Geophysical Prospecting最新文献

{"title":"Three methods of visco-acoustic migration based on the De Wolf approximation and comparison of their migration images","authors":"Huachao Sun,&nbsp;Jianguo Sun,&nbsp;Zhenghui Gao","doi":"10.1111/1365-2478.13637","DOIUrl":"https://doi.org/10.1111/1365-2478.13637","url":null,"abstract":"<p>The viscosity of a medium affects the amplitude attenuation and velocity dispersion of seismic waves. Therefore, it is necessary to consider these factors during migration. First, to eliminate the viscous effect of a medium, we combine the Futterman model with the integral equation of the De Wolf approximation to construct a compensation operator of the De Wolf approximation for a visco-acoustic medium. Next, we use the visco-acoustic screen approximation method to realize the continuation operator then establish a prestack depth migration algorithm. Finally, an error analysis, impulse response test and model test are performed. The results show that three different generalized visco-acoustic screen methods (phase screen method, generalized screen method and extended local Born Fourier method) can satisfactorily compensate for the attenuation of deep interface amplitude. Among these methods, the visco-acoustic extended local Born Fourier method has the highest accuracy and the best compensation effect.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"266-283"},"PeriodicalIF":1.8,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Full-tensor magnetic gradiometry: Comparison with scalar total magnetic intensity, processing and visualization guidelines","authors":"Hernan Ugalde,&nbsp;Bill Morris,&nbsp;Akshay Kamath,&nbsp;Brian Parsons","doi":"10.1111/1365-2478.13629","DOIUrl":"https://doi.org/10.1111/1365-2478.13629","url":null,"abstract":"<p>Full-tensor magnetic gradiometry data have been collected commercially for the last few years. However, to date, there is still no clarity on how to compare these data to scalar total field surveys. Some users display the vertical gradient of the vertical component (<i>B</i>_zz) and compare that to a first vertical derivative of total field with the caveat that ‘they are similar’. Others compute the length of the measured vector and call that total field. We establish the basic formulas to calculate total field from the tensor components and demonstrate this with a real data example from Thompson, Manitoba, Canada. Another key question is whether full-tensor interpolation is required to obtain total field from tensor data. We compare the results from using a commercial full-tensor interpolation algorithm with standard minimum curvature of the tensor components individually and with another open-source code that uses a radial basis function interpolator on the individual tensor components. All three applications produced a total field grid of superior quality to that calculated from a scalar total field survey available for the area of study.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"303-314"},"PeriodicalIF":1.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inversion of downhole resistivity properties through infrared spectroscopy and whole-rock geochemistry using machine-learning","authors":"Mehdi Serdoun,&nbsp;Frédéric Sur,&nbsp;Gaétan Milesi,&nbsp;Elodie Williard,&nbsp;Pierre Martz,&nbsp;Julien Mercadier","doi":"10.1111/1365-2478.13627","DOIUrl":"https://doi.org/10.1111/1365-2478.13627","url":null,"abstract":"<p>The electrical properties of rocks are widely used in the geophysical exploration of natural resources, such as minerals, hydrocarbons and groundwater. In mining exploration, the primary goal is to map electrically anomalous geological features associated with different mineralization styles, such as clay alteration haloes, metal oxides and sulphides, weathered crystalline rocks or fractured zones. As such, the reconciliation of geophysical data with geological information (geochemistry, mineralogy, texture and lithology) is a critical step and can be performed based on petrophysical properties collected either on core samples or as downhole measurements. Based on data from 189 diamond drill cores collected for uranium exploration in the Athabasca Basin (Saskatchewan, Canada), this paper presents a case study of reconciliation of downhole resistivity probing with core sample geochemistry and short-wave infrared spectroscopy (350–2500 nm) through three successive steps: (i) multivariate analysis of resistivity and other petrophysical properties (porosity, density) against geochemical and infrared spectroscopy information to characterize electrical properties of rocks with respect to other physical parameters, (ii) a machine-learning workflow integrating geochemistry and spectral signatures in order to infer synthetic resistivity logs along with uncertainties. The best model in the basin was Light Gradient-Boosting Machine with pairwise log-ratio, which yielded a coefficient of determination <i>R</i>² = 0.80 (root mean square error = 0.16), and in the basement, support vector regression with data fusion of infrared spectroscopy and pairwise log-ratios on geochemistry yielded <i>R</i>² = 0.82 (root mean square error = 0.35); (iii) the best model was then fitted on an area that was excluded from the original dataset (Getty Russell property) in order to infer synthetic resistivity logs for that zone. Software code is publicly available. This workflow can be re-used for the valorization of legacy datasets.