Search Results (45)

In marine seismic surveys, towed streamers record only pressure data with limited offsets and insufficient low-frequency content, whereas Ocean Bottom Nodes (OBNs) acquire multi-component data with wider offset and sufficient low-frequency content, albeit with sparser spatial sampling. Elastic full waveform inversion (EFWI) is used to estimate subsurface elastic properties by matching observed and synthetic data. However, using only towed streamer data makes it impossible to reliably estimate shear-wave velocities due to the absence of direct S-wave recordings and limited illumination. Inversion using OBN data is prone to acquisition footprint artifacts. To overcome these challenges, we propose a hierarchical joint inversion method based on P- and S-wave separation (PS-JFWI). We first derive novel acoustic-elastic coupled equations based on wavefield separation. Then, we design a two-stage inversion framework. In Stage I, we use OBN data to jointly update the P- and S-wave velocity models. In Stage II, we apply a gradient decoupling algorithm: we construct the P-wave velocity gradient by combining the gradient using PP-waves from both towed streamer and OBN data and construct the S-wave velocity gradient using the gradient using PS-waves. Numerical experiments demonstrate that the proposed method enhances the inversion accuracy of both velocity models compared with single-source and conventional joint inversion methods. Full article

The northwestern slope of the Dongdao Platform in the Xisha Sea exhibits a complex geomorphological structure. Utilizing high-resolution multibeam bathymetric data and 2D seismic profiles, this study systematically reconstructs the slope morphology and its evolutionary processes. The study area displays a distinct threefold zonation: the upper slope (160–700 m water depth) has a steep gradient of 15°–25°, characterized by deeply incised V-shaped channels and slump deposits, primarily shaped by gravity-driven erosion; the middle slope (700–1200 m water depth) features a gentler gradient of 10°–15°, where channels stabilize, adopting U-shaped cross-sections with the development of lateral accretion deposits; the lower slope (1200–1500 m water depth) exhibits a milder gradient of 5°–10°, dominated by a mixture of fine-grained carbonate sediments and hemipelagic mud–marine sediments originating partly from the open ocean and partly from the nearby continental margin. The slope extends from 160 m to 1500 m water depth, hosting the C1–C4 channel system. Seismic facies analysis reveals mass-transport deposits, channel-fill facies, and facies modified by bottom currents—currents near the seafloor that redistribute sediments laterally—highlighting the interplay between fluid activity and gravity-driven processes. The slope evolution follows a four-stage model: (1) the pockmark formation stage, where overpressured gas migrates vertically through chimneys, inducing localized sediment instability and forming discrete pockmarks; (2) the initial channel development stage, during which gravity flows exploit the pockmark chains as preferential erosional pathways, establishing nascent incised channels; (3) the channel expansion and maturation stage, marked by intensified erosion from high-density debris flows, resulting in a stepped longitudinal profile, while bottom-current reworking enhances lateral sediment differentiation; (4) the stable transport stage, wherein the channels fully integrate with the Sansha Canyon, forming a well-connected “platform-to-canyon” sediment transport system. Full article

The generation of an earthquake and a tsunami is a coupled process of radiating seismic waves and exciting tsunamis, and the two types of waves are simultaneously recorded by ocean-bottom pressure sensors. In order to constrain the earthquake source and evaluate the tsunami hazards, it is necessary to separate the tsunami waves. It is traditional to apply a low-pass filter such that the seismic waves are filtered and the tsunami waves remain. However, filtering may also cause distortion of the tsunami waves. In this study, we first use the finite-element method to simulate the generation of seismic and tsunami waves and show that the coupling is a linear superposition of the two waves. We then propose a new method to extract the tsunami waves. First, a low-pass filter with relatively high cutoff frequency that does not affect the tsunami waves is adopted, so that only tsunami waves and low-frequency seismic waves remain. The low-frequency seismic waves satisfy a theoretical equation $p=\rho ha$ (p pressure, $\rho$ water density, h water depth, and a seafloor vertical acceleration), and they can be predicted and removed by utilizing the records of ocean-bottom acceleration. We demonstrate the procedure by numerical simulations and show that the method successfully extracts clean tsunami signals, which is important for earthquake source characterization and tsunami hazard assessment. Full article

