Full waveform inversion (FWI) is an advanced geophysical inversion technique. FWI provides images of subsurface structures with higher resolution in fields such as oil exploration and geology. The conventional algorithm minimises the misfit error by calculating the least squares of the wavefield solutions between observed data and simulated data, followed by gradient direction and model update increment. Since the gradient is calculated by forward and backward wavefields, the high-accuracy model update relies on accurate forward and backward wavefield modelling. However, the quality of wavefield solutions obtained in practical situations could be poor and does not meet the requirements of high-resolution FWI. Specifically, the low-frequency wavefield is easily affected by noise and downsampling, which influences data quality, while the high-frequency wavefield is susceptible to spatial aliasing effects that produce imaging artefacts. Therefore, we propose using an algorithm called sparse relaxation regularised regression (SR3) to optimise the wavefield solution in frequency domain FWI, which is the forward and backward wavefield obtained from the Helmholtz equation, thus improving the FWI's accuracy. The sparse relaxation regularised regression algorithm combines sparsity and regularisation, allowing the broadband FWI to reduce the effects of noise and outliers, which can provide data supplementation in the low-frequency band and anti-aliasing in the high-frequency band. Our numerical examples demonstrate the wavefield optimisation effect of the sparse relaxation regularised regression-based algorithm in various cases. The improved algorithm's accuracy and stability are verified compared to the Tikhonov regularisation algorithm.
{"title":"Improving full-waveform inversion based on sparse regularisation for geophysical data","authors":"Jiahang Li, H. Mikada, J. Takekawa","doi":"10.1093/jge/gxae036","DOIUrl":"https://doi.org/10.1093/jge/gxae036","url":null,"abstract":"\u0000 Full waveform inversion (FWI) is an advanced geophysical inversion technique. FWI provides images of subsurface structures with higher resolution in fields such as oil exploration and geology. The conventional algorithm minimises the misfit error by calculating the least squares of the wavefield solutions between observed data and simulated data, followed by gradient direction and model update increment. Since the gradient is calculated by forward and backward wavefields, the high-accuracy model update relies on accurate forward and backward wavefield modelling. However, the quality of wavefield solutions obtained in practical situations could be poor and does not meet the requirements of high-resolution FWI. Specifically, the low-frequency wavefield is easily affected by noise and downsampling, which influences data quality, while the high-frequency wavefield is susceptible to spatial aliasing effects that produce imaging artefacts. Therefore, we propose using an algorithm called sparse relaxation regularised regression (SR3) to optimise the wavefield solution in frequency domain FWI, which is the forward and backward wavefield obtained from the Helmholtz equation, thus improving the FWI's accuracy. The sparse relaxation regularised regression algorithm combines sparsity and regularisation, allowing the broadband FWI to reduce the effects of noise and outliers, which can provide data supplementation in the low-frequency band and anti-aliasing in the high-frequency band. Our numerical examples demonstrate the wavefield optimisation effect of the sparse relaxation regularised regression-based algorithm in various cases. The improved algorithm's accuracy and stability are verified compared to the Tikhonov regularisation algorithm.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140214674","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}
Lixin Tian, Wenxu Peng, Wenming Han, Shixin Zhang, Danping Cao
Digital rock physics (DRP) offers an effective method of deriving elastic parameters from digital rock images, but its practical application is always limited to limited datasets. Recently, deep learning techniques have presented a promising avenue for generating more extensive and cost-effective samples. However, generating controllable samples according to user definition remains very difficult due to high dependence on sufficient datasets. To resolve this problem, a new network was proposed based on the UNet framework through image translation (UNet-IT) to expand rock castings by given porosity in relatively fewer datasets. Practical tests on carbonate rock images demonstrate that the proposed method can generate samples tailored to specific porosity requirements, which achieved a minimum porosity relative error of less than 1%. Compared with the unextended samples, the generated ones have completely different pore structures in terms of two-point probability, two-point cluster and lineal path functions. Furthermore, the elastic parameters of the generated images obtained through the finite element method (FEM) and practical logging data matched well, with an average relative error of approximately 9%. This indicates that the generated samples can be used as effective data to estimate fine rock physics templates and then improve inversion accuracy.
