Ocean Bottom Seismometer Data Research Articles

Accurate Trace-Cut and Phase Alignment of Active Ocean-Bottom Seismometer Data

Abstract Accurate positions of ocean-bottom seismometers (OBSs) on the seafloor are critical parameters and can only be obtained by inversion modeling of first-arrival travel times of overhead cross-line airgun shootings. With an increased sampling interval of ≤20 ms for long-term earthquake studies, apparent artifacts affect the phase alignment of first arrivals on the seismic sections of trace-cut airgun shots. Our analysis shows that these apparent misalignments are caused by timing inconsistencies and inaccuracies during the trace-cut, which are so-called rounding errors. To eliminate these rounding errors, a simple interpolation is used to resample traces. Further analysis shows the simple interpolation satisfactorily retains the original waveform. The improved timing accuracy significantly reduces the uncertainty of seafloor locations as shown by Hadal OBS data.

Seismic noise in South China Sea: High-quality time-frequency analysis from 0.01 to 125 Hz.

An efficient and precise time-frequency analysis method for real-time ocean bottom seismometer (RTOBS) data in the South China Sea (SCS) is presented. Overcoming the limitations of conventional methods, the method involves temporal segmentation, unique frequency octaves, and Fourier transforms to generate power spectral density (PSD) and probability density function profiles. The method demonstrates superior precision, computational efficiency, and full-bandwidth (0 to Nyquist) capability compared to traditional techniques, as validated through theoretical and empirical evaluations. Applied to SCS RTOBS data, it unveils temporal PSD variations, shedding light on underwater noise sources like earthquakes, offshore blasting, ship-induced disturbances, and tidal effects. Establishing background noise levels in the SCS supports noise source categorization and ocean environment monitoring. Furthermore, comparing onshore and offshore seismic stations advances interdisciplinary research, fostering a comprehensive understanding of acoustics and seismology in the region.

Origin of a high-velocity layer: Insights from seismic reflection imaging (South China Sea)

Magmatism and crustal structural characteristics are well-known phenomena that support understanding the marginal sea's tectonic evolution. The South China Sea (SCS) has become a natural laboratory for researchers to investigate the evolution of the marginal sea because of its complex tectonic background and multiple phases of magmatic activities. However, due to limitations in resolution and depth of observations, the evolutionary process of the SCS is still controversial. We use the latest processed multi-channel seismic reflection profile Line AB to evaluate the sedimentary sequences, crustal tectonics, and the Cenozoic magmatism in the southern SCS. The gravity modeling results indicate a reduction in crustal thickness from 18 km in the proximal domain to 7 km in the Continent-Ocean Transition (COT). The COT is roughly 35 km wide, representing a rapid break up process of the SCS. A high-velocity layer (HVL) of about 3 km thickness was formed in the continental slope crust, interpreted as post-spreading magmatic underplating. In addition, four sedimentary sequences, post-spreading magmatic intrusion, and the possible magma conduits were identified in the profile. Combining with previous ocean bottom seismometer (OBS) data, multi-channel seismic data, fault system data, and petrologic observations, we indicate the deep mantle magmatic material upwelled to the crust and further intruded into sediments along the hyper-extended crust or detachment faults during the post-spreading stage. We contend that cooling and heat shrinking within the SCS oceanic lithosphere have the potential to initiate decompression melt deformation in the continental slope. In summary, we construct a model of the origin and migration mechanism of extensive post-spreading magmatism in the SCS. Our research reveals the Cenozoic evolution and the HVL of the southern SCS, and provides a possible genesis for the anomalously young magmatism observed at the continental margin.

