Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113227
P. Yang, D. Gajewski
Summary Time reverse imaging has become a standard technique for locating and characterising seismic events. No identification of events or their onset times is required for locating events with time reverse imaging. Nevertheless, because of the resolution limits of the source signals, it can not reliably locate the sources that are close to each other, i.e., a small concentrating source distribution. We propose a new time reverse imaging method to address this issue. First, we divide the wavefields into several small parts according to the bounds of the maximum absolute amplitude at each time step. The neighboring wavefields of each small part are extracted, and they are centred at the picked points that correspond to the maximum absolute amplitude of each small part and given by a circle with a radius of half the dominant wavelength of the source signal. Then we introduce the Gaussian-type weights to weight these neighboring wavefields. Finally, these extracted wavefields are cross correlated. The crosscorrelation creates a new imaging condition. It yields good location results, deviating from the actual source locations by far less than half the prevailing wavelength of the signal, even in the case of sparse acquisition and poor S/N ratio.
{"title":"High-resolution time reverse imaging for microseismic event location","authors":"P. Yang, D. Gajewski","doi":"10.3997/2214-4609.202113227","DOIUrl":"https://doi.org/10.3997/2214-4609.202113227","url":null,"abstract":"Summary Time reverse imaging has become a standard technique for locating and characterising seismic events. No identification of events or their onset times is required for locating events with time reverse imaging. Nevertheless, because of the resolution limits of the source signals, it can not reliably locate the sources that are close to each other, i.e., a small concentrating source distribution. We propose a new time reverse imaging method to address this issue. First, we divide the wavefields into several small parts according to the bounds of the maximum absolute amplitude at each time step. The neighboring wavefields of each small part are extracted, and they are centred at the picked points that correspond to the maximum absolute amplitude of each small part and given by a circle with a radius of half the dominant wavelength of the source signal. Then we introduce the Gaussian-type weights to weight these neighboring wavefields. Finally, these extracted wavefields are cross correlated. The crosscorrelation creates a new imaging condition. It yields good location results, deviating from the actual source locations by far less than half the prevailing wavelength of the signal, even in the case of sparse acquisition and poor S/N ratio.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131589127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113297
Tengku Mohd Syazwan Tengku Hassan, C. S. Lee, R. Bekti, J. Ting
Summary The conventional low frequency model (LFM) have limitations: uncertainty of spatial variability away from the wells, the uncertainty of the structural model and stratigraphic architecture. It is also challenging to build complex geology structural model. We propose using Deep Feed-forward Neural Network (DFNN) with attributes from seismic partial stacks and seismic velocity to create LFM of elastic properties for Constrained Sparse Spike Inversion. The methodology incorporates training of well curves, additional information from seismic partial stacks and trend from seismic velocity and wells. It has shorter turnaround by not having to include structural model, and is suitable for complex geological settings.
{"title":"Building low frequency model with Deep Learning for seismic inversion in complex geology without structural model","authors":"Tengku Mohd Syazwan Tengku Hassan, C. S. Lee, R. Bekti, J. Ting","doi":"10.3997/2214-4609.202113297","DOIUrl":"https://doi.org/10.3997/2214-4609.202113297","url":null,"abstract":"Summary The conventional low frequency model (LFM) have limitations: uncertainty of spatial variability away from the wells, the uncertainty of the structural model and stratigraphic architecture. It is also challenging to build complex geology structural model. We propose using Deep Feed-forward Neural Network (DFNN) with attributes from seismic partial stacks and seismic velocity to create LFM of elastic properties for Constrained Sparse Spike Inversion. The methodology incorporates training of well curves, additional information from seismic partial stacks and trend from seismic velocity and wells. It has shorter turnaround by not having to include structural model, and is suitable for complex geological settings.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130691901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202010381
J. Tang, C. Peng, M. O’Briain, C. Shih
Summary In the deep-water Campeche Bay area of the southern Gulf of Mexico, there are many complex shallow salt bodies and carbonate rafts that generate a significant amount of coherent noise energies, collectively for all non-primary reflection energies, as a result of the high impedance contrast between these bodies and the surrounding sediments. These noise energies include surface-related salt-diffracted multiples, interbed/internal multiples, bounces between salt bodies, and other types of prismatic waves as well as converted shear waves. These coherent noises cause difficulties in interpreting base of salt and subsalt seismic events. Identifying and removing them is crucial for optimal seismic imaging of subsalt targets. We propose a method to model these noises using a geological imaging model and elastic finite-difference forward modeling. The method requires that the shallow part of the geological imaging model be accurate. We first compute elastic synthetic data using the model. Then, we migrate the synthetic data to generate a noise model in the image domain and use this noise model to pattern-match with another image volume migrated using field data. In this way, we can identify noises in the field data and remove them adaptively to obtain a cleaner image of the recorded reflectivity.