{"title":"Controllable image expansion of rock castings based on deep learning","authors":"Lixin Tian, Wenxu Peng, Wenming Han, Shixin Zhang, Danping Cao","doi":"10.1093/jge/gxae033","DOIUrl":"https://doi.org/10.1093/jge/gxae033","url":null,"abstract":"\u0000 Digital rock physics (DRP) offers an effective method of deriving elastic parameters from digital rock images, but its practical application is always limited to limited datasets. Recently, deep learning techniques have presented a promising avenue for generating more extensive and cost-effective samples. However, generating controllable samples according to user definition remains very difficult due to high dependence on sufficient datasets. To resolve this problem, a new network was proposed based on the UNet framework through image translation (UNet-IT) to expand rock castings by given porosity in relatively fewer datasets. Practical tests on carbonate rock images demonstrate that the proposed method can generate samples tailored to specific porosity requirements, which achieved a minimum porosity relative error of less than 1%. Compared with the unextended samples, the generated ones have completely different pore structures in terms of two-point probability, two-point cluster and lineal path functions. Furthermore, the elastic parameters of the generated images obtained through the finite element method (FEM) and practical logging data matched well, with an average relative error of approximately 9%. This indicates that the generated samples can be used as effective data to estimate fine rock physics templates and then improve inversion accuracy.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221289","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}
Electrical resistivity method has been widely used to study permafrost and to monitor the process of freezing-thawing. However, a thorough understanding of the mechanism of electrical response during thawing is missing. In this study, we investigated the thawing behavior of saline soils in the temperature range ∼-10 to 15 °C considering the effects of soil type and salinity. A total of nine experiments were performed with three soil types (silica sand, sandy soil and silt) and three salinities (0.01 S/m, 0.1 S/m and 1 S/m). The results show that resistivity variations with temperature can be divided into three stages. In Stage I, tortuosity and unfrozen water content play major roles in the decrease of resistivity. In Stage Ⅱ, which is an isothermal or near isothermal process, resistivity still decreases slightly due to the thawing of residual ice and pore water movement. In Stage III, ionic mobility plays an important impact on decreasing resistivity. In addition, the isothermal process is found to only occur in silica sand which can be explained by latent heat effect. Exponential and linear models linking temperature with resistivity are used to fit the experimental data in Stage I and Stage III. The fitting parameter in different models shows great correlation with soil type and salinity. Furthermore, unfrozen water content below 0 °C is also estimated and uncertainty of estimation is analyzed.
电阻率法已被广泛用于研究冻土和监测冻融过程。然而，人们对解冻过程中的电反应机制还缺乏透彻的了解。在本研究中，我们考虑了土壤类型和盐度的影响，研究了盐碱土在温度范围 ∼-10 至 15 ° C 的解冻行为。在三种土壤类型（硅砂、砂土和淤泥）和三种盐度（0.01 S/m、0.1 S/m 和 1 S/m）下共进行了九次实验。结果表明，电阻率随温度的变化可分为三个阶段。在第Ⅰ阶段，迂回度和不冻水含量对电阻率的下降起主要作用。在第Ⅱ阶段，即等温或接近等温的过程中，由于残冰解冻和孔隙水运动，电阻率仍会略有下降。在第Ⅲ阶段，离子迁移率对电阻率的下降有重要影响。此外，等温过程只发生在硅砂中，这可以用潜热效应来解释。温度与电阻率之间的指数模型和线性模型被用来拟合第一阶段和第三阶段的实验数据。不同模型的拟合参数与土壤类型和盐度有很大关系。此外，还估算了 0 °C 以下的不冻水含量，并分析了估算的不确定性。
{"title":"Experimental investigation of thawing behavior of saline soils using resistivity method","authors":"Cihai Chen, Zhilong Yang, Yaping Deng, Haichun Ma, Jiazhong Qian","doi":"10.1093/jge/gxae037","DOIUrl":"https://doi.org/10.1093/jge/gxae037","url":null,"abstract":"\u0000 Electrical resistivity method has been widely used to study permafrost and to monitor the process of freezing-thawing. However, a thorough understanding of the mechanism of electrical response during thawing is missing. In this study, we investigated the thawing behavior of saline soils in the temperature range ∼-10 to 15 °C considering the effects of soil type and salinity. A total of nine experiments were performed with three soil types (silica sand, sandy soil and silt) and three salinities (0.01 S/m, 0.1 S/m and 1 S/m). The results show that resistivity variations with temperature can be divided into three stages. In Stage I, tortuosity and unfrozen water content play major roles in the decrease of resistivity. In Stage Ⅱ, which is an isothermal or near isothermal process, resistivity still decreases slightly due to the thawing of residual ice and pore water movement. In Stage III, ionic mobility plays an important impact on decreasing resistivity. In addition, the isothermal process is found to only occur in silica sand which can be explained by latent heat effect. Exponential and linear models linking temperature with resistivity are used to fit the experimental data in Stage I and Stage III. The fitting parameter in different models shows great correlation with soil type and salinity. Furthermore, unfrozen water content below 0 °C is also estimated and uncertainty of estimation is analyzed.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140222677","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}
