Geophysical Prospecting - Special Issue: Seabed Prospecting Technology, 2024

High‐precision P‐wave and S‐wave velocity models are very important for seabed exploration. Elastic full‐waveform inversion is an effective way to obtain the velocity models. Full‐waveform inversion is strongly dependent on source wavelet, and inaccurate wavelet estimation will severely affect the inversion results. Furthermore, elastic full‐waveform inversion is a highly non‐linear problem and has larger calculation and memory cost. Based on the deconvolution method, we develop a multi‐scale source‐independent elastic full‐waveform inversion method in a hybrid domain to alleviate the non‐linearity, to improve computation efficiency and decrease memory cost and to reduce the influence of source wavelet. We reconstruct the deconvoluted objective function and further derive the adjoint source and gradient formulas for elastic full‐waveform inversion with the adjoint‐state method. We can obtain good inversion results by using this objective function, adjoint source and gradient even if the source wavelet is unknown. In order to illustrate the influence of different reference traces on the objective function, we provide the theoretical formula of the objective function changing with the variation of reference traces. Experiments show that the deconvolution objective function cannot completely eliminate the influence of inaccurate source wavelet on the inversion, and the inversion effect depends on the selection of reference trace. Different inversion results of the Marmousi2 model indicate that the minimum offset trace is the best reference trace. Comparison of the inverted Marmousi2 model by the proposed method and the conventional elastic full‐waveform inversion method using correct wavelet shows that the two inversion results are very close, which proves the effectiveness of the proposed method and indicates a potential application of the method in seabed exploration.

Offshore seismic surveys are typically conducted using streamers, which can make it challenging to accurately image deep and steep reflectors due to their horizontal layout and limited offset range. In recent years, vertical cable seismic acquisition has been developed as an alternative approach that utilizes a vertical layout to obtain wave field information not achievable by conventional streamers. However, relying solely on vertical cable seismic data does not produce high‐quality images due to its sparse detector distribution. To address this issue, we propose a joint imaging method based on reverse‐time migration that incorporates both streamer and vertical cable records. Our approach combines the back‐propagated wave fields of the streamer and vertical cable at each step of wave field extrapolation, providing more comprehensive wave field information than either one could provide individually. We then utilize zero‐delay cross‐correlation imaging conditions to generate a migration profile. To improve the efficiency of our reverse‐time migration algorithm, we employ a plane‐wave source encoding strategy for joint imaging. The effectiveness of our proposed method is validated using both the wedge model and the Marmousi model. By utilizing reverse‐time migration in the plane‐wave domain, our joint imaging method overcomes the limitations of poor imaging of steep‐dip formations with conventional streamer data. Additionally, it suppresses migration noise caused by insufficient illumination range from either streamer or vertical cable data alone. Overall, our joint imaging approach offers a promising solution for improving the accuracy and quality of offshore seismic surveys.

Most seismic data used for conventional exploration lack far‐offset signals and effective low‐frequency components. This makes it difficult for conventional full waveform inversion methods to recover the low‐wavenumber components of mid‐ and deep‐layer models. To resolve the bottleneck of ray‐theoretical reflection traveltime tomography, wave equations, such as reflection inversion methods, are receiving increasing attention. We reconstructed a wide‐spectrum velocity model based on the multiscale wave‐equation reflection inversion method for model decomposition. First, using the dynamic image warping method to obtain reflection traveltime residuals, the wave‐equation reflection traveltime inversion was used to recover the low‐wavenumber components of the background model, which also solved the cycle‐skipping problem. Second, the medium‐wavenumber component of the model is then supplemented by the reflection waveform inversion, while the reflectivity model is obtained relying on the least‐squares reverse time migration method. Based on this method, the background and perturbation models are updated alternately and iteratively. At the same time, the stratum structural tensor information was extracted using the perturbed model image to construct a preconditioned operator for the stratum structural constraint, suppress unreasonably high‐wavenumber components in the generalized gradient and improve the geological consistency of the inversion results. Testing of the model using the Sigsbee2b model and seismic field data from the East China Sea showed that, compared with the conventional reflection traveltime tomography method based on prestack depth migration, the wave‐equation reflection inversion strategy with well‐matched information from traveltimes to waveforms significantly improved the accuracy of the middle and deep velocity modelling and enhanced the imaging quality of the whole body.