{"title":"Use of Elastic Forward Modeling to Remove Complex Coherent Noises","authors":"J. Tang, C. Peng, M. O’Briain, C. Shih","doi":"10.3997/2214-4609.202010381","DOIUrl":"https://doi.org/10.3997/2214-4609.202010381","url":null,"abstract":"Summary In the deep-water Campeche Bay area of the southern Gulf of Mexico, there are many complex shallow salt bodies and carbonate rafts that generate a significant amount of coherent noise energies, collectively for all non-primary reflection energies, as a result of the high impedance contrast between these bodies and the surrounding sediments. These noise energies include surface-related salt-diffracted multiples, interbed/internal multiples, bounces between salt bodies, and other types of prismatic waves as well as converted shear waves. These coherent noises cause difficulties in interpreting base of salt and subsalt seismic events. Identifying and removing them is crucial for optimal seismic imaging of subsalt targets. We propose a method to model these noises using a geological imaging model and elastic finite-difference forward modeling. The method requires that the shallow part of the geological imaging model be accurate. We first compute elastic synthetic data using the model. Then, we migrate the synthetic data to generate a noise model in the image domain and use this noise model to pattern-match with another image volume migrated using field data. In this way, we can identify noises in the field data and remove them adaptively to obtain a cleaner image of the recorded reflectivity.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132292916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113153
S. Korevaar, M. Leewis, H. González, P. Doulgeris, P. Benitez
Summary Optimal and successful geothermal development depends on the identification of aquifers with good reservoir properties combined with appropriate subsurface temperatures. Uncertainties need to be kept to a minimum to achieve a robust and positive business case for investors to commit. This paper demonstrates wave-equation based AVO (WEB-AVO) inversion on pre-stack seismic data as a useful technique to better assess the feasibility of geothermal projects in low data density areas. One unique feature of this method is that it solves directly for compressibility and shear compliance, which are commonly more sensitive for reservoir property changes compared to acoustic and shear impedance. Although the technique has its limitations related to data quality and availability, it showed that it could very well be used for optimisation of a geothermal project. For a case study in the Blaricum region (the Netherlands), top, base and thickness interpretations of the targeted geothermal reservoir could be enhanced, gPOS could be uplifted, and baffles and barriers (sedimentary, diagenetic and/or structural in origin), could be better identified. Consequently, a more optimal well placement can be achieved.
{"title":"The added value of WEB-AVO inversion for geothermal project development: a 2D reservoir characterization case study.","authors":"S. Korevaar, M. Leewis, H. González, P. Doulgeris, P. Benitez","doi":"10.3997/2214-4609.202113153","DOIUrl":"https://doi.org/10.3997/2214-4609.202113153","url":null,"abstract":"Summary Optimal and successful geothermal development depends on the identification of aquifers with good reservoir properties combined with appropriate subsurface temperatures. Uncertainties need to be kept to a minimum to achieve a robust and positive business case for investors to commit. This paper demonstrates wave-equation based AVO (WEB-AVO) inversion on pre-stack seismic data as a useful technique to better assess the feasibility of geothermal projects in low data density areas. One unique feature of this method is that it solves directly for compressibility and shear compliance, which are commonly more sensitive for reservoir property changes compared to acoustic and shear impedance. Although the technique has its limitations related to data quality and availability, it showed that it could very well be used for optimisation of a geothermal project. For a case study in the Blaricum region (the Netherlands), top, base and thickness interpretations of the targeted geothermal reservoir could be enhanced, gPOS could be uplifted, and baffles and barriers (sedimentary, diagenetic and/or structural in origin), could be better identified. Consequently, a more optimal well placement can be achieved.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133162744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113161
R. Wang, C. Bao, L. Qiu
Summary In recent decades, Full-waveform inversion (FWI) has suffered from the cycle-skipping issue, which we found can be mitigated by changing the source signature of the observed data. Compared with a physical source such as the Ricker source, seismic data with the Gaussian source can provide a better landscape of the objective function while improving the gradient's quality in the iterative reconstruction. In the synthetic experiments, we transform band-limited seismic data simulated with the Ricker wavelet into seismic data with the Gaussian source and apply it to FWI. Neural networks are employed to provide an efficient solution to this problem. Numerical experiments on the Marmousi model are conducted to demonstrate the effectiveness of our proposed method.
