Leading Edge最新文献

{"title":"Aeromagnetic attitude compensation for uninhabited aircraft systems without high-altitude calibration patterns using hybrid recurrent neural networks","authors":"M. Cunningham, L. Tuck, C. Samson, J. Laliberté, M. Goldie, Alan Wood, David Birkett","doi":"10.1190/tle42020112.1","DOIUrl":"https://doi.org/10.1190/tle42020112.1","url":null,"abstract":"Since the 1950s, Tolles-Lawson-based aeromagnetic compensation methods have been used to separate an aircraft's magnetic signal from signal associated with ground geologic and cultural features. This is done by performing a high-altitude figure-of-merit (FOM) flight and fitting the band-pass-filtered magnetic data to determine compensation parameters. This paper describes a supervised hybrid recurrent neural network (HRNN) algorithm trained on low-altitude survey data to perform aeromagnetic compensation. The proposed HRNN attitude compensation method can be employed for aeromagnetic surveys where traditional FOM and compensation are not possible. It has particular relevance for surveying via uninhabited aircraft systems (UAS). Firstly, the HRNN was tested on data from a fixed-wing airplane survey, and the results were compared to hardware-based compensation results. The standard deviation of the difference between the two methods for magnetic attitude correction (MAC) was 0.1 nT for the training region and 0.4 nT for the application region, respectively. Secondly, a UAS FOM flight at the highest permitted altitude in Canada, 120 m above ground level, showed similar improvement ratios for software-based least squares (LS) and the proposed HRNN algorithm of 3.5 and 2.6, respectively. The percent change and deviation in differences in MACs from LS to HRNN was 0.0% and 0.9 nT across small-box loops and –2.7% and 0.4 nT across large-box loops. Finally, LS and the proposed HRNN algorithm were applied to a 50 m altitude UAS data set for which no FOM flight was possible. LS did not successfully model aircraft noise, whereas the HRNN demonstrated effective removal of the magnetic signal due to aircraft attitude variations. The modeled HRNN MAC had a standard deviation of 2.4 nT.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47502168","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}

{"title":"Toward high-fidelity imaging: Dynamic matching FWI and its applications","authors":"Y. Huang, J. Mao, J. Sheng, M. Perz, Yang He, F. Hao, Faqi Liu, Bin Wang, S. L. Yong, Daniel H. Chaikin, A. Ramirez, M. Hart, H. Roende","doi":"10.1190/tle42020124.1","DOIUrl":"https://doi.org/10.1190/tle42020124.1","url":null,"abstract":"Full-waveform inversion (FWI) is firmly established within our industry as a powerful velocity model building tool. FWI carries significant theoretical advantages over conventional velocity model building methods such as refraction and reflection tomography. Specifically, by solving a nonlinear inverse problem through the wave equation, FWI is able to recover a broadband velocity model containing both high and low spatial wavenumbers, thus extending the approximation of residual moveout correction inherent in traditional velocity model building approaches. Moreover, FWI is capable of inverting information from the entire wavefield (i.e., early arrivals, reflections, refractions, and multiple energy) rather than from a subset as in conventional approaches (i.e., first break and primary reflections), thereby availing itself of more information to better constrain its model estimate. However, these theoretical benefits cannot be realized easily in practice because various complexities of real seismic data often conspire to violate algorithmic assumptions, leading to unsatisfactory results. Dynamic matching FWI (DMFWI) is a newly developed algorithm that solves an inversion problem that maximizes the cross correlation of two dynamically matched data sets — one recorded and the other synthetic. Dynamic matching of the two data sets de-emphasizes the amplitude impact, which allows the algorithm to focus on minimizing their kinematic differences rather than amplitude in the data-fitting process. The multichannel correlation makes the algorithm robust for data with low signal-to-noise ratio. Applications of DMFWI across different types of acquisition and geologic settings demonstrate that this novel FWI approach can resolve complex velocity errors and provide high-quality migrated images that exhibit a high degree of geologic plausibility. Additionally, reflectivity images can be obtained in a straightforward manner as natural byproducts through computation of the directional derivative of the inverted FWI velocity models.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42059721","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}

{"title":"Board Report","authors":"","doi":"10.1190/tle42020138.1","DOIUrl":"https://doi.org/10.1190/tle42020138.1","url":null,"abstract":"SEG Board of Directors and Executive Committee actions in November and December 2022.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46942883","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}