With the increasing maturity of ocean bottom seismometer technology in gas hydrate exploration, more and more researchers apply ocean bottom seismometer exploration technology for offshore oil exploration. However, due to the special observation mode of ocean bottom seismometer, it is impracticable to process ocean bottom seismometer data using traditional data processing methods, such as velocity analysis and pre‐stack time migration. This manuscript proposed a new velocity analysis method for ocean bottom seismometer data, which obtains more accurate root‐mean‐square velocity than the existing method. Then we deduced the Kirchhoff pre‐stack time migration formula for ocean bottom seismometer data. Two models demonstrate the correctness of the velocity analysis and migration methods. Finally, the two methods were applied to the actual ocean bottom seismometer data, and the obtained migration profile is consistent well with the profile of towed streamer data in the nearby area.

As one of wavefield separation methods in elastic reverse time migration, the decoupled wave equation succeeds to separate the particle velocity of P‐ and S‐wave without mode crosstalk. However, it fails to decouple the stress of P‐ and S‐wave which encounters with the problem of crosstalk. To overcome the crosstalk, the paper has proposed the quasi‐stress equations to decouple the stress tensor wavefields of P‐ and S‐wave based on the decoupled stress–strain relationship. Because P‐ and S‐wave stress wavefields are second‐order tensors, different from the imaging method of vector decoupled particle velocity wavefields, a double dot product imaging condition is proposed to obtain scalar images. Based on the total source‐stress tensor wavefields and separated receiver‐stress tensor wavefields, we obtain the scalar images of quasi‐PP and quasi‐PS from the stress information by the double dot product imaging condition. Several numerical examples are evaluated to demonstrate the effectiveness of the separation of P‐ and S‐wave stress and advantage of the proposed imaging condition from the second‐order stress tensor wavefields.

Seismic waves propagating in anisotropic earth media suffer from waveform distortion, which leads to degraded resolution in seismic data imaging if this adverse effect is not corrected. Additionally, the wavefields simulated by the traditional coupled pseudo‐acoustic wave equations contain undesired shear wave artefacts and are unstable when Thomsen's anisotropy parameter ε is less than δ. To address these issues, many pure qP‐wave equations are proposed to describe wave propagation in anisotropic media. However, these equations are either low precision or high accuracy but computationally expensive. Considering the trade‐off between the computation efficiency and simulation accuracy, we derive a pure qP‐wave equation from the exact dispersion formula in tilted transverse isotropic media. The newly derived equation has only two higher order partial derivatives, which can better balance the accuracy and efficiency than the previous pure qP‐wave equations. The least‐squares reverse time migration method can balance amplitudes, suppress migration artefacts and improve imaging resolution. Then, based on the newly derived pure qP‐wave equation, we derive the Born modelling operator and adjoint migration operator and develop an anisotropic least‐squares reverse time migration method. To achieve the accelerated convergence, we introduce a generalized minimum residual algorithm to implement the anisotropic least‐squares reverse time migration method. Numerical simulation results show that the proposed pure qP‐wave equation outperforms the previous equations in balancing the trade‐off between accuracy and efficiency. In addition, synthetic examples demonstrate the effectiveness and robustness of our proposed anisotropic least‐squares reverse time migration method in correcting the deviations due to anisotropic effects.

Clock drift is common for the autonomous ocean bottom seismometer, and a technique of linear drift correction is usually used to fix this kind of timing error assuming that the internal clock has constant drift rate during deployment. However, non‐linear component of clock drift has also been found recently. In order to test the behaviours of the clock drift for 19 Chinese ocean bottom seismometers from a three‐dimensional controlled‐source seismic survey, we have analysed two types of differences in direct wave travel‐times, including one from all shots to the ocean bottom seismometer onboard and another one from the nearest two shots on different shooting lines to the ocean bottom seismometer on the seafloor. Comparison of the differences in direct wave travel‐times from seismic data with and without clock drift correction shows that linear drift correction can commendably eliminate them for most of the ocean bottom seismometers, but some large residuals remain for part of the ocean bottom seismometers. These residuals are inferred to be possibly related to the non‐linear component of clock drift caused by the temperature shock during deployment. The non‐linear clock drift usually varies from different ocean bottom seismometers and thus difficult to correct completely and resultantly will influence the timing accuracy somewhat. Hence, higher uncertainties are suggested to assign to the picked travel‐times for the ocean bottom seismometer with possibly larger non‐linear clock drift in the further tomographic modelling and the internal clock for this kind of ocean bottom seismometer should be recalibrated prior to the next experiment if which requires higher timing accuracy.

