"},"PeriodicalIF":4.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707137","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}

{"title":"Robust frequency-domain acoustic waveform inversion using a measurement of smooth radical function derived from compressive sensing","authors":"Chao Lang,&nbsp;Ning Wang,&nbsp;Shi-Li Pang","doi":"10.1016/j.cageo.2024.105778","DOIUrl":"10.1016/j.cageo.2024.105778","url":null,"abstract":"<div><div>A smooth radical function derived from compressive sensing is introduced, aiming to measure the misfit in frequency-domain acoustic waveform inversion. The purpose of employing this function is to improve inverse accuracy and reliability. With a novel approximation of L1 norm, the objective function constructed by this measurement can exhibit favorable robustness throughout the inverse iteration. By exploiting the smoothness property, the misfit can be minimized through a cost-effective approach of taking derivatives. The inverse framework of the smooth radical function is derived which indicates comparable computing complexity per iterative step to L2 case, theoretically. The experiential data with outliers are employed for inversion and compared with the traditional optimization-based L1 norm and L2 norm. The obtained results are consistent with theoretical analysis and demonstrate the superiority of the proposed measurement.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105778"},"PeriodicalIF":4.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707102","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}

{"title":"Augmented formulation for a Bayesian approach for frequency-domain full-waveform inversion to estimate the material properties of a layered half-space","authors":"Hieu Van Nguyen,&nbsp;Jin Ho Lee","doi":"10.1016/j.cageo.2024.105782","DOIUrl":"10.1016/j.cageo.2024.105782","url":null,"abstract":"<div><div>Seismic full-waveform inversion (FWI) facilitates the generation of high-resolution subsurface images using wavefield measurements. Seismic FWI in the frequency domain is preferable because it allows consideration of the multiscale nature of FWI, controls the numerical dispersion of the media, and represents the hysteretic damping of the material. The Bayesian approach can be considered for FWI problems to alleviate the ill-posedness of inverse problems and quantify the uncertainty of the estimated parameters. This study rigorously formulates a Bayesian approach for seismic FWI in the frequency domain, assuming Gaussian probability distributions for the prior information of parameters to be estimated and the likelihood functions of observations. Conventional and augmented formulations are provided. In the augmented formulation, complex dynamic responses in the frequency domain are augmented by their complex conjugates. Rigorous expressions are derived for the posterior covariance matrix of estimated parameters to assess the uncertainty in these parameters. The proposed augmented formulation is demonstrated using various elastic inverse problems to estimate the shear-wave velocities of layered half-spaces. Excellent inverted profiles for the shear-wave velocities are obtained, and their posterior probability distributions are estimated using the Bayesian approach.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105782"},"PeriodicalIF":4.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707105","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}

{"title":"ProbShakemap: A Python toolbox propagating source uncertainty to ground motion prediction for urgent computing applications","authors":"Angela Stallone ,&nbsp;Jacopo Selva ,&nbsp;Louise Cordrie ,&nbsp;Licia Faenza ,&nbsp;Alberto Michelini ,&nbsp;Valentino Lauciani","doi":"10.1016/j.cageo.2024.105748","DOIUrl":"10.1016/j.cageo.2024.105748","url":null,"abstract":"<div><div>Seismic urgent computing enables early assessment of an earthquake’s impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions about rescue and mitigation activities in the affected areas. Uncertainty quantification for urgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present <span>ProbShakemap</span>, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. <span>ProbShakemap</span> incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS <span>ShakeMap</span> software and the <span>OpenQuake-engine</span>. <span>ShakeMap</span> modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the <span>OpenQuake-engine</span> libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. <span>ProbShakemap</span> provides the user with a set of tools to explore, at each POI, the predictive distribution of ground motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between <span>ProbShakemap</span> and <span>ShakeMap</span> output.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105748"},"PeriodicalIF":4.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707097","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}