Abstract In Antarctica, more than 98% of the continent is covered by ice sheets. Seismic exploration is recognized as one of the most effective methods for high-resolution imaging of ice sheets and the underlying bedrock. However, conducting active-source seismic surveys remains highly challenging due to the extreme environmental conditions and limited logistical support. Passive seismic exploration, which does not require active sources, has therefore emerged as a promising approach for efficient seismic investigations on Antarctic ice sheets. However, its imaging capability under field conditions still requires direct validation against active-source results. This study provides a field-scale validation of passive seismic imaging by directly comparing passive results with co-located active-source results acquired along a survey line on the ice sheet in the Larsemann Hills, East Antarctica. Two seismic processing workflows are developed for surface-wave and body-wave imaging. For surface waves, least-squares noise suppression and beamforming-based stationary-phase selection are used to improve the quality of reconstructed virtual shot gathers. Multichannel analysis of surface waves (MASW) is conducted on both active and passive datasets, and the resulting dispersion characteristics and inverted shear-wave velocity models show strong consistency. For body-wave imaging, conventional reflection processing techniques used for active-source data are adapted for virtual shot gathers retrieved from passive recordings. The stacked passive seismic profile reproduces the main reflection events observed in the active-source profile, particularly the ice-bed interface and shallow reflections within the bedrock. These comparative results demonstrate that, in the Larsemann Hills region of Antarctica, passive seismic methods provide an environmentally sustainable, cost-effective, and reliable alternative to active seismic surveys for large-scale imaging of ice sheets and shallow bedrock structures.

Research on Antarctic subglacial lakes and bedrock carries profound implications for understanding Earth's history, exploring extremophile ecosystems, predicting climate change patterns, and addressing global sea-level rise. The controlled seismic method proves effective for detecting these subglacial features, yielding high-resolution data on ice sheets, subglacial lakes, and bedrock formations. This paper introduces an electromagnetic controlled seismic source based on a linear synchronous motor (LSM), specifically engineered for polar environments characterized by subzero temperatures and snow/ice cover. The system employs alternating current to drive the LSM, generating vibrations at either fixed or swept frequencies. Motor displacement is precisely monitored via a grating scale position sensor, ensuring accurate seismic wave excitation. The developed seismic source generates continuous swept-frequency signals spanning 5 - 250 Hz, with adjustable start/stop frequencies and output energy amplitude ratios. Capable of maximum output force up to 8 kN, its modular sub-source design ensures both transportability and scalable vibration intensity. During the 41st Chinese Antarctic Research Expedition, the LSM seismic source underwent field testing in Princess Elizabeth Land's Kirin Lake area – marking the world's first active-source seismic exploration targeting Kirin Subglacial Lake. Single-shot record successfully detected reflections from the lake surface and bedrock at ~3,500 m depth. The seismic source we designed is China's first set of seismic sources for polar environments and critical technical support for advanced subglacial lake research. It comprehensively takes into account extremely low temperatures, ice and snow interference, the convenience of assembly and transportation, as well as the excitation ability and flexibility. This work provides a novel seismic excitation solution. Results demonstrate the system's key advantages: operational user-friendliness, high field efficiency, superior broadband excitation performance, environmentally friendly and reusable.

The thickness of the firn layer on the Antarctic ice sheet varies from a few meters to over a hundred meters and exhibits a porous structure with density increasing with depth. Accurate characterization of its densification structure is crucial for understanding ice sheet dynamics. Active-source seismic methods are effective for probing firn properties, but conventional Herglotz-Wiechert and traveltime tomography approaches are limited by one-dimensional assumptions and low resolution. In this study, we reconstruct the P-wave velocity structure of the firn using Wave-equation Traveltime inversion (WT) based on guided-wave first arrivals, and derive its density distribution through empirical relationships. Validation with both synthetic data including an ice shelf and field measurements from Thwaites Glacier, West Antarctica, demonstrates that WT effectively captures spatial variations in firn density and resolves small-scale structures, providing a robust tool for high-precision densification modeling.

