Guofeng Liu

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 provided 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 were developed for surface-wave and body-wave imaging. For surface waves, least-squares noise suppression and beamforming-based stationary-phase selection were used to improve the quality of reconstructed virtual shot gathers. Multichannel analysis of surface waves (MASW) was conducted on active and passive data sets, and the resulting dispersion characteristics and inverted shear-wave velocity models showed strong consistency. For body-wave imaging, conventional reflection processing techniques used for active-source data were adapted for virtual shot gathers retrieved from passive recordings. The stacked passive seismic profile reproduced the main reflection events observed in the active-source profile, particularly the ice–bed interface and shallow reflections within the bedrock. These comparative results demonstrated that, in the Larsemann Hills region of Antarctica, passive seismic methods provided an environmentally sustainable, cost-effective, and reliable alternative to active seismic surveys for large-scale imaging of ice sheets and shallow bedrock structures.

The firn layer in the upper part of the Antarctic ice sheet represents a crucial transitional zone during the transformation from snow to glacial ice, and its structural characteristics play an essential role in evaluating the surface mass balance of the ice sheet. Surface-wave inversion offers strong constraints on shallow subsurface stratification, and compared with conventional ambient noise, traffic-induced seismic vibrations provide higher-frequency signals that can significantly enhance near-surface imaging resolution. In this study, we identified traffic noise signatures within ambient seismic data recorded during the 40th Chinese National Antarctic Research Expedition in the Larsemann Hills, East Antarctica. Using these data, we performed high-resolution shear-wave velocity imaging of the firn layer through surface-wave inversion based on seismic interferometry and one-dimensional Monte Carlo inversion. The retrieved virtual shot gathers revealed clear surface-wave energy, and the inverted shear-wave velocity profiles were in good agreement with borehole data obtained at the center of the survey line. Vibrations generated by vehicles such as PB300 can serve as an effective, low-cost and environmentally friendly seismic source. This traffic-source-based approach provides a practical pathway for high-resolution firn imaging and offers valuable potential for future large-scale, long-term monitoring of ice-sheet structure and mass balance.

Antarctic drilling projects provide critical information for investigating ice-sheet stability, reconstructing paleoclimate evolution, and characterizing subglacial geological structures through ice-core and bedrock recovery. Drilling site selection currently relies on high-resolution geophysical methods such as radio echo sounding and active-source seismic methods; however, radar imaging near the ice–bedrock interface is limited by electromagnetic attenuation, while active-source seismic methods in polar regions are constrained by logistical complexity and high cost. To address these limitations, this study proposes a passive integrated imaging approach that integrates P-wave responses and vertical-component Rayleigh-wave information retrieved from continuous ambient noise recordings near drilling sites using seismic interferometry. Based on their distinct propagation characteristics, signal selection and processing workflows are developed to jointly image near-surface firn structure, ice-sheet thickness, and subglacial bedrock structure. Application to the Princess Elizabeth Land drilling project in East Antarctica demonstrates that high- signal-to-noise-ratio P-wave responses and vertical-component Rayleigh-wave signals can be retrieved from as little as 24 h of ambient noise data, while stacking the full 20-day record further suppresses incoherent noise and yields more reliable imaging of the ice–bedrock interface. These results indicate that passive seismic imaging provides a rapid, cost-effective, and environmentally friendly complement for drilling site selection and operational support.

Seismic exploration is a geophysical method used for imaging subsurface structures, capable of providing high-resolution images of the underground. In seismic data processing, Kirchhoff Pre-Stack Depth Migration (KPSDM) serves as one of the key techniques, playing a critical role in significantly enhancing the lateral resolution of imaging and providing accurate characterization of subsurface media. However, with the continuous growth in high-density seismic data volumes, the computational efficiency of KPSDM is primarily constrained by substantial computational loads, end-to-end I/O bottlenecks, and data storage pressures. To address the performance optimization challenges of computation-intensive applications that require frequent large-scale data transfers between the host and accelerator devices, this paper proposes GAP-DCCS, a GPU-based Generic Acceleration Paradigm with efficient Data Compression and Caching Strategy, which includes the following core strategies: (1) For compute-intensive modules, a GPU-based three-dimensional parallel acceleration is implemented, combined with memory access optimization techniques and overlapping strategies for data transfer and computation, to improve GPU resource utilization; (2) To alleviate the storage pressure of large-scale datasets, the BitComp compression algorithm is introduced to efficiently compress task data while maintaining output stability, significantly reducing storage requirements and end-to-end data transfer volume; (3) To tackle the I/O bottleneck caused by frequent large-scale data transfers between the host and devices, an adaptive dynamic caching data management mechanism is designed to effectively increase data reuse rates and markedly reduce end-to-end transfer frequency. Experimental results demonstrate that the proposed optimization method significantly enhances the computational performance of KPSDM, achieving a speedup of 123.51× on a single NVIDIA Tesla A800 GPU compared to a 16-core CPU. This optimization paradigm has not only been effectively validated in KPSDM but also offers a referable high-performance computing solution for other large-scale data processing tasks.

