Pub Date : 2023-07-20DOI: 10.1080/08123985.2023.2236119
Ashok Yadav, S. Mondal, Jay Yadav, S. Chakraborty
The amplitude Variations with Offset (AVO) technique is extensively used in hydrocarbon exploration and production for de-risking of the prospect before drilling. The interpretation of AVO results brings more confidence in the presence or absence of hydrocarbon. The AVO and Direct Hydrocarbon Indicator (DHI) analysis have their own inherent limitations. Therefore, to ensure the presence of true AVO in seismic, other attribute supports are crucial. The present prospect in this study was deposited as a channel-levee complex in the shelf-slope of the Krishna-Godavari basin. There is evidence of false AVO within this complex and thus additional attributes were used in this study to support the AVO analysis. The conventional AVO analysis shows the presence of Class-III AVO for a probable reservoir in seismic. However, it is unable to ensure the presence of hydrocarbon in the stacked reservoir unit. The spectral AVO technique was used to check AVO response at low frequencies for laminated and stacked reservoir units. Additionally, seismic attenuation and dispersion attribute studies were incorporated into the de-risking analysis. The drill result showed two distinct reservoir intervals in this prospect, which validated the predrill interpretation. This integrated approach added significant value in de-risking the prospect using AVO and other seismic attributes in complex geologic setups.
AVO (amplitude variation with Offset)技术广泛应用于油气勘探和生产中，以降低钻井前的勘探风险。AVO结果的解释提高了油气存在与否的可信度。AVO和直接含烃指标(DHI)分析都有其固有的局限性。因此，为了确保地震中存在真正的AVO，其他属性支持是至关重要的。本研究的远景区为克里希纳-戈达瓦里盆地陆架斜坡上的河道-堤防复合体。在这个复合体中有虚假AVO的证据，因此在本研究中使用了额外的属性来支持AVO分析。常规AVO分析表明，地震中可能储层存在iii类AVO。然而，它无法保证叠合储层单元中油气的存在。利用频谱AVO技术对层状和堆叠储层单元的低频AVO响应进行了检测。此外，将地震衰减和频散属性研究纳入了去风险分析。钻探结果显示该远景区有两个不同的储层层段，验证了钻前解释。这种综合方法为在复杂地质环境中利用AVO和其他地震属性降低勘探风险增加了重要价值。
{"title":"Direct hydrocarbon indicator analysis to predict reservoir in Deepwater Krishna-Godavari basin: a case study","authors":"Ashok Yadav, S. Mondal, Jay Yadav, S. Chakraborty","doi":"10.1080/08123985.2023.2236119","DOIUrl":"https://doi.org/10.1080/08123985.2023.2236119","url":null,"abstract":"The amplitude Variations with Offset (AVO) technique is extensively used in hydrocarbon exploration and production for de-risking of the prospect before drilling. The interpretation of AVO results brings more confidence in the presence or absence of hydrocarbon. The AVO and Direct Hydrocarbon Indicator (DHI) analysis have their own inherent limitations. Therefore, to ensure the presence of true AVO in seismic, other attribute supports are crucial. The present prospect in this study was deposited as a channel-levee complex in the shelf-slope of the Krishna-Godavari basin. There is evidence of false AVO within this complex and thus additional attributes were used in this study to support the AVO analysis. The conventional AVO analysis shows the presence of Class-III AVO for a probable reservoir in seismic. However, it is unable to ensure the presence of hydrocarbon in the stacked reservoir unit. The spectral AVO technique was used to check AVO response at low frequencies for laminated and stacked reservoir units. Additionally, seismic attenuation and dispersion attribute studies were incorporated into the de-risking analysis. The drill result showed two distinct reservoir intervals in this prospect, which validated the predrill interpretation. This integrated approach added significant value in de-risking the prospect using AVO and other seismic attributes in complex geologic setups.","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"54 1","pages":"625 - 635"},"PeriodicalIF":0.9,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42659567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.1080/08123985.2023.2222766
H. Seillé, S. Thiel, K. Brand, S. Mulé, G. Visser, A. Fabris, T. Munday
{"title":"Bayesian fusion of MT and AEM probabilistic models with geological data: examples from the eastern Gawler Craton, South Australia","authors":"H. Seillé, S. Thiel, K. Brand, S. Mulé, G. Visser, A. Fabris, T. Munday","doi":"10.1080/08123985.2023.2222766","DOIUrl":"https://doi.org/10.1080/08123985.2023.2222766","url":null,"abstract":"","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45033079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-17DOI: 10.1080/08123985.2023.2225538
R. Liu, Huaifeng Sun, Zhen Wang, Qiuyan Fan, Shangbin Liu, Jiuqing Lin, Yang Yang
Subsurface karst features are significantly developed in Guangxi Province, China. This area mainly contains fractured subsurface rock, abundant karst channels, and widely distributed underground fissure networks. Such adverse geological conditions could potentially create hydrogeological hazards such as collapses, water inrush, and mud inrush during infrastructure construction. The Hejing limestone mine is an opencast mine in Pingnan County, Guangxi, that produces cement. Mining activities have altered the seepage fields in this area, causing large amounts of groundwater to flood into the mining pit; this has caused many ground collapses while severely reducing limestone production. More than 24 km of surface electrical resistivity tomography (ERT) profiles have been previously recorded in the region to identify potential karst positions and explore groundwater inrush paths. In this study. we employed surface and cross-borehole ERT surveys to delineate specific groundwater inrush paths on the eastern side of the mine and characterise karst distribution in the study area. Resistivity imaging results revealed some low-resistivity anomaly distributions and provided reliable geological information about the distribution of subsurface karst for future grouting work.
