首页 > 最新文献

Journal of Applied Geophysics最新文献

英文 中文
Magnetic diagnosis model for heavy metal pollution in beach sediments of Qingdao, China 中国青岛海滨沉积物重金属污染磁力诊断模型
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-14 DOI: 10.1016/j.jappgeo.2024.105553
Wang Yong-Hong , Huang Yi-Heng , Liang Wei-Qiang
Magnetic techniques have been widely used in recent decades to determine heavy metal pollution in sediments due to their high sensitivity to magnetic particles and considerable measurement convenience. Beaches are usually greatly influenced by human activities, but pollution problems such as heavy metal pollution due to sewage discharge, nearby factories, and garbage disposal have reduced the tourism value and ecological environmental quality of beaches. In this study, three beaches in Qingdao city were chosen as examples, and a magnetic diagnostic model for heavy metal pollution in beach sediments was established using statistical methods. The results showed that beach No. 1 in Qingdao was not polluted, while the pollution level of beach No. 2 was lower than that of beach No. 3. Beach No. 2 exhibited slight Cr and Zn pollution and slight Fe enrichment, while beach No. 3 exhibited slight to severe Cr, Ni, and Zn pollution and severe Fe enrichment. The statistical model results indicated that χ, saturation isothermal remanent magnetization (SIRM), SOFT, and χARM are more suitable for establishing magnetic diagnostic models, and the pollution level, pollution source and diffusion range of heavy metal elements could be detected with this model. The main causes of pollution are sewage outlets and the disposal of artificial coal ash. When the magnetic susceptibility value of the 0.063–0.125 mm particle size fraction of Qingdao beach sediments is greater than 6000 × 10−8 m3kg−1, attention should be given to possible contamination by heavy metals. In this study, we revealed that environmental magnetic methods can be employed to effectively determine the pollution level, source, and diffusion of heavy metals in beach sediments, which can facilitate the management of heavy metals and other pollutants in beach sediments and ecological environmental protection.
近几十年来,磁性技术因其对磁性颗粒的高灵敏度和相当大的测量便利性而被广泛用于测定沉积物中的重金属污染。海滩通常受人类活动影响较大,但由于污水排放、附近工厂、垃圾处理等造成的重金属污染问题,降低了海滩的旅游价值和生态环境质量。本研究选取青岛市的三个海滩为例,利用统计学方法建立了海滩沉积物重金属污染磁性诊断模型。结果表明,青岛 1 号海滩未受污染,2 号海滩的污染程度低于 3 号海滩。2 号海水浴场表现为轻度铬、锌污染和轻度铁富集,3 号海水浴场表现为轻度至重度铬、镍、锌污染和重度铁富集。统计模型结果表明,χ、饱和等温剩磁(SIRM)、SOFT 和 χARM 更适合建立磁诊断模型,利用该模型可检测重金属元素的污染程度、污染源和扩散范围。污染的主要原因是排污口和人工煤灰的处理。当青岛海滨沉积物 0.063-0.125 mm 粒径部分的磁感应强度值大于 6000×10-8 m3kg-1 时,应注意可能受到重金属污染。本研究揭示了利用环境磁法可以有效地确定海滩沉积物中重金属的污染程度、来源和扩散情况,有利于海滩沉积物中重金属等污染物的治理和生态环境保护。
{"title":"Magnetic diagnosis model for heavy metal pollution in beach sediments of Qingdao, China","authors":"Wang Yong-Hong ,&nbsp;Huang Yi-Heng ,&nbsp;Liang Wei-Qiang","doi":"10.1016/j.jappgeo.2024.105553","DOIUrl":"10.1016/j.jappgeo.2024.105553","url":null,"abstract":"<div><div>Magnetic techniques have been widely used in recent decades to determine heavy metal pollution in sediments due to their high sensitivity to magnetic particles and considerable measurement convenience. Beaches are usually greatly influenced by human activities, but pollution problems such as heavy metal pollution due to sewage discharge, nearby factories, and garbage disposal have reduced the tourism value and ecological environmental quality of beaches. In this study, three beaches in Qingdao city were chosen as examples, and a magnetic diagnostic model for heavy metal pollution in beach sediments was established using statistical methods. The results showed that beach No. 1 in Qingdao was not polluted, while the pollution level of beach No. 2 was lower than that of beach No. 3. Beach No. 2 exhibited slight Cr and Zn pollution and slight Fe enrichment, while beach No. 3 exhibited slight to severe Cr, Ni, and Zn pollution and severe Fe enrichment. The statistical model results indicated that χ, saturation isothermal remanent magnetization (SIRM), SOFT, and χ<sub>ARM</sub> are more suitable for establishing magnetic diagnostic models, and the pollution level, pollution source and diffusion range of heavy metal elements could be detected with this model. The main causes of pollution are sewage outlets and the disposal of artificial coal ash. When the magnetic susceptibility value of the 0.063–0.125 mm particle size fraction of Qingdao beach sediments is greater than 6000 × 10<sup>−8</sup> m<sup>3</sup>kg<sup>−1</sup>, attention should be given to possible contamination by heavy metals. In this study, we revealed that environmental magnetic methods can be employed to effectively determine the pollution level, source, and diffusion of heavy metals in beach sediments, which can facilitate the management of heavy metals and other pollutants in beach sediments and ecological environmental protection.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105553"},"PeriodicalIF":2.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Deep learning-based geophysical joint inversion using partial channel drop method 利用部分通道下降法进行基于深度学习的地球物理联合反演
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-08 DOI: 10.1016/j.jappgeo.2024.105554
Jongchan Oh , Shinhye Kong , Daeung Yoon , Seungwook Shin
Joint inversion stands as a critical technique for the precise determination of subsurface structures by mitigating the ill-posedness inherent in separate geophysical inversion procedures. Recently, the integration of deep learning (DL) into joint inversion has shown promise in achieving more precise interpretations. However, existing DL-based joint inversion approaches face challenges, particularly when survey configurations between training and test datasets vary, and are prone to overfitting towards specific types of data. In response to these limitations, we introduce the Partial Channel Drop (PCD) method applied to DL joint inversion, resulting in a DL-PCD joint inversion model. Our study utilizes gravity, magnetic, and direct current resistivity data as the multiple geophysical data sources and employs 3D U-Net for the DL joint inversion model. The PCD method is implemented during the DL joint inversion training process, yielding a robust and versatile DL-based joint inversion model that can adapt to differing data configurations and manage scenarios with missing data while preventing overfitting and consequent bias in inversion results. Our proposed approach demonstrates superior generalization performance and robustness compared to separate inversion and DL joint inversion without the PCD method, exhibiting resilience even when faced with added noise. The results validate the effectiveness of the PCD method in bolstering the generalization performance of DL joint inversion, laying the groundwork for transformative possibilities in future 3D joint inversion research.