{"title":"EcoSeis: A novel acquisition method for optimizing seismic resolution while minimizing environmental footprint","authors":"M. Naghizadeh, P. Vermeulen, A. Crook, A. Birce, S. Ross, A. Stanton, Maximo Rodriguez, Warren Cookson","doi":"10.1190/tle42010061.1","DOIUrl":"https://doi.org/10.1190/tle42010061.1","url":null,"abstract":"All exploration and production projects, whether for oil-and-gas, mining, or clean-technology applications, begin with an accurate image of the subsurface. Many technologies have been developed to enable the acquisition of cost-effective seismic data, with high-density land seismic programs becoming commonplace. However, as the industry progresses and the long-term surface footprint associated with these programs becomes better understood, new methods are needed to reduce the environmental impact of seismic data acquisition while maintaining sufficient subsurface resolution for accurate resource development. New acquisition geometries are typically easier to create than test in the field due to the high cost of field acquisition and processing. However, by using existing data acquired in a grid, one can decimate the original data set into multiple geometries and process them. This provides an opportunity to fully test new geometries without the expense of field acquisition. In this paper, we present processing, interpretation, and inversion tests from an existing ultra-high-density oil-sands seismic data set decimated based on ecologically improved program designs. We then measure and compare the results to understand the impact of these geometries on subsurface resolution.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42227530","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}

{"title":"A brief overview of seismic resolution in applied geophysics","authors":"J. Reilly, M. Aharchaou, R. Neelamani","doi":"10.1190/tle42010008.1","DOIUrl":"https://doi.org/10.1190/tle42010008.1","url":null,"abstract":"The high resolving power of seismic measurements has promoted wide adoption of the seismic method in oil and gas and other industries. Studying the evolution of seismic resolution, the different factors affecting it, and the remaining barriers enables an improved understanding of where we are today and what lies ahead. The need to improve seismic resolution is best framed in the context of the interpretation questions being raised and the project stage (e.g., new frontier, appraisal, development, or production). Improvements in resolution do not depend on a single aspect of the seismic workflow but on multiple interconnected components including acquisition, processing, imaging, and interpretation methods and technologies. This paper highlights some of the key milestones in improving seismic resolution. We also conjecture on progress likely to be made in the years ahead and remaining opportunities to enhance seismic resolution.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46097522","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}

{"title":"From FWI to ultra-high-resolution imaging","authors":"I. Espin, N. Salaun, Hao Jiang, Mathieu Reinier","doi":"10.1190/tle42010016.1","DOIUrl":"https://doi.org/10.1190/tle42010016.1","url":null,"abstract":"In recent years, the development of time-lag full-waveform inversion (FWI) has enabled the use of the full wavefield (primary reflections, diving waves, and their multiples and ghosts) in the inversion process. With this advancement, it is possible to obtain a very detailed velocity model, ultimately reaching the point of deriving from the velocity a migration-like reflectivity image called the FWI image. When the FWI maximum frequency is increased, high-resolution velocity models are obtained, revealing superior reservoir information compared to conventional imaging results. Two case studies will be discussed in this paper. The first is in the Greater Castberg area where the 150 Hz FWI image greatly surpassed the Q Kirchhoff prestack depth migration image from the water-bottom level down to the reservoir (located at a depth of about 1.5 km). The second case study is over the Nordkapp Basin. The use of the full wavefield for the shallow ultra-high-resolution (UHR) FWI image (run at 200 Hz) revealed reverse faulting and pockmark details that were invisible with Kirchhoff prestack depth migration and reverse time migration. By using additional information present in multiples, ghosts, and diving waves, a spatial resolution down to 3 m was achieved. This made it possible to image very thin features without the need for a dedicated high-resolution acquisition design. The current UHR FWI image workflow provides velocity and reflectivity information in the near surface that is important in identifying optimal locations for various purposes such as well placement and wind-turbine installation.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45007481","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}

{"title":"Introduction to this special section: Seismic resolution","authors":"M. Aharchaou, R. Neelamani, Chengbo Li","doi":"10.1190/tle42010007.1","DOIUrl":"https://doi.org/10.1190/tle42010007.1","url":null,"abstract":"The science of modern seismology was born more than 100 years ago (1889) when the first teleseismic record was identified and the seismograph was developed ( Ben-Menahem, 1995 ). In 1921, earth exploration was revolutionized when a team led by Clarence Karcher conducted the first field tests of the reflection seismograph in Oklahoma City ( Dragoset, 2005 ). That experiment showed that the subsurface can be imaged using seismic data. Businesses boomed as the seismic method started establishing its track record in finding hydrocarbons. Over the last century, the seismic method has emerged as the cornerstone of exploration geophysics, providing us with increasingly accurate characterizations of the subsurface and enabling us to better discover and describe hydrocarbon prospects, geothermal anomalies, seafloor hazards, aquifers, and much more.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46937830","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}

