Marine Seismic Surveys Research Articles

A quantitative analysis of acoustic field directionality from a seismic airgun array

Marine seismic reflection surveys generate high amplitude impulsive acoustic events using airgun arrays to study geophysical characteristics of the seabed. These data can be used beyond seismic imaging – modeling short range propagation, considering impacts on marine mammals, extracting seabed properties and their effect on the acoustic field, etc. Knowledge of the source characteristics is necessary to utilize these data and proper modeling of sound propagation from these arrays requires characterization of the array beam pattern. Complex simulations of airgun arrays have been used in the past to model airgun array spectra, but beam patterns have not been thoroughly considered in the literature. Delay-and-sum combinations of these airgun signatures provide a simple beam pattern estimate, but this approach ignores variability in airgun position, timing, amplitude, interactions between airguns, etc. The use of more complex notional airgun signatures can yield more accurate estimates, but these are more challenging to model and still ignores shot-to-shot variability. Experimentally-determined beam patterns are evaluated here and the variability in the results considered, showing both similarities and notable differences from simulated results. The experimental results indicate that the source array depth impacts the ghost-free array beam pattern and that variability between shots is enough to significantly alter beam patterns. Overall, the observations suggest that accurate simulation of array beam patterns may require more complexity than is currently considered and that inclusion of uncertainty due to environmental and airgun shot variability is essential.

Deep-learning viscoelastic seismic inversion for mapping subsea permafrost

Marine seismic surveys can be used to map ice-bearing subsea permafrost on a large scale. However, current seismic processing technologies have limited capacity to image permafrost distribution at depth, mainly due to the low sensitivity of primary reflections and refractions to the velocity inversion found at the base of permafrost. Guided waves and multiples are more sensitive to the velocity variations below the top of permafrost, but they remain challenging to use in physics-based inversion approaches. A deep-learning-based seismic inversion has the potential to improve seismic imaging below the top of permafrost by automatically extracting information from all wave modes. We develop a multi-input neural network (NN) to estimate seismic velocities from marine seismic data. The network is trained on synthetic data generated from the representative distributions of the seismic properties of subsea permafrost. We find that our network can image large velocity contrasts and reversals in depth, typical of subsea permafrost. We use our network to estimate the P- and S-wave velocity and Q-factor models from a seismic line in the Beaufort Sea. The NN indicates highly discontinuous subsea permafrost with variable thickness in the area. Our work shows that deep-learning-based seismic inversion could become a cost-effective technology to map the distribution of subsea permafrost on a large scale and, more generally, high-velocity geologic layers located in shallow waters.

Reflected acoustic energy from geological layers during seismic reflection surveys.

Acoustic propagation is significantly impacted by seabed characteristics, which play a large role in propagation modeling. Shallow seabed characteristics comprise a notable area of research due to their impacts on bottom loss, but deep seabed characteristics are often ignored. At low frequencies (several hundred Hertz, particularly below 100 Hz) and at ranges less than that corresponding to the seafloor critical angle, these deep layer characteristics have non-negligible effects. Those effects are explored here using a subset of data from a marine seismic reflection survey, MGL2104, in an environment with a nearly constant ∼2.6 km bathymetry. The source is a 5700 in.3 airgun array and reflections are measured by a 1200 channel, ∼15 km streamer, with both arrays at 12 m depth. The results show that in one-third-octave bands below 100 Hz, a significant fraction of the reflected energy (sometimes >50%) at certain ranges in the water column is attributable to sub-seabed layers, and the seafloor reflections only become the dominant source at ranges where the reflection path approaches a critical angle. The analysis also considers the effects of layer depths on reflected energy, demonstrating that increased depth does not necessarily correlate with decreased energy reflected in the water column.

Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data

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.

Free-surface multiple attenuation and seismic deghosting for blended data using convolutional neural networks

Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g., for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.

Exploiting passive acoustic recordings of seismic survey datasets

Marine seismic reflection surveys provide a dense and abundant dataset covering a large region of geological interest in the ocean. These data provide an opportunity for acoustic analysis with varying environmental characteristics, both in the water column and the seabed. In a typical survey, an airgun array is fired tens to hundreds of thousands of times, and hundreds of receiver channels record the acoustic signal reflected from the seabed after each shot. Additionally, the high amplitude signal broadcast from airgun arrays used in these surveys may be measured passively by hydrophones located close to the survey. In this work, we consider the data measured by Ocean Observatories Initiative (OOI) hydrophones adjacent to two seismic surveys, MGL1905, and MGL2104. In each case, a 6600 in3 airgun array is fired ∼every 37.5 m along multiple survey lines extending 10s to 100s of kilometers. Each survey generates multiple terabytes of acoustic data, and many of the shots are also captured by OOI hydrophones. This work aims to combine these two datasets to evaluate the acoustic behavior of airgun shots over a variety of conditions and examine the relationship between various environmental factors and acoustic propagation behavior. [Work supported by ONR.]