Distributed Acoustic Sensing (DAS) offers numerous advantages, including resistance to electromagnetic interference, long-range dynamic monitoring, dense spatial sensing, and low deployment costs. We initially deployed a water–land DAS system at the Xinfengjiang (XFJ) Reservoir in Guangdong Province, China, to monitor earthquake events. Environmental noise analysis identified three distinct noise zones based on deployment conditions: periodic 18 Hz signals near surface-laid segments, attenuated low-frequency signals (<10 Hz) in the buried terrestrial sections, and elevated noise at transition zones due to water–cable interactions. The system successfully detected hundreds of teleseismic and regional earthquakes, including a Mw7.3 earthquake in Hualien and a local ML0.5 microseismic event. One year later, the DAS system was upgraded with two types of spiral sensor cables at the end of the submarine cable, extending the total length to 5.51 km. The results of detecting both active (transducer) and passive sources (cooperative vessels) highlight the potential of integrating DAS interrogators with spiral sensor cables for the accurate tracking of underwater moving targets. This field trial demonstrates that DAS technology holds promise for the integrated joint monitoring of underwater acoustics and seismic signals beneath lake or ocean bottoms. Full article

Ocean Bottom Node (OBN) is a seismic data acquisition technique, comprising a hydrophone and a three-component geophone. In practice, the vertical component is susceptible to high-amplitude, low-velocity, and low-frequency shear wave noise, which negatively impacts the subsequent processing of dual-sensor data. The most commonly used method is adaptive matching subtraction, which estimates shear wave noise in the vertical component by solving an optimization problem. Neural networks, as robust nonlinear fitting tools, offer superior performance in resolving nonlinear mapping relationship and exhibit computational efficiency. In this paper, we introduce a self-supervised shear wave suppression approach for 3D OBN seismic data, using a neural network in place of the traditional adaptive matching subtraction operator. This method adopts the horizontal components as the input to the neural network, and measures the output and the noisy vertical component to establish a loss function for the network training. The network output is the predicted shear wave noise. To better balance signal leakage and noise suppression, the method incorporates a local normalized cross-correlation regularization term in the loss function. As a self-supervised method, it does not need clean data to serve as labels, thereby negating the tedious work of obtaining clean field data. Extensive experiments on both synthetic and field data demonstrate the effectiveness of the proposed method on shear wave noise suppression for 3D OBN seismic data. Full article

Nonlinear solitary waves influence the Earth’s crust because wave pressure on the ocean bottom contains non-hydrostatic components. Our physical-mathematical model allows us to calculate the surplus super-hydrostatic pressure on the Earth’s crust. It depends on the amplitudes of solitary waves and the depth of an ocean. The surplus wave pressure averages 50% from hydrostatic pressure on the shallow ocean shelves. Thus, the solitary wave’s tsunami class can provoke novel (repeated) earthquakes (or landslides) because surplus stresses affect the seismic focus. Theoretical results and experimental physical modeling of soliton waves have shown good agreement. A calculated example of the mega-tsunami in Lituya Bay and a described example of Dickson Fjord (AK, USA) indicate changes in the dynamic pressure after the onset of the tsunami. The presented studies demonstrate a first attempt at creating a numerical model of this phenomenon. Full article

In this study, we used machine learning (ML) to estimate time-lapse velocity variations in a reservoir region using seismic data. To accomplish this task, we needed an adequate training set that could map seismic data to velocity perturbation. We generated a synthetic seismic database by simulating reservoirs of varying velocities using a 2D velocity model typical of the Brazilian pre-salt ocean bottom node (OBN) acquisition, located in the Santos basin, Brazil. The largest velocity change in the injector well was around 3% of the empirical velocity model, which mimicked a realistic scenario. The acquisition geometry was formed by the geometry of 1 shot and 49 receivers. For each synthetic reservoir, the corresponding seismic data were obtained by estimating a one-shot forward-wave propagation using acoustic approximation. We studied the reservoir illumination to optimize the input data of the ML inversion. We split the set of synthetic reservoirs into two subsets: training (80%) and testing (20%) sets. We point out that the ML inversion was restricted to the reservoir zone, which means that it was inversion-oriented to a target. We obtained a good similarity between true and ML-inverted reservoir anomalies. The similarity diminished for a situation with non-repeatability noise. Full article