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"355-379"},"PeriodicalIF":1.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Underwater unexploded ordnance discrimination based on intrinsic target polarizabilities – A case study","authors":"Erika Gasperikova,&nbsp;Ugo Conti,&nbsp;H. Frank Morrison","doi":"10.1111/1365-2478.13631","DOIUrl":"https://doi.org/10.1111/1365-2478.13631","url":null,"abstract":"<p>Seabed unexploded ordnance that resulted partly from the high failure rate among munitions from more than 80 years ago and from decades of military training and testing of weapons systems poses an increasing concern all around the world. Although existing magnetic systems can detect clusters of debris, they are not able to tell whether a munition is still intact requiring special removal (e.g. in situ detonation) or is harmless scrap metal. The marine environment poses unique challenges, and transferring knowledge and approaches from land to a marine environment has not been easy and straightforward. On land, the background soil conductivity is much lower than the conductivity of the unexploded ordnance and the electromagnetic response of a target is essentially the same as that in free space. For those frequencies required for target characterization in the marine environment, the seawater response must be accounted for and removed from the measurements. The system developed for this study uses fields from three orthogonal transmitters to illuminate the target and four three-component receivers to measure the signal arranged in a configuration that inherently cancels the system's response due to the enclosing seawater, the sea–bottom interface and the air–sea interface for shallow deployments. The system was tested as a cued system on land and underwater in San Francisco Bay – it was mounted on a simple platform on top of a support structure that extended 1 m below and allowed the diver to place metal objects to a specific location even in low-visibility conditions. The measurements were stable and repeatable. Furthermore, target responses estimated from marine measurements matched those from land acquisition, confirming that the seawater and air–sea interface responses were removed successfully. Thirty-six channels of normalized induction responses were used for the classification, which was done by estimating the target principal dipole polarizabilities. Our results demonstrated that the system can resolve the intrinsic polarizabilities of the target, with clear distinctions between those of symmetric intact unexploded ordnance and irregular scrap metal. The prototype system was able to classify an object based on its size, shape and metal content and correctly estimate its location and orientation.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"315-329"},"PeriodicalIF":1.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective elastic properties of fractured rocks considering fracture interactions","authors":"Junxin Guo,&nbsp;Bo-Ye Fu","doi":"10.1111/1365-2478.13632","DOIUrl":"https://doi.org/10.1111/1365-2478.13632","url":null,"abstract":"<p>Fracture interactions are an important factor that affects rock effective elastic properties. We study the fracture interaction effects by combinations of theoretical modelling, numerical simulations and experiments in this work. First, we propose a simplified differential effective medium scheme and self-consistent approximation for effective elastic properties of rocks with aligned fractures, and we compare their results to those of non-interaction approximation. The results show that the predictions by differential effective medium scheme and self-consistent approximation are lower than those by non-interaction approximation. This indicates that the differential effective medium scheme and self-consistent approximation quantify the stress amplification effects but not stress shielding effects. To validate this, we carry out numerical simulations for cases with coplanar cracks and stacked cracks, respectively. We find that the stress shielding effect (stacked cracks) causes a significant increment of effective elastic stiffnesses of fractured rocks. However, the stress amplification (coplanar cracks) has the opposite effect, which induces a slight reduction in rock effective elastic stiffnesses. The differential effective medium scheme quantifies the lower bound for this effect well. Besides numerical simulations, applying theoretical models in experimental measurements also shows a pronounced effect of stress shielding but a small influence of stress amplification on fractured rock elastic properties. This work indicates that the stress shielding is an important effect that affects fractured rock elastic properties. Without considering this effect, the fracture density may be largely underestimated by the seismic or sonic logging inversion. Hence, the models accounting for stress shielding effects need to be developed in the future, which can combine with the above models for the seismic or sonic logging inversion of fracture properties.