In Antarctica, seismic monitoring and detection is significant for understanding longterm glacier activity, glacial melting, and other related scientific issues. We deployed 8 three-component short-period seismometers near the grounding line of the Dalk Glacier ice tongue in Southeast Antarctica, collecting seismic signals from 19 October 2022 to 28 February 2023. We applied unsupervised deep learning, using an autoencoder to encode the features of the input spectrograms into low-dimensional latent vectors, which were then input into a Gaussian mixture model for clustering. We ultimately identified four groups of microseismic signals: Group A wideband events (20-100 Hz) are associated with temperature variation; it is a group of crevasse events. Group B includes low-frequency (< 20 Hz) events that may be resonances generated by fluid-filled subglacial fractures, corresponding to the minimum and maximum tidal speeds at the Dalk Glacier coast. Group C events are vibrations caused by wind, and group D events are noise from commuter vehicles. In particular, we discuss the highenergy pulse events included in group A as well as the possible potential sources of group B events. Our research results indicate that deep clustering can effectively identify various types of microseismic signals. The icequakes are closely related to glacier activity driven by environmentally related factors. This may improve further study on the internal structure of glaciers.

Subglacial lakes offer unique opportunities to study ice sheet dynamics, microbial life, and climate evolution. Motivated by the upcoming seismic survey of Qilin Subglacial Lake, this study constructs a representative subglacial lake model and extends it to six geological scenarios to examine their effects on seismic wavefield characteristics, revealing the prominent occurrence of guided waves and multiple reflections. Simulated data are then analyzed to identify key imaging challenges - particularly those arising from strong multiples, low-velocity firn layers, and source-side ghosts - and to assess processing strategies, such as salt flooding, that enhance imaging accuracy and suppress noise. Commonly used seismic acquisition systems in polar regions are also reviewed to summarize their characteristics and implications for survey design. Finally, four seismic lines from Lago Subglacial CECs are processed, demonstrating strong consistency with the simulation results in terms of key seismic features, thereby validating the proposed theoretical approach. Collectively, these efforts provide a robust and broadly applicable framework for understanding seismic wave propagation in subglacial lake environments, offering both theoretical insights and methodological guidance for future Antarctic investigations.

Antarctica is mostly covered by snow, firn, and glacier ice, and the transformation from snow to firn and glacier ice influences energy transfer and material transport in polar regions. In this paper, we deployed three linear seismic arrays near Dome A in East Antarctica during China's 39th and 40th Antarctic scientific expeditions and used seismic ambient-noise to reconstruct the firn structure nearby. The result shows that the ambient noise mainly comes from the Kunlun Station and is related to human activities. We resolved the empirical Green's function that contains abundant multi-modal surface waves from 3 to 35 Hz, and reconstructed the shallow S-wave velocity, density, and radial anisotropy structures by inverting them. The reliability of the structure was validated by the ice-core data, which demonstrates the effectiveness of using cultural seismic noise for the reconstruction of shallow structures in Antarctica. The result shows that the S-wave velocity increases rapidly with a weak negative radial anisotropy (SH wave travels slower than SV wave) in the top 28 m, which corresponds to the transformation from snow to firn. The firn layer shows a fairly strong positive radial anisotropy (SH wave travels faster than SV wave) between 40 and 70 m in depth. The radial anisotropy vanishes to zero at around 84 m in depth, denoting the transformation from firn to glacier ice. Overall, the multi-parameter results clearly show the transformation from snow to ice, and the internal evolution of firn at the Dome A region. Furthermore, we compared several existing S-wave velocity profiles of firn structures from different areas in Antarctica, which indicate relatively higher S-wave velocities at the same depth in the four study areas located in West Antarctica.