联合反演可以减轻单独地球物理反演程序固有的不确定性,是精确测定地下结构的关键技术。最近,将深度学习(DL)整合到联合反演中,有望实现更精确的解释。然而,现有的基于深度学习的联合反演方法面临着挑战,尤其是当训练数据集和测试数据集之间的勘测配置不同时,容易出现对特定类型数据的过拟合。针对这些局限性,我们引入了应用于 DL 联合反演的部分通道下降(PCD)方法,从而产生了 DL-PCD 联合反演模型。我们的研究利用重力、磁力和直流电阻率数据作为多种地球物理数据源,并采用三维 U-Net 建立 DL 联合反演模型。PCD 方法是在 DL 联合反演训练过程中实施的,它产生了一种基于 DL 的稳健且通用的联合反演模型,能够适应不同的数据配置并管理数据缺失的情况,同时防止过拟合和反演结果的偏差。与不使用 PCD 方法的单独反演和 DL 联合反演相比,我们提出的方法具有更优越的泛化性能和鲁棒性,即使在面临额外噪声时也能表现出弹性。结果验证了 PCD 方法在增强 DL 联合反演的泛化性能方面的有效性,为未来三维联合反演研究的变革性可能性奠定了基础。
{"title":"Deep learning-based geophysical joint inversion using partial channel drop method","authors":"Jongchan Oh ,&nbsp;Shinhye Kong ,&nbsp;Daeung Yoon ,&nbsp;Seungwook Shin","doi":"10.1016/j.jappgeo.2024.105554","DOIUrl":"10.1016/j.jappgeo.2024.105554","url":null,"abstract":"<div><div>Joint inversion stands as a critical technique for the precise determination of subsurface structures by mitigating the ill-posedness inherent in separate geophysical inversion procedures. Recently, the integration of deep learning (DL) into joint inversion has shown promise in achieving more precise interpretations. However, existing DL-based joint inversion approaches face challenges, particularly when survey configurations between training and test datasets vary, and are prone to overfitting towards specific types of data. In response to these limitations, we introduce the Partial Channel Drop (PCD) method applied to DL joint inversion, resulting in a DL-PCD joint inversion model. Our study utilizes gravity, magnetic, and direct current resistivity data as the multiple geophysical data sources and employs 3D U-Net for the DL joint inversion model. The PCD method is implemented during the DL joint inversion training process, yielding a robust and versatile DL-based joint inversion model that can adapt to differing data configurations and manage scenarios with missing data while preventing overfitting and consequent bias in inversion results. Our proposed approach demonstrates superior generalization performance and robustness compared to separate inversion and DL joint inversion without the PCD method, exhibiting resilience even when faced with added noise. The results validate the effectiveness of the PCD method in bolstering the generalization performance of DL joint inversion, laying the groundwork for transformative possibilities in future 3D joint inversion research.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105554"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra 使用嵌套四面体的改进型目标导向自适应有限元方法,用于三维直流电阻率各向异性正向建模
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-08 DOI: 10.1016/j.jappgeo.2024.105555
Lewen Qiu , Jingtian Tang , Zhengguang Liu
We developed a novel adaptive finite element method (FEM) to address the problem of 3-D direct current (DC) resistivity forward modeling with complex surface topography and arbitrary conductivity anisotropy. The tetrahedra-based FEM and secondary virtual potential algorithm are first used to handle arbitrary complex geo-models. Then, to ensure the accuracy of the simulation solution, an improved goal-oriented adaptive mesh refinement (AMR) algorithm is proposed to realize an optimized mesh density distribution. To avoid the drawback of the traditional goal-oriented AMR algorithm for the DC forward modeling problem, we incorporate a volume-based weighting factor into the posterior error estimation procedure to further optimize the density distribution of the forward modeling grid. In addition, instead of traditional open source mesh generation software, we propose using the longest-edge bisection (LEB) algorithm to perform the mesh refinement process, which can preserve the topological structure between different-level meshes. Finally, the comprehensive test using a two-layered model and two complex 3-D models demonstrate the capability of our newly developed code to obtain highly accurate solutions even on relatively coarse initial grids. By incorporating the volume factor, our novel AMR algorithm achieves a more uniform and reasonable mesh density distribution during these experiments. The LEB refinement technique can generate a series of nested tetrahedral elements and provide fewer tetrahedral elements compared to the traditional Delaunay-based AMR method. The proposed 3-D DC forward modeling method has been implemented into an open source C++ code, which will contribute to the advancement of the 3-D DC resistivity imaging field.
