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The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070416.1

Kelsy Taylor

引用次数: 0

Improving GPR interpretation by applying image-enhancing attributes and machine learning techniques: A case study over Green Hill Cemetery in Frankfort, Kentucky 应用图像增强属性和机器学习技术改进 GPR 解译：肯塔基州法兰克福绿山公墓案例研究

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070422.1

C. Buist, Heather Bedle

{"title":"Improving GPR interpretation by applying image-enhancing attributes and machine learning techniques: A case study over Green Hill Cemetery in Frankfort, Kentucky","authors":"C. Buist, Heather Bedle","doi":"10.1190/tle43070422.1","DOIUrl":"https://doi.org/10.1190/tle43070422.1","url":null,"abstract":"Ground-penetrating radar (GPR) enables noninvasive imaging in structural mapping applications such as unmarked grave detection. However, burial signatures can be cryptic and require expert analysis. This study investigates attribute enhancement and machine learning to automate identification of variable-signature graves at Green Hill Cemetery in Frankfort, Kentucky. Nine complementary seismic attributes were computed from the GPR envelope reflectivity volume to boost discontinuities and patterns indicative of shafts. Coherent energy, pseudofrequency, and similarity transforms showed optimal visualization enhancement. These volumes were input into unsupervised k-means and self-organizing map (SOM) machine learning models to cluster potential burial sites. Both methods accurately characterized strong anomaly reflections associated with apparent grave boundaries. However, limitations emerged in classifying subtler signatures that are likely linked to deteriorated or deeper interments. SOM clustering provided finer segmentation between noise and targets. Collectively, attributes amplified burial edges for easier recognition, while machine learning clustering expedited identification of most vault structures and unmarked sites. However, edge case discrimination remains a challenge. Results suggest that hybrid human-machine learning analysis can enhance efficiency over purely manual interpretation. With further method refinement, automated attribute and machine learning workflows show strong potential for accelerating GPR-based cemetery and archaeological mapping.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706503","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}

引用次数: 0

SEG Summer Field Camp: Multigeophysical imaging of an aquifer in the Astara region of Azerbaijan SEG 野外夏令营：阿塞拜疆阿斯塔拉地区含水层的多重物理成像

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070453.1

Clara Jodry, Dashgin Abakarov, Zaur Bayramov, Javid Aliyev, Nigar Karimova, Murad Abdulla-Zada

{"title":"SEG Summer Field Camp: Multigeophysical imaging of an aquifer in the Astara region of Azerbaijan","authors":"Clara Jodry, Dashgin Abakarov, Zaur Bayramov, Javid Aliyev, Nigar Karimova, Murad Abdulla-Zada","doi":"10.1190/tle43070453.1","DOIUrl":"https://doi.org/10.1190/tle43070453.1","url":null,"abstract":"Groundwater faces growing pressure due to human activities and the impacts of global climate change. Therefore, it is imperative to characterize near-surface aquifers, especially those that are unconfined because they are particularly vulnerable to these challenges. In Azerbaijan, alluvial plain aquifers represent a critical facet of the nation's water resources, yet they remain largely understudied. The objective of our study is to employ a multigeophysical survey (including electrical resistivity, seismic refraction, and ground-penetrating radar) to describe the subsurface attributes of an unconfined alluvial aquifer situated within an agricultural field in Astara, Azerbaijan. These data were acquired during the 2023 SEG Summer Field Camp by global students and specialists. Based on our general knowledge of the area, we interpret our findings as a subsurface with four distinct layers. The first is an initial 1 m thick soil layer covering the second layer, which is a more permeable unconfined aquifer likely consisting of a mixture of sand, pebbles, and gravel with a silty matrix (3 m thick). The third is a potential confining layer that is possibly clayey. The fourth layer is a presumed confined aquifer at a depth of 10 m. These results shed light on previously unstudied alluvial plain aquifers and contribute to comprehension of the hydrogeologic conditions at the local scale. To provide a broader understanding at the regional scale, the survey area should be extended and linked to borehole data to improve the interpretation of the geophysical results.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693153","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}

