Geophysical Prospecting最新文献

{"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":"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}

{"title":"Understanding the effects of pore pressure–induced crack deformation on the acoustic anisotropy of rocks with aligned cracks","authors":"Han Wang,&nbsp;Tongcheng Han,&nbsp;Li-Yun Fu","doi":"10.1111/1365-2478.13623","DOIUrl":"https://doi.org/10.1111/1365-2478.13623","url":null,"abstract":"<p>Cracks are extensively existing in rocks and play a significant role in the acoustic anisotropy of cracked rocks. Rocks in nature are affected by pore pressure, whereas the crack deformation with pore pressure and the impacts of the crack deformation on the anisotropic acoustic properties remain little known. Combining the theoretical model with the laboratory measurements of the anisotropic velocities of artificial sandstone samples with and without aligned penny-shaped cracks, we invert for the crack parameters that characterize the crack deformation as a function of pore pressure and theoretically simulate the impacts of pore pressure–induced variation in the crack parameters on the anisotropic velocities. The results show that with increasing pore pressure, the inverted crack porosity increases exponentially, whereas the inverted crack aspect ratio decreases exponentially and the two crack parameters are linearly correlated. Moreover, model calculation demonstrates that the anisotropic velocities exhibit distinct reductions with the variation in the crack parameters caused by increasing pore pressure. In particular, the reduction in the velocity of the shear wave travelling parallel to the crack plane with polarization perpendicular to the crack plane is the most pronounced. We also demonstrate that the effects of the pore pressure–induced increasing crack porosity on the anisotropic velocities are more pronounced than the impacts of the decreasing crack aspect ratio. The findings not only reveal the variation of the crack geometry with pore pressure and the effects of the crack deformation on the anisotropic velocities of the cracked rocks but also can provide theoretical support for improving the characterization of the cracks through seismic survey.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"418-429"},"PeriodicalIF":1.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113957","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":"Transient electromagnetic data denoising based on cluster analysis and locally weighted linear regression","authors":"Cheng Wang,&nbsp;Jianhui Li,&nbsp;Xushan Lu","doi":"10.1111/1365-2478.13625","DOIUrl":"https://doi.org/10.1111/1365-2478.13625","url":null,"abstract":"<p>In transient electromagnetic surveys, the collected data inevitably contain noise originating from both natural and cultural sources. This noise has the potential to mask transient electromagnetic responses linked to geological features, thereby posing challenges in accurately interpreting subsurface structures. Hence, the implementation of effective noise reduction techniques is crucial in ensuring the accuracy and reliability of inversion outcomes in transient electromagnetic surveys. This study introduces a novel approach that merges <i>k</i>-means clustering with locally weighted linear regression to denoise transient electromagnetic data. The results from synthetic examples illustrate that the <i>k</i>-means locally weighted linear regression method can predict transient electromagnetic data closely resembling true values, similar to the long short-term memory autoencoder. Occam's inversion results derived from denoised data using both the <i>k</i>-means locally weighted linear regression and long short-term memory–autoencoder methods can well reflect the true model. Notably, a key advantage of the <i>k</i>-means locally weighted linear regression method is its independence from labelled data as the sample set. The <i>k</i>-means locally weighted linear regression method was applied to field data collected at the Narenbaolige coalfield in Inner Mongolia, China. Occam's inversion models generated from the denoised field data delineate the boundary between the basaltic body and sedimentary rocks, aligning with drilling data. The inversion models derived from the noisy field data also can capture this boundary, but deep section views reveal the presence of numerous intricate high-resistivity anomalous bodies. These observations highlight the effectiveness of the <i>k</i>-means locally weighted linear regression method in denoising transient electromagnetic data.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"430-444"},"PeriodicalIF":1.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113951","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":"Elastic least-squares reverse time migration from topography through anisotropic tensorial elastodynamics","authors":"Tugrul Konuk,&nbsp;Jeffrey Shragge","doi":"10.1111/1365-2478.13619","DOIUrl":"https://doi.org/10.1111/1365-2478.13619","url":null,"abstract":"<p>Least-squares reverse time migration is an increasingly popular technique for subsurface imaging, especially in the presence of complex geological structures. However, elastic least-squares reverse time migration algorithms face significant practical and numerical challenges when migrating multi-component seismic data acquired from irregular topography. Many associated issues can be avoided by abandoning the Cartesian coordinate system and migrating the data to a generalized topographic coordinate system conformal to surface topology. We introduce a generalized anisotropic elastic least-squares reverse time migration methodology that uses the numerical solutions of tensorial elastodynamics for propagating wavefields in computational domains influenced by free-surface topography. We define a coordinate mapping assuming unstretched vertically translated meshes that transform an irregular physical domain to a regular computational domain on which calculating numerical elastodynamics solutions is straightforward. This allows us to obtain numerical solutions of forward and adjoint elastodynamics and generate subsurface images directly in topographic coordinates using a tensorial energy-norm imaging condition. Numerical examples demonstrate that the proposed generalized elastic least-squares reverse time migration algorithm is suitable for generating high-quality images with reduced artefacts and better balanced reflectivity that can accurately explain observed data acquired from topography in a medium characterized by arbitrary heterogeneity and anisotropy. Finally, the computational cost of our method is comparable to that of an equivalent Cartesian elastic least-squares reverse time migration numerical implementation.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"650-663"},"PeriodicalIF":1.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113508","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":"Novel brittleness index construction and pre-stack seismic prediction for gas hydrate reservoirs","authors":"Wenqiang Yang,&nbsp;Zhaoyun Zong,&nbsp;Xinxin Liu,&nbsp;Dewen Qin,&nbsp;Qingwen Liu","doi":"10.1111/1365-2478.13628","DOIUrl":"https://doi.org/10.1111/1365-2478.13628","url":null,"abstract":"<p>Reservoir transformation is essential for developing gas hydrate reservoirs. Predicting sediment brittleness is key to optimizing drilling design and evaluating engineering sweet spots. Constructing a brittleness index reflecting the brittle mineral content of a rock based on elastic parameters and predicting it using seismic data is a feasible solution for assessing reservoir brittleness. In addition, the elastic brittleness index can characterize the effect of complex pore types, fractures and pore fillings on rock brittleness. With the shallow hydrate reservoir in the sea as the research target. First, a novel brittleness index characterized by multiplying the Lamé parameter (<span></span><math>\u0000 <semantics>\u0000 <mi>λ</mi>\u0000 <annotation>$lambda $</annotation>\u0000 </semantics></math>) by Poisson's ratio (<span></span><math>\u0000 <semantics>\u0000 <mi>σ</mi>\u0000 <annotation>$sigma $</annotation>\u0000 </semantics></math>) is proposed. Its superiority in indicating brittle mineral content is verified by a rock-physics model. Second, a reflection coefficient approximation equation including the novel brittleness index is derived, enabling direct estimation of reservoir brittleness from seismic data. The new brittleness index has proven to better reflect brittle mineral content and effectively indicate the high brittleness characteristics of hydrate reservoirs. The accuracy of the proposed approximate equation is verified by a layered medium model, and the viability of predicting the new brittleness index using seismic data is also theoretically supported by the model test. Finally, the proposed method has obtained favourable results in the application of hydrate work area data collected at the South China Sea, confirming its availability and practicality.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"380-396"},"PeriodicalIF":1.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113955","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}