我们开发了一种新颖的自适应有限元法(FEM),以解决具有复杂表面地形和任意电导率各向异性的三维直流(DC)电阻率正演建模问题。首先使用基于四面体的有限元法和二次虚拟电位算法来处理任意复杂的地质模型。然后,为确保仿真解的精度,提出了一种改进的目标导向自适应网格细化(AMR)算法,以实现优化的网格密度分布。为避免传统面向目标的自适应网格细化算法在直流前向建模问题上的缺陷,我们在后向误差估计过程中加入了基于体积的加权因子,以进一步优化前向建模网格的密度分布。此外,我们建议使用最长边分割(LEB)算法代替传统的开源网格生成软件来执行网格细化过程,这样可以保留不同层次网格之间的拓扑结构。最后,使用一个两层模型和两个复杂的三维模型进行的综合测试表明,我们新开发的代码即使在相对较粗的初始网格上也能获得高精度的解。通过加入体积因子,我们的新型 AMR 算法在这些实验中实现了更均匀、更合理的网格密度分布。与传统的基于 Delaunay 的 AMR 方法相比,LEB 细分技术可以生成一系列嵌套的四面体元素,并提供更少的四面体元素。所提出的三维直流正演建模方法已被应用到开源的 C++ 代码中,这将有助于推动三维直流电阻率成像领域的发展。
{"title":"An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra","authors":"Lewen Qiu ,&nbsp;Jingtian Tang ,&nbsp;Zhengguang Liu","doi":"10.1016/j.jappgeo.2024.105555","DOIUrl":"10.1016/j.jappgeo.2024.105555","url":null,"abstract":"<div><div>We developed a novel adaptive finite element method (FEM) to address the problem of 3-D direct current (DC) resistivity forward modeling with complex surface topography and arbitrary conductivity anisotropy. The tetrahedra-based FEM and secondary virtual potential algorithm are first used to handle arbitrary complex geo-models. Then, to ensure the accuracy of the simulation solution, an improved goal-oriented adaptive mesh refinement (AMR) algorithm is proposed to realize an optimized mesh density distribution. To avoid the drawback of the traditional goal-oriented AMR algorithm for the DC forward modeling problem, we incorporate a volume-based weighting factor into the posterior error estimation procedure to further optimize the density distribution of the forward modeling grid. In addition, instead of traditional open source mesh generation software, we propose using the longest-edge bisection (LEB) algorithm to perform the mesh refinement process, which can preserve the topological structure between different-level meshes. Finally, the comprehensive test using a two-layered model and two complex 3-D models demonstrate the capability of our newly developed code to obtain highly accurate solutions even on relatively coarse initial grids. By incorporating the volume factor, our novel AMR algorithm achieves a more uniform and reasonable mesh density distribution during these experiments. The LEB refinement technique can generate a series of nested tetrahedral elements and provide fewer tetrahedral elements compared to the traditional Delaunay-based AMR method. The proposed 3-D DC forward modeling method has been implemented into an open source C++ code, which will contribute to the advancement of the 3-D DC resistivity imaging field.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105555"},"PeriodicalIF":2.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques 利用机器学习技术为岩土工程和地质环境应用建立电阻率高级预测模型
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-06 DOI: 10.1016/j.jappgeo.2024.105557
Soumitra Kumar Kundu , Ashim Kanti Dey , Sanjog Chhetri Sapkota , Prasenjit Debnath , Prasenjit Saha , Arunava Ray , Manoj Khandelwal
Electrical Resistivity (ER) is one of the best geophysical methods for subsurface investigation, especially for geotechnical and geo-environmental studies. Being non-invasive, economical and rapid, this method is highly preferable to geotechnical engineers for continuous evaluation of soil properties along the resistivity profile. Numerous studies have been conducted to correlate the subsurface properties with the ER. However, most of the studies consider a single input variable, which is correlated with the resistivity values using some conventional regression analyses. Very few studies have been conducted to obtain the resistivity value with multiple input parameters, like unit weight, temperature, porosity, moisture content, etc. Since, the soil parameters have a combined effect on resistivity, hence, correlations between the resistivity and the multiple input parameters are urgently required for a better and more reliable result. Moreover, the non-linear properties of soil make the task more complicated. To fill up this research gap, in the present study, 2772 ER tests were conducted using seven different types of soil with different combinations of temperature, density, and water content. Using this database, a Support Vector Regression (SVR), Artificial Neural Network (ANN) model and Extreme Gradient Boosting (XGB) were developed for prediction of ER. It has been understood that all the models are acknowledged as trustworthy data modelling tools. However, the XGB model performs better with an R2 of 0.99 during the training and testing phase. Further, a parametric study was also done to determine, how each input parameter affects the ER. An error analysis was also performed to see the consistent discrepancy between the experimental and projected values of ER. The outcomes validate the robustness of the XGB model, indicating that it can serve as a substitute method for ER prediction.
