Journal of Applied Geophysics最新文献

{"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_ARM 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.</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}

{"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}

{"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}

{"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>^<em>2</em> 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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}