This study presents a geophysical investigation of the lamproite fields located in the Dharwar craton, aiming to map conductivity variations using contemporary techniques. The study employs very low-frequency electromagnetic (VLF-EM) methods, applying Hilbert transformations and first-order vertical derivatives to the Fraser and Karous-Hjelt filtered contoured of VLF-EM data. The Peninsular Gneissic Complex (PGC) granitic rocks in the study area experienced tectonic forces, resulting in fractures along specific WNW-ESE to NW-SE trends. Within these crustal weak zones, these lamproites are emplaced. The lamproite pipes are volcanic rocks. Hence, the top portions are weathered and tend to conductive, and the conductivity tend to decreases with the depth. The volumetric size of lamproites ranges from centimetres to hundreds of meters, unlike kimberlites, which are larger. Hence, the exploration of lamproites poses challenges. The contours of in-phase and quadrature components were used to identify the cluster of lamproite zones within the study area. From this study, the boundaries of the lamproite pipes were clearly identified using real component's analytical and first-order vertical derivative signal contour maps. The VLF-EM pseudo depth current density section was used to identify anomalous lamproite, pipes, and their subsurface extensions, along with the surrounding formations. The current investigation findings specify that the lamproites exhibit weak conductive. These results provide valuable insights for exploration efforts within the Dharwar craton, and can aid in the identification and mapping of the lamproite fields.
{"title":"Geophysical Characterization of Lamproite Fields in the Dharwar Craton Using VLF-EM and Advanced Filtering Techniques: Insights from Conductivity Analysis and Analytical Signal Mapping","authors":"Ravi Jonnalagadda, R. R. Mathur, A. Sridhar","doi":"10.1093/jge/gxae035","DOIUrl":"https://doi.org/10.1093/jge/gxae035","url":null,"abstract":"\u0000 This study presents a geophysical investigation of the lamproite fields located in the Dharwar craton, aiming to map conductivity variations using contemporary techniques. The study employs very low-frequency electromagnetic (VLF-EM) methods, applying Hilbert transformations and first-order vertical derivatives to the Fraser and Karous-Hjelt filtered contoured of VLF-EM data. The Peninsular Gneissic Complex (PGC) granitic rocks in the study area experienced tectonic forces, resulting in fractures along specific WNW-ESE to NW-SE trends. Within these crustal weak zones, these lamproites are emplaced. The lamproite pipes are volcanic rocks. Hence, the top portions are weathered and tend to conductive, and the conductivity tend to decreases with the depth. The volumetric size of lamproites ranges from centimetres to hundreds of meters, unlike kimberlites, which are larger. Hence, the exploration of lamproites poses challenges. The contours of in-phase and quadrature components were used to identify the cluster of lamproite zones within the study area. From this study, the boundaries of the lamproite pipes were clearly identified using real component's analytical and first-order vertical derivative signal contour maps. The VLF-EM pseudo depth current density section was used to identify anomalous lamproite, pipes, and their subsurface extensions, along with the surrounding formations. The current investigation findings specify that the lamproites exhibit weak conductive. These results provide valuable insights for exploration efforts within the Dharwar craton, and can aid in the identification and mapping of the lamproite fields.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221255","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}
As the transport channels of oil and gas, fracture networks can greatly improve the reservoir seepage, which is of great significance to the hydraulic fracturing and hydrocarbon deposit exploitation in petroleum science and engineering. In this paper, our target reservoirs are deep karsted carbonates at depth of more than 6000 m and with highly heterogeneous, leading to complex seismic responses with weak energy and low resolution. Therefore, it is challenging to predict the spatial distribution of carbonate fracture-cavern reservoir and to characterize its delicate structure. We present a characterization method for an excellent fracture description by integrating several attribute results on 3D seismic field data. Firstly, we use a noise elimination method to remove the noise interference in seismic data without damaging the fault structure characteristics. Next, we propose a novel spatially windowed 2D Hilbert transform-based operator to perform volumetric edge detection on 3D seismic field data. Then, the volumetric edge results are co-rendered with other seismic geometric attributes to generate multi-attribute fusion results for a comprehensive prediction that can excellently delineate geologic anomalies at different scales in deep carbonates. The results indicate that integrating multiple scale attributes can obtain more rich geological discontinuity and reveal more subtle fractures than using single attribute. The multi-attribute fusion results can effectively delineate some small-medium-sized faults, and they provide practical support for the exploration and production of Tahe carbonate fracture-cavern reservoirs.