{"title":"Investigation of subsurface karst in an opencast mine in southwestern China via surface and cross-borehole electrical resistivity tomography","authors":"R. Liu, Huaifeng Sun, Zhen Wang, Qiuyan Fan, Shangbin Liu, Jiuqing Lin, Yang Yang","doi":"10.1080/08123985.2023.2225538","DOIUrl":"https://doi.org/10.1080/08123985.2023.2225538","url":null,"abstract":"Subsurface karst features are significantly developed in Guangxi Province, China. This area mainly contains fractured subsurface rock, abundant karst channels, and widely distributed underground fissure networks. Such adverse geological conditions could potentially create hydrogeological hazards such as collapses, water inrush, and mud inrush during infrastructure construction. The Hejing limestone mine is an opencast mine in Pingnan County, Guangxi, that produces cement. Mining activities have altered the seepage fields in this area, causing large amounts of groundwater to flood into the mining pit; this has caused many ground collapses while severely reducing limestone production. More than 24 km of surface electrical resistivity tomography (ERT) profiles have been previously recorded in the region to identify potential karst positions and explore groundwater inrush paths. In this study. we employed surface and cross-borehole ERT surveys to delineate specific groundwater inrush paths on the eastern side of the mine and characterise karst distribution in the study area. Resistivity imaging results revealed some low-resistivity anomaly distributions and provided reliable geological information about the distribution of subsurface karst for future grouting work.","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"54 1","pages":"685 - 695"},"PeriodicalIF":0.9,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44270243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-02DOI: 10.1080/08123985.2023.2218870
Yulan Yang, Lihua Fu, Kun Qian, Hongwei Li
Random noise, which has a significant impact on subsequent processing and interpretation, easily interferes with seismic data. Current convolutional neural networks (CNN) use a single-stage technique to boost network capacity by exploiting the complicated network structure, but the performance of the network becomes saturated and prone to overfitting at a certain stage. Hence, we propose a two-stage U-Net denoising network with a supervised attention module (UNet-SAM). In this supervised algorithm, the first stage obtains the pre-denoising results, while the second stage achieves more accurate data. The supervised attention module (SAM) block is inserted in the first stage, extracting features with supervised attention to utilise as a priori information and guide the fine denoising in the second stage. The combination of the attention mechanism and two-stage strategy provides prior information that helps to train a network with better denoising performance. Experiments on synthetic and field data illustrate that the proposed UNet-SAM not only has a superior denoising effect but also retains more of the original effective signal.