Submarine pyroclastic deposits from 7.3 ka caldera-forming Kikai-Akahoya eruption

Extensive submarine pyroclastic deposits were discovered around the Kikai caldera, Southwest Japan, which originated from the caldera-forming Kikai-Akahoya (K-Ah) eruption that occurred at 7.3 ka. This submarine pyroclastic unit originated from a pyroclastic density current (PDC) that entered the sea and was transformed into a subaqueous density current. Our high-resolution marine seismic reflection surveys reveal that the uppermost unit exhibiting prominent seismic facies thins radially away from the caldera and covers >4500 km2 of the seafloor. The estimated volume of the submarine deposits is >71 km3 based on the isopach map. Seismic sequence stratigraphic analyses and correlation of shard glass compositions from the piston core and remotely operated vehicle samples with those from subaerial outcrops identified the unit as pyroclastic deposits produced by the K-Ah eruption. The geomorphological features of the unit indicate a long runout distance (i.e., 40 km), exponential thinning with increasing distance from the source, and a depression-filling nature of the deposits suggesting that the unit was formed predominantly by a density current that entered the water. The quantitative comparisons of geometric shape and its thinning rate, including that for non-volcanic density currents, indicate that the subaerial PDC entering into the water was transported as a dilute density current in the water. We propose that the formation of these dilute density currents transformed by PDCs probably requires sufficient water depth, as inferred also from previous analog experiments. Adding on-land ignimbrite of 5 km3 and widespread tephra of 249–374 km3, our estimate of submarine pyroclastic deposits of >71 km3 leads to a total bulk volume of the K-Ah eruption of 332–457 km3 (133–183 km3 in DRE), making it probably the largest eruption of the Holocene.

National Archive of Marine Seismic Surveys Web Portal Provides Public Access to US Exclusive Economic Zone Marine Seismic Surveys

AbstractSeismic reflection data collected within the US Exclusive Economic Zone (EEZ) represent a scientific resource with inestimable value. The US Geological Survey (USGS), in partnership with the Bureau of Ocean Energy Management (BOEM), provide unrestricted public access to seismic data acquired since 1975 through the National Archive of Marine Seismic Surveys (NAMSS) portal. This map‐searchable web portal provides access to over 1,100 two‐ and three‐dimensional (2D, 3D) marine seismic data sets collected by the USGS and by the offshore resource industry. The industry data sets are released by BOEM under The Code of Federal Regulations 30 CFR Parts 551 and 580, allowing for the release of seismic reflection data after the completed 25‐year proprietary term from the issuance of the Geological and Geophysical (G&G) permit. The web portal provides tools to filter and identify surveys within the US EEZ and navigation and shapefiles can be downloaded with each survey. Seismic data sets downloaded from NAMSS have been instrumental in numerous scientific investigations along the US EEZ. These include studies focused on offshore marine geohazards, such as fault systems and submarine landslides, as well as gas hydrates and other geologic processes. Acquisition of such data sets, especially pneumatic‐sourced surveys, is becoming progressively more difficult due to environmental concerns and associated costs, thus preservation and public access to such high‐quality data sets is critical to maintaining the ability to conduct offshore seismic reflection scientific research along the US EEZ.

Data augmentation for 3D seismic fault interpretation using deep learning

Manual seismic interpretation of faults is a tedious and complicated process, which is prone to human error and bias. A semi-automatic approach for interpreting faults on seismic data is to use attributes that highlight discontinuities. These methods need to be optimized by the interpreter, thus constantly rely on the interpreter's knowledge. Recently, Machine Learning (ML) techniques in general and Convolutional Neural Networks (CNN) as part of Deep Neural Networks (DNN), in particular, have been used to detect and image faults on seismic data in order to make the process more automated and accurate. Supervised CNN learns and evolves from manually annotated or labeled fault interpretations.In this study, we have applied supervised 2D CNN to image faults on seismic data through binary segmentation. The study was performed on 3D marine seismic surveys in off-shore Norway collected from three separate locations along the Norwegian Continental Shelf, utilizing Efficient UNET CNN architecture. We applied data augmentation (geometric transformations) and hyperparameter tuning to improve the learning process and performance of the deep learning algorithm.We used noise content, acquisition type, imaging type, and fault scale as the main criteria to select seismic surveys. The application of data augmentation to the training and testing data generally led to improvement in the performance of 2D CNN on fault predictions, although the amount of improvement varied with respect to different surveys. The initial 2D CNN fault prediction improvement mainly relied on the quality, and size of faults present in the 3D seismic volumes. Further, improvement was achieved by the adjustment of certain hyperparameters affecting the training and testing process of the 2D CNN. However, we found little to no improvement on one seismic volume containing high levels of noise.