Refraction seismic data acquired by wide‐angle ocean‐bottom seismometers are typically used to invert geological structures. Accurate and reliable first‐arrival traveltime picks are therefore critical to successful refraction data processing. Unfortunately, signal‐to‐noise ratios of refraction data acquired by wide‐angle ocean‐bottom seismometers at far offsets are usually low; thus, conventional methods such as the energy ratio method and correlation analysis method usually fail to accurately and efficiently determine the refraction phase. We therefore develop a novel first‐arrival wave recognition method based on the optimal dominant energy spectrum to improve the accuracy of refraction wave picking on wide‐angle ocean‐bottom seismometer data. The method consists of three steps. The first step is to obtain the optimal dominant energy spectrum according to the power spectrum density of sample scanning. Second, the initial position of the first‐arrival wave is recognized by combining the range of the optimal dominant energy spectrum and the maximum point of its derivative. Third, the modified cross‐correlation method adjusts the initial position to obtain a more accurate arrival time. In this paper, we illustrate the workflow and feasibility of the proposed method via testing on model data. Then, the method is used to determine the first‐arrival time of field wide‐angle ocean‐bottom seismometer data acquired in the Southwest Pacific Ocean and Xisha Islands. The results show that our method can accurately pick the refraction phase at offsets as far as 80 km, thus significantly reducing the uncertainties in first‐arrival phase picking.

Seabed fluid flow happens due to the complex interactionamong the accumulation, migration and escape of hydrocarbon and/or fluids. As shallow gas accumulations are anomalously developed in Bohai Bay, studying fluid flow features provides insights for understanding environment fluid in the Bohai Sea. Therefore, in our study, we aimed to make practical and effective use of single‐channel seismic reflection data to delineate and interpret subsurface fluid flow features by analysing the variance, sweetness, instantaneous frequency, and root mean square amplitude of seismic attributes. Four types of submarine fluid escape features, including pockmarks, mounds, fluid vents, dimmed or chaotic seafloor reflections, and two shallow fluid migration pathways (gas chimneys and faults) are distinguished on the single‐channel seismic reflection profiles. Gas chimneys are divided into strict columnar and irregular columnar chimneys, fluid migration along faults is critically dependent on the activity of faults. Enhanced reflections and bright spots may be good indicators of lateral fluid migration and accumulation of fluids in porous formations. We propose a shallow fluid flow model in the study area that illustrates the dynamic fluid migration process in terms of possible fluid sources, shallow migration pathways, and submarine fluid escape features.

Amplitude Versus Offset forward modelling plays a crucial role in identifying gas hydrate reservoirs. However, since gas hydrates commonly exist in thin intermediate seafloor layers, traditional Amplitude Versus Offset techniques based on the Zoeppritz equation with the semi‐infinite medium assumption may not accurately depict the reflection amplitude of hydrates. In this paper, multi‐component Amplitude Versus Offset forward modelling using the Brekhovskikh equation was performed to simulate the Amplitude Versus Offset response of thin‐layered gas hydrate reservoirs more accurately. The numerical experiments demonstrate that the overall trends of the P‐wave and S‐wave Amplitude Versus Offset curves calculated by the Brekhovskikh equation and the Zoeppritz equation are consistent under various reservoir thicknesses, porosities and hydrate saturations, but the Amplitude Versus Offset curves calculated by the Brekhovskikh equation reveal more details. When the angle is greater than 30°, the gradient of the P‐wave Amplitude Versus Offset curve calculated from the Brekhovskikh equation as a function of hydrate saturation and porosity is greater than that calculated by the Zoeppritz equation, whereas the S‐wave Amplitude Versus Offset is not sensitive to changes in hydrate reservoir parameters. To further validate the Brekhovskikh equation in practical Amplitude Versus Offset analysis of gas hydrates, a geological model of gas hydrates is established based on logging data in the South China Sea. Subsequently, the bottom simulation reflection Amplitude Versus Offset of the P‐wave was computed using the Brekhovskikh equation and the Zoeppritz equation. Comparison with the actual vertical cable seismic data indicates that the Amplitude Versus Offset curve using Brekhovskikh equation is more consistent with the amplitude trend than the Zoeppritz equation. This finding suggests that the Brekhovskikh equation holds great potential for establishing an Amplitude Versus Offset identification marker for gas hydrates and improving seismic data interpretation in gas hydrate exploration.

