Journal of Applied Geophysics最新文献_第3页

Lithology identification using electrical imaging logging image: A case study in Jiyang Depression, China 利用电成像测井图像识别岩性：中国济阳凹陷案例研究

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-12 DOI: 10.1016/j.jappgeo.2024.105536

Juan Liu , Xuanlin Min , Zhongli Qi , Jun Yi , Wei Zhou

Lithology identification plays a significant role in stratigraphic evaluation and geological analysis. Traditional lithology identification method is by modeling the relationship between well logging and lithology. However, well logging are not always sufficient to identify lithology since sometimes the curves are similar for different lithologies. Recently, electrical imaging logging image (EILI) with high resolution plays an increasingly important role in logging interpretation since EILI can intuitively reflect the characteristics of lithology. Unlike traditional lithology identification method by using well logging, in this paper, we propose a novel multi-dimensional automatic lithology identification method by applying deep learning to EILI. First, Filtersim algorithm is employed to fill the blank strip of the EILI. Then, an integrated convolutional neural networks (CNNs) model is designed to extract the resistivity feature, texture feature, and holistic feature of the EILI, respectively. Specifically, the integrated CNNs model can realize automatic recognition for different geological structures (massive, bedded, lamellar) and lithology (mudstone, sand-mudstone, lime-mudstone). Finally, lithology identification can be achieved by combining with multi-dimensional features. The efficacy of proposed integrated model is validated experimentally on the EILI of shale oil reservoir in the Jiyang Depression of China. Experimental results show the effectiveness and superiority of the integrated CNNs method for lithology identification.

岩性识别在地层评价和地质分析中发挥着重要作用。传统的岩性识别方法是建立测井与岩性之间的关系模型。然而，测井并不总是足以识别岩性，因为有时不同岩性的测井曲线是相似的。最近，高分辨率的电成像测井图像（EILI）在测井解释中发挥着越来越重要的作用，因为 EILI 可以直观地反映岩性的特征。与传统的测井岩性识别方法不同，本文通过对 EILI 进行深度学习，提出了一种新颖的多维度岩性自动识别方法。首先，采用 Filtersim 算法填补 EILI 的空白带。然后，设计一个集成卷积神经网络（CNNs）模型，分别提取 EILI 的电阻率特征、纹理特征和整体特征。具体来说，集成卷积神经网络模型可实现对不同地质结构（块状、层状、片状）和岩性（泥岩、砂泥岩、石灰泥岩）的自动识别。最后，结合多维特征可实现岩性识别。在中国济阳凹陷页岩油藏的 EILI 试验中验证了所提出的综合模型的有效性。实验结果表明了集成 CNNs 方法在岩性识别方面的有效性和优越性。

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引用次数: 0

Study on ground-penetrating radar wave field characteristics for earth dam disease considering the medium randomness 考虑介质随机性的土坝病害探地雷达波场特性研究

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-11 DOI: 10.1016/j.jappgeo.2024.105535

Binghan Xue , Siye Zhang , Zhifeng Dong , Hongyuan Fang , Jianwei Lei , Kejie Zhai , Jianguo Chen

Ground-Penetrating Radar (GPR) has been widely used for non-destructive testing of earth dam disease. However, the forward simulation of GPR for earth dam disease often employs layered homogeneous models, neglecting the influence of medium randomness on its wave field characteristics. Therefore, considering the randomness of the medium, a geoelectrical model for earth dam disease is established, which is based on the mixed-type autocorrelation function and the finite element time-domain method. The influence of random medium model parameters on the single-channel wave of GPR is analyzed. The electromagnetic wave propagation characteristics under different medium models are explored. The forward simulation of GPR for earth dam disease such as panel voiding, concentrated seepage, and loosening are performed. The differences in propagation characteristics for earth dam disease between uniform medium model and random medium model are compared. Compared to the calculation results of the uniform medium model, the propagation speed and amplitude of electromagnetic waves in the random medium model changes, and a number of diffraction waves are present. When performing forward simulation of GPR for earth dam disease, considering medium randomness can deepen the understanding of the GPR section view and help improve the accuracy of image interpretation.