{"title":"Integrated characterization of deep karsted carbonates in Tahe Oilfield, Tarim Basin","authors":"B. Lv, Xuehua Chen, Cuncai Qie, Wei Jiang","doi":"10.1093/jge/gxae031","DOIUrl":"https://doi.org/10.1093/jge/gxae031","url":null,"abstract":"\u0000 As the transport channels of oil and gas, fracture networks can greatly improve the reservoir seepage, which is of great significance to the hydraulic fracturing and hydrocarbon deposit exploitation in petroleum science and engineering. In this paper, our target reservoirs are deep karsted carbonates at depth of more than 6000 m and with highly heterogeneous, leading to complex seismic responses with weak energy and low resolution. Therefore, it is challenging to predict the spatial distribution of carbonate fracture-cavern reservoir and to characterize its delicate structure. We present a characterization method for an excellent fracture description by integrating several attribute results on 3D seismic field data. Firstly, we use a noise elimination method to remove the noise interference in seismic data without damaging the fault structure characteristics. Next, we propose a novel spatially windowed 2D Hilbert transform-based operator to perform volumetric edge detection on 3D seismic field data. Then, the volumetric edge results are co-rendered with other seismic geometric attributes to generate multi-attribute fusion results for a comprehensive prediction that can excellently delineate geologic anomalies at different scales in deep carbonates. The results indicate that integrating multiple scale attributes can obtain more rich geological discontinuity and reveal more subtle fractures than using single attribute. The multi-attribute fusion results can effectively delineate some small-medium-sized faults, and they provide practical support for the exploration and production of Tahe carbonate fracture-cavern reservoirs.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226283","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}
In shallow water ocean bottom cable (OBC) seismic data, the ineffectiveness of conventional surface-related multiple elimination(SRME) methods due to poor seabed records is addressed. This research utilizes the seismic wavefield received by multiple cables from a single shot gather to predict shallow water multiple models for that shot gather. Initially, the seismic data within a finite aperture around a seismic trace in the time domain shot gather is treated as the known seismic wavefield. This seismic wavefield is then extrapolated along the water layer to this seismic trace, following the Fresnel diffraction principle. The extrapolated data becomes the shallow water multiple model for this seismic trace. This process is repeated for each trace in the shot gather to obtain the shallow water multiple model of the entire shot gather. Forward modeling tests have shown that smaller data apertures can effectively avoid the impact of spatial aliasing on multiple model prediction. To address the overlap of primary waves and shallow water multiples in deep seismic data, which have lower dominant frequencies, the multiple model data is used as a known seismic wavefield and extrapolated along the water layer again. This produces second-order and higher-order multiple models. Applying this model to suppress multiple waves can minimize primary waves loss. This entirely data-driven approach necessitates solely water depth information, imposing no additional conditions. Both forward modeling and real seismic data testing validate the efficacy of this method in shallow water.