{"title":"Seismic random noise attenuation via a two-stage U-net with supervised attention","authors":"Yulan Yang, Lihua Fu, Kun Qian, Hongwei Li","doi":"10.1080/08123985.2023.2218870","DOIUrl":"https://doi.org/10.1080/08123985.2023.2218870","url":null,"abstract":"Random noise, which has a significant impact on subsequent processing and interpretation, easily interferes with seismic data. Current convolutional neural networks (CNN) use a single-stage technique to boost network capacity by exploiting the complicated network structure, but the performance of the network becomes saturated and prone to overfitting at a certain stage. Hence, we propose a two-stage U-Net denoising network with a supervised attention module (UNet-SAM). In this supervised algorithm, the first stage obtains the pre-denoising results, while the second stage achieves more accurate data. The supervised attention module (SAM) block is inserted in the first stage, extracting features with supervised attention to utilise as a priori information and guide the fine denoising in the second stage. The combination of the attention mechanism and two-stage strategy provides prior information that helps to train a network with better denoising performance. Experiments on synthetic and field data illustrate that the proposed UNet-SAM not only has a superior denoising effect but also retains more of the original effective signal.","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"54 1","pages":"636 - 646"},"PeriodicalIF":0.9,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44588760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-30DOI: 10.1080/08123985.2023.2217192
Jianghao Chang, Junjie Xue, Yongshuai Guo, Hongchun Yi, Maofei Li
AbstractIn this study, a recently developed time-domain electromagnetic method called the short-offset grounded-wire transient electromagnetic (SOTEM) method, which is a near-source observation method, was adopted to obtain strong signals and great detection depths. The responses of the SOTEM and long-offset transient electromagnetic (LOTEM) methods were compared to further guide and promote the SOTEM method. Currently, the comparison between SOTEM and LOTEM methods is primarily based on one-dimensional (1D) models. However, most geological bodies are three-dimensional (3D) structures. We investigated the responses of a grounded-wire transient electromagnetic method based on 3D models using the 3D finite-difference time-domain method. In addition, the signal strengths, detection sensitivities and detection depths of the SOTEM and LOTEM methods were compared. The results revealed that the field amplitudes of Ex (electrical component parallel to the transmitting source) and ∂By/∂t (magnetic component perpendicular to the transmitting source horizontally) were higher at the short offsets than at the long offsets. For the ∂Bx/∂t (magnetic component parallel to the transmitting source) and the vertical magnetic component ∂Bz/∂t, at initial times, the responses received would be stronger when closer to the transmitting source, whereas at later times, the responses would be stronger when farther from the source. Ex detection sensitivity increased with an increase in the offset at initial times, and increased with a decrease in the offset at later times. The detection sensitivities of the three magnetic field components at short offsets were higher than those at long offsets. The ∂By/∂t effective detection depth was the greatest. Generally, the effective detection depths of the three magnetic field components increased with decreasing offset. The range of the ratio of the horizontal distance, r, between the transmitting source and the target body to the effective detection depth H was 0.5-1.1.KEYWORDS: Transient electromagnetic methodgrounded-wire3D modellingSOTEM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key R&D Program of China [grant number 2022YFC2903505]; Natural Science Foundation of China (NSFC) [grant number 42030106]; Science and Technology Project of Hebei Education Department [grant number QN2022041]; Fund from the Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources [grant number ZB2022003]; Natural Science Foundation of Chongqing [grant number cstc2020jcyj-msxmX0676]; and National Pre-research Funds of Hebei GEO University in 2023 [grant number KY202301].
{"title":"Comparison of short-offset and long-offset grounded-wire transient electromagnetic responses based on the 3D model","authors":"Jianghao Chang, Junjie Xue, Yongshuai Guo, Hongchun Yi, Maofei Li","doi":"10.1080/08123985.2023.2217192","DOIUrl":"https://doi.org/10.1080/08123985.2023.2217192","url":null,"abstract":"AbstractIn this study, a recently developed time-domain electromagnetic method called the short-offset grounded-wire transient electromagnetic (SOTEM) method, which is a near-source observation method, was adopted to obtain strong signals and great detection depths. The responses of the SOTEM and long-offset transient electromagnetic (LOTEM) methods were compared to further guide and promote the SOTEM method. Currently, the comparison between SOTEM and LOTEM methods is primarily based on one-dimensional (1D) models. However, most geological bodies are three-dimensional (3D) structures. We investigated the responses of a grounded-wire transient electromagnetic method based on 3D models using the 3D finite-difference time-domain method. In addition, the signal strengths, detection sensitivities and detection depths of the SOTEM and LOTEM methods were compared. The results revealed that the field amplitudes of Ex (electrical component parallel to the transmitting source) and ∂By/∂t (magnetic component perpendicular to the transmitting source horizontally) were higher at the short offsets than at the long offsets. For the ∂Bx/∂t (magnetic component parallel to the transmitting source) and the vertical magnetic component ∂Bz/∂t, at initial times, the responses received would be stronger when closer to the transmitting source, whereas at later times, the responses would be stronger when farther from the source. Ex detection sensitivity increased with an increase in the offset at initial times, and increased with a decrease in the offset at later times. The detection sensitivities of the three magnetic field components at short offsets were higher than those at long offsets. The ∂By/∂t effective detection depth was the greatest. Generally, the effective detection depths of the three magnetic field components increased with decreasing offset. The range of the ratio of the horizontal distance, r, between the transmitting source and the target body to the effective detection depth H was 0.5-1.1.KEYWORDS: Transient electromagnetic methodgrounded-wire3D modellingSOTEM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key R&D Program of China [grant number 2022YFC2903505]; Natural Science Foundation of China (NSFC) [grant number 42030106]; Science and Technology Project of Hebei Education Department [grant number QN2022041]; Fund from the Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources [grant number ZB2022003]; Natural Science Foundation of Chongqing [grant number cstc2020jcyj-msxmX0676]; and National Pre-research Funds of Hebei GEO University in 2023 [grant number KY202301].","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135643515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.1080/08123985.2023.2214166