{"title":"Seismic Waveform Inversion with Source Manipulation","authors":"R. Wang, C. Bao, L. Qiu","doi":"10.3997/2214-4609.202113161","DOIUrl":"https://doi.org/10.3997/2214-4609.202113161","url":null,"abstract":"Summary In recent decades, Full-waveform inversion (FWI) has suffered from the cycle-skipping issue, which we found can be mitigated by changing the source signature of the observed data. Compared with a physical source such as the Ricker source, seismic data with the Gaussian source can provide a better landscape of the objective function while improving the gradient's quality in the iterative reconstruction. In the synthetic experiments, we transform band-limited seismic data simulated with the Ricker wavelet into seismic data with the Gaussian source and apply it to FWI. Neural networks are employed to provide an efficient solution to this problem. Numerical experiments on the Marmousi model are conducted to demonstrate the effectiveness of our proposed method.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115941864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113323
Z. Hou, D. Cao, Q. Liu
Summary Segmentation of digital rock images is a crucial and basic step in digital rock process, and equivalent elastic parameter and fluid properties calculated from the digital rock can be affected by the result of segmentation. Conventional segmentation algorithm based on thresholding algorithm cannot perform a satisfying result in small structure due to noise impact. To address issues, a modified guided by prior information, edge feature, is proposed to improve accuracy of small structure. Edge feature reflects information of the effect of transport, weathered, and eroded in the deposition process, but the shape of noise and artifacts can’t reflect these information, rather show regularity due to the influence of instruments, hence boundary feature can improve the discrimination of noise. Furthermore, conventional SegNet was used to compare with modified SegNet, the former obtains 90.21% accuracy using 38-layers network, proposed approach using prior information achieves 93.07% accuracy using 30-layers network, which demonstrates less computational time and better anti-noise property. In addition, connectivity was used to evaluate segmentation result, modified SegNet shows a better similarity with origin image.
{"title":"Segmentation of Digital Rock Images Guided by Edge Feature Using Deep Learning","authors":"Z. Hou, D. Cao, Q. Liu","doi":"10.3997/2214-4609.202113323","DOIUrl":"https://doi.org/10.3997/2214-4609.202113323","url":null,"abstract":"Summary Segmentation of digital rock images is a crucial and basic step in digital rock process, and equivalent elastic parameter and fluid properties calculated from the digital rock can be affected by the result of segmentation. Conventional segmentation algorithm based on thresholding algorithm cannot perform a satisfying result in small structure due to noise impact. To address issues, a modified guided by prior information, edge feature, is proposed to improve accuracy of small structure. Edge feature reflects information of the effect of transport, weathered, and eroded in the deposition process, but the shape of noise and artifacts can’t reflect these information, rather show regularity due to the influence of instruments, hence boundary feature can improve the discrimination of noise. Furthermore, conventional SegNet was used to compare with modified SegNet, the former obtains 90.21% accuracy using 38-layers network, proposed approach using prior information achieves 93.07% accuracy using 30-layers network, which demonstrates less computational time and better anti-noise property. In addition, connectivity was used to evaluate segmentation result, modified SegNet shows a better similarity with origin image.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114976771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202010573
Q. Li, G. Wu, P. Duan
Summary In land seismic exploration, low-velocity zone causes the ray path of reflected wave propagating to the detector perpendicularly. Therefore, single-component data is regarded as P-wave data. In this paper, we first derive a new pseudo acoustic wave equation (PAE) in elastic world based on acoustic approximation. Compared with the acoustic modeling, pseudo acoustic modeling has obvious elastic AVO effect and S-P converted energy. Then we propose a pseudo acoustic wave full waveform inversion method for elastic parameters inversion using P-wave data only. The gradients of the misfit function with respect to updating the perturbations of elastic parameters based on PAE theory are derived. A field data example in eastern china is carried out by our new method using only the p-wave data. The results of pseudo acoustic full waveform inversion shows that S-wave velocity inverted is reliable and the passion ratio profile is well fitted to the natural potential logging curve.