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"330-344"},"PeriodicalIF":1.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing cycle-skipping in full-waveform inversion using acoustic wave energy","authors":"Zhonglei Li,&nbsp;Gang Yao","doi":"10.1111/1365-2478.13630","DOIUrl":"https://doi.org/10.1111/1365-2478.13630","url":null,"abstract":"<p>Limitations in acquisition technologies lead to insufficient low-frequency signals in field seismic data. Local optimization methods are the common approaches for full-waveform inversion. Inaccurate initial velocity models and lack of low-frequency signals in seismic data typically cause the local-gradient-based full-waveform inversion to converge to a local minimum due to cycle-skipping. The existing energy-based objective functions can generate artificial low-frequency signals successfully by squaring the pressure but overlook the law of energy conservation, which may mislead model updates. To overcome this issue, we combine acoustic wave potential energy and kinetic energy to develop a new objective function that fits the acoustic wave energy. The new acoustic-wave-energy-based full-waveform inversion considers the law of energy conservation. The new system creates low-frequency signals to avoid cycle-skipping and produce an accurate smooth background velocity model, which provides a sufficient starting model for conventional full-waveform inversion. Numerical examples demonstrate that the combination of acoustic-wave-energy-based full-waveform inversion and conventional full-waveform inversion can deliver more faithful and accurate final results than conventional full-waveform inversion alone.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 3","pages":"960-976"},"PeriodicalIF":1.8,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic fault detection with sliding windowed differential cepstrum–based coherence analysis","authors":"Qi Ran,&nbsp;Kang Chen,&nbsp;Cong Tang,&nbsp;Long Wen,&nbsp;Ming Zeng,&nbsp;Han Liang,&nbsp;Guang-rong Zhang,&nbsp;Han Xiao,&nbsp;Ya-juan Xue","doi":"10.1111/1365-2478.13633","DOIUrl":"https://doi.org/10.1111/1365-2478.13633","url":null,"abstract":"<p>Cepstral decomposition is beneficial for highlighting certain geological features within the particular quefrency bands which may be deeply buried within the wide quefrency range of the seismic data. Converting seismic traces into the corresponding cepstrum components can better analyse some characteristics of underground strata than the traditional spectral decomposition methods. We propose the sliding windowed differential cepstrum–based coherence analysis approach to delineate the fault features. First, the data are decomposed using a sliding windowed differential cepstrum, which results in multi-cepstrum data of corresponding quefrency of certain bandwidth. These different multi-cepstrum data may highlight the different stratigraphic features in a certain quefrency band. We select the first-order common quefrency volume as the featured attribute. Then, eigenstructure-based coherence is applied on the first-order common quefrency data volume to statistically obtain the fault detection result with a finer and sharper image. Synthetic data and field data examples show that the proposed method has the ability to better visualize all the possible subtle and minor faults present in the data more accurately and discernibly than the traditional coherence method. Compared with the ant-tracking method, the proposed method is more effective in revealing the major faults. It is hoped that this work will complement current fault detection methods with the addition of the cepstral-based method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"345-354"},"PeriodicalIF":1.8,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Removing random noise and improving the resolution of seismic data using deep-learning transformers","authors":"Qifeng Sun,&nbsp;Yali Feng,&nbsp;Qizhen Du,&nbsp;Faming Gong","doi":"10.1111/1365-2478.13617","DOIUrl":"https://doi.org/10.1111/1365-2478.13617","url":null,"abstract":"<p>Post-stack data are susceptible to noise interference and have low resolution, which impacts the accuracy and efficiency of subsequent seismic data interpretation. To address this issue, we propose a deep learning approach called Seis-SUnet, which achieves simultaneous random noise suppression and super-resolution reconstruction of seismic data. First, the Conv-Swin-Block is designed to utilize ordinary convolution and Swin transformer to capture the long-distance dependencies in the spatial location of seismic data, enabling the network to comprehensively comprehend the overall structure