C. Finn, M. Zientek, Benjamin R. Bloss, Heather L. Parks, J. Modroo
Modelling and analysis of helicopter electromagnetic data result in resistivity and susceptibility models and derivatives of magnetic data that characterise shallow parts of the Stillwater Complex, critical for aiding exploration and expansion of globally scarce critical and battery mineral resources that include platinum group elements, nickel, copper and chromium. The magnetic susceptibly models derived from the electromagnetic data and the tilt derivative of the magnetic data image layering, mafic dikes, banded iron formation, and serpentinised peridotite. Known areas with contact-type mineralisation are generally characterised by low resistivities and susceptibilities where the volume of mineralised rock is large and/or the depth is shallow. We use iso-cluster and edge detection analysis of both resistivities and susceptibilities to identify potential mineralisation in poorly characterised regions as well as faults. Low resistivity layers beneath large landslides reflect water saturated porous slip surfaces which can interfere with drilling. This uncommon approach of tightly linking the resistivity and susceptibility models and magnetic anomaly data to rock property, surficial geologic, drill hole and soil geochemistry data to image the geology in the upper ∼100 m, aids identification of prospective mineralised regions as well landslides and faults that can impact mineral exploration and local hazards.
{"title":"Electromagnetic and magnetic imaging of the Stillwater Complex, Montana, USA","authors":"C. Finn, M. Zientek, Benjamin R. Bloss, Heather L. Parks, J. Modroo","doi":"10.1080/08123985.2023.2214166","DOIUrl":"https://doi.org/10.1080/08123985.2023.2214166","url":null,"abstract":"Modelling and analysis of helicopter electromagnetic data result in resistivity and susceptibility models and derivatives of magnetic data that characterise shallow parts of the Stillwater Complex, critical for aiding exploration and expansion of globally scarce critical and battery mineral resources that include platinum group elements, nickel, copper and chromium. The magnetic susceptibly models derived from the electromagnetic data and the tilt derivative of the magnetic data image layering, mafic dikes, banded iron formation, and serpentinised peridotite. Known areas with contact-type mineralisation are generally characterised by low resistivities and susceptibilities where the volume of mineralised rock is large and/or the depth is shallow. We use iso-cluster and edge detection analysis of both resistivities and susceptibilities to identify potential mineralisation in poorly characterised regions as well as faults. Low resistivity layers beneath large landslides reflect water saturated porous slip surfaces which can interfere with drilling. This uncommon approach of tightly linking the resistivity and susceptibility models and magnetic anomaly data to rock property, surficial geologic, drill hole and soil geochemistry data to image the geology in the upper ∼100 m, aids identification of prospective mineralised regions as well landslides and faults that can impact mineral exploration and local hazards.","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"54 1","pages":"553 - 570"},"PeriodicalIF":0.9,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44086559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1080/08123985.2023.2212698
Xiaodong Luan, M. Becken, A. Thiede, P. Kotowski
{"title":"3D tomographic inversion for frequency semi-airborne EM under multinary constraints","authors":"Xiaodong Luan, M. Becken, A. Thiede, P. Kotowski","doi":"10.1080/08123985.2023.2212698","DOIUrl":"https://doi.org/10.1080/08123985.2023.2212698","url":null,"abstract":"","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41824465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-21DOI: 10.1080/08123985.2023.2210155
S. Piro, V. Beolchini, L. Peña-Chocarro, A. Pizzo
{"title":"High resolution multi-methodological geophysical investigations to enhance the knowledge of Tusculum archaeological site (Roma, Italy)","authors":"S. Piro, V. Beolchini, L. Peña-Chocarro, A. Pizzo","doi":"10.1080/08123985.2023.2210155","DOIUrl":"https://doi.org/10.1080/08123985.2023.2210155","url":null,"abstract":"","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49539720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.1080/08123985.2023.2205582
Jingnan Yue, Lihua Fu, Xiao Niu, Wenqian Fang
Due to geological conditions, acquisition environment, and economic restrictions, acquired seismic data are often incomplete and irregularly distributed, and this affects subsequent migration imaging and inversion. Sparse constraint-based methods are widely used for seismic data interpolation, including fixed-base transform and dictionary learning. Fixed-base transform methods are fast and simple to implement, but the basis function needs to be pre-selected. The dictionary learning method is more adaptive, and provides a means of learning the sparse representation from corrupted data. K-singular value decomposition (K-SVD) is a classical dictionary learning method that combines sparse coding and dictionary updating iteratively. However, the dictionary atoms are updated column-by-column, leading to high computational complexity due to long SVD calculation times. In this study, we evaluated the dictionary learning method via l 4-norm maximisation using an orthogonal dictionary, which is different from the traditional l 0-norm or l 1-norm minimisation, and interpolated the missing traces in the projection onto convex sets (POCS) framework. The optimal objection function is convex, but can be solved using a simple and efficient Matching, Stretching and Projection (MSP) algorithm, which greatly reduces the dictionary learning time. Numerical experiments using synthetic and field data demonstrate the effectiveness of the proposed method.