Abstract. Subglacial lakes are crucial for studying life evolution, ice sheet dynamics, and climate change. Lake Qilin, located in the Princess Elizabeth Land region of East Antarctica, is the second largest subglacial lake discovered in Antarctica. Studying its microbial communities and biogeochemical cycles provides valuable insights into Earth's life evolution and the search for extraterrestrial life. To advance this research, China plans to conduct clean drilling and water sampling in Lake Qilin from 2025 to 2027. Additionally, during the 41th Chinese National Antarctica Research Expedition, active-source seismic exploration will be conducted to obtain high-resolution imaging, guiding drilling site selection. This study provides theoretical support for these efforts by developing a representative velocity model for subglacial lakes, simulating wavefields to characterize seismic responses, processing simulated data to identify processing challenges, and evaluating acquisition systems to determine the optimal survey geometry for Lake Qilin exploration. The results demonstrate that, in addition to primary reflections, multiples and guided waves will prominently develop in the seismic wavefield. Conventional seismic data processing of multiples introduce false coherent events, complicating seismic interpretation. Furthermore, the full-coverage acquisition system is identified as the optimal approach for the Lake Qilin exploration. To validate our simulation results, seismic data from Thwaites Glacier were processed and analyzed, and the results aligning well with part of our simulations, thus confirming the accuracy of the theoretical framework.

Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal. However, the cycle-skipping problem has become an urgent focus of this method because of the nonlinearity of the inversion problem. To mitigate the issue of local minima, the optimal transport problem has been introduced into full-waveform inversion in this study. The Wasserstein distance derived from the optimal transport problem is defined as the mismatch function in the FWI objective function, replacing the L2 norm. In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. Additionally, this study presents the objective function for dual-parameter full-waveform inversion of ground-penetrating radar, with the Wasserstein distance as the mismatch function. By normalizing with the Softplus function, the electromagnetic wave signals are adjusted to meet the non-negativity and mass conservation assumptions of the Wasserstein distance, and the convexity of the method has been proven. A multi-scale frequency-domain Wasserstein distance full-waveform inversion method based on the Softplus normalization approach is proposed, enabling the simultaneous inversion of relative permittivity and conductivity from ground-penetrating radar data. Numerical simulation cases demonstrate that this method has low initial model dependency and low noise sensitivity, allowing for high-precision inversion of relative permittivity and conductivity. The inversion results show that it, in particular, significantly improves the accuracy of conductivity inversion.

This study provides the first long-time series of spatial and temporal distributions for small lakes in the Larsemann Hills (69 degrees 23 ' S, 76 degrees 20 ' E) in the East Antarctic. In the Larsemann oasis, there is a significant number of over 150 small lakes, which can be observed with high spatial resolution in remote sensing imagery. However, accurately identifying and analyzing these small water bodies and elongated rivers has been challenging due to the mixed pixels effect and limitations in available middle spatial resolution imagery. In our study, we propose a data-driven approach within the conditional random fields framework, which considers three scales: superpixel, pixel, and subpixel, to refine the boundaries of small water bodies efficiently. The superpixel level quickly identifies the main water body and normalized difference water index provides a buffer region, while the pixel level employs support vector machine (SVM) to obtain a more precise boundary. Subpixel mapping technology within the pixel level further reduces mixed pixel effects for improved accuracy. The waterbodies were extracted from Sentinel-2 images with a spatial resolution of 10 m. The lake boundaries derived from the proposed algorithm in this study showed good agreement with in situ measurements of the lake shoreline delineated by aerial images from the 39th Chinese Antarctic Scientific Expedition. The analysis revealed distinct seasonal patterns across the Larsemann Hills, while the lake areas achieved their peak extents earlier, specifically in February before 2020 and in January after 2020. The water body mapping based on the proposed algorithm can contribute to Antarctic remote sensing hydrological observations, particularly in the monitoring of outburst events. These findings demonstrate the potential of extending this method to other Antarctic oases to enhance intra-annual lake observations. Moreover, Sentinel-2 images provide valuable remote sensing data for studying the seasonal cycles of water bodies, including those of varying sizes in the Larsemann Hills, based on long-term time series imagery.

During the 2019–2020 field season of the 36th Chinese National Antarctic Research Expedition, two seismic arrays were deployed in the Dalk Glacier area of Larsemann Hills, East Antarctica. The arrays consisted of 100 short-period nodal stations and were intended to investigate the physical properties and seismic events in the region, with the goal of enhancing our understanding of the glacier’s structure and dynamics. With these data, we use horizontal-to-vertical spectral ratio (HVSR) analysis to estimate the ice thickness. Noise cross-correlation functions and multimode dispersion curves of Rayleigh waves were extracted from the vertical-component ambient noise data to illuminate near-surface glacial structures. Teleseismic events with Mw >5.5 and two typical kinds of icequakes were observed via visual inspection. These initial results improve the understanding of the physical properties of the ice sheet as well as the glacial seismicity in Dalk Glacier.