Due to the limitations of the actual acquisition environment in the field, especially in ocean bottom seismometer (OBS) acquisition, the acquired seismic data are often irregular and incomplete, which affects the subsequent data imaging, interpretation and hydrocarbon‐bearing reservoir prediction. The interpolation reconstruction algorithm based on the compressive sensing theory can reconstruct the data without the limitation of Nyquist sampling interval. However, the reconstruction accuracy and effect are different for different sparse representations of the data. On the basis of compressed sensing theory, we propose a Bregman iterative seismic data reconstruction method based on the sparse decomposition of discrete orthonormal Coiflets and Symlets wavelet transforms. First, the discrete orthonormal matrix is constructed by using the above two wavelet functions to make the original seismic data sparse, then the Bregman iterative algorithm is used to reconstruct the sparse coefficients in the discrete wavelet domain, and finally the recovery matrix is used to reconstruct the seismic data. The discrete orthonormal Coiflets and Symlets wavelet transforms have good sparse representation ability and can compensate for the problem that discrete Fourier transform cannot well sparse representation of the data. After numerical experiments on horizontal‐layered model, Marmousi2 model and actual data, it is verified that the proposed method can reconstruct the missing seismic data under the regular observation system and under the irregular observation system with non‐uniform OBS distribution. Furthermore, this method can hardly bring the interference of random noise, and the reconstruction result is of high accuracy.

The Mariana subduction system is an ideal place for ocean–ocean convergence boundary research. As a typical example of an extensional subduction model, the subduction process, subduction mechanism and subduction effect of this system have long been popular research topics. The seismic reflection information of oceanic sedimentary layers shown on multichannel seismic profiles is an intuitive record of small‐ to medium‐scale structural activity and sedimentary sequence evolution and is an important source for studying plate‐coupling processes and tectonic dynamic action. This paper is based on the raw multichannel seismic data of MGL1204 in the Mariana arc forearc region; these data were obtained by using linear interference filter technique noise attenuation technology and multiple velocity iterations to accurately image the oceanic sedimentary layer, and the imagery is used to study the Cenozoic fracture activity and residual strata thickness distribution characteristics of the middle part of the Mariana forearc region and to explore the tectonic migration characteristics of the Cenozoic sedimentary basins. We believe that due to the influence of regional stress field changes caused by the movement directions of the Pacific Plate and Philippine Sea Plate towards the Eurasian Plate, the development of secondary structures in the western Pacific subduction zone shows a characteristic west‐to‐east migration, and the structural characteristics of the Mariana forearc Cenozoic sedimentary basin are consistent with the regional tectonic features. The faults, stratigraphy and depocentres of the oceanic sedimentary layer show a migration pattern from west to east and from north to south overall; 25 and 13 Ma are identified as two key tectonic migration periods, and the tectonic migration characteristics and time records of the basin also provide direct evidence for the plate reconstruction model of trench retreat and Philippine Sea plate rotation.

The source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections.

Seafloor ambient noise in the Arctic Ocean is related to sea ice. The characteristics of low‐frequency seafloor ambient noise (<10 Hz) in the northern Chukchi Sea have rarely been reported. Here, we investigated the seafloor ambient noise recorded using ocean‐bottom seismometers in the northern Chukchi Sea under four different sea ice‐concentration periods from 3 to 23 August 2020, in the 11th Chinese National Arctic Research Expedition. The causes and mechanisms of the changes in seafloor ambient noise that correspond to the variation in sea ice concentration were discussed. The energy of infragravity waves and primary microseisms is weak compared with other oceans. Combined with the analysis of land station, we argue that the variations in sea ice extent have little effect on the energy of the primary microseisms and infragravity waves. The power of secondary microseisms is growth with the loss of sea ice and is influenced by storms. We find that a high concentration of sea ice can impede the process of storm‐sea surface interaction, which in turn affects the power of microseisms inspired by storm.

Ocean bottom seismometer data usually contain a large amount of random noise, which seriously reduces the signal‐to‐noise ratio of the data and affects subsequent imaging. Hence, random noise attenuation is one of the most essential steps in ocean bottom seismometer data processing. In this paper, a novel approach is proposed to attenuate the marine seismic random noise of ocean bottom seismometers based on a six‐dense‐layer denoising autoencoder. We input the domain data into the denoising autoencoder, the encoder compresses the signal and noise and extracts the main features and the decoder finally reconstructs the denoised data with the same dimension as the input. In this approach, because few raw labelled examples are available, we first constructed the pretraining, training and test data sets by patch processing. Then, we pretrained the encoder based on clean synthetic seismic data through unsupervised learning and pretrained the decoder based on noisy synthetic seismic data through supervised learning. Next, the pretrained model was fine‐tuned with the encoder–decoder on a raw seismic data set in an unsupervised manner. Finally, we used the model to attenuate the random noise in raw ocean bottom seismometer data for testing. Synthetic and raw examples are used to compare the deconvolution, multichannel singular spectrum analysis, deep denoising autoencoder and substep deep denoising autoencoder approaches. Experimental tests demonstrate that the proposed method has higher processing efficiency and precision.

First‐arrival travel‐time tomography is frequently used in ocean‐bottom seismometer surveys for estimating subsurface velocity. However, due to a lack of seismometer stations or ray sampling, the tomographic images’ spatial resolution and quality are typically low. Inspired by the multiple imaging of ocean‐bottom seismometer data, in this study, I developed a mirror tomography method to incorporate the travel times of refraction multiples in first‐arrival travel‐time tomography. Specifically, the travel times of refraction multiples were treated as virtual first‐arrival travel times from the mirror positions of the stations. This technique enhances ray coverage and stabilizes the inversion. I confirmed that the travel times of refraction multiples were consistent with the first‐arrival travel times calculated using numerical modelling at the mirror position of the station. Synthetic examples showed that the mirror tomography scheme may enhance ray coverage and model resolution. Mirror tomography may compensate for the uneven distribution of travel‐time picks caused by the loss of the ocean‐bottom seismometer.

The Sulu orogenic belt, formed by the collision between Sino‐Korean and South China blocks, has important geological implications due to its unique tectonic setting. To understand the deep crustal structure of the extension of the Sulu orogenic belt in the Yellow Sea, we presented a 220‐km long active‐source ocean‐bottom seismometer wide‐angle reflection/refraction profile across the Yellow Sea. The obtained P‐wave velocity structure exhibits significant horizontal and vertical variations. The middle and upper crustal regions of the north beneath the survey line exhibit a higher velocity and larger thickness than those of the south, whereas the opposite is the case with the lower crust. The lower crustal high‐velocity zone in the Northern Depression of the South Yellow Sea Basin is a manifestation of the mantle material upwelling and accretion to the lower crust. Magmatic intrusions occurred along the faults and formed a high‐velocity zone in the middle and upper crusts. Due to undulating Moho interface and varying crustal velocities, the Qingdao–Rongcheng fault is identified as a fault in the Sulu orogenic belt without the significance of tectonic zoning. This study shows that the Sino‐Korean Block, Sulu orogenic belt and South China Block are bounded by the Jimo–Muping faults (an extension to the sea), Qianliyan fault and deep fault of the South Yellow Sea. Furthermore, the Sulu orogenic belt may have been formed by the northward subduction of the South China Block beneath the Sino‐Korean Block during the Indosinian orogeny to the east of the Shandong Peninsula.