{"title":"OBC shallow water de-multiple based on the principle of Fresnel diffraction","authors":"Qiang Xu","doi":"10.1093/jge/gxae034","DOIUrl":"https://doi.org/10.1093/jge/gxae034","url":null,"abstract":"\u0000 In shallow water ocean bottom cable (OBC) seismic data, the ineffectiveness of conventional surface-related multiple elimination(SRME) methods due to poor seabed records is addressed. This research utilizes the seismic wavefield received by multiple cables from a single shot gather to predict shallow water multiple models for that shot gather. Initially, the seismic data within a finite aperture around a seismic trace in the time domain shot gather is treated as the known seismic wavefield. This seismic wavefield is then extrapolated along the water layer to this seismic trace, following the Fresnel diffraction principle. The extrapolated data becomes the shallow water multiple model for this seismic trace. This process is repeated for each trace in the shot gather to obtain the shallow water multiple model of the entire shot gather. Forward modeling tests have shown that smaller data apertures can effectively avoid the impact of spatial aliasing on multiple model prediction. To address the overlap of primary waves and shallow water multiples in deep seismic data, which have lower dominant frequencies, the multiple model data is used as a known seismic wavefield and extrapolated along the water layer again. This produces second-order and higher-order multiple models. Applying this model to suppress multiple waves can minimize primary waves loss. This entirely data-driven approach necessitates solely water depth information, imposing no additional conditions. Both forward modeling and real seismic data testing validate the efficacy of this method in shallow water.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239990","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}
In many cases, 1D inversion is still an important step in transient electromagnetic data processing. Potential issues may arise in the calculation of apparent resistivity using induced electromotive force (EMF) due to overshoot and the presence of multi-valued functions. Obtaining reliable and consistent inversion results using a uniform half-space as the initial model is challenging, especially when aiming for efficient inversion. Focusing on these problems, we use the land-based transient electromagnetic (TEM) sounding data, which was acquired by using a large fixed-loop transmitter, and adopt a quasi-2D inversion scheme to generate improved images of the subsurface resistivity structure. First, we have considered directly using magnetic field data or converting induced EMF into magnetic field, and then calculating the apparent resistivity over the whole zone. Next, a resistivity profile that varies with depth is obtained through fast smoke ring imaging. This profile serves as the initial model for the subsequent optimal inversion. The inversion scheme uses a nonlinear least-squares method, incorporating lateral and vertical constraints, to produce a quasi-2D subsurface image. The potentiality of the proposed methodology has been exemplified through the interpretation of synthetic data derived from a 3D intricate resistivity model, as well as field data obtained from a TEM survey conducted in a coalmine field. In both cases, the inversion process yields quasi-2D subsurface images that exhibit a reasonable level of accuracy. These images appear to be less moulded by 3D effects and demonstrate a satisfactory level of agreement with the known target area.
在许多情况下，一维反演仍然是瞬态电磁数据处理的重要步骤。由于过冲和多值函数的存在，在使用感应电动势（EMF）计算视电阻率时可能会出现潜在问题。使用均匀半空间作为初始模型，获得可靠一致的反演结果具有挑战性，尤其是在追求高效反演时。针对这些问题，我们利用大型固定环路发射机获取的陆基瞬变电磁（TEM）探测数据，采用准二维反演方案生成改进的地下电阻率结构图像。首先，我们考虑直接使用磁场数据或将感应电磁场转换为磁场，然后计算整个区域的视电阻率。然后，通过快速烟圈成像获得随深度变化的电阻率剖面。该剖面可作为后续优化反演的初始模型。反演方案采用非线性最小二乘法，结合横向和纵向约束，生成准二维地下图像。通过解释从三维错综电阻率模型中获得的合成数据，以及从在煤矿矿区进行的 TEM 勘测中获得的实地数据，证明了所提方法的潜力。在这两种情况下，反演过程产生的准二维地下图像都显示出合理的精确度。这些图像受三维效应的影响较小，与已知目标区域的吻合程度令人满意。