探地雷达（GPR）已被广泛用于土坝病害的无损检测。然而，土坝病害 GPR 的正演模拟通常采用分层均质模型，忽略了介质随机性对其波场特性的影响。因此，考虑到介质的随机性，建立了基于混合型自相关函数和有限元时域法的土坝病害地质电学模型。分析了随机介质模型参数对 GPR 单通道波的影响。探讨了不同介质模型下的电磁波传播特性。对土坝病害（如面板空隙、集中渗流和松动）的 GPR 进行了正演模拟。比较了均匀介质模型和随机介质模型在土坝病害传播特性上的差异。与均匀介质模型的计算结果相比，随机介质模型中电磁波的传播速度和振幅发生了变化，出现了一些衍射波。在对土坝病害进行 GPR 正演模拟时，考虑介质随机性可以加深对 GPR 断面视图的理解，有助于提高图像判读的准确性。

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引用次数: 0

Efficient self-attention based joint optimization for lithology and petrophysical parameter estimation in the Athabasca Oil Sands 阿萨巴斯卡油砂岩性和岩石物理参数估计的高效自注意联合优化技术

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-09 DOI: 10.1016/j.jappgeo.2024.105532

M Quamer Nasim , Paresh Nath Singha Roy , Adway Mitra

Accurately identifying lithology and petrophysical parameters, such as porosity and water saturation, are essential in reservoir characterization. Manual interpretation of well-log data, the conventional approach, is not only labor-intensive but also susceptible to human errors. To address these challenges of lithology identification and petrophysical parameter estimation in the Athabasca Oil Sands area, this study introduces an AutoRegressive Vision Transformer (ARViT) model for lithology and petrophysical parameter prediction. The effectiveness of ARViT lies in its self-attention mechanism and its ability to handle data sequentially, allowing the model to capture important spatial dependencies within the well-log data. This mechanism enables the model to identify subtle spatial and temporal relationships among various geophysical measurements. The model is also interpretable and can serve as an assistive tool for geoscientists, enabling faster interpretation while reducing human bias. The interpretable nature of the model should assist geoscientists in conducting faster quality checks of the predictions, ensuring that errors are not propagated to subsequent stages. This study adopts a multitask learning approach, jointly optimizing the model's performance across multiple tasks simultaneously. To evaluate the effectiveness of the ARViT model, we conducted series of experiments and comparisions, testing it against traditional artificial neural networks (ANN), Long Short-Term Memory (LSTM), and Vision Transformer (ViT) models. To showcase the versatility of ARViT, we apply Low-Rank Adaptation (LoRA) to a different smaller dataset, showing its potential to adapt to different geological contexts. LoRA not only helps in model adaptability but also helps to reduce the number of trainable parameters. Our findings demonstrate that ARViT outperforms ANN, LSTM, and ViT in estimating lithological and petrophysical parameters. While lithology prediction has been a well-explored field, ARViT's unique blend of features, including its self-attention mechanism, autoregression, and multitask approach along with efficient fine tuning using LoRA, sets it apart as a valuable tool for the complex task of lithology prediction and petrophysical parameter estimation.

准确识别岩性和岩石物理参数（如孔隙度和含水饱和度）对储层特征描述至关重要。人工解释井记录数据是一种传统方法，不仅耗费大量人力，而且容易出现人为错误。为了应对阿萨巴斯卡油砂地区岩性识别和岩石物理参数估计方面的挑战，本研究引入了用于岩性和岩石物理参数预测的自动回归视觉转换器（ARViT）模型。ARViT 的有效性在于其自我关注机制和按顺序处理数据的能力，使模型能够捕捉到井记录数据中重要的空间依赖关系。这种机制使模型能够识别各种地球物理测量数据之间微妙的时空关系。该模型还具有可解释性，可作为地球科学家的辅助工具，加快解释速度，同时减少人为偏差。模型的可解释性应有助于地球科学家对预测结果进行更快的质量检查，确保错误不会传播到后续阶段。本研究采用多任务学习方法，同时在多个任务中联合优化模型的性能。为了评估 ARViT 模型的有效性，我们进行了一系列实验和比较，将其与传统的人工神经网络（ANN）、长短期记忆（LSTM）和视觉转换器（ViT）模型进行了测试。为了展示 ARViT 的多功能性，我们在另一个较小的数据集上应用了低级自适应（LoRA）技术，以显示其适应不同地质环境的潜力。LoRA 不仅有助于提高模型的适应性，还有助于减少可训练参数的数量。我们的研究结果表明，在估计岩性和岩石物理参数方面，ARViT 优于 ANN、LSTM 和 ViT。岩性预测一直是一个被广泛探索的领域，而 ARViT 的独特功能（包括自注意机制、自回归、多任务方法以及使用 LoRA 进行高效微调）使其脱颖而出，成为岩性预测和岩石物理参数估计这一复杂任务的重要工具。