{"title":"Multi-Parameter Pseudo Acoustic Full Waveform Inversion Method in Elastic World","authors":"Q. Li, G. Wu, P. Duan","doi":"10.3997/2214-4609.202010573","DOIUrl":"https://doi.org/10.3997/2214-4609.202010573","url":null,"abstract":"Summary In land seismic exploration, low-velocity zone causes the ray path of reflected wave propagating to the detector perpendicularly. Therefore, single-component data is regarded as P-wave data. In this paper, we first derive a new pseudo acoustic wave equation (PAE) in elastic world based on acoustic approximation. Compared with the acoustic modeling, pseudo acoustic modeling has obvious elastic AVO effect and S-P converted energy. Then we propose a pseudo acoustic wave full waveform inversion method for elastic parameters inversion using P-wave data only. The gradients of the misfit function with respect to updating the perturbations of elastic parameters based on PAE theory are derived. A field data example in eastern china is carried out by our new method using only the p-wave data. The results of pseudo acoustic full waveform inversion shows that S-wave velocity inverted is reliable and the passion ratio profile is well fitted to the natural potential logging curve.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122169425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202112946
X. Fang, F. Niu, D. Wu
Summary The perfectly matched layer (PML) boundary condition has been widely used as a very effective absorbing boundary condition for seismic wavefield simulations. Convolutional PML (CPML) achieved by using a complex frequency-shifted stretch function was the latest development to further improve PML’s absorption performance for near-grazing angle incident waves as well as for low-frequency incident waves. However, the mathematical theory of most PML methods are derived from the first-order equation system; When implementing the PML technique to second-order wave equations, all the existing methods involve adding auxiliary terms and rewriting the CPML wave equations into the original coordinate, which will lead to the increase of calculation, more auxiliary variables, and complicate the implementation more than is necessary. We propose a new implementation of CPML for the second-order wave equation system. It does not need to introduce auxiliary variables or auxiliary equations for transforming the second-order CPML equations into the original coordinate, and furthermore, the implementation is simple and efficient.
{"title":"A new Implementation of CPML for the Second-Order Wave Equation","authors":"X. Fang, F. Niu, D. Wu","doi":"10.3997/2214-4609.202112946","DOIUrl":"https://doi.org/10.3997/2214-4609.202112946","url":null,"abstract":"Summary The perfectly matched layer (PML) boundary condition has been widely used as a very effective absorbing boundary condition for seismic wavefield simulations. Convolutional PML (CPML) achieved by using a complex frequency-shifted stretch function was the latest development to further improve PML’s absorption performance for near-grazing angle incident waves as well as for low-frequency incident waves. However, the mathematical theory of most PML methods are derived from the first-order equation system; When implementing the PML technique to second-order wave equations, all the existing methods involve adding auxiliary terms and rewriting the CPML wave equations into the original coordinate, which will lead to the increase of calculation, more auxiliary variables, and complicate the implementation more than is necessary. We propose a new implementation of CPML for the second-order wave equation system. It does not need to introduce auxiliary variables or auxiliary equations for transforming the second-order CPML equations into the original coordinate, and furthermore, the implementation is simple and efficient.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202010665
H. Li, M. Cai, X. Du, G. Li, B. Zhou
Summary Sparse spike inversion (SSI), imposes a sparseness constraint term along seismic trace, can evidently broaden the effective band of seismic data. However, this method frequently suffers from instability and poor continuity issues due to neglecting of the spatial dependence among reflectivity at adjacent traces. Although some methods add a lateral constraint item into cost function to consider above spatial correlations, the complicate coupling effect between the triggered two trade-off parameters severely limits the algorithm’s performance. We develop a two-step multichannel reflectivity inversion algorithm (TS-MRI) to retrieve spatially correlated reflectivity while avoiding opting for the two weights simultaneously. In the first step, we apply SSI to fast obtain sparse reflectivity estimation. In the second step, we exploit the result from SSI, a data-driven structural constraint term, and a least-square framework to reconstruct multi-trace reflectivity. The reflection structure characteristics (RSC) estimation plays a key role in building the structural constraint term, which has ability to map the spatial geometrical association in data into inverted reflectivity image. A model and a field data examples confirm the merits of TS-MRI than SSI on guaranteeing the continuity of structures and protecting weak events.
