Computers & Geosciences最新文献

{"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}

{"title":"Functional multiple-point simulation","authors":"Oluwasegun Taiwo Ojo ,&nbsp;Marc G. Genton","doi":"10.1016/j.cageo.2024.105767","DOIUrl":"10.1016/j.cageo.2024.105767","url":null,"abstract":"<div><div>We present a new paradigm, called functional multiple-point simulation, in which multiple-point geostatistical simulation can be performed when functions or curves are observed at each location of a random field. Multiple-point simulation is a non-parametric method used for conditional geostatistical simulation of complex spatial patterns by inferring multiple-point statistics from a training image, rather than from a two-point variogram or covariance model. When the observable at each spatial location is a functional random variable, such multiple-point simulation must take into account not only the spatial correlation among locations but also the similarity of functions or curves observed at each location. The data events to be compared in this case are now functional, in the sense that they consist of spatial arrangements of functions. Consequently, we propose four distances, inspired by the functional data analysis literature, for measuring similarities between functional data events and use these to extend the direct sampling method to perform multiple-function geostatistical simulation with functional fields. We coin the new method Functional Direct Sampling and carry out extensive qualitative and quantitative performance comparison between the four proposed distances using simulation techniques on two well-known applications of multiple-point simulation: simulating copies of a functional random field and gap-filling of locations in a functional random field. We apply the proposed method to a gap-filling task of simulated wind profiles spatial functions over the Arabian Peninsula.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105767"},"PeriodicalIF":4.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707104","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":"Multimodal feature integration network for lithology identification from point cloud data","authors":"Ran Jing ,&nbsp;Yanlin Shao ,&nbsp;Qihong Zeng ,&nbsp;Yuangang Liu ,&nbsp;Wei Wei ,&nbsp;Binqing Gan ,&nbsp;Xiaolei Duan","doi":"10.1016/j.cageo.2024.105775","DOIUrl":"10.1016/j.cageo.2024.105775","url":null,"abstract":"<div><div>Accurate lithology identification from outcrop surfaces is crucial for interpreting geological 3D data. However, challenges arise due to factors such as severe weathering and vegetation coverage, which hinder achieving ideal identification results with both accuracy and efficiency. The integration of 3D point cloud technology and deep learning methodologies presents a promising solution to address these challenges. In this study, we propose a novel multimodal feature integration network designed to distinguish various rock types from point clouds. Our network incorporates a multimodal feature integration block equipped with multiple attention mechanisms to extract representative deep features, along with a hierarchical feature separation block to leverage these features for precise segmentation of points corresponding to different lithologies. Furthermore, we introduce a specialized loss function tailored for rock type identification to enhance network training. Through experiments involving point cloud sampling strategies and loss function evaluation, we identify the optimal network configuration. Comparative analyses against baseline methods demonstrate the superiority of our proposed network across diverse study areas reconstructed from UAV images and laser scanner data, exhibiting improved visual appearance and metric values (Accuracy = 0.978, mean Accuracy = 0.895, mean IoU = 0.857). These findings underscore the efficacy of the multimodal feature integration network as a promising approach for lithology identification tasks in various digital outcrop models derived from heterogeneous data sources.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105775"},"PeriodicalIF":4.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659531","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":"A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network","authors":"Nian Yu ,&nbsp;Chenkai Wang ,&nbsp;Huang Chen ,&nbsp;Wenxin Kong","doi":"10.1016/j.cageo.2024.105765","DOIUrl":"10.1016/j.cageo.2024.105765","url":null,"abstract":"<div><div>Magnetotelluric (MT) inversion is an important means of MT data interpretation. The use of deep learning technology for MT inversion has attracted much attention because it is not limited to the initial model, avoids falling into local optimal solutions, and has the strong ability to process large amounts of data. However, obtaining highly reliable deep learning inversion results remains a challenge. In this paper, we have proposed a two-dimensional (2-D) MT inversion method based on the improved Dense Convolutional Network (DenseNet), with the aim of improving the reliability of the 2-D deep learning MT inversion results. First, the MARE2DEM is used to compute the 2-D MT forward responses when establishing the sample set. Then, an improved DenseNet is proposed by incorporating depthwise separable convolution in lieu of standard convolution within dense connection blocks, and embedding the attention mechanism. Depthwise separable convolution splits the standard convolution operation into depthwise and pointwise convolution, effectively capturing spatial features of input data and correlations between channels. Meanwhile, attention mechanism allows the network to assign varying degrees of importance (or attention) to different elements in a sequence of data, thus enhancing its ability of key feature extraction. This design not only retains the inherent feature reuse and alleviates gradient vanishing of DenseNet but also further enhances network performance. The optimized network parameters for the improved DenseNet are obtained by training on the training set, while the validation set is used to adjust hyperparameters and evaluate model performance. Finally, the proposed 2-D deep learning approach is verified by using both synthetic and field data. Experimental results with synthetic data show that the reliability of inversion results obtained by using the proposed algorithm is improved, and the inversion results obtained by using both TE- and TM-mode data is more accurate than those obtained by using the single mode data. The inversion results of field data show that the proposed 2-D MT deep learning inversion approach can effectively detect the subsurface resistivity structure and has a good application prospect.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105765"},"PeriodicalIF":4.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659031","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":"Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network","authors":"George Brencher ,&nbsp;Scott T. Henderson ,&nbsp;David E. Shean","doi":"10.1016/j.cageo.2024.105771","DOIUrl":"10.1016/j.cageo.2024.105771","url":null,"abstract":"<div><div>Atmospheric noise in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscures real displacement signals, especially in mountainous regions. As climate change disproportionately impacts the mountain cryosphere, a reliable technique for atmospheric correction in high-relief terrain is increasingly important. We developed and implemented a statistical machine learning atmospheric correction approach that relies on the differing spatial and topographic characteristics of slow-moving periglacial features and atmospheric noise. Our correction is applied at the native spatial and temporal resolution of the InSAR data, does not require external atmospheric reanalysis data, and can correct both stratified and turbulent atmospheric noise.</div><div>Using Sentinel-1 data from 2017 to 2022, we trained a convolutional neural network (CNN) on observed atmospheric noise from 330 short-baseline interferograms and observed displacement signals from time series inversion of 1322 interferograms. We applied our trained CNN to correct 251 additional interferograms over an out-of-region application area, which were inverted to create displacement time series. We used the Rocky Mountains in New Mexico, Colorado, and Wyoming as our training, validation, testing, and application areas. When applied to our testing dataset, our correction offered performance improvements of 131%, 208%, and 68% in structural similarity index measure over corrections using atmospheric reanalysis data, phase correlation with topography, and high-pass filtering, respectively. The CNN-corrected time series reveals previously obscured kinematic behavior of rock glaciers and other features in the application dataset. Our flexible, robust approach can be used to correct arbitrary InSAR data to analyze subtle surface deformation signals for a range of science and engineering applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105771"},"PeriodicalIF":4.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659533","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}

{"title":"Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding","authors":"Bahman Abbassi,&nbsp;Li-Zhen Cheng","doi":"10.1016/j.cageo.2024.105768","DOIUrl":"10.1016/j.cageo.2024.105768","url":null,"abstract":"<div><div>Understanding deformation networks, visible as curvilinear lineaments in images, is crucial for geoscientific explorations. However, traditional manual extraction of lineaments is expertise-dependent, time-consuming, and labor-intensive. This study introduces an automated method to extract and identify geological faults from aeromagnetic images, integrating Bayesian Hyperparameter Optimization (BHO), Principal Component Wavelet Analysis (PCWA), and Hysteresis Thresholding Algorithm (HTA). The continuous wavelet transform (CWT), employed across various scales and orientations, enhances feature extraction quality, while Principal Component Analysis (PCA) within the CWT eliminates redundant information, focusing on relevant features. Using a Gaussian Process surrogate model, BHO autonomously fine-tunes hyperparameters for optimal curvilinear pattern recognition, resulting in a highly accurate and computationally efficient solution for curvilinear lineament mapping. Empirical validation using aeromagnetic images from a prominent fault zone in the James Bay region of Quebec, Canada, demonstrates significant accuracy improvements, with 23% improvement in <em>F</em>_<em>β</em> Score over the unoptimized PCWA-HTA and a marked 300% improvement over traditional HTA methods, underscoring the added value of fusing BHO with PCWA in the curvilinear lineament extraction process. The iterative nature of BHO progressively refines hyperparameters, enhancing geological feature detection. Early BHO iterations broadly explore the hyperparameter space, identifying low-frequency curvilinear features representing deep lineaments. As BHO advances, hyperparameter fine-tuning increases sensitivity to high-frequency features indicative of shallow lineaments. This progressive refinement ensures that later iterations better detect detailed structures, demonstrating BHO's robustness in distinguishing various curvilinear features and improving the accuracy of curvilinear lineament extraction. For future work, we aim to expand the method's applicability by incorporating multiple geophysical image types, enhancing adaptability across diverse geological contexts.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105768"},"PeriodicalIF":4.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659020","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}