{"title":"Quasi-2D inversion of surface large fixed-loop transient electromagnetic sounding data","authors":"Feng-Ping Li, Jian-Hua Yue, Hai-Yan Yang, Yun Wu, Zhi-Xin Liu, Zhi-Hai Jiang","doi":"10.1093/jge/gxae013","DOIUrl":"https://doi.org/10.1093/jge/gxae013","url":null,"abstract":"In many cases, 1D inversion is still an important step in transient electromagnetic data processing. Potential issues may arise in the calculation of apparent resistivity using induced electromotive force (EMF) due to overshoot and the presence of multi-valued functions. Obtaining reliable and consistent inversion results using a uniform half-space as the initial model is challenging, especially when aiming for efficient inversion. Focusing on these problems, we use the land-based transient electromagnetic (TEM) sounding data, which was acquired by using a large fixed-loop transmitter, and adopt a quasi-2D inversion scheme to generate improved images of the subsurface resistivity structure. First, we have considered directly using magnetic field data or converting induced EMF into magnetic field, and then calculating the apparent resistivity over the whole zone. Next, a resistivity profile that varies with depth is obtained through fast smoke ring imaging. This profile serves as the initial model for the subsequent optimal inversion. The inversion scheme uses a nonlinear least-squares method, incorporating lateral and vertical constraints, to produce a quasi-2D subsurface image. The potentiality of the proposed methodology has been exemplified through the interpretation of synthetic data derived from a 3D intricate resistivity model, as well as field data obtained from a TEM survey conducted in a coalmine field. In both cases, the inversion process yields quasi-2D subsurface images that exhibit a reasonable level of accuracy. These images appear to be less moulded by 3D effects and demonstrate a satisfactory level of agreement with the known target area.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108087","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}
Wen Gu, Xingyao Yin, Furong Wu, Ying Luo, Hong Liang, Song pei, Yaming Yang
Igneous reservoir has become an important exploration target for increasing reserves and production of oil and gas in Junggar Basin. However, the igneous reservoir exploration is restricted because the seismic exploration of high-quality igneous reservoir is difficult and the anisotropy induced by high angle fractures cannot be neglected. To implement the characterization of igneous reservoir, we first study the correlation between anisotropy parameters and physical properties of igneous rock, and we propose a five-dimensional facies-driven inversion method based on rock physics, which means we employ 3D seismic data at different incidence angles and azimuths to implement the estimation of hydrocarbon reservoir constrained by the igneous rock facies. We also present an anisotropic igneous rock physics model, in which micro petrophysical characteristics, strong heterogeneity of skeleton minerals, pore structures are considered. Since a reasonable initial model is important for seismic inversion, we propose a facies-driven modeling seismic inversion method, in which we use facies obtained based on the difference between rock composition, reservoir physical parameters and elastic parameters of different lithofacies igneous rocks to constrain the seismic inversion. Finally, we present a step seismic inversion method of employing seismic data to estimate multi-parameters of HTI media. Therefore, the comprehensive processes of rock-physics modelling, inversion model establishment, and reservoir prediction of high-quality igneous rocks are proposed in this study, which demonstrates effective application for igneous reservoirs in China.
{"title":"Five-dimensional facies-driven seismic inversion for igneous reservoirs based on rock physics modelling","authors":"Wen Gu, Xingyao Yin, Furong Wu, Ying Luo, Hong Liang, Song pei, Yaming Yang","doi":"10.1093/jge/gxae025","DOIUrl":"https://doi.org/10.1093/jge/gxae025","url":null,"abstract":"\u0000 Igneous reservoir has become an important exploration target for increasing reserves and production of oil and gas in Junggar Basin. However, the igneous reservoir exploration is restricted because the seismic exploration of high-quality igneous reservoir is difficult and the anisotropy induced by high angle fractures cannot be neglected. To implement the characterization of igneous reservoir, we first study the correlation between anisotropy parameters and physical properties of igneous rock, and we propose a five-dimensional facies-driven inversion method based on rock physics, which means we employ 3D seismic data at different incidence angles and azimuths to implement the estimation of hydrocarbon reservoir constrained by the igneous rock facies. We also present an anisotropic igneous rock physics model, in which micro petrophysical characteristics, strong heterogeneity of skeleton minerals, pore structures are considered. Since a reasonable initial model is important for seismic inversion, we propose a facies-driven modeling seismic inversion method, in which we use facies obtained based on the difference between rock composition, reservoir physical parameters and elastic parameters of different lithofacies igneous rocks to constrain the seismic inversion. Finally, we present a step seismic inversion method of employing seismic data to estimate multi-parameters of HTI media. Therefore, the comprehensive processes of rock-physics modelling, inversion model establishment, and reservoir prediction of high-quality igneous rocks are proposed in this study, which demonstrates effective application for igneous reservoirs in China.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263329","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}