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引用次数: 0

Vehicle noise characteristics in magnetotelluric data and vehicle noise removal using waveform fitting 磁无线电数据中的车辆噪声特征以及利用波形拟合去除车辆噪声

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-09 DOI: 10.1016/j.jappgeo.2024.105534

KyeongHo Ryu , Seokhoon Oh , Hyoung-Seok Kwon

Magnetic field fluctuations due to vehicle noise were observed in magnetotelluric (MT) time-series data measured near roads. The observed vehicle noise had magnitudes ranging from tens to thousands of μA/m, whereas the observed weak natural MT signal magnitudes were approximately tens of μA/m. A small signal-to-noise ratio made it difficult to apply robust processing for removing vehicle noise. In addition, vehicle noise severely distorts the MT response in the MT deadband from 0.01 Hz to 0.3 Hz, where the MT signal is very weak, and methods to remove it are required for deep structure imaging. In this study, magnetic field fluctuations due to moving vehicles were simulated with a magnetic dipole and attempted to be removed using a waveform fitting method. A total of 378 vehicle noises were extracted from the near-road MT data and synthesized with the remote MT data without vehicle noises to investigate the effect of vehicle noise on the MT response. Removal of vehicle noise from synthesized remote MT data resulted in substantial restoration of the apparent resistivity and phase curves around the MT deadband and below 0.001 Hz. In the MT field data, the vehicle noise was simulated and removed with two moving dipoles; the magnitude of the remaining vehicle noise was reduced by approximately half compared to a single dipole, and very stable apparent resistivity and phase curves were obtained. Although electromagnetic noise distortion remains after vehicle noise removal, the waveform fitting method significantly improves the apparent resistivity and phase curve response in the 0.01–0.3 Hz frequency band.

在道路附近测量的磁电潜能（MT）时间序列数据中观测到了由车辆噪声引起的磁场波动。观测到的车辆噪声幅度从几十到几千微安/米不等，而观测到的弱自然 MT 信号幅度约为几十微安/米。由于信噪比较小，因此很难采用稳健的处理方法去除车辆噪声。此外，在 MT 信号非常微弱的 0.01 Hz 至 0.3 Hz 死区，车辆噪声严重扭曲了 MT 响应，因此深部结构成像需要去除车辆噪声的方法。在这项研究中，利用磁偶极子模拟了移动车辆引起的磁场波动，并尝试使用波形拟合方法将其去除。从近路 MT 数据中总共提取了 378 个车辆噪声，并与不含车辆噪声的远程 MT 数据进行合成，以研究车辆噪声对 MT 响应的影响。从合成的远程 MT 数据中去除车辆噪声后，MT 死区附近和 0.001 Hz 以下的视电阻率和相位曲线得到了大幅恢复。在 MT 现场数据中，使用两个移动偶极子模拟并消除了车辆噪声；与单个偶极子相比，剩余车辆噪声的幅度减少了约一半，并获得了非常稳定的视电阻率和相位曲线。虽然去除车辆噪声后电磁噪声失真仍然存在，但波形拟合方法显著改善了 0.01-0.3 Hz 频段的视电阻率和相位曲线响应。

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引用次数: 0

Enhancing seismic feature orientations: A novel approach using directional derivatives and Hilbert transform of gradient structure tensor 增强地震特征方向：利用梯度结构张量的方向导数和希尔伯特变换的新方法

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-06 DOI: 10.1016/j.jappgeo.2024.105528

Gagandeep Singh , William K. Mohanty , Aurobinda Routray , Rahul Mahadik , S.K. Singh

Seismic dip calculation serves as a widely employed technique in the realms of seismic interpretation and reservoir characterization, strategically employed to highlight faults and attributes within the seismic volume. Among the various methodologies utilized for estimating structural dip and azimuth, the Gradient Structure Tensor (GST) stands out. This approach involves leveraging the dominant eigenvector of the positive definite GST matrix to ascertain the inline and crossline dip of seismic data.

In the initial phase of our innovative proposal, we employed the spectral balancing technique to enhance the fidelity of seismic data. Subsequently, leveraging this groundwork, we introduced an Analytical Directional Gradient Structure Tensor technique, a distinctive adaptation of GST. This novel approach involves the calculation of directive derivatives in both perpendicular and parallel directions to seismic features. By incorporating directive derivatives, our method excels in capturing subtle stratigraphic nuances, particularly in the dipping direction of interest. To validate the accuracy and effectiveness of our approach, we present compelling evidence through the examination of synthetic and real-field seismic volume outcomes. This underscores the robustness and reliability of our proposed method in enhancing the precision of seismic dip calculations and providing valuable insights into subsurface geological features.