Constructing an accurate three-dimensional (3D) geological model is crucial for advancing our understanding of subsurface structures and their evolution, particularly in complex regions such as the South China Sea (SCS). This study introduces a novel approach that integrates multimodal deep learning with multipoint statistics (MPS) to develop a high-resolution 3D crustal P-wave velocity structure model of the SCS. Our method addresses the limitations of traditional algorithms in capturing non-stationary geological features and effectively incorporates heterogeneous data from multiple geophysical sources, including 44 wide-angle seismic crustal structure profiles obtained by ocean bottom seismometers (OBSs), gravity anomalies, magnetic anomalies, and topographic data. The proposed model is rigorously validated against existing methods such as Kriging interpolation and MPS alone, demonstrating superior performance in reconstructing both global and local spatial features of the crustal structure. The integration of diverse datasets significantly enhances the model’s accuracy, reducing errors and improving the alignment with known geological information. The resulting 3D model provides a detailed and reliable representation of the SCS crust, offering critical insights for studies on tectonic evolution, resource exploration, and geodynamic processes. This work highlights the potential of combining deep learning with geostatistical methods for geological modeling, providing a robust framework for future applications in geosciences. The flexibility of our approach also suggests its applicability to other regions and geological attributes, paving the way for more comprehensive and data-driven investigations of Earth’s subsurface. Full article

Numerical modeling of acoustic-elastic media is helpful for seismic exploration in the deepwater environment. We propose an algorithm based on the staggered grid finite difference to simulate wave propagation in the interface between fluid and transversely isotropic media, where the interface does not need to consider the boundary condition. We also derive the stability conditions of the proposed method. Scholte waves, which are generated at the seafloor, exhibit distinctly different propagation behaviors than body waves in ocean-bottom seismograms. Numerical examples are used to characterize the wavefield of Scholte waves and discuss the relationship between travel time and the Thomsen parameters. Thomsen parameters are assigned clear physical meanings, and the magnitude of their values directly indicates the strength of the anisotropy in the media. Numerical results show that the velocity of the Scholte wave is positively correlated with ε and negatively correlated with δ. And the curve of the arrival time of the Scholte wave as a whole is sinusoidal and has no symmetry in inclination. The velocity of the Scholte wave in azimuth is positively related to the polar angle. The energy of the Scholte wave is negatively correlated with the distance from the source to the fluid-solid interface. The above results provide a basis for studying oceanic Scholte waves and are beneficial for utilizing the information provided by Scholte waves. Full article

Time-shifted seismic research plays an important role in monitoring changes in the gas-water interface uplift, the weakening of amplitude attributes, and gas distribution due to mining. When time-shifted seismic research involves non-repeatable data with significant differences between data sets due to variations in seismic data acquisition parameters and seismic geometries, it necessitates consistent processing before time-shifted monitoring comparisons. In this paper, a study of time-shifted seismic monitoring using two non-repetitive data sets based on the ocean bottom cable (OBC) and towed streamer data is presented. First, amplitude, frequency, wavelet, and time difference are processed to achieve consistency for time-shifted comparisons. Secondly, three modes of seismic geometry normalization are compared to optimize the appropriate offset, azimuth, and signal-to-noise ratio (SNR). Finally, after eliminating the fault surface wave, the maximum trough amplitude attribute is extracted for the same position in the two data sets to analyze time-shifted differences under the three modes using the ratio method and difference method. The conclusions show the following: the OBC and towed streamer data can achieve consistency in terms of amplitude, frequency, wavelet, azimuth, SNR, and time difference; the data reconstruction method outperforms other methods in normalizing offset, azimuth, and SNR; and the time-shifted comparison method of the amplitude attribute ratio method proves more effective than the difference method. This study offers a reliable foundation for future time-shifted seismic research with non-repetitive data to monitor changes in subsurface oil and gas. It also provides a methodological basis for carbon capture and storage (CCS) monitoring technology. Full article

The Amapá margin, part of the Brazilian Equatorial Margin (BEM), is a key region that plays a strategic role in the global climate balance between the North and South Atlantic Ocean as it is strictly tied to equatorial heat conveyance and the fresh/salt water equilibrium with the Amazon River. We performed a new scientific expedition on the Amapá continental shelf (ACS, northern part of the Amazon continental platform) collecting sediment and using instrumental observation at an unstudied site. We show here the preliminary outcomes following the applied methodologies for investigation. Geophysical, geological, and biological surveys were carried out within the ACS to (1) perform bathymetric and sonographic mapping, high-resolution sub-surface geophysical characterization of the deep environment of the margin of the continental platform, (2) characterize the habitats and benthic communities through underwater images and biological sampling, (3) collect benthic organisms for ecological and taxonomic studies, (4) define the mineralogical and (5) elemental components of sediments from the study region, and (6) identify their provenance. The geophysical data collection included the use of bathymetry, a sub-bottom profiler, side scan sonar, bathythermograph acquisition, moving vessel profiler, and a thermosalinograph. The geological data were obtained through mineralogical, elemental, and grain size analysis. The biological investigation involved epifauna/infauna characterization, microbial analysis, and eDNA analysis. The preliminary results of the geophysical mapping, shallow seismic, and ultrasonographic surveys endorsed the identification of a hard substrate in a mesophotic environment. The preliminary geological data allowed the identification of amphibole, feldspar, biotite, as well as other minerals (e.g., calcite, quartz, goethite, ilmenite) present in the substrata of the Amapá continental shelf. Silicon, iron, calcium, and aluminum composes ~85% of sediments from the ACS. Sand and clay are the main fraction from these sediments. Within the sediments, Polychaeta (Annelida) dominated, followed by Crustacea (Arthropoda), and Ophiuroidea (Echinodermata). Through TowCam videos, 35 taxons with diverse epifauna were recorded, including polychaetes, hydroids, algae, gastropods, anemones, cephalopods, crustaceans, fishes, and sea stars. Full article

Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal. Full article

This paper is devoted to the features of seismological observations in the Arctic seas, which are complicated by harsh climatic conditions, the presence of ice cover, stamukhi and icebergs, and limited navigation. Despite the high risk of losing expensive equipment, the deployment of local networks of bottom seismographs or stations installed on ice is still necessary for studying the seismotectonic characteristics and geodynamic processes of the region under consideration, the deep structure of the crust and upper mantle, seismic hazards, and other marine geohazards. Various types of seismic stations used for long-term and short-term deployments in the Russian sector of the Arctic Ocean, as well as various schemes and workflows for their deployment/recovery, are described. The characteristics of seafloor seismic noise and their features are also considered. The results of deployments demonstrate that the characteristics of the stations make it possible to reliably record earthquake signals and seismic noise. Based on the experience gained, it was concluded that the preferred schemes for deploying ocean-bottom seismographs are those in which their subsequent recovery does not depend on their power resources. Usually, such schemes allow for the possibility of dismantling stations via trawling and are suitable for the shelf depths of the sea. The advantages of such schemes include the possibility of installing additional hydrophysical and hydrobiological equipment. When using pop-up ocean-bottom seismographs, special attention should be paid to the careful planning of the recovery because its success depends on the possibility of a passage to the deployment site, which is not always possible due to changing meteorological and ice conditions. Seismic records obtained on the seafloor are characterized by a high noise level, especially during periods of time when there is no ice cover. Therefore, it is recommended to install bottom stations for periods of time when ice cover is present. The frequency range of the prevailing noise significantly overlaps with the frequency range of earthquake signals that must be taken into account when processing bottom seismic records. Full article

This paper is devoted to the detection and analysis of overpressure zones in unconsolidated seafloor sediments using an ocean-bottom seismometer. The methodological aspects of creating a system of anomalous pore pressure zone detection in marine sediments are studied. The aim of this study is to establish the requirements for a pore pressure monitoring system necessary to successfully detect overpressure zones based on seismic response, and to analyze temporal changes in pore pressure distribution. Data from a certain offshore field are used as a basis from which to construct synthetic models of overpressure distribution in marine sediments. Synthetic models are constructed using specially developed rock physics models for unconsolidated saturated media. Seismic responses are calculated for these synthetic models to represent data that otherwise would be obtained from bottom seismometers placed on the seafloor. Resultant seismic responses are studied with respect to the detection of overpressure zones. Possibilities and limitations of bottom seismometer data are discussed. Requirements for the frequency bands of bottom seismometers are formulated based on the results that are obtained. Full article

Ambient noise plays a crucial role in influencing the observation quality at seismic stations. By studying the distribution patterns of ambient noise, we can gain initial insights into the noise conditions within a specific research area. This paper investigates the properties of ambient noise in different frequency bands under environmental settings in the Chukchi Sea region, utilizing data collected from ocean bottom seismometers (OBSs) deployed during the Ninth Chinese National Arctic Scientific Survey. The probability density function (PDF) method is used to reveal the distinctive features of ambient noise. In addition, by comparing the crowed number values of ambient noise in the Chukchi Sea area with the global new low-noise model (NLNM) and new high-noise model (NHNM), a more comprehensive understanding of the patterns, distribution characteristics, and sources of ambient noise in the Arctic Chukchi Sea area is gained. The study suggests that the overlying sea ice in the Arctic Chukchi Sea area can suppress the microseismic band ambient noise, and the overall level of ambient noise in the Chukchi Sea area lies between the land seismic ambient noise level and the ambient noise level in the middle- and low-latitude sea areas. Meanwhile, an abnormal power spectrum caused by different levels of natural earthquakes is observed. This study fills the gap by using seafloor seismic instruments to investigate ambient noise in the Chukchi Sea area. Full article