We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training with the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in complex field data. By excluding filters with coherent noise features, our method can further attenuate coherent noise and separate ground roll.

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

We have developed the theory and practice of 3D supervirtual interferometry (SVI) for enhancing the signal-to-noise ratio (S/N) of refraction arrivals in 3D data. Unlike 2D SVI, 3D SVI requires an extra integration along the inline direction to compute the stationary source-receiver pairs for enhanced stacking of the refraction events. The result is a significant increase in the S/N of first arrivals in the far-offset traces. We have evaluated 3D synthetic and field data examples to demonstrate the effectiveness of the proposed method. For the synthetic data tests, SVI has extended the source-receiver offset range of pickable traces from 11 to 15 km. In the field data example, SVI has extended the source-receiver offset of traces with pickable first-arrival traveltimes from 12 km to a maximum of 18 km, and the total number of reliable travel-time picks has increased by 12%, which contributes to a deeper velocity update in the traveltime tomogram.

Previous No Access2nd SEG Rock Physics Workshop: Challenges in Deep and Unconventional Oil/Gas Exploration, Qingdao, China, 25–27 October 2019High resolution wave-equation traveltime inversion utilizing the first arriving signalsAuthors: Han YU*Jing LiKai LuYuqing ChenGerard SchusterHan YU*Nanjing University of Posts and TelecommunicationsSearch for more papers by this author, Jing LiJilin UniversitySearch for more papers by this author, Kai LuKing Abdullah University of Science and TechnologySearch for more papers by this author, Yuqing ChenKing Abdullah University of Science and TechnologySearch for more papers by this author, and Gerard SchusterKing Abdullah University of Science and TechnologySearch for more papers by this authorhttps://doi.org/10.1190/rpwk2019-044.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract This abstract describes the theory and the application of wave-equation traveltime inversion using multi-frequency bands for gradually producing highly resolved underground structures. The ground truths, the proposed zero-offset gathers and surface wave inversion results validate the effectiveness of this method. Keywords: traveltime, inversion, transmission, high-resolution, wave equationPermalink: https://doi.org/10.1190/rpwk2019-044.1FiguresReferencesRelatedDetails 2nd SEG Rock Physics Workshop: Challenges in Deep and Unconventional Oil/Gas Exploration, Qingdao, China, 25–27 October 2019ISSN (online):2159-6832Copyright: 2020 Pages: 58 publication data© 2019 Published in electronic format with permission by the Society of Exploration Geophysicists, China University of Petroleum (East China), Hohai UniversityPublisher:Society of Exploration Geophysicists HistoryPublished Online: 14 Apr 2020 CITATION INFORMATION Han YU*, Jing Li, Kai Lu, Yuqing Chen, and Gerard Schuster, (2020), "High resolution wave-equation traveltime inversion utilizing the first arriving signals," SEG Global Meeting Abstracts : 58-58. https://doi.org/10.1190/rpwk2019-044.1 Plain-Language Summary Keywordstraveltimeinversiontransmissionhigh-resolutionwave equationPDF DownloadLoading ...

A 5.6-km-long line of refraction and reflection seismic data spanning the Pliocene-Pleistocene fill of the Olduvai Basin, Tanzania is presented. The line is oriented along a northwest-southeast profile through the position of Olduvai Gorge Coring Project (OGCP) Borehole 2A. Our aims are to (1) delineate the geometry of the basin floor by tracing bedrock topography of the metaquartzitic and gneissic basement, (2) map synsedimentary normal faults and trace individual strata at depth, and (3) provide context for the sequence observed in OGCP cores. Results with refraction tomography and poststack migration show that the maximum basin depth is around 405 m (+/- 25 m) in the deepest portion, which quadruples the thickness of the basin-fill previously known from outcrops. Variations in seismic velocities show the positions of lower density lake claystones and higher density well-cemented sedimentary sequences. The Bed I Basalt lava is a prominent marker in the refraction seismic results. Bottom-most sediments are dated to > 2.2 Ma near where Borehole 2A bottoms out at the depth of 245 m. However, the seismic line shows that the basin-fill reaches a maximum stratigraphic thickness of around 380 m deep at Borehole 2A, in the western basin where the subsidence was greatest. This further suggests that potential hominin palaeoenvironments were available and preserved within the basin-fill possibly as far back as around 4 Ma, applying a temporal extrapolation using the average sediment accretion rate.