引用次数: 0

Introduction to this special section: General submissions 本专栏简介：一般呈件

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070420.1

Steve Brown, Chester W. Weiss

引用次数: 0

Steckman Ridge: A naturally fractured underground gas storage field 斯特克曼岭天然裂缝地下储气田

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070459.1

Brian Edward Toelle, M. Stellas

{"title":"Steckman Ridge: A naturally fractured underground gas storage field","authors":"Brian Edward Toelle, M. Stellas","doi":"10.1190/tle43070459.1","DOIUrl":"https://doi.org/10.1190/tle43070459.1","url":null,"abstract":"This paper presents a field development case study of a gas storage field in Pennsylvania within a fractured reservoir. Integration of multiple data sets was required for the project's success. During the study, the interpretation of 3D isofrequency seismic volumes, generated through spectral decomposition, was shown to be the key step in this data integration. The interpretation of these isofrequency volumes successfully identified previously unresolved subtle structural features, which controlled the natural fracture system. The existence of these subtle structural features was subsequently confirmed with eight horizontal gas storage wells. Accurate characterization of a fracture trend requires the integration of seismic data, available well data, surface geology, drilling, and production data. This study was performed to develop the Oriskany reservoir in the Steckman Ridge underground gas storage field located in Bedford County, Pennsylvania. This field had undergone rapid depletion during its primary production phase. During the early portion of the study, the reservoir was determined to be a type 1 fractured reservoir with little to no gas storage available in the sandstone matrix. Multiple data types were acquired and integrated to detect and characterize the structural features that compose the reservoir. Isofrequency volumes, developed through spectral decomposition of 3D seismic data over the field, helped identify and map subtle shear faults that proved to be controlling the location and orientation of the dominant fracture trend in the field. Horizontal boreholes, needed to convert the field from gas production to gas storage, were planned and drilled based on this seismic interpretation. The existence of these shear faults, their location, and their orientation were confirmed by image logs acquired along the length of six horizontal gas storage boreholes. Additionally, flow tests performed to determine gas deliverability supported this interpretation.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690077","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}

引用次数: 0

Editorial Calendar 编辑日历

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070413.1

引用次数: 0

Self-supervised denoising at low signal-to-noise ratios: A seismic-while-drilling application 低信噪比下的自监督去噪：地震钻探应用

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070436.1

C. Birnie, Sixiu Liu, A. Aldawood, A. Bakulin, I. Silvestrov, T. Alkhalifah

{"title":"Self-supervised denoising at low signal-to-noise ratios: A seismic-while-drilling application","authors":"C. Birnie, Sixiu Liu, A. Aldawood, A. Bakulin, I. Silvestrov, T. Alkhalifah","doi":"10.1190/tle43070436.1","DOIUrl":"https://doi.org/10.1190/tle43070436.1","url":null,"abstract":"Self-supervised blind-mask denoising networks overcome the challenge of requiring clean training targets by employing a mask on raw noisy data to form the input for training while using the unmasked version as the network's target. The application of such networks has shown considerable strength in suppressing both random and coherent noise in seismic data. However, because such networks need to figure out the target (clean signal) on their own, they struggle at low signal-to-noise ratios. Seismic-while-drilling acquisitions result in seismic data of very low quality because the drilling operation introduces significant noise propagating from the rig site. Due to its consistent and low-frequency nature, it is hard to design a noise mask to hide the rig noise from the network without also hiding useful information required for predicting the signal. However, by reframing the task from a noise suppression to a noise prediction task and utilizing a mask to hide the signal from the network, the rig noise can be predicted accurately. Therefore, the difference between the network's prediction and the raw data results in common bit (equivalent to shot, but continuous) gathers with a significantly higher signal-to-noise ratio due to the removal of rig noise. Illustrated on six common bit gathers, this reversed methodology is shown to separate the rig noise and signal, even in their shared bandwidth. The additional use of explainable artificial intelligence is investigated as a means of avoiding the manual step of creating the signal mask, providing promising results. This study lays the ground work for suppression of high-amplitude, consistent noises, such as those arising from well site operations like fluid injection procedures for carbon sequestration or geothermal energy production purposes.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716531","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}