Ambient Noise Interferometry Using Ocean Bottom Seismometer Data From Active Source Experiments Conducted in the Southernmost Mariana Trench

AbstractOcean bottom seismometers (OBSs) have been used to detect submarine structural and tectonic information for decades. According to signal source controllability, OBS data have generally been classified into active and passive source data categories. The former mainly focuses on the compressional wave (P‐wave) velocity inversion and always lacks valid information about the shear wave (S‐wave) velocity structure. While the latter provides structural information with limited resolution due to the aperture of the stations. Overcoming the barriers between processing these two data types will allow the reuse of a vast amount of data from active source experiments to explore the submarine S‐wave velocity structural properties. Here, we creatively applied ambient noise interferometry to invert the S‐wave velocity structure using data from active source OBS deployment conducted in the southernmost Mariana subduction zone, which had already been utilized to detect submarine P‐wave velocity structure. Considering the short time duration and relatively low quality of this type of data, a combined method of short‐segment cross‐correlation and selected time‐frequency domain phase‐weighted stacking was adopted to obtain stable cross‐correlation functions, which were subsequently used to invert S‐wave velocity structures. Compared to previous studies using different methods, our result sheds new light on the crust and upper mantle structure of the southernmost Mariana subduction zone. This method could be used to detect more information based on the reutilization of existing active source OBS data.

3D Local Earthquake Tomography of the Andaman–Nicobar Subduction Zone Using Ocean-Bottom Seismometer Data

ABSTRACT The Andaman–Nicobar (A–N) subduction zone is one of the most seismically active subduction zones of the world where the Indian plate subducts beneath the Burmese–Sunda plate. Imaging the subducting Indian plate (SIP) geometry in this region is important to understand the subduction process, earthquake genesis, and associated seismic hazards. Therefore, we imaged the SIP for the first time using local earthquake data recorded from a network of nine ocean-bottom seismometers and six surface seismic stations. We inverted 2819 P and 2171 S phases picked from 410 local earthquakes recorded between December 2013 and May 2014 to obtain the tomographic images in the A–N region. The images show high-VP and VP/VS anomalies linked to colder and thicker SIP in the A–N region. We also observed seismic signatures of strong structural heterogeneity all along the SIP. The low-velocity anomaly at 60–100 km depth beneath the Andaman back-arc spreading center indicates mantle upwelling. Likewise, low-VP anomalies beneath the active volcano Barren Island indicate production of arc magmas by slab dehydration and corner flow in the mantle wedge.

A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.

OBSTransformer: a deep-learning seismic phase picker for OBS data using automated labelling and transfer learning

SUMMARY Accurate seismic phase detection and onset picking are fundamental to seismological studies. Supervised deep-learning phase pickers have shown promise with excellent performance on land seismic data. Although it may be acceptable to apply them to Ocean Bottom Seismometer (OBS) data that are indispensable for studying ocean regions, they suffer from a significant performance drop. In this study, we develop a generalized transfer-learned OBS phase picker—OBSTransformer, based on automated labelling and transfer learning. First, we compile a comprehensive data set of catalogued earthquakes recorded by 423 OBSs from 11 temporary deployments worldwide. Through automated processes, we label the P and S phases of these earthquakes by analysing the consistency of at least three arrivals from four widely used machine learning pickers (EQTransformer, PhaseNet, Generalized Phase Detection and PickNet), as well as the Akaike Information Criterion (AIC) picker. This results in an inclusive OBS data set containing ∼36 000 earthquake samples. Subsequently, we use this data set for transfer learning and utilize a well-trained land machine learning model—EQTransformer as our base model. Moreover, we extract 25 000 OBS noise samples from the same OBS networks using the Kurtosis method, which are then used for model training alongside the labelled earthquake samples. Using three groups of test data sets at subglobal, regional and local scales, we demonstrate that OBSTransformer outperforms EQTransformer. Particularly, the P and S recall rates at large distances (>200 km) are increased by 68 and 76 per cent, respectively. Our extensive tests and comparisons demonstrate that OBSTransformer is less dependent on the detection/picking thresholds and is more robust to noise levels.

PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning

AbstractDetecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments.

Noise and site response characterization in Makassar Strait and Celebes Sea based on Ocean Bottom Seismometer

To overcome the limited information in the northern waters of Sulawesi Island, ocean bottom seismometers (OBS) were installed in the northern part of the Makassar Strait and the Sulawesi Sea. OBS data were analysed using passive seismic methods to determine the characteristics of noise and site response in the Makassar Strait and the Celebes Sea. Probabilistic Power Spectral Density (PPSD) from OBS station showed microtremor in the period 0.06 – 0.7 s, secondary microseismic peak in the period 0.9 – 2 s, secondary microseismic peak in the period 4 – 8 s, and hum in the period more than 10 s. The amplitude of the noise power is in the range of New Low Noise Model (NLNM) and New High Noise Model (NHNM) for a period <10 s and slightly above NHNM for a period >10 s for OBS IGGCAS F-type. For OBS IGGCAS G-type, the noise power amplitude is in the period < 0.7 s in the NLNM and NHNM ranges. However, there is a tendency for the noise power amplitude to be flat with an amplitude above NHNM for a period > 0.7 s. The noise power’s amplitude depends on the station’s location, climate, instruments, daily variations, and seasonal variations. Horizontal-to-Vertical Spectral Ratio (HVSR) in the North Makassar Strait shows an H/V curve with an unclear peak in the middle of the strait and multiple peaks in the eastern part of the strait with a frequency range of 1.35 – 4.7 Hz. Between the Makassar Strait and the Celebes Sea, the H/V curve shows a flat peak and multiple peaks with a frequency range of 1.10 – 4.03 Hz. Meanwhile, in the Celebes Sea, the H/V curve shows an unclear peak (unclear peak) and several peaks (multiple peaks) with a frequency range of 0.65 – 6.25 Hz.

Subsurface structures and nature of seafloor mounds in the northern South China Sea margin: Implications for Mesozoic hydrocarbon exploration

The Chaoshan Depression in the northern South China Sea (SCS) margin hosts thick (ca. 4–9 km) Mesozoic strata including two sets of hydrocarbon source rocks (the Late Triassic-Early Jurassic and Late Jurassic marine facies sequences). It was predicted to be petroliferous but unproven. Numerous submarine mounds are distributed on its seafloor, but whether they are mud volcanoes or magmatic volcanoes remains controversial due to poor knowledge of the subsurface structures. To reveal the nature of these mounds and the Mesozoic hydrocarbon potential in the Chaoshan Depression, multi-channel seismic (MCS) and ocean bottom seismometer (OBS) data along line CS-L1 across three seafloor mounds are processed and analyzed. The MCS profile reveals the existence of fuzzy reflection zones (FRZs) within the Mesozoic strata beneath these mounds. The overlying sedimentary sequences appear uplifted and deformed. The OBS velocity results exhibit that these FRZs feature significantly lower velocities than their host strata with velocity differences ranging from ∼0.25 km/s to 0.71 km/s, likely resulting from vigorous fluid charging rather than magma intrusion. Thus these FRZs are identified as mud-fluid diapirs. Between these diapirs and the overlying seafloor mounds, numerous high-angle faults exist, serving as vertical fluid feeder conduits. Additionally, several bright spots, acting as indicators of gas reservoirs, are observed near these faults. Moreover, bottom simulating reflectors with reverse polarity are imaged within the shallow strata around the seafloor mounds on an adjacent MCS profile, indicating rich gas trapping and/or gas hydrate accumulation. Therefore, we propose that these seafloor mounds are mud volcanoes with fluids coming from the underlying diapirs. As the diapirs extend downward into the Jurassic and Triassic strata, the hydrocarbon gases feeding the mud volcanoes and diapirs are reasoned to originate from the deep Mesozoic source rocks, implying significant Mesozoic hydrocarbon potential in the Chaoshan Depression of the SCS.

An Efficient Transfer Learning-Based OBS Seismic Phase Picker (OBSPD) Trained on Cascadia Subduction Zone Earthquake Dataset

Abstract Earthquake monitoring and many seismological studies depend on seismic phase arrivals. Thus, detecting seismic events and picking the phase arrival times are fundamentally important. In the recent years, seismic phase picking models based on deep learning approaches have been widely developed. These deep learning models can achieve better performances than traditional phase picking methods and improve the quality of phase picking for land-based earthquake monitoring. However, these models might not perform well on data from ocean-bottom seismometers (OBSs), because they are trained exclusively using onshore seismic data and have limited out-of-distribution generalization ability. Nevertheless, there are insufficient labeled OBS phase arrivals dataset to train a deep learning model from scratch. In this study, we developed an automatic phase detection model for OBS data (OBS phase detection [OBSPD]) using the transfer learning approach based on an existing U-GPD model with pretrained weights from a generalized phase detection model feature extraction system. We developed OBSPD with a limited amount of training data (2784 three-component event waveforms) from the Cascadia subduction zone (CSZ) OBS deployments. Our results show that transfer learning can achieve lower model loss with less overfitting compared to when training a model from scratch. Our new OBSPD model outperforms four existing deep learning pickers in terms of phase detection accuracy with smaller arrival time residuals on a test OBS dataset at CSZ, especially for P phases.