{"title":"Seismic Soundoff: The necessity and benefits of mentorship","authors":"A. Geary","doi":"10.1190/tle42010080.1","DOIUrl":"https://doi.org/10.1190/tle42010080.1","url":null,"abstract":"Cara Hunter and Teresa Santana discuss the value and importance of mentoring for geoscientists. They explain the need for Mentoring365, explore networking, and offer tips for first-time mentors and mentees. They also elaborate on the two-way aspect of mentorship and how any professional can build their network. The conversation concludes with a reflection on how mentorship has influenced their careers and the value of engaging as a mentor.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41741800","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}

{"title":"Inversion-based multistage seismic data processing with physics-driven priors","authors":"Rajiv Kumar, Y. Kamil, P. Bilsby, A. Narayan, A. Mahdad, W. G. Brouwer, A. Misbah, M. Vassallo, A. Zarkhidze, Peter Watterson","doi":"10.1190/tle42010052.1","DOIUrl":"https://doi.org/10.1190/tle42010052.1","url":null,"abstract":"Various aspects of survey design have a profound impact on how noise appears on the coherent signal of interest, thus impacting conventional inversion methods in complex environments. We propose a multistage physics-driven prior-based processing technique that is versatile and can be used in a wide range of inversion-based processing applications such as source separation and/or interpolation for any acquisition environments (e.g., land, marine, and ocean-bottom nodes). The inversion-based multistage approach progressively builds the coherent signal model while eliminating the aliasing, blending, and background noise in a signal-safe manner. To stabilize the inversion process, we include physics-driven priors in the multiple stage process, which enhances the sparsity of the coherent signal in the transform domain. Results using real data from land and ocean-bottom node surveys validate the potential of the proposed approach to produce optimal processing results while dealing with the common geophysical challenges related to different seismic acquisitions.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44933230","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}

{"title":"Pushing seismic resolution to the limit with FWI imaging","authors":"Zhiyuan Wei, J. Mei, Zedong Wu, Zhigang Zhang, Rongxin Huang, Ping Wang","doi":"10.1190/tle42010024.1","DOIUrl":"https://doi.org/10.1190/tle42010024.1","url":null,"abstract":"Although the resolution of a seismic image is ultimately bound by the spatial and temporal sampling of the acquired seismic data, the seismic images obtained through conventional imaging methods normally fall very short of this limit. Conventional seismic imaging methods take a piecemeal approach to imaging problems with many steps designed in preprocessing, velocity model building, migration, and postprocessing to solve one or a few specific issues at each step. The inefficacies of each step and the disconnects between them lead to various issues such as velocity errors, residual noise and multiples, illumination holes, and migration swings that prevent conventional imaging methods from obtaining a high-resolution image with good signal-to-noise (S/N) and well-focused details. In contrast, full-waveform inversion (FWI) imaging models and uses the full-wavefield data including primaries and multiples and reflection and transmission waves to iteratively invert for the velocity and reflectivity in one go. It is a systemic approach to address imaging issues. FWI imaging has proven to be a superior method over conventional imaging methods because it provides seismic images with greatly improved illumination, S/N, focusing, and resolution. We demonstrate with a towed-streamer data set and an ocean-bottom-node (OBN) data set that FWI imaging with a frequency close to the temporal resolution limit of seismic data (100 Hz or higher) can provide seismic images with unprecedented resolution from the acquired seismic data. This has been impossible to achieve with conventional imaging methods. Moreover, incorporating more accurate physics into FWI imaging (e.g., upgrading the modeling engine from acoustic to elastic) can further improve the seismic resolution substantially. Elastic FWI imaging can further reduce the mismatch between modeled and recorded data, especially around bodies of large impedance contrast such as salt. It appreciably improves the S/N and resolution of the inverted images. We show with an OBN data set in the Gulf of Mexico that elastic FWI imaging further improves the resolution of salt models and subsalt images over its acoustic counterpart.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42959375","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}