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

PreviousNext No AccessSEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17–19 September 2018Auto-windowed Super-virtual Interferometry via Machine Learning: A Strategy of First-arrival Traveltime Automatic Picking for Noisy Seismic DataAuthors: Kai LuShihang FengKai LuKing Abdullah University of Science and TechnologySearch for more papers by this author and Shihang FengKing Abdullah University of Science and TechnologySearch for more papers by this authorhttps://doi.org/10.1190/AIML2018-03.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Supervirtual interferometry (SVI) was developed to significantly enhance the signal-to-noise ratio of noisy first arrivals. However, a time window must be specified that contains these first arrivals, and the window should be no wider than several times the dominant period of the source wavelet. The accurate specification of this window is very challenging for noisy data and involves manual picking. To overcome this problem, we propose to automatically pick these windows via machine learning methods. Convolutional neural network (CNN) and density-based spatial clustering of applications with noise (DBSCAN) are used to distinguish first-arrival signals completely buried in noise. Numerical tests validate that this method can accurately specify the correct window as well as that of a human interpreter. The benefit is an automatic means for picking first-arrival traveltimes in noisy traces from a large 3D data set. Keywords: interferometry, noise, numericalPermalink: https://doi.org/10.1190/AIML2018-03.1FiguresReferencesRelatedDetailsCited byEnhancing the accuracy of DBSCAN’s first arrival traveltime picking using super-virtual refraction interferometryMuhammad Awais, Sherif M. Hanafy, and King Fahd15 August 2022Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNsIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Superpixel-Based Convolutional Neural Network for Georeferencing the Drone ImagesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14Physically realistic training data construction for data-driven full-waveform inversion and traveltime tomographyShihang Feng, Youzuo Lin, and Brendt Wohlberg30 September 2020Automated first break picking with constrained pooling networksDavid Cova, Peigen Xie, and Phuong-Thu Trinh30 September 2020Deep convolutional neural network and sparse least-squares migrationZhaolun Liu, Yuqing Chen, and Gerard Schuster13 June 2020 | GEOPHYSICS, Vol. 85, No. 4 SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17–19 September 2018ISSN (online):2159-6832Copyright: 2018 Pages: 244 publication data© 2018 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 14 Dec 2018 CITATION INFORMATION Kai Lu and Shihang Feng, (2018), "Auto-windowed Super-virtual Interferometry via Machine Learning: A Strategy of First-arrival Traveltime Automatic Picking for Noisy Seismic Data," SEG Global Meeting Abstracts : 10-14. https://doi.org/10.1190/AIML2018-03.1 Plain-Language Summary KeywordsinterferometrynoisenumericalPDF DownloadLoading ...

Many explorationists think of surface waves as the most damaging noise in land seismic data. Thus, much effort is spent in designing geophone arrays and filtering methods that attenuate these noisy events. It is now becoming apparent that surface waves can be a valuable ally in characterizing the near-surface geology. This review aims to find out how the interpreter can exploit some of the many opportunities available in surface waves recorded in land seismic data. For example, the dispersion curves associated with surface waves can be inverted to give the S-wave velocity tomogram, the common-offset gathers can reveal the presence of near-surface faults or velocity anomalies, and back-scattered surface waves can be migrated to detect the location of near-surface faults. However, the main limitation of surface waves is that they are typically sensitive to S-wave velocity variations no deeper than approximately half to one-third the dominant wavelength. For many exploration surveys, this limits the depth of investigation to be no deeper than approximately 0.5–1.0 km.