of seismic data. Second, to address the problem of weakening the effective signal during network mapping, we use a hybrid training strategy of L1 loss, edge loss and multi-scale structural similarity loss. The edge loss function directs the network training to focus more on the high-frequency information at the edges of seismic data by amplifying the weight. Additionally, the verification of synthetic and field seismic datasets confirms that Seis-SUnet can effectively improve the signal-to-noise ratio and resolution of seismic data. By comparing it with traditional methods and two deep learning reconstruction methods, experimental results demonstrate that Seis-SUnet excels in removing random noise, preserving the continuity of rock layers and maintaining faults as well as being strong robustness.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"611-627"},"PeriodicalIF":1.8,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention mechanism-assisted recurrent neural network for well log lithology classification","authors":"Yining Gao,&nbsp;Miao Tian,&nbsp;Dario Grana,&nbsp;Zhaohui Xu,&nbsp;Huaimin Xu","doi":"10.1111/1365-2478.13618","DOIUrl":"https://doi.org/10.1111/1365-2478.13618","url":null,"abstract":"<p>Lithology classification is a fundamental aspect of reservoir classification. Due to the limited availability of core samples, computational modelling methods for lithology classification based on indirect measurements are required. The main challenge for standard clustering methods is the complex vertical dependency of sedimentological sequences as well as the spatial coupling of well logs. Machine learning methods, such as recurrent neural networks, long short-term memory and bidirectional long short-term memory, can account for the spatial correlation of the measured data and the predicted model. Based on these developments, we propose a novel approach using two distinct models: a self-attention-assisted bidirectional long short-term memory model and a multi-head attention-based bidirectional long short-term memory model. These models consider spatial continuity and adaptively adjust the weight in each step to improve the classification using the attention mechanism. The proposed method is tested on a set of real well logs with limited training data obtained from core samples. The prediction results from the proposed models and the benchmark one are compared in terms of the accuracy of lithology classification. Additionally, the weight matrices from both attention mechanisms are visualized to elucidate the correlations between depth steps and to help analyse how these mechanisms contribute to improved prediction accuracy. The study shows that the proposed multi-head attention-based bidirectional long short-term memory model improves classification, especially for thin layers.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"628-649"},"PeriodicalIF":1.8,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-lapse inversion of self-potential data through particle filtering","authors":"Yi-An Cui,&nbsp;Yuankang Peng,&nbsp;Jing Xie","doi":"10.1111/1365-2478.13626","DOIUrl":"https://doi.org/10.1111/1365-2478.13626","url":null,"abstract":"<p>In environmental sciences, comprehending the movement of subsurface contaminants is crucial for formulating effective remediation measures. The self-potential (SP) method has become a common tool for delineating landfill contamination plumes. Contaminant diffusion or migration represents dynamic processes, with corresponding SP responses evolving over time. However, conventional SP interpretation approaches have predominantly relied on static single-frame inversion, overlooking the temporal correlation in time-series SP data and resulting in cumulative errors. To tackle this challenge, we introduce a novel method for time-lapse inversion of SP data leveraging particle filtering. This approach recursively refines the priori state model through posteriori observations to achieve precise estimations of dynamic models. Specifically, a spherical polarization model is deployed to establish the state equations of underground contaminant diffusion and transport models, whereas the observation model is derived through forward modeling. The proposed method is validated using two synthetic examples and one lab-measured dataset. The findings demonstrate the efficacy of the time-lapse inversion algorithm in precisely estimating dynamic models, outperforming static single-frame inversion based on the particle swarm optimization algorithm. The posteriori distribution of particles approximates a bell-shaped distribution, with the true state closely positioned near the peak probability. Therefore, we affirm that conducting time-lapse inversion of time-series SP data through particle filtering is an effective and dependable approach for accurately estimating dynamic model states.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"445-454"},"PeriodicalIF":1.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113952","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}