地震倾角计算是地震解释和储层特征描述领域中广泛使用的一种技术，战略性地用于突出地震剖面中的断层和属性。在用于估算构造倾角和方位角的各种方法中，梯度结构张量（GST）最为突出。这种方法是利用正定 GST 矩阵的主导特征向量来确定地震数据的内线和横线倾角。在创新提案的初始阶段，我们采用了频谱平衡技术来提高地震数据的保真度。随后，在此基础上，我们引入了分析定向梯度结构张量技术，这是对 GST 的独特调整。这种新方法包括计算与地震特征垂直和平行方向的方向导数。通过加入方向导数，我们的方法能够很好地捕捉到地层的细微差别，尤其是在感兴趣的倾角方向。为了验证我们方法的准确性和有效性，我们通过对合成和实际现场地震体积结果的检验，提出了令人信服的证据。这凸显了我们提出的方法在提高地震倾角计算精度方面的稳健性和可靠性，并提供了对地下地质特征的宝贵见解。

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引用次数: 0

Evidence of palaeoenvironmental and climatic changes from the interpreted radar wave pictures of near surface sediments around the River Nile, Assiut, Egypt 从埃及阿苏特尼罗河附近近地表沉积物的雷达波图片解释中获得古环境和气候变化的证据

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-05 DOI: 10.1016/j.jappgeo.2024.105533

Mohamed Osman Ebraheem , Hamza Ahmed Ibrahim

Different palaeoenvironmental features that pose natural geological, environmental, and engineering hazards to human operations occur frequently around the Nile Valley. Moreover, where these features were initially created, their relevance focuses on how the urban communities responded to the processes. So, a ground penetrating radar (GPR) field survey was carried out on different paleoenvironments of Pre-Quaternary and Quaternary sediment around Assiut. Deep and critical analyses of georadar facies were made to obtain clear images of these features with unprecedented resolution. The main objective of this study is to find some reasonable geological interpretations for these features. From this study, it is possible to identify and differentiate these features originating from different geological environments and climatological conditions in arid regions such as those around Assiut. In addition, the study serves as guidelines for environmental management and climatic changes for enhancing knowledge of urban development. Also, the study demonstrates how georadar can be used to create precise images of intricate shallow subsurface anatomy with possible palaeoenvironmental and palaeoclimatic indicators.

尼罗河谷周围经常出现不同的古环境特征，对人类活动造成自然地理、环境和工程方面的危害。此外，在这些地貌最初形成的地方，其相关性侧重于城市社区如何应对这些过程。因此，对阿苏特周围不同的前第四纪和第四纪沉积物古环境进行了地面穿透雷达（GPR）实地勘测。为了以前所未有的分辨率获得这些特征的清晰图像，对地质雷达面进行了深入和严格的分析。这项研究的主要目的是为这些特征找到一些合理的地质解释。通过这项研究，有可能识别和区分这些源自不同地质环境和气候条件的地貌，如阿苏特周边干旱地区的地貌。此外，这项研究还可作为环境管理和气候变化的指导方针，以增进对城市发展的了解。此外，该研究还展示了如何利用测地雷达为错综复杂的浅层地下解剖结构绘制精确图像，并提供可能的古环境和古气候指标。

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引用次数: 0

Multichannel Analysis of Surface Waves based on Common Virtual Source Gathers of Seismic Ambient Noise Cross-Correlations: A Case Study at an Earth Dam in Brazil 基于地震环境噪声交叉相关的共同虚拟源采集的面波多通道分析：巴西土坝案例研究

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-05 DOI: 10.1016/j.jappgeo.2024.105529

Victor José Cavalcanti Bezerra Guedes , Susanne Tainá Ramalho Maciel , Marcelo Peres Rocha , Welitom Rodrigues Borges , Luciano Soares da Cunha

The S-wave velocity (Vs) is a valuable parameter for assessing the mechanical properties of subsurface materials for geotechnical purposes. Seismic surface wave methods have become prominent for estimating near-surface Vs models. Researchers have proposed methods based on passive seismic signals as efficient alternatives to enhance depth of investigation, lateral resolution and reduce field effort. This study presents the Multichannel Analysis of Surface Waves (MASW) utilizing Common Virtual Source Gathers (CVSGs) derived from seismic ambient noise cross-correlations, based on Ambient Noise Seismic Interferometry concepts. The method is applied to passive data acquired with an array of receivers at the Paranoá earth dam in Brasília, Brazil, to construct a pseudo-2D Vs image of the massif for interpretation. Our findings showcase the adopted processing flow and combination of methods as an effective approach for near-surface Vs estimation, demonstrating its usability also for large earth dam embankments.