电阻率(ER)是进行地下勘测,特别是岩土工程和地质环境研究的最佳地球物理方法之一。这种方法具有非侵入性、经济性和快速性等特点,非常适合岩土工程师沿电阻率剖面对土壤特性进行连续评估。为了将地下属性与电阻率相关联,已经进行了大量研究。然而,大多数研究考虑的是单一输入变量,并通过一些传统的回归分析将其与电阻率值相关联。很少有研究利用单位重量、温度、孔隙度、含水量等多个输入参数来获取电阻率值。由于土壤参数会对电阻率产生综合影响,因此迫切需要将电阻率与多个输入参数相关联,以获得更好、更可靠的结果。此外,土壤的非线性特性使这项工作变得更加复杂。为了填补这一研究空白,本研究使用七种不同类型的土壤,以不同的温度、密度和含水量组合进行了 2772 次 ER 试验。利用该数据库,开发了支持向量回归(SVR)、人工神经网络(ANN)模型和极梯度提升(XGB)模型,用于预测 ER。据了解,所有模型都被认为是值得信赖的数据建模工具。不过,在训练和测试阶段,XGB 模型的 R2 值为 0.99,表现更佳。此外,还进行了参数研究,以确定每个输入参数对 ER 的影响。还进行了误差分析,以了解 ER 的实验值和预测值之间的一致差异。结果验证了 XGB 模型的稳健性,表明它可以作为 ER 预测的替代方法。
{"title":"Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques","authors":"Soumitra Kumar Kundu ,&nbsp;Ashim Kanti Dey ,&nbsp;Sanjog Chhetri Sapkota ,&nbsp;Prasenjit Debnath ,&nbsp;Prasenjit Saha ,&nbsp;Arunava Ray ,&nbsp;Manoj Khandelwal","doi":"10.1016/j.jappgeo.2024.105557","DOIUrl":"10.1016/j.jappgeo.2024.105557","url":null,"abstract":"<div><div>Electrical Resistivity (ER) is one of the best geophysical methods for subsurface investigation, especially for geotechnical and geo-environmental studies. Being non-invasive, economical and rapid, this method is highly preferable to geotechnical engineers for continuous evaluation of soil properties along the resistivity profile. Numerous studies have been conducted to correlate the subsurface properties with the ER. However, most of the studies consider a single input variable, which is correlated with the resistivity values using some conventional regression analyses. Very few studies have been conducted to obtain the resistivity value with multiple input parameters, like unit weight, temperature, porosity, moisture content, etc. Since, the soil parameters have a combined effect on resistivity, hence, correlations between the resistivity and the multiple input parameters are urgently required for a better and more reliable result. Moreover, the non-linear properties of soil make the task more complicated. To fill up this research gap, in the present study, 2772 ER tests were conducted using seven different types of soil with different combinations of temperature, density, and water content. Using this database, a Support Vector Regression (SVR), Artificial Neural Network (ANN) model and Extreme Gradient Boosting (XGB) were developed for prediction of ER. It has been understood that all the models are acknowledged as trustworthy data modelling tools. However, the XGB model performs better with an <em>R</em><sup><em>2</em></sup> of 0.99 during the training and testing phase. Further, a parametric study was also done to determine, how each input parameter affects the ER. An error analysis was also performed to see the consistent discrepancy between the experimental and projected values of ER. The outcomes validate the robustness of the XGB model, indicating that it can serve as a substitute method for ER prediction.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105557"},"PeriodicalIF":2.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Recognition and classification of microseismic event waveforms based on histogram of oriented gradients and shallow machine learning approach 基于定向梯度直方图和浅层机器学习方法的微震事件波形识别与分类
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-01 DOI: 10.1016/j.jappgeo.2024.105551
Hongmei Shu , Ahmad Yahya Dawod , Longjun Dong
Accurate identification of microseismic events is vital for understanding underground rock deformation, rupture behavior, and mechanical properties. This study proposes a method that combines the Histogram of Orientation Gradient (HOG) and shallow machine learning techniques for microseismic waveform recognition. HOG features are extracted from event waveform images, and five classifiers including Linear classifier (LC), Fisher Discriminant (FD), Decision Tree (DT), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are compared. Experimental results show good accuracy and efficiency, with the SVM classifier and FD classifier achieving the best performance at 97.1 % and 96.9 % accuracy, respectively. Compared to previous studies, this method offers simplicity, ease of use, and low computational resource requirements, making it valuable for real-time monitoring and disaster prediction applications. It provides a foundation for evaluating mine geological structure stability.
准确识别微震事件对于了解地下岩石变形、破裂行为和机械特性至关重要。本研究提出了一种结合方位梯度直方图(HOG)和浅层机器学习技术的微震波形识别方法。从事件波形图像中提取 HOG 特征,并比较了线性分类器 (LC)、费雪判别式 (FD)、决策树 (DT)、K-近邻 (KNN) 和支持向量机 (SVM) 等五种分类器。实验结果显示了良好的准确性和效率,其中 SVM 分类器和 FD 分类器表现最佳,准确率分别为 97.1 % 和 96.9 %。与之前的研究相比,该方法具有简单、易用、计算资源要求低等特点,因此在实时监测和灾害预测应用中具有重要价值。它为评估矿山地质结构的稳定性奠定了基础。
{"title":"Recognition and classification of microseismic event waveforms based on histogram of oriented gradients and shallow machine learning approach","authors":"Hongmei Shu ,&nbsp;Ahmad Yahya Dawod ,&nbsp;Longjun Dong","doi":"10.1016/j.jappgeo.2024.105551","DOIUrl":"10.1016/j.jappgeo.2024.105551","url":null,"abstract":"<div><div>Accurate identification of microseismic events is vital for understanding underground rock deformation, rupture behavior, and mechanical properties. This study proposes a method that combines the Histogram of Orientation Gradient (HOG) and shallow machine learning techniques for microseismic waveform recognition. HOG features are extracted from event waveform images, and five classifiers including Linear classifier (LC), Fisher Discriminant (FD), Decision Tree (DT), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are compared. Experimental results show good accuracy and efficiency, with the SVM classifier and FD classifier achieving the best performance at 97.1 % and 96.9 % accuracy, respectively. Compared to previous studies, this method offers simplicity, ease of use, and low computational resource requirements, making it valuable for real-time monitoring and disaster prediction applications. It provides a foundation for evaluating mine geological structure stability.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105551"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Prospection of faults on the Southern Erftscholle (Germany) with individually and jointly inverted refraction seismics and electrical resistivity tomography 利用单独和联合反演折射地震学和电阻率层析成像技术勘探德国 Erftscholle 南部的断层
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-01 DOI: 10.1016/j.jappgeo.2024.105549
Nino Menzel , Norbert Klitzsch , Michael Altenbockum , Lisa Müller , Florian M. Wagner
As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that influences hydrological and geological conditions on a local as well as regional level. The study area is located in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Since the tectonic structures were located by past studies based on a sparse foundation of geological data, the positions include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements. Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. In addition to compiling individual resistivity and velocity models for all deduced measurements, ERT and SRT datasets were cooperatively inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The cooperative inversions do not improve the geophysical models qualitatively, since the individually inverted datasets already provide results of good quality and resolution.
作为下莱茵湾(LRE)的一部分,南埃尔夫特区块的构造环境十分复杂,对当地和区域的水文和地质条件都有影响。研究区域位于北莱茵-威斯特法伦州南部,被多个西北-东南走向的断层结构穿越。由于过去的研究是在地质数据稀少的基础上对这些构造进行定位的,因此其位置具有相当大的不确定性。因此,决定通过地球物理测量来重新评估和完善假定的断层位置。沿着七条长达 1.1 千米的测量剖面进行了地震折射断层扫描(SRT)和电阻率断层扫描(ERT)。除了为所有推导出的测量数据编制单独的电阻率和速度模型外,还利用结构耦合合作反演(SCCI)对 ERT 和 SRT 数据集进行了合作反演。该算法通过在每次模型迭代后调整各个正则化,加强速度和电阻率之间的结构相似性。之前假定的构造位置与 ERT 和 SRT 勘测的新证据存在差异。特别是在研究区域的西部和东部,勘测结果与之前假定的位置相差约 100 米。地震和地电测量结果进一步表明,该区域南部存在断层结构,而过去的研究一直没有发现这一结构。合作反演并没有从质量上改进地球物理模型,因为单独反演的数据集已经提供了质量很 好、分辨率很高的结果。
{"title":"Prospection of faults on the Southern Erftscholle (Germany) with individually and jointly inverted refraction seismics and electrical resistivity tomography","authors":"Nino Menzel ,&nbsp;Norbert Klitzsch ,&nbsp;Michael Altenbockum ,&nbsp;Lisa Müller ,&nbsp;Florian M. Wagner","doi":"10.1016/j.jappgeo.2024.105549","DOIUrl":"10.1016/j.jappgeo.2024.105549","url":null,"abstract":"<div><div>As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that influences hydrological and geological conditions on a local as well as regional level. The study area is located in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Since the tectonic structures were located by past studies based on a sparse foundation of geological data, the positions include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements. Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. In addition to compiling individual resistivity and velocity models for all deduced measurements, ERT and SRT datasets were cooperatively inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The cooperative inversions do not improve the geophysical models qualitatively, since the individually inverted datasets already provide results of good quality and resolution.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"231 ","pages":"Article 105549"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Salt dome identification using incremental semi-supervised learning and unsupervised learning-based label generation 利用增量半监督学习和基于无监督学习的标签生成技术识别盐丘
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-01 DOI: 10.1016/j.jappgeo.2024.105552
Kui Wu , Wei Hu , Yu Qi , Yixin Yu , Sanyi Yuan
Salt domes represent distinctive geological anomalies in seismic data, crucial for pinpointing hydrocarbon reservoirs and strategizing drilling paths. Conventional seismic attributes or computer vision methods usually fail to capture the intricate details of salt domes, resulting in interpretation results marred by noise. While deep learning presents a promising approach for intelligent 3D salt dome interpretation, its effectiveness is heavily dependent on the availability of labeled samples. To facilitate accurate interpretation, we propose an innovative workflow that integrates an unsupervised label generation component with an incremental semi-supervised learning framework utilizing the U-Net architecture. To generate salt dome labels, we prioritize both the root mean square (RMS) amplitude attribute and variance attribute (VA) as foundational data. Utilizing convolutional autoencoders (CAE), we establish a relationship between the input RMS attribute and the output reconstructed attribute. The intermediate features extracted by CAE are transformed into the salt boundary feature via principal component analysis and K-Means clustering. Concurrently, we employ K-Means clustering on VA to ascertain the salt internal feature. We further propose a feature aggregation method to consolidate the salt boundary feature and the salt internal feature for label generation of the salt dome. For 3D salt dome interpretation, we begin by predicting adjacent test datasets using labels generated by the unsupervised salt dome label generation module. The prediction results of these test datasets are then integrated into the training datasets to enhance the interpretation performance of U-Net, steering it towards an incremental semi-supervised learning method for salt dome interpretation. Additionally, we extend this research by applying transfer learning techniques for identifying mound-shoals using the same semi-supervised model parameters initially developed for interpreting salt domes. This method is validated using datasets from the Netherlands F3 block for salt domes and the North China block for mound-shoals. The results demonstrate that this innovative process only requires a minimal number of labels from unsupervised methods to precisely interpret salt domes across 3D seismic data. Furthermore, the low-level features of salt domes learned from neural network can be seamlessly transferred to mound-shoal identification. This automated approach significantly streamlines the interpretation process, reducing the time and resources traditionally necessary for reservoir analysis.
盐穹是地震数据中独特的地质异常现象,对于精确定位油气储层和制定钻探路径至关重要。传统的地震属性或计算机视觉方法通常无法捕捉到盐穹的复杂细节,导致解释结果受到噪声的影响。虽然深度学习为智能三维盐穹解释提供了一种前景广阔的方法,但其有效性在很大程度上取决于是否有标记样本。为了促进精确判读,我们提出了一种创新的工作流程,将无监督标签生成组件与利用 U-Net 架构的增量半监督学习框架集成在一起。为了生成盐穹标签,我们优先将均方根振幅属性和方差属性作为基础数据。利用卷积自动编码器(CAE),我们在输入的均方根属性和输出的重构属性之间建立了一种关系。CAE 提取的中间特征通过主成分分析和 K-Means 聚类转换为盐边界特征。与此同时,我们采用 K-Means 聚类对 VA 进行聚类,以确定盐的内部特征。我们进一步提出了一种特征聚合方法,以整合盐边界特征和盐内部特征,从而生成盐穹顶标签。对于三维盐穹顶解释,我们首先使用无监督盐穹顶标签生成模块生成的标签预测相邻的测试数据集。然后将这些测试数据集的预测结果整合到训练数据集中,以提高 U-Net 的解释性能,使其成为盐穹解释的增量半监督学习方法。此外,我们还扩展了这项研究,利用最初为解释盐穹而开发的相同半监督模型参数,将迁移学习技术用于识别土墩shoals。我们利用荷兰 F3 区块的盐穹窿数据集和华北区块的土墩shoals数据集对这一方法进行了验证。结果表明,这种创新方法只需要从无监督方法中提取极少量的标签,就能精确解释三维地震数据中的盐穹隆。此外,从神经网络中学习到的盐穹隆低层特征可无缝转移到土墩shoal识别中。这种自动化方法大大简化了解释过程,减少了传统储层分析所需的时间和资源。
{"title":"Salt dome identification using incremental semi-supervised learning and unsupervised learning-based label generation","authors":"Kui Wu ,&nbsp;Wei Hu ,&nbsp;Yu Qi ,&nbsp;Yixin Yu ,&nbsp;Sanyi Yuan","doi":"10.1016/j.jappgeo.2024.105552","DOIUrl":"10.1016/j.jappgeo.2024.105552","url":null,"abstract":"<div><div>Salt domes represent distinctive geological anomalies in seismic data, crucial for pinpointing hydrocarbon reservoirs and strategizing drilling paths. Conventional seismic attributes or computer vision methods usually fail to capture the intricate details of salt domes, resulting in interpretation results marred by noise. While deep learning presents a promising approach for intelligent 3D salt dome interpretation, its effectiveness is heavily dependent on the availability of labeled samples. To facilitate accurate interpretation, we propose an innovative workflow that integrates an unsupervised label generation component with an incremental semi-supervised learning framework utilizing the U-Net architecture. To generate salt dome labels, we prioritize both the root mean square (RMS) amplitude attribute and variance attribute (VA) as foundational data. Utilizing convolutional autoencoders (CAE), we establish a relationship between the input RMS attribute and the output reconstructed attribute. The intermediate features extracted by CAE are transformed into the salt boundary feature via principal component analysis and K-Means clustering. Concurrently, we employ K-Means clustering on VA to ascertain the salt internal feature. We further propose a feature aggregation method to consolidate the salt boundary feature and the salt internal feature for label generation of the salt dome. For 3D salt dome interpretation, we begin by predicting adjacent test datasets using labels generated by the unsupervised salt dome label generation module. The prediction results of these test datasets are then integrated into the training datasets to enhance the interpretation performance of U-Net, steering it towards an incremental semi-supervised learning method for salt dome interpretation. Additionally, we extend this research by applying transfer learning techniques for identifying mound-shoals using the same semi-supervised model parameters initially developed for interpreting salt domes. This method is validated using datasets from the Netherlands F3 block for salt domes and the North China block for mound-shoals. The results demonstrate that this innovative process only requires a minimal number of labels from unsupervised methods to precisely interpret salt domes across 3D seismic data. Furthermore, the low-level features of salt domes learned from neural network can be seamlessly transferred to mound-shoal identification. This automated approach significantly streamlines the interpretation process, reducing the time and resources traditionally necessary for reservoir analysis.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105552"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Microseismic precursor response characteristics of rockburst in the super-long working face: A case study 超长工作面岩爆的微震前兆响应特征:案例研究
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-01 DOI: 10.1016/j.jappgeo.2024.105550
Fei Tang , Yueping Qin
Rockburst is one of the significant dynamic hazards of coal and rock bodies during super-long working face mining. Microseismic (MS) technology has been widely used to monitor the dynamic hazards of coal and rock bodies. By analyzing the parameters and statistics of seismic events, the level of rock burst hazard can be assessed. Then, the prevention and control measures taken in advance in the working face should be guided to reduce the impact damage. This study analyzed the precursor characteristics of rockburst MS signals in super-long working faces from spatial distribution, total daily energy, number of MS events, spectrograms, and b-value of MS signals. The results show that the MS events are mainly distributed in the coal seam roof three days before the occurrence of rockburst, the proportion of daily MS events in the coal seam roof increases, and the number of MS events shows a continuous decline. The proportion of large energy MS signals is higher than that of conventional and inclined seam workings in super-long workings before rockbursts; the amplitude of the MS signals from the super-long working face is large, the vibration duration is long (0.8–1.4 s) and the frequency is low; with the approach of rockburst, the low-energy frequency band tends to increase and the frequency decreases. The proportion of the low-energy frequency band (0–40 Hz) of the precursor of impact ground pressure is high. The main frequency of the MS signal of the super-long working face is lower than that of the conventional working face and the inclined coal seam working face when the rockburst occurs; rockburst often occurs in the b-value decreasing stage, and the number of MS events and b-value changes before the rockburst shows the same downward trend, rockburst occurs when the occurrence of the b-value is less than 0.8. The study results for the safety of the super-long working face mining back to provide a scientific basis.
岩爆是超长工作面开采过程中煤体和岩体的重要动态危险之一。微震(MS)技术已被广泛用于监测煤体和岩体的动态危险。通过分析地震事件的参数和统计数据,可以评估岩爆危害程度。进而指导工作面提前采取防治措施,减少冲击破坏。本研究从空间分布、日总能量、MS 事件数量、频谱图和 MS 信号 b 值等方面分析了超长工作面岩爆 MS 信号的前兆特征。结果表明,岩爆发生前三天,MS 事件主要分布在煤层顶板,煤层顶板日 MS 事件所占比例增大,MS 事件数量呈持续下降趋势。岩爆前超长工作面大能量 MS 信号所占比例高于常规工作面和倾斜工作面;超长工作面 MS 信号振幅大,振动持续时间长(0.8-1.4 s),频率低;随着岩爆的临近,低能量频段趋于增加,频率降低。冲击地压前兆低能频段(0-40 Hz)所占比例较高。岩爆发生时,超长工作面 MS 信号的主频低于常规工作面和倾斜煤层工作面;岩爆往往发生在 b 值下降阶段,岩爆前 MS 事件发生次数与 b 值变化呈同步下降趋势,岩爆发生时 b 值小于 0.8。该研究结果为超长工作面回采安全提供了科学依据。
{"title":"Microseismic precursor response characteristics of rockburst in the super-long working face: A case study","authors":"Fei Tang ,&nbsp;Yueping Qin","doi":"10.1016/j.jappgeo.2024.105550","DOIUrl":"10.1016/j.jappgeo.2024.105550","url":null,"abstract":"<div><div>Rockburst is one of the significant dynamic hazards of coal and rock bodies during super-long working face mining. Microseismic (MS) technology has been widely used to monitor the dynamic hazards of coal and rock bodies. By analyzing the parameters and statistics of seismic events, the level of rock burst hazard can be assessed. Then, the prevention and control measures taken in advance in the working face should be guided to reduce the impact damage. This study analyzed the precursor characteristics of rockburst MS signals in super-long working faces from spatial distribution, total daily energy, number of MS events, spectrograms, and b-value of MS signals. The results show that the MS events are mainly distributed in the coal seam roof three days before the occurrence of rockburst, the proportion of daily MS events in the coal seam roof increases, and the number of MS events shows a continuous decline. The proportion of large energy MS signals is higher than that of conventional and inclined seam workings in super-long workings before rockbursts; the amplitude of the MS signals from the super-long working face is large, the vibration duration is long (0.8–1.4 s) and the frequency is low; with the approach of rockburst, the low-energy frequency band tends to increase and the frequency decreases. The proportion of the low-energy frequency band (0–40 Hz) of the precursor of impact ground pressure is high. The main frequency of the MS signal of the super-long working face is lower than that of the conventional working face and the inclined coal seam working face when the rockburst occurs; rockburst often occurs in the b-value decreasing stage, and the number of MS events and b-value changes before the rockburst shows the same downward trend, rockburst occurs when the occurrence of the b-value is less than 0.8. The study results for the safety of the super-long working face mining back to provide a scientific basis.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105550"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Improved sub-ice platelet layer mapping with multi-frequency EM induction sounding 利用多频电磁感应探测改进冰下血小板层测绘
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-01 DOI: 10.1016/j.jappgeo.2024.105540
Mara Neudert , Stefanie Arndt , Stefan Hendricks , Mario Hoppmann , Markus Schulze , Christian Haas
In Antarctica, sub-ice platelet layers (SIPL) accumulate beneath sea ice where ice crystals emerge from adjacent ice shelf cavities, serving as a unique habitat and indicator of ice-ocean interaction. Atka Bay in the eastern Weddell Sea, close to the German overwintering base Neumayer Station III, is well known for hosting a SIPL linked to ice shelf water outflow from beneath the Ekström Ice Shelf. This study presents a comprehensive analysis of an extensive multi-frequency electromagnetic (EM) induction sounding dataset in Atka Bay. Employing an open-source inversion scheme, the dataset was inverted to determine fast ice and platelet layer thicknesses along with their electrical conductivities. From electrical conductivity of the SIPL, we derive the SIPL solid fraction. Our results demonstrate the capability of obtaining high-resolution maps of SIPL thickness over extensive areas, providing unprecedented insights into accumulation patterns and identifying regions of ice-shelf water outflow in Atka Bay. Calibration in a zero-conductivity environment on the ice shelf proves effective, reducing logistical efforts for correcting electronic offsets and drift. Moreover, we demonstrate that both instrument noise and motion noise are sufficiently low to accurately determine SIPL thickness, with uncertainties within the decimeter range. Notably, this investigation is the first to cover the entirety of Atka Bay, including ice shelf fringes, overcoming limitations of prior studies. Our approach represents a significant advancement in studying ocean/ice-shelf interactions using non-destructive EM methods, emphasizing the potential to assess future changes in sub-ice shelf processes. In the future, the adaptation of this method to airborne multi-frequency EM measurements using drones or aircraft has the potential to further extend spatial coverage.
在南极洲,冰下小板层(SIPL)积聚在海冰之下,冰晶从邻近的冰架洞穴中涌出,是冰与海洋相互作用的独特栖息地和指示器。威德尔海东部的阿特卡湾(Atka Bay)靠近德国越冬基地诺伊迈尔三号站(Neumayer Station III),这里的冰下血小板层(SIPL)与埃克斯特伦冰架(Ekström Ice Shelf)下的冰架水外流有关。本研究对阿特卡湾广泛的多频电磁感应探测数据集进行了全面分析。采用开源反演方案对数据集进行反演,以确定快速冰层和板块层厚度及其导电率。根据 SIPL 的电导率,我们得出了 SIPL 固体分数。我们的研究结果表明,我们有能力获得大面积 SIPL 厚度的高分辨率地图,为了解积聚模式和确定阿特卡湾冰架水外流区域提供前所未有的见解。在冰架上的零传导环境中进行校准证明是有效的,减少了校正电子偏移和漂移的后勤工作。此外,我们还证明仪器噪声和运动噪声都很低,足以准确测定 SIPL 厚度,不确定性在分米范围内。值得注意的是,这项研究首次覆盖了整个阿特卡湾,包括冰架边缘,克服了以往研究的局限性。我们的方法代表了利用非破坏性电磁方法研究海洋/冰架相互作用的重大进展,强调了评估未来冰架下过程变化的潜力。未来,利用无人机或飞机将这种方法调整为机载多频电磁测量,有可能进一步扩大空间覆盖范围。
{"title":"Improved sub-ice platelet layer mapping with multi-frequency EM induction sounding","authors":"Mara Neudert ,&nbsp;Stefanie Arndt ,&nbsp;Stefan Hendricks ,&nbsp;Mario Hoppmann ,&nbsp;Markus Schulze ,&nbsp;Christian Haas","doi":"10.1016/j.jappgeo.2024.105540","DOIUrl":"10.1016/j.jappgeo.2024.105540","url":null,"abstract":"<div><div>In Antarctica, sub-ice platelet layers (SIPL) accumulate beneath sea ice where ice crystals emerge from adjacent ice shelf cavities, serving as a unique habitat and indicator of ice-ocean interaction. Atka Bay in the eastern Weddell Sea, close to the German overwintering base Neumayer Station III, is well known for hosting a SIPL linked to ice shelf water outflow from beneath the Ekström Ice Shelf. This study presents a comprehensive analysis of an extensive multi-frequency electromagnetic (EM) induction sounding dataset in Atka Bay. Employing an open-source inversion scheme, the dataset was inverted to determine fast ice and platelet layer thicknesses along with their electrical conductivities. From electrical conductivity of the SIPL, we derive the SIPL solid fraction. Our results demonstrate the capability of obtaining high-resolution maps of SIPL thickness over extensive areas, providing unprecedented insights into accumulation patterns and identifying regions of ice-shelf water outflow in Atka Bay. Calibration in a zero-conductivity environment on the ice shelf proves effective, reducing logistical efforts for correcting electronic offsets and drift. Moreover, we demonstrate that both instrument noise and motion noise are sufficiently low to accurately determine SIPL thickness, with uncertainties within the decimeter range. Notably, this investigation is the first to cover the entirety of Atka Bay, including ice shelf fringes, overcoming limitations of prior studies. Our approach represents a significant advancement in studying ocean/ice-shelf interactions using non-destructive EM methods, emphasizing the potential to assess future changes in sub-ice shelf processes. In the future, the adaptation of this method to airborne multi-frequency EM measurements using drones or aircraft has the potential to further extend spatial coverage.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105540"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 and application of joint-constrained inversion of transient electromagnetic multivariate parameter 瞬态电磁多变量参数联合约束反演的研究与应用
IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2024-11-01 DOI: 10.1016/j.jappgeo.2024.105548
Jian-lei Guo , Yan-wei Hou , Xiong-wei Li , Zhi-peng Qi , Ke-rui Fan , Wen-han Li , Wei-hua Yao , Xiu Li
Due to the phenomena of stratigraphic inclination, complex structure, and lateral discontinuity of resistivity or layer thickness in most of the coal seams, the traditional one-dimensional transient electromagnetic inversion method has limitations in interpretation accuracy. In addition, two- and three-dimensional inversion and artificial intelligence inversion have problems of large computation and large sample size, respectively, which limit their application in small- and medium-sized engineering exploration. To improve the inversion effect, this study proposes a method of joint-constrained inversion of transient electromagnetic multivariate parameters. This method achieves the joint constraint inversion of the transient electromagnetic multi-parameter by making full use of the geological data and a priori information to construct the initial model and adding the constraints such as the resistivity, the thickness, and the layer interface of each layer in the inversion objective function, and at the same time, taking into account the spatial correlation of the stratigraphic structure between the neighboring measurement points, as well as the transverse and vertical constraints between the measurement points along the direction of the survey line and perpendicular to the survey line. First, a series of typical geoelectric models are established and numerically simulated, and the results are compared with those of the traditional inversion method to verify the applicability and effectiveness of the method. Then, the constrained inversion is carried out on the physical simulation and measured data, and the results are in good agreement with the actual geological conditions. The numerical simulation, physical simulation and measured data inversion results consistently prove that this method can effectively reduce the uncertainty of the inversion at the isolated measuring points, improve the spatial continuity of the formation boundary, and better reflect the actual geoelectric characteristics of the formation.
由于大部分煤层存在地层倾斜、构造复杂、电阻率或层厚横向不连续等现象,传统的一维瞬变电磁反演方法在解释精度上存在局限性。此外,二维、三维反演和人工智能反演分别存在计算量大、样本量大等问题,限制了其在中小型工程勘探中的应用。为了提高反演效果,本研究提出了一种瞬态电磁多变量参数联合约束反演方法。该方法通过充分利用地质资料和先验信息构建初始模型,并在反演目标函数中加入各层电阻率、厚度、层界面等约束条件,同时考虑相邻测点之间地层结构的空间相关性,以及测点之间沿测线方向和垂直于测线方向的横向和纵向约束条件,实现了瞬变电磁多参数的联合约束反演。首先,建立一系列典型的地电模型并进行数值模拟,将结果与传统反演方法进行比较,以验证该方法的适用性和有效性。然后,对物理模拟和实测数据进行约束反演,结果与实际地质条件吻合良好。数值模拟、物理模拟和实测数据反演结果一致证明,该方法能有效降低孤立测点反演的不确定性,提高地层边界的空间连续性,更好地反映地层的实际地电特征。
{"title":"Research and application of joint-constrained inversion of transient electromagnetic multivariate parameter","authors":"Jian-lei Guo ,&nbsp;Yan-wei Hou ,&nbsp;Xiong-wei Li ,&nbsp;Zhi-peng Qi ,&nbsp;Ke-rui Fan ,&nbsp;Wen-han Li ,&nbsp;Wei-hua Yao ,&nbsp;Xiu Li","doi":"10.1016/j.jappgeo.2024.105548","DOIUrl":"10.1016/j.jappgeo.2024.105548","url":null,"abstract":"<div><div>Due to the phenomena of stratigraphic inclination, complex structure, and lateral discontinuity of resistivity or layer thickness in most of the coal seams, the traditional one-dimensional transient electromagnetic inversion method has limitations in interpretation accuracy. In addition, two- and three-dimensional inversion and artificial intelligence inversion have problems of large computation and large sample size, respectively, which limit their application in small- and medium-sized engineering exploration. To improve the inversion effect, this study proposes a method of joint-constrained inversion of transient electromagnetic multivariate parameters. This method achieves the joint constraint inversion of the transient electromagnetic multi-parameter by making full use of the geological data and a priori information to construct the initial model and adding the constraints such as the resistivity, the thickness, and the layer interface of each layer in the inversion objective function, and at the same time, taking into account the spatial correlation of the stratigraphic structure between the neighboring measurement points, as well as the transverse and vertical constraints between the measurement points along the direction of the survey line and perpendicular to the survey line. First, a series of typical geoelectric models are established and numerically simulated, and the results are compared with those of the traditional inversion method to verify the applicability and effectiveness of the method. Then, the constrained inversion is carried out on the physical simulation and measured data, and the results are in good agreement with the actual geological conditions. The numerical simulation, physical simulation and measured data inversion results consistently prove that this method can effectively reduce the uncertainty of the inversion at the isolated measuring points, improve the spatial continuity of the formation boundary, and better reflect the actual geoelectric characteristics of the formation.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"230 ","pages":"Article 105548"},"PeriodicalIF":2.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
期刊
Journal of Applied Geophysics
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1