{"title":"Orthogonal dictionary learning based on l 4-Norm maximisation for seismic data interpolation","authors":"Jingnan Yue, Lihua Fu, Xiao Niu, Wenqian Fang","doi":"10.1080/08123985.2023.2205582","DOIUrl":"https://doi.org/10.1080/08123985.2023.2205582","url":null,"abstract":"Due to geological conditions, acquisition environment, and economic restrictions, acquired seismic data are often incomplete and irregularly distributed, and this affects subsequent migration imaging and inversion. Sparse constraint-based methods are widely used for seismic data interpolation, including fixed-base transform and dictionary learning. Fixed-base transform methods are fast and simple to implement, but the basis function needs to be pre-selected. The dictionary learning method is more adaptive, and provides a means of learning the sparse representation from corrupted data. K-singular value decomposition (K-SVD) is a classical dictionary learning method that combines sparse coding and dictionary updating iteratively. However, the dictionary atoms are updated column-by-column, leading to high computational complexity due to long SVD calculation times. In this study, we evaluated the dictionary learning method via l 4-norm maximisation using an orthogonal dictionary, which is different from the traditional l 0-norm or l 1-norm minimisation, and interpolated the missing traces in the projection onto convex sets (POCS) framework. The optimal objection function is convex, but can be solved using a simple and efficient Matching, Stretching and Projection (MSP) algorithm, which greatly reduces the dictionary learning time. Numerical experiments using synthetic and field data demonstrate the effectiveness of the proposed method.","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"54 1","pages":"589 - 600"},"PeriodicalIF":0.9,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44579890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.1080/08123985.2023.2183114
Jizhong Wu, Ying Shi, Xiangguo Dong
The viscoelasticity of an underground medium will cause absorption and attenuation of seismic waves, resulting in energy attenuation and phase distortion. This absorption and attenuation is often quantified by the quality Factor Q. The strong attenuation effect resulting from geology is a challenging problem for high-resolution imaging. To compensate for the attenuation effect, it is necessary to estimate the attenuation parameters accurately. However, it is difficult to directly derive a heterogeneous attenuation Q model. This research letter proposes a method to derive a Q model from reflection seismic data using a backpropagation neural network (BPNN), one of the most widely used neural network models. We treated the Q detection problem as a pattern recognition task and train a network to assign the correct Q classes to a set of input patterns. The proposed method uses synthetic data for network training and validation. Finally, we used a set of model data and a set of field data to demonstrate the effectiveness of this method, and the high-resolution imaging results in the time domain with appropriate compensation are obtained.
{"title":"Estimating effective Q parameters from reflection seismic data using BPNN","authors":"Jizhong Wu, Ying Shi, Xiangguo Dong","doi":"10.1080/08123985.2023.2183114","DOIUrl":"https://doi.org/10.1080/08123985.2023.2183114","url":null,"abstract":"The viscoelasticity of an underground medium will cause absorption and attenuation of seismic waves, resulting in energy attenuation and phase distortion. This absorption and attenuation is often quantified by the quality Factor Q. The strong attenuation effect resulting from geology is a challenging problem for high-resolution imaging. To compensate for the attenuation effect, it is necessary to estimate the attenuation parameters accurately. However, it is difficult to directly derive a heterogeneous attenuation Q model. This research letter proposes a method to derive a Q model from reflection seismic data using a backpropagation neural network (BPNN), one of the most widely used neural network models. We treated the Q detection problem as a pattern recognition task and train a network to assign the correct Q classes to a set of input patterns. The proposed method uses synthetic data for network training and validation. Finally, we used a set of model data and a set of field data to demonstrate the effectiveness of this method, and the high-resolution imaging results in the time domain with appropriate compensation are obtained.","PeriodicalId":50460,"journal":{"name":"Exploration Geophysics","volume":"54 1","pages":"526 - 532"},"PeriodicalIF":0.9,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41786642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：