S 波速度（Vs）是评估岩土工程地下材料力学特性的重要参数。地震面波方法已成为估算近地表 Vs 模型的重要方法。研究人员提出了基于被动地震信号的方法，作为提高勘探深度、横向分辨率和减少现场工作量的有效替代方法。本研究以环境噪声地震干涉测量概念为基础，介绍了利用地震环境噪声交叉相关性衍生的通用虚拟震源采集（CVSG）进行地表波多通道分析（MASW）的方法。该方法应用于巴西巴西利亚帕拉诺亚土坝接收器阵列获取的被动数据，以构建用于解释的地块伪二维 Vs 图像。我们的研究结果表明，所采用的处理流程和方法组合是估算近地表 Vs 的有效方法，证明其也适用于大型土坝堤坝。

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引用次数: 0

Fast concrete crack depth detection using low frequency ultrasound array SH waves data 利用低频超声阵列 SH 波数据快速检测混凝土裂缝深度

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-05 DOI: 10.1016/j.jappgeo.2024.105530

Jian Shen , Liu Liu , Zhenming Shi , Shaojun Li , Ming Peng , Yao Wang , Chunsheng Liu

Fast detection of the depths of surface-open cracks plays an important role in evaluating the damage conditions of concrete elements. The presence of surface-open cracks and other anomalies inside concrete complicates the ultrasonic wave field and thus severely undermines the precision of traditional nondestructive testing methods. This study introduces independently developed low-frequency ultrasonic array detection equipment. The detector adopts a doubled-ray coverage strategy to enhance the imaging stability under noisy conditions. Moreover, we propose an imaging method called the crack focusing-synthetic aperture focusing technique (CF-SAFT), through which both reflected and transmitted surface waves are removed so that only diffracted SH waves converge to their origins. An extra instantaneous phase analysis is supplemented to highlight the diffraction points. We test the effectiveness of our method through a multitude of numerical examples and a model experiment. Successful depth identification was obtained regardless of different geometries of the cracks or interference from the steel reinforcements. The superiority of our method is further verified through noisy ultrasonic data and complex scenarios.

在评估混凝土构件的损坏情况时，快速检测表面开裂深度起着重要作用。混凝土内部存在的表面开裂和其他异常现象使超声波场变得复杂，从而严重影响了传统无损检测方法的精度。本研究介绍了自主研发的低频超声阵列检测设备。探测器采用双射线覆盖策略，提高了噪声条件下的成像稳定性。此外，我们还提出了一种名为 "裂纹聚焦-合成孔径聚焦技术（CF-SAFT）"的成像方法，通过这种方法，反射波和透射表面波都被去除，从而只有衍射 SH 波汇聚到其源头。此外还辅以瞬时相位分析，以突出衍射点。我们通过大量的数值示例和模型试验来检验我们方法的有效性。无论裂缝的几何形状如何，也无论钢筋的干扰如何，我们都成功地识别了裂缝的深度。通过噪声超声波数据和复杂场景，我们进一步验证了我们方法的优越性。

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引用次数: 0

MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification MS-CGAN：融合条件生成对抗网络和多尺度时空特征进行岩性识别

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-04 DOI: 10.1016/j.jappgeo.2024.105531

Pengwei Zhang , Jiadong Ren , Fengda Zhao , Xianshan Li , Haitao He , Yufeng Jia , Xiaoqing Shao

Lithology identification constitutes a crucial undertaking in formation evaluation and reservoir characterization. However, the need for improved precision arises in conventional lithology identification models due to the difficulties presented by unequal distributions of small-sample logging data. An effective combination of domain expertise and data-driven models to predict lithology is essential due to the intricate and nonlinear connection between logging parameters and lithology, combined with the distinct characteristics of the oilfield environments. In this paper, we proposed a multi-scale conditional generative adversarial network(MS-CGAN) method, which combines conditional generative adversarial networks with multi-scale spatio-temporal features to address data imbalance issues and enhance the accuracy of lithology classification. Our approach, tested on two small datasets from the Hugoton and Panoma fields, USA, and the Daqing production wells, China, stands out as the optimal choice compared to other models. Comprehensive evaluation results indicate promising practical applications and potential benefits of the new model in enhancing lithology identification using limited data.

岩性识别是地层评价和储层特征描述中的一项重要工作。然而，由于小样本测井数据分布不均造成的困难，传统岩性识别模型需要提高精度。由于测井参数与岩性之间存在着错综复杂的非线性联系，再加上油田环境的独特性，因此将领域专业知识与数据驱动模型有效结合起来预测岩性至关重要。本文提出了一种多尺度条件生成对抗网络（MS-CGAN）方法，该方法将条件生成对抗网络与多尺度时空特征相结合，解决了数据不平衡问题，提高了岩性分类的准确性。我们的方法在来自美国 Hugoton 和 Panoma 油田以及中国大庆生产井的两个小型数据集上进行了测试，与其他模型相比，我们的方法是最佳选择。综合评估结果表明，新模型在利用有限数据加强岩性识别方面具有良好的实际应用前景和潜在效益。

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引用次数: 0

Electrical resistivity tomography: A reliable tool to monitor the efficiency of different irrigation systems in horticulture field 电阻断层扫描：监测园艺领域不同灌溉系统效率的可靠工具

IF 2.2 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2024-10-03 DOI: 10.1016/j.jappgeo.2024.105527

Agnese Innocenti , Veronica Pazzi , Marco Napoli , Rossano Ciampalini , Simone Orlandini , Riccardo Fanti

Water management in agricultural systems is essential for optimal crop yields without incurring excessive water costs and wastage. The choice of irrigation method is crucial for better water management and distribution. The drip system appears to be among the best methods in the field of precision agriculture. In addition to the irrigation system, mulching with ridge plastic film to drain excess water is widely used to increase crop yields in terms of plant water availability. In this study, the time-lapse Electrical Resistivity Tomography (ERT), a not-invasive geophysical technique, is proposed as a simple and reliable method to evaluate the effectiveness of the irrigation systems and to monitor the changes in water content over time and over a volume of soil. ERTs data were compared to moisture ones retrieved from sensors that record continuously over time, but punctually. The ERT investigations were conducted in melon-growing lands in southern Tuscany (Italy). Measurements were carried out on two different fields in two periods: spring and summer. The aim of the work was to evaluate, by means of volumetric measures of the soil conductivity, the effectiveness of three different drip systems and of the mulch ridge. In both the monitored fields the ridge was created in a half portion of the field itself, while the other part of the land was left plat. Geoelectrical investigations associated with humidity sensors have shown that in the summer a too high mulch ridge quickly drains the irrigation water, bringing the root zone into a water deficit. The ERTs also provided good results relating to the irrigation system, demonstrating that a three-lines drip irrigation system, compared to a two-lines one, manages to distribute the irrigation water homogeneously, guaranteeing a constant water content for the plants over time.

农业系统中的水管理对于作物获得最佳产量而又不会造成过高的水成本和水浪费至关重要。灌溉方法的选择对于更好地管理和分配水资源至关重要。滴灌系统似乎是精准农业领域的最佳方法之一。除灌溉系统外，还广泛使用塑料薄膜覆盖以排出多余水分，从而提高作物产量，增加植物水分供应。在这项研究中，提出了一种非侵入性地球物理技术--延时电阻率断层扫描（ERT），作为一种简单可靠的方法来评估灌溉系统的有效性，并监测土壤含水量随时间和体积的变化。ERT 数据与通过传感器获取的水分数据进行了比较，后者可持续记录一段时间内的水分变化。ERT 研究在意大利托斯卡纳南部的甜瓜种植地进行。测量在春季和夏季两个时期的两块不同的土地上进行。这项工作的目的是通过测量土壤导电率的体积来评估三种不同滴灌系统和地膜覆盖脊的效果。在两块受监测的田地中，地脊都是在田地本身的一半面积上铺设的，而另一部分土地则保持平整。与湿度传感器相关的地质电学研究表明，在夏季，过高的地膜覆盖会迅速排干灌溉水，使根区缺水。ERT 还提供了与灌溉系统有关的良好结果，表明三线滴灌系统与两线滴灌系统相比，能够均匀地分配灌溉水，保证植物长期保持稳定的含水量。

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