Sequencing Seismic Noise Correlations for Improving Surface Wave Retrieval and Characterizing Noise Sources

Abstract Cross-correlating continuous seismic data is a commonly employed technique to extract coherent signals to image and monitor the subsurface. However, due largely to site effects and poorly characterized noise sources in oceanic environments, its application to ocean-bottom seismometer (OBS) recordings often requires additional processing. In this contribution, we propose a method to improve the quality of the retrieved surface waves from OBS data and characterize the noise sources. We first cluster the pre-stack noise cross-correlation functions (NCFs) based on a sequencing algorithm, followed by selectively stacking those consisting of coherent and stable signals that are consistent with predicted surface-wave arrival times. Synthetic tests show that the sequenced NCFs can be used to recover the spatial and temporal distribution of noise sources. Applying the method to an OBS array offshore California increases the signal-to-noise ratios of the obtained Rayleigh waves. In addition, we find that the annual temporal distribution of selected NCFs with frequencies ranging from 0.04 to 0.1 Hz is nearly homogeneous during the recording period. In contrast, many NCFs excluded for stacking are temporally clustered. This method has the potential to be applied to other OBS recordings or possibly onland deployments, thus helping to obtain high-quality surface waves and to analyze temporal noise source characteristics.

Dispersion inversion for P- and S-wave velocities based on guided P and Scholte waves

Inversion based on dispersion curves is widely used for the estimation of subsurface velocity structures. However, conventional surface-wave dispersion inversion usually uses normal modes, and only the S-wave velocities are retrieved. To retrieve P-wave velocities, the dispersion of guided P waves should be considered. We develop an inversion scheme that uses the guided-P-wave and Scholte-wave modes to implement an integrated inversion for P- and S-wave velocity structures of shallow marine sediments. The forward modeling of dispersion curves is based on the spectral-element method and the perfectly matched layer technique. A quantitative sensitivity analysis of guided P modes is then carried out, which not only confirms the capability of guided P modes for estimating the P-wave velocity but also indicates that guided P modes can be combined with Scholte modes to better constrain the S-wave velocity. Based on the Levenberg-Marquardt algorithm, a joint inversion scheme that uses guided P and Scholte modes is developed. The validity of the inversion scheme is assessed by testing synthetic and field ocean-bottom seismometer data obtained in shallow marine environments. The results demonstrate the effectiveness of the joint inversion for retrieving the P- and S-wave velocity structures of shallow marine sediments.

Analysis of Regional Ambient Seismic Noise in the Chukchi Sea Area in the Arctic Based on OBS Data from the Ninth Chinese National Arctic Scientific Survey

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.

Post-rift magmatism controlled by detachment faults in a microplate, northwestern South China Sea

Post-rift magmatism along continental margins is usually focused on highly stretched basins or aborted rifts. Adjacent microplates with relatively thick lithosphere are not expected to exhibit intense post-rift magmatism. This study identifies 20 mounded structures and associated pathways using two-dimensional, multichannel seismic data and ocean bottom seismometer (OBS) data across the southeastern Xisha Massif of the northwestern South China Sea. This massif is a relatively thick (&amp;gt;20 km) region of crust that forms a microplate between two rift branches. The mounded structures are interpreted as volcanoes, based on their seismic reflections and morphological characteristics. Detachment faults that extend into the middle crust captured the magma and provided pathways for vertical migration. During the rise of magma into the sedimentary stratum, detachment faults still served as the main channels of magmatic migration. The rigidity differences between the basement and the overlying sediments, as well as the stress field, facilitated subordinate pathways for magmatic migration, particularly at the depocenters and flanks of half-grabens. Consequently, larger volcanoes are present above the basement highs, while smaller volcanoes are located in the centers of half-grabens. This study provides criteria for identifying submarine mounded structures of different origins that are applicable beyond the study area. Moreover, this study highlights that detachment faults play a key role in the volcanic systems of the relatively rigid microplates of heterogeneous crustal structure. It also promotes our understanding of post-rift magmatism and the dynamic evolution of continental margins, and the results could be applicable to other areas with similar geological settings.

Event recognition in marine seismological data using Random Forest machine learning classifier

SUMMARYAutomatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogues containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalogue screening and event recognition among different signals [i.e. earthquakes, short duration events (SDE) and marine noise sources]. The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalogue of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogues. Our final model reaches an average accuracy of 87 per cent in recognition between the classes, comparable to classification results for data from land. We find that, from the used set of features, the most important in separating the different classes of events are related to the kurtosis of the envelope of the signal in different frequencies, the frequency with the highest energy and overall signal duration. We illustrate the implementation of the approach by using the temporal and spatial distribution of SDEs as a case study. We used recordings from six OBSs deployed between 2019 and 2020 off the west-Svalbard coast to investigate the potential link of SDEs to fluid dynamics and discuss the robustness of the approach by analysing SDE intensity, periodicity and distance to seepage sites in relation to other published studies on SDEs.

CASIE21-OBS: An Open-Access, OBS Controlled-Source Seismic Data Set for Investigating the Structure and Properties of the Cascadia Accretionary Wedge and the Downgoing Explorer-Juan de Fuca-Gorda Plate System

Abstract Geological processes at subduction zones and their associated geohazards (e.g., megathrust earthquakes, submarine landslides, tsunamis, and arc volcanism) are, to a large extent, controlled by the structure, physical properties and fluid content of the subducting plate, the accreted sediments, and the overriding plate. In these settings, modern seismic modeling and imaging techniques based on controlled-source, multicomponent ocean-bottom seismometer (OBS) data are some of the best tools available for determining the subseafloor elastic properties, which can be linked to the aforementioned properties. Here, we present CASIE21-OBS, a controlled-source marine wide-angle OBS data set recently collected across the Cascadia convergent margin as part of the larger CAscadia Seismic Imaging Experiment 2021 (CASIE21). The main component of CASIE21 is a long-offset multichannel seismic (MCS) survey of the Cascadia margin conducted in June–July 2021 onboard R/V M.G. Langseth (cruise MGL2104) aiming to characterize the incoming plate, the plate interface geometry and properties, and the overlying sediment stratigraphy and physical properties. CASIE21-OBS was conducted during R/V M.G. Langseth cruise MGL2103 (May 2021) and R/V Oceanus cruise OC2106A (June–July 2021). It consisted of 63 short-period four-component OBSs deployed at a total 120 stations along 10 across-trench profiles extending from ∼50 km seaward of the deformation front to the continental shelf, and from offshore northern Vancouver Island to offshore southern Oregon. The OBSs recorded the airgun signals of the CASIE21-MCS survey as well as natural seismicity occurring during the deployment period (24 May 2021 19:00 UTC–9 July 2021 09:00 UTC). The OBS data are archived and available at the Incorporated Research Institutions for Seismology Data Management Center under network code YR_2021 for continuous time series (miniSEED) and identifier 21-008 for assembled data set (SEG-Y).

Seismicity Properties of the Chain Transform Fault Inferred Using Data From the PI‐LAB Experiment

AbstractOceanic transform faults are intriguing in that they do not produce earthquakes as large as might be expected given their dimensions. We use 1‐year of local seismicity (370 events aboveMC = 2.3) recorded on an array of ocean bottom seismometers (OBSs) and geophysical data to study the seismotectonic properties of the Chain transform, located in the equatorial Mid‐Atlantic. We extend our analysis back in time by considering stronger earthquakes (MW ≥ 5.0) from global catalogs. We divide Chain into three areas (east, central, and west) based on historical event distribution, morphology, and multidimensional OBS seismicity cluster analysis. Seismic activity recorded by the OBS is the highest at the eastern area of Chain where there is a lozenge‐shaped topographic high, a negative rMBA gravity anomaly, and only a few historicalMW ≥ 5.5 events. OBS seismicity rates are lower in the western and central areas. However, these areas accommodate the majority of seismic moment release, as inferred from both OBS and historical data. Higherb‐values are significantly correlated with lower rMBA and with shallower bathymetry, potentially related to thickened crust. Our results suggest high lateral heterogeneity along Chain. Patches with moderate to low OBS seismicity rates that occasionally hostMW ≥ 6.0 earthquakes are interrupted by segments with abundant OBS activity but few historical events with 5.5 ≤ MW &lt; 6.0. This segmentation is possibly due to variable fluid circulation and alteration, which may also change in time.