The diffusive viscous (DV) model is a useful tool for interpreting low-frequency seismic attenuation and the influence of fluid saturation on frequency-dependent reflections. Among present methods for the numerical solution of corresponding DV wave equation, the finite-difference frequency-domain (FDFD) method with complex-valued adaptive coefficients (CVAC) has the advantage of efficiently suppressing both numerical dispersion and numerical attenuation. In this research, the FDFD method with CVAC is first generalized to 3D DV equation. In addition, the current calculation of CVAC involves the numerical integration of propagation angles, conjugate gradient (CG) iterative optimization and the sequential selection of initial values, which is difficult and inefficient for implementation. An improved method is developed for calculating CVAC, where a complex-valued least-squares problem is constructed by substituting the 3D complex-valued plane-wave solutions into the FDFD scheme. The QR decomposition method is utilized to efficiently solve the least-squares problem. Numerical dispersion and attenuation analyses reveal that the FDFD method with CVAC requires about 2.5 spatial points in a wavelength within a dispersion deviation of 1% and an attenuation deviation of 10% for 3D DV equation. An analytic solution for 3D DV wave equation in homogeneous media is proposed to verify the effectiveness of the proposed method. And numerical examples demonstrate that the FDFD method with CVAC can obtain accurate wavefield modelling results for 3D DV models with a limited number of spatial points in a wavelength, and the FDFD method with QR-based CVAC requires less computational time than the FDFD method with CG-based CVAC.
扩散粘性（DV）模型是解释低频地震衰减和流体饱和对频率相关反射影响的有用工具。在目前对相应 DV 波方程进行数值求解的方法中，带有复值自适应系数（CVAC）的有限差分频域（FDFD）方法具有有效抑制数值色散和数值衰减的优点。本研究首先将带有 CVAC 的 FDFD 方法推广到三维 DV 方程。此外，目前 CVAC 的计算涉及传播角的数值积分、共轭梯度（CG）迭代优化和初始值的顺序选择，实施起来难度大、效率低。本文提出了一种计算 CVAC 的改进方法，即通过将三维复值平面波解代入 FDFD 方案来构建复值最小二乘问题。利用 QR 分解法有效地解决了最小二乘问题。数值频散和衰减分析表明，对于三维 DV 方程，在 1%的频散偏差和 10%的衰减偏差范围内，使用 CVAC 的 FDFD 方法在一个波长内需要约 2.5 个空间点。提出了均匀介质中三维 DV 波方程的解析解，以验证所提方法的有效性。数值实例表明，对于波长内空间点数量有限的三维 DV 模型，使用 CVAC 的 FDFD 方法可以获得精确的波场建模结果，而且使用基于 QR 的 CVAC 的 FDFD 方法比使用基于 CG 的 CVAC 的 FDFD 方法所需的计算时间更短。
{"title":"Finite difference frequency domain method with QR-decomposition-based complex-valued adaptive coefficients for 3D diffusive viscous wave modelling","authors":"Wenhao Xu, Jing Ba, Shaoru Wang, Haixia Zhao, Chunfang Wu, Jianxiong Cao, Xu Liu","doi":"10.1093/jge/gxae026","DOIUrl":"https://doi.org/10.1093/jge/gxae026","url":null,"abstract":"\u0000 The diffusive viscous (DV) model is a useful tool for interpreting low-frequency seismic attenuation and the influence of fluid saturation on frequency-dependent reflections. Among present methods for the numerical solution of corresponding DV wave equation, the finite-difference frequency-domain (FDFD) method with complex-valued adaptive coefficients (CVAC) has the advantage of efficiently suppressing both numerical dispersion and numerical attenuation. In this research, the FDFD method with CVAC is first generalized to 3D DV equation. In addition, the current calculation of CVAC involves the numerical integration of propagation angles, conjugate gradient (CG) iterative optimization and the sequential selection of initial values, which is difficult and inefficient for implementation. An improved method is developed for calculating CVAC, where a complex-valued least-squares problem is constructed by substituting the 3D complex-valued plane-wave solutions into the FDFD scheme. The QR decomposition method is utilized to efficiently solve the least-squares problem. Numerical dispersion and attenuation analyses reveal that the FDFD method with CVAC requires about 2.5 spatial points in a wavelength within a dispersion deviation of 1% and an attenuation deviation of 10% for 3D DV equation. An analytic solution for 3D DV wave equation in homogeneous media is proposed to verify the effectiveness of the proposed method. And numerical examples demonstrate that the FDFD method with CVAC can obtain accurate wavefield modelling results for 3D DV models with a limited number of spatial points in a wavelength, and the FDFD method with QR-based CVAC requires less computational time than the FDFD method with CG-based CVAC.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266791","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}
Ibrar Iqbal, Bin Xiong, Shanxi Peng, Huanghua Wang
In this research, our focus lies in exploring the effectiveness of a frequency-velocity convolutional neural network (CNN) in the efficient and non-intrusive acquisition of 2D wave velocity visuals of near-surface geological substances, accomplished through the analysis of data from ground penetrating radar (GPR). In order to learn complex correlations between antenna readings and subsurface velocities, the proposed CNN model makes use of the spatial features present in the GPR data. By employing a network architecture capable of accurately detecting both local and global patterns within the data, it becomes feasible to efficiently extract valuable velocity information from ground penetrating radar (GPR) readings. The CNN model is trained and validated using a substantial dataset consisting of GPR readings along with corresponding ground truth velocity images. Diverse subsurface settings, encompassing different soil types and geological characteristics, are employed to gather the GPR measurements. In the supervised learning approach employed to train the CNN model, the GPR measurements serve as input, while the associated ground truth velocity images are utilized as target outputs. The model is trained using backpropagation and optimized using a suitable loss function to reduce the difference between the predicted velocity images and the actual images. The experimental results demonstrate the effectiveness of the proposed CNN method in accurately deriving 2D velocity images of near-surface materials from GPR antenna observations. Compared to traditional techniques, the CNN model exhibits superior velocity calculation precision and achieves high levels of accuracy. Moreover, when applied to unseen GPR data, the trained model exhibits promising generalization abilities, highlighting its potential for practical subsurface imaging applications.
{"title":"A convolutional neural network for Creating Near Surface 2D Velocity Images from GPR Antenna Measurements","authors":"Ibrar Iqbal, Bin Xiong, Shanxi Peng, Huanghua Wang","doi":"10.1093/jge/gxae023","DOIUrl":"https://doi.org/10.1093/jge/gxae023","url":null,"abstract":"\u0000 In this research, our focus lies in exploring the effectiveness of a frequency-velocity convolutional neural network (CNN) in the efficient and non-intrusive acquisition of 2D wave velocity visuals of near-surface geological substances, accomplished through the analysis of data from ground penetrating radar (GPR). In order to learn complex correlations between antenna readings and subsurface velocities, the proposed CNN model makes use of the spatial features present in the GPR data. By employing a network architecture capable of accurately detecting both local and global patterns within the data, it becomes feasible to efficiently extract valuable velocity information from ground penetrating radar (GPR) readings. The CNN model is trained and validated using a substantial dataset consisting of GPR readings along with corresponding ground truth velocity images. Diverse subsurface settings, encompassing different soil types and geological characteristics, are employed to gather the GPR measurements. In the supervised learning approach employed to train the CNN model, the GPR measurements serve as input, while the associated ground truth velocity images are utilized as target outputs. The model is trained using backpropagation and optimized using a suitable loss function to reduce the difference between the predicted velocity images and the actual images. The experimental results demonstrate the effectiveness of the proposed CNN method in accurately deriving 2D velocity images of near-surface materials from GPR antenna observations. Compared to traditional techniques, the CNN model exhibits superior velocity calculation precision and achieves high levels of accuracy. Moreover, when applied to unseen GPR data, the trained model exhibits promising generalization abilities, highlighting its potential for practical subsurface imaging applications.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437168","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}
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