{"title":"Spatially Correlated Reflectivity Reconstruction via a Two-Step Scheme","authors":"H. Li, M. Cai, X. Du, G. Li, B. Zhou","doi":"10.3997/2214-4609.202010665","DOIUrl":"https://doi.org/10.3997/2214-4609.202010665","url":null,"abstract":"Summary Sparse spike inversion (SSI), imposes a sparseness constraint term along seismic trace, can evidently broaden the effective band of seismic data. However, this method frequently suffers from instability and poor continuity issues due to neglecting of the spatial dependence among reflectivity at adjacent traces. Although some methods add a lateral constraint item into cost function to consider above spatial correlations, the complicate coupling effect between the triggered two trade-off parameters severely limits the algorithm’s performance. We develop a two-step multichannel reflectivity inversion algorithm (TS-MRI) to retrieve spatially correlated reflectivity while avoiding opting for the two weights simultaneously. In the first step, we apply SSI to fast obtain sparse reflectivity estimation. In the second step, we exploit the result from SSI, a data-driven structural constraint term, and a least-square framework to reconstruct multi-trace reflectivity. The reflection structure characteristics (RSC) estimation plays a key role in building the structural constraint term, which has ability to map the spatial geometrical association in data into inverted reflectivity image. A model and a field data examples confirm the merits of TS-MRI than SSI on guaranteeing the continuity of structures and protecting weak events.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128246883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3997/2214-4609.202113223
M. Mukesh, K. Sarkar, U. K. Singh
Summary Employing the hybrid optimization technique with Gibb’s Sampler in association with joint inversion of MT and DC methods over 1D layered earth structures. The hybrid optimization algorithms have ability to balance the exploration and exploitation characteristics required for obtaining global solution. This hybrid technique uses the exploitation characteristic of PSO algorithm and the exploration characteristic of GWO algorithm, and the arrangement are generated from model parameters according to the Gibb’s sampling. The inherent problem of suppression is also studied due to conductive layer above a resistive layer. The results of hybrid algorithm with Gibb’s Sampler converges the solution faster than standard hybrid algorithm and it depicts large number of good fitting solutions lies in narrow region within the search space. Therefore, it is better to analyze histogram and calculate global mean model based on probability distribution function (PDF) with 68.27% confidence interval (CI) for all accepted models instead of selecting global model based on least error. In the present study, two different subsurface structures are optimized with noise free and noisy synthetic data. The efficiency of the algorithm is demonstrated by optimization in the paper.
{"title":"Joint Inversion of MT and DC Resistivity using Meta-Heuristic Algorithm with Gibb’s Sampler","authors":"M. Mukesh, K. Sarkar, U. K. Singh","doi":"10.3997/2214-4609.202113223","DOIUrl":"https://doi.org/10.3997/2214-4609.202113223","url":null,"abstract":"Summary Employing the hybrid optimization technique with Gibb’s Sampler in association with joint inversion of MT and DC methods over 1D layered earth structures. The hybrid optimization algorithms have ability to balance the exploration and exploitation characteristics required for obtaining global solution. This hybrid technique uses the exploitation characteristic of PSO algorithm and the exploration characteristic of GWO algorithm, and the arrangement are generated from model parameters according to the Gibb’s sampling. The inherent problem of suppression is also studied due to conductive layer above a resistive layer. The results of hybrid algorithm with Gibb’s Sampler converges the solution faster than standard hybrid algorithm and it depicts large number of good fitting solutions lies in narrow region within the search space. Therefore, it is better to analyze histogram and calculate global mean model based on probability distribution function (PDF) with 68.27% confidence interval (CI) for all accepted models instead of selecting global model based on least error. In the present study, two different subsurface structures are optimized with noise free and noisy synthetic data. The efficiency of the algorithm is demonstrated by optimization in the paper.","PeriodicalId":265130,"journal":{"name":"82nd EAGE Annual Conference & Exhibition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127292257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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：