Deep crustal structure across the Challenger Deep: Tectonic deformation and strongly serpentinized layer

The southern Mariana subduction zone features the Earth’s deepest trench, named the Challenger Deep, and tectonic deformation is obvious in the overriding and subducting plates. We conducted a 230 km-long wide-angle reflection/refraction survey across the Challenger Deep. Based on ocean bottom seismometer (OBS) data, we image the velocity structure using forward and inverse modeling. The velocity model shows that the Pacific plate entering the subduction zone has normal crustal thickness (6.5–7.2 km) but lower velocity than mature Pacific oceanic crust. PnP seismic phases with large offsets and strong seismic reflectivity are observed at eight OBS seismic record sections, which constrain the thickness (4.0–6.5 km) and velocity (7.1–7.5 km/s) of the upper mantle low-velocity layer. We suggest that the observed velocity reduction in the crust and upper mantle of the subducting plate is caused by bending-related normal faults, which not only fracture the crust but also provide pathways for water infiltration and serpentinization of the dry mantle. The upper mantle reflector constrained by PnP seismic phases possibly indicates a rheological and fault-slip interface derived from rapid variations in the mechanical strength of peridotite. The inner trench slope (ITS) near the Challenger Deep is characterized by indented topography, where the upper crust is obviously thinned and the lower crust is uplifted; the isovelocity contour of 6.5 km/s is 1.5 km below the seafloor. This may be caused by the difference in the degree of subducting plate rollback on either side of the western boundary of the diffuse deformation zone.

Seismic characterization of a fluid escape structure in the North Sea: the Scanner Pockmark complex area

SUMMARYSubsurface fluid escape structures are geological features which are commonly observed in sedimentary basins worldwide. Their identification and description have implications for various subsurface fluid flow applications, such as assuring integrity of overburden rocks to geological CO2 storage sites. In this study, we applied 3-D first-arrival traveltime tomography to a densely sampled wide-azimuth and wide-angle ocean bottom seismometer (OBS) data set collected over the Scanner Pockmark complex, a site of active gas venting in the North Sea. Seismic reflection data show a chimney structure underlying the Scanner Pockmark. The objective of this study was to characterize this chimney as a representative fluid escape structure in the North Sea. An area of 6$\times $6 km2 down to a depth of 2 km below sea level was investigated using a regularized tomography algorithm. In total, 182 069 manually picked traveltimes from 24 OBS were used. Our final velocity model contains compressional wave velocity perturbations ranging from −125 to +110 ms−1 relative to its average 1-D model and compares favourably with a coincident seismic reflection data set. The tomographic velocity model reveals that the chimney as observed in seismic reflection data is part of a larger complex fluid escape structure, and discriminates the genuine chimney from seismic artefacts. We find that part of the seeping gas migrates from a deep source, accumulates beneath the Crenulate Reflector unconformity at ∼250 m below seafloor (mbsf) before reaching the porous sediments of the Ling Bank and Coal Pit formation at &amp;lt;100 mbsf. In addition, the model shows that the venting gas at Scanner Pockmark is also being fed laterally through a narrow NW–SE shallow channel. Quantitative velocity analysis suggests a patchy gas saturation within the gas-charged sediments of the Ling Bank and the Coal Pit formations. Confined to the well-resolved regions, we estimate a base case average gas saturation of ∼9 per cent and in-situ gas volume of ∼1.64 $\times {10^6}\ {{\rm{m}}^3}$ across the Ling Bank and Coal Pit Fm. that can sustain the observed methane flux rate at the Scanner Pockmark for about 10 to 17 yr.