Near Surface Geophysics最新文献_第10页

Characterisation of a river embankment using a non‐destructive DC electrical survey 用非破坏性直流电测量法描述河堤特征

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2022-02-19 DOI: 10.1002/nsg.12202

R. Umezawa, M. Jinguuji, T. Yokota

引用次数: 1

Modeling 3D elastodynamic wave scattering due to density and Lamé parameter contrasts of near surface scatterers 基于近表面散射体密度和Lamé参数对比的三维弹性动力波散射建模

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2022-02-06 DOI: 10.1002/nsg.12199

U. Harmankaya, A. Kaslilar

引用次数: 0

First application of a new seismo‐electric streamer for combined resistivity and seismic measurements along linearly extended earth structures 首次应用一种新的地震电拖缆，沿线性扩展的大地结构进行电阻率和地震测量

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2022-02-03 DOI: 10.1002/nsg.12198

A. A., V. F., C. Comina

引用次数: 3

An integrated long‐wavelength statics method applied to seismic processing of Tibetan permafrost 一种应用于西藏多年冻土地震处理的长波长静校正方法

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2022-01-17 DOI: 10.1002/nsg.12197

Zhiwei Liu, Haixin Feng, Ying Lu, Yang Gao, Lei Zhou

引用次数: 0

Issue Information 问题信息

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2022-01-14 DOI: 10.1002/nsg.12195

引用次数: 0

Choosing optimal model parameterization for improving the accuracy of refraction seismic tomography 选择最优模型参数提高折射地震层析成像精度

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2022-01-10 DOI: 10.1002/nsg.12196

G. Chernyshov, A. Duchkov, Ivan Yurievich Koulakov

引用次数: 0

Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders 随机电阻率断层成像与集成平滑和深度卷积自编码器

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2021-12-30 DOI: 10.1002/nsg.12194

M. Aleardi, A. Vinciguerra, E. Stucchi, A. Hojat

{"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}

引用次数: 0

Optimising GPR time‐zero adjustment and two‐way travel time wavelet measurements using a realistic 3D numerical model 优化GPR时零调整和双向行程时间小波测量使用现实的三维数值模型

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2021-12-28 DOI: 10.1002/nsg.12193

Hossain Zadhoush, A. Giannopoulos

引用次数: 2

Improving the galvanic contact resistance for geoelectrical measurements in debris areas: a case study 提高碎片地区地电测量的触电电阻:一个案例研究

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2021-12-15 DOI: 10.1002/nsg.12192

M. Pavoni, A. Carrera, J. Boaga

引用次数: 6

Investigating soil conditions around buried water pipelines using VLF‐AC electrical resistivity survey 利用VLF - AC电阻率测量法调查埋地输水管道周围的土壤状况

IF 1.6 4区地球科学

Near Surface Geophysics Pub Date : 2021-12-13 DOI: 10.1002/nsg.12191

M. Jinguuji, T. Yokota

引用次数: 0