{"title":"Characterisation of a river embankment using a non‐destructive DC electrical survey","authors":"R. Umezawa, M. Jinguuji, T. Yokota","doi":"10.1002/nsg.12202","DOIUrl":"https://doi.org/10.1002/nsg.12202","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43198562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling 3D elastodynamic wave scattering due to density and Lamé parameter contrasts of near surface scatterers","authors":"U. Harmankaya, A. Kaslilar","doi":"10.1002/nsg.12199","DOIUrl":"https://doi.org/10.1002/nsg.12199","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44755607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"First application of a new seismo‐electric streamer for combined resistivity and seismic measurements along linearly extended earth structures","authors":"A. A., V. F., C. Comina","doi":"10.1002/nsg.12198","DOIUrl":"https://doi.org/10.1002/nsg.12198","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42088450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Chernyshov, A. Duchkov, Ivan Yurievich Koulakov
{"title":"Choosing optimal model parameterization for improving the accuracy of refraction seismic tomography","authors":"G. Chernyshov, A. Duchkov, Ivan Yurievich Koulakov","doi":"10.1002/nsg.12196","DOIUrl":"https://doi.org/10.1002/nsg.12196","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44578018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To reduce both the computational cost of probabilistic inversions and the ill-posedness of geophysical problems, model and data spaces can be reparameterized into low-dimensional domains where the inverse solution can be computed more efficiently. Among the many compression methods, deep learning algorithms based on deep generative models provide an efficient approach for model and data space reduction. We present a probabilistic electrical resistivity tomography inversion in which the data and model spaces are compressed through deep convolutional variational autoencoders, while the optimization procedure is driven by the ensemble smoother with multiple data assimilation, an iterative ensemble-based algorithm. This method iteratively updates an initial ensemble of models that are generated according to a previously defined prior model. The inversion outcome consists of the most likely solution and a set of realizations of the variables of interest from which the posterior uncertainties can be numerically evaluated. We test the method on synthetic data computed over a schematic subsurface model, and then we apply the inversion to field measurements. The model predictions and the uncertainty assessments provided by the presented approach are also compared with the results of a Markov Chain Monte Carlo sampling working in the compressed domains, a gradient-based algorithm and with the outcomes of an ensemble-based inversion running in the un-compressed spaces. A finite-element code constitutes the forward operator. Our experiments show that the implemented inversion provides most likely solutions and uncertainty quantifications comparable to those yielded by the ensemble-based inversion running in the full model and data spaces, and the Markov Chain Monte Carlo sampling, but with a significant reduction of the computational cost.
{"title":"Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders","authors":"M. Aleardi, A. Vinciguerra, E. Stucchi, A. Hojat","doi":"10.1002/nsg.12194","DOIUrl":"https://doi.org/10.1002/nsg.12194","url":null,"abstract":"To reduce both the computational cost of probabilistic inversions and the ill-posedness of geophysical problems, model and data spaces can be reparameterized into low-dimensional domains where the inverse solution can be computed more efficiently. Among the many compression methods, deep learning algorithms based on deep generative models provide an efficient approach for model and data space reduction. We present a probabilistic electrical resistivity tomography inversion in which the data and model spaces are compressed through deep convolutional variational autoencoders, while the optimization procedure is driven by the ensemble smoother with multiple data assimilation, an iterative ensemble-based algorithm. This method iteratively updates an initial ensemble of models that are generated according to a previously defined prior model. The inversion outcome consists of the most likely solution and a set of realizations of the variables of interest from which the posterior uncertainties can be numerically evaluated. We test the method on synthetic data computed over a schematic subsurface model, and then we apply the inversion to field measurements. The model predictions and the uncertainty assessments provided by the presented approach are also compared with the results of a Markov Chain Monte Carlo sampling working in the compressed domains, a gradient-based algorithm and with the outcomes of an ensemble-based inversion running in the un-compressed spaces. A finite-element code constitutes the forward operator. Our experiments show that the implemented inversion provides most likely solutions and uncertainty quantifications comparable to those yielded by the ensemble-based inversion running in the full model and data spaces, and the Markov Chain Monte Carlo sampling, but with a significant reduction of the computational cost.","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47101335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimising GPR time‐zero adjustment and two‐way travel time wavelet measurements using a realistic 3D numerical model","authors":"Hossain Zadhoush, A. Giannopoulos","doi":"10.1002/nsg.12193","DOIUrl":"https://doi.org/10.1002/nsg.12193","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":"7 5","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41257480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving the galvanic contact resistance for geoelectrical measurements in debris areas: a case study","authors":"M. Pavoni, A. Carrera, J. Boaga","doi":"10.1002/nsg.12192","DOIUrl":"https://doi.org/10.1002/nsg.12192","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46049559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigating soil conditions around buried water pipelines using VLF‐AC electrical resistivity survey","authors":"M. Jinguuji, T. Yokota","doi":"10.1002/nsg.12191","DOIUrl":"https://doi.org/10.1002/nsg.12191","url":null,"abstract":"","PeriodicalId":49771,"journal":{"name":"Near Surface Geophysics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42151142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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