Reverse time migration (RTM) is a wave equation migration method that has high imaging accuracy and superior imaging effects in complex underground structures, and has been extensively employed in industrial production. Surface offset gathers (SOGs) are crucial for migration velocity updating and muting the stretched waveform in the shallow layers of seismic data. However, RTM SOGs cannot be obtained directly. Usually, SOGs obtained through Kirchhoff migration are used for velocity updating, which fails to form a closed-loop process for RTM migration calculation and velocity updating. Attribute migration is a stable, simple and efficient method to obtained RTM SOGs. Based on attribute migration, this paper applied the constrained least-squares inversion instead of division to get a more accurate RTM SOGs, uses residual curvature analyze (RCA) to update the velocity. RTM SOGs can also be obtained by grouping seismic data according to offset and migrating each group separately. By comparison, SOGs obtained by these two methods had identical characteristics. Further, the residual curvature spectrum after velocity analysis had identical characteristics. Therefore, we believe that the attribute migration method using constrained least-squares inversion can be employed for velocity updating, and verify its effectiveness with model data and actual data. In summary, this paper presents a stable and direct method to efficiently calculate SOGs for RTM and thus form a closed loop of RTM migration calculation and velocity updating.

Elevation static correction is an important step in seismic data processing used to eliminate distortions in events caused by surface fluctuations. There is a well-established processing sequence for active-source seismic data, with static correction applied first, followed by subsequent processing. For passive-source data, static correction can be carried out using two methods. The first method is similar to the static correction of active-source seismic data and involves performing static correction on virtual shot gathers. The second method involves directly applying static correction to ambient noise and then using the corrected ambient noise to retrieve the virtual shot gathers. In this study, we derived and compared these two methods to perform static correction on passive-source data. We compared the above two methods through the forward modeling of passive-source data and field data from the Daqiao Gold Mine. The results showed that applying static correction to ambient noise and virtual shot gathers can correct passive-source data from an undulating surface to a flat surface. The equivalence of static correction on virtual shot gathers, and ambient noise offers more choices for conducting static correction of passive-source seismic reflection exploration on undulating surfaces.

Surface offset gathers (SOGs) are crucial for velocity updating in seismic data migration and muting the stretched waveform in shallow parts of migrated seismic data. Kirchhoff-based migration methods can output SOGs because they take every seismic trace as the input. As a result, the imaging results can be rearranged according to the offset value of the input data to obtain SOGs. However, regarding more accurate wave equation methods (such as reverse time migration, RTM), the SOGs cannot be obtained directly with a single migration calculation. Attribute migration is an easy-to-implement method to output SOGs of wave equation migration methods. It calculates the offset value of each imaging point by dividing the migration result modulated by the offset attribute with the conventional migration result. However, the division error may cause the calculation result outside the given offset value. This paper proposes a constrained least-squares inversion method for stable calculation of the RTM offset range. The method ensures that the calculated results are within the given offset range. We tested the method against the direct division and least-squares methods without constraints using the Marmousi model and a real 3D dataset. We show that the SOGs obtained by the constrained least-squares inversion attribute migration method had the same characteristics as those derived using the division-based attribute migration method, but they also had higher vertical resolution, better energy distribution, and improved lateral continuity. In a further study, we expect to develop a stable and direct method to efficiently calculate SOGs for RTM and an iterative closed loop for RTM velocity updating.

Seismic exploration is a geophysical method with deeper exploration capabilities and higher resolution than potential geophysical methods