引用次数: 0

Seismic modeling using pseudo-impedance derived from physical models 利用物理模型得出的伪阻抗进行地震建模

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070444.1

Chris Willacy, Tim P. Dooley

{"title":"Seismic modeling using pseudo-impedance derived from physical models","authors":"Chris Willacy, Tim P. Dooley","doi":"10.1190/tle43070444.1","DOIUrl":"https://doi.org/10.1190/tle43070444.1","url":null,"abstract":"Building accurate numerical models of the earth for simulation is both challenging and time consuming. The subsurface complexity must be captured in sufficient detail for the results to be of practical use for geophysical analysis. Physical models provide an efficient way to add realistic detail to numerical models. Physical models, for example, are scaled simulations of large-scale geologic systems that provide great insight into understanding the interplay between sedimentation, salt tectonics, and faulting. The integration of geophysical workflows with sandbox models has been the subject of previous research, primarily to create synthetic data to compare with observed field data. However, capturing the detail from the physical model is challenging. A novel workflow is presented that uses pixel intensity values from photographic images of the analogue model to create a pseudo-impedance perturbation, mimicking the acoustic impedance contrasts that are encountered in the earth. This solution enables the detailed sedimentary architecture from the sandbox to be transferred to the digital earth model. Acoustic wave equation forward modeling and reverse time migration imaging have been applied using the generated pseudo-impedance model for a Gulf of Mexico-style salt canopy model. The images produced show clear definition of the salt/sediment interface and capture the sedimentary stratigraphy and faulting observed in the original sandbox model.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703532","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}

引用次数: 0

Seismic Soundoff: The untapped potential of nonseismic methods 地震消声：非地震方法尚未开发的潜力

The Leading Edge Pub Date : 2024-07-01 DOI: 10.1190/tle43070472.1

Andrew Geary

引用次数: 0

Identifying imaging challenges for 1D and 3D geometric complexity (LG space) 确定一维和三维几何复杂性（LG 空间）的成像挑战

The Leading Edge Pub Date : 2024-06-01 DOI: 10.1190/tle43060338.1

August Lau, Alfonso Gonzalez

{"title":"Identifying imaging challenges for 1D and 3D geometric complexity (LG space)","authors":"August Lau, Alfonso Gonzalez","doi":"10.1190/tle43060338.1","DOIUrl":"https://doi.org/10.1190/tle43060338.1","url":null,"abstract":"In some situations, it is difficult to image seismic data even when factors such as illumination, anisotropy, and attenuation are resolvable. An often-overlooked but dominant factor affecting imaging is the geometric complexity of subsurface reflectors. In some of these settings, we propose that geometric complexity plays a dominant role in the quality of seismic imaging. This has been documented in subsalt regions with poor image quality where 3D vertical seismic profiles (VSPs) are available. VSPs often demonstrate that there is good illumination below salt, but the complexity of the observed downgoing wave and diffractions cannot be simulated with smooth earth models. We use synthetic examples with high geometric complexity and no other complications to analyze the imaging process: a 2D sediment-salt model with rough salt-top topography, and a 1D model with complex reflectivity. In these models, it is difficult to estimate velocity, and it is challenging to image the data. These synthetic examples are useful to understand the limitations of imaging with smooth models, to create guidelines to identify geometric complexity in field data, and to develop possible mitigations to improve imaging. Further real data examples will be needed to test geometric complexity.","PeriodicalId":507626,"journal":{"name":"The Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281658","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}

引用次数: 0