Application of Optimization Algorithms for Solving Marine Seismic Survey Planning Problems with Bottom Stations in the Arctic Shelf

Application of Optimization Algorithms for Solving Marine Seismic Survey Planning Problems with Bottom Stations in the Arctic Shelf

Acoustic energy reflections from deep seabed layers in seismic reflection surveys

Data from marine seismic reflection surveys are generally used to study the composition of the seabed 100s to 1000s of meters below the seafloor. These surveys consist of an airgun array that broadcasts an impulsive signal towards the seafloor, the reflections of which are measured by towed hydrophone streamers and used to invert for geophysical properties. The abundance of data from these surveys provides many opportunities to conduct acoustic analyses and to utilize data-driven approaches. Here, the extensive datasets are used for analyzing the impacts of seabed characteristics on the acoustic fields in the water column. Various seabed components are considered and their influence on the acoustic field quantified. It is shown that layers well below the seafloor can contribute up to 50% of the acoustic energy reflected into the water column at frequencies below 100 Hz and ranges approximately three times the channel depth. Additionally, non-intuitive relationships between layer depths and acoustic energy are explored. [Work supported by ONR.]

Effects of marine seismic surveys on free-ranging fauna: a systematic literature review

Among anthropogenic activities, marine seismic surveys are a fundamental tool for oil and gas explorations, geophysical research, environmental hazard, and risk analysis. This resulting noise may affect a range of species, eliciting masking, behavioral responses, and changes in acoustic repertoires. There is an urgency to understand in depth the potential effects of seismic surveys on marine ecosystems since the information available is still scarce. Using Scopus® and Web of Science™ 2023 Clarivate bibliographic databases, we systematically reviewed the scientific literature addressing seismic surveys’ effects on free-ranging marine fauna. The first selection of articles matching selected keywords yielded 680 articles from Scopus and 320 from Web of Science. Screening for research articles written in English investigating marine fauna in its natural environment and performing a quality assessment process resulted in selecting 31 articles since 2001. We found a trend of increasing research efforts in this field with a decrease after 2020 and a broad spectrum of journals that hosted the publications (31 papers published in 12 journals). Although several taxa are investigated, most studies focused on effects on marine mammals. There is a lack of research on diverse animal taxa, and no research papers compare the effects on different taxa along the food chain. Behavioral and physiological effects are the most found by authors in the field. However, observed behavioral changes cannot always be uniquely attributed to the exposure to seismic surveys, as many authors report the influence of other variables (e.g., environmental conditions) during the observations.

Understanding seismic velocity variations of subsea permafrost: A sensitivity study

Monitoring the change in permafrost conditions and distribution is crucial for forecasting global warming. As seismic velocities increase with ice content, marine seismic surveys can map the top of ice-bearing subsea permafrost on a large scale. However, conventional seismic methods cannot fully map internal velocity variations linked to changes in ice content because the removal of guided waves and multiples is challenging for reflection processing. Nevertheless, these arrivals carry information about velocity variations with depth. We investigate if a joint analysis of various wave arrivals could provide information about velocity variations within permafrost and accurately estimate the permafrost thickness. Through a sensitivity analysis, we estimate the feasibility of using different wave arrivals, such as reflections, refractions, multiples, and guided waves, to better characterize permafrost conditions. We find that guided waves are sensitive to velocity variations within permafrost and can detect the depth of the permafrost’s base under certain geologic conditions. Then, we combine the analysis of all seismic arrivals to derive a subsurface permafrost model for a seismic line collected in the Beaufort Sea. The joint analysis reveals the transitions in depth between ice-bonded and partially ice-bonded permafrost and offers a crude estimate of permafrost thickness.

Comments on Singh et al. (2022) ‘Marine seismic surveys for hydrocarbon exploration: What’s at stake?’

Comments on Singh et al. (2022) ‘Marine seismic surveys for hydrocarbon exploration: What’s at stake?’

A processing for ocean-bottom multicomponent data with seismic interferometry: a case study of southern offshore in Korea

Marine seismic surveys are widely used to explore oil and gas resources and monitor CO2 storage. In particular, ocean-bottom seismic surveys allow the acquisition of high-quality data without the influence of the water-layer environment. However, ocean-bottom seismic surveys are more expensive than streamer surveys and P-wave survey data exhibit free-surface multiples, which are persistent problems. PZ summation can be applied to process multicomponent ocean-bottom seismic data, which effectively reduces ghosting and water-layer reverberation by combining hydrophone and vertical geophone data. In this study, we processed and analysed ocean-bottom cable data from a shallow gas area offshore from Korea. In our field-survey simulation, PZ summation was applied to the synthetic data to separate up-going and down-going wavefields and attenuate free-surface multiples. In addition, improved image can be obtained compared to conventional stack section by applying seismic interferometry to the acquired up-going wavefield data.

Impacts of seabed characteristics on short-range acoustic propagation in seismic surveys

An abundance of marine seismic reflection surveys with publicly available datasets have been collected over the last several decades to study the composition of the seabed up to multiple kilometers below the sea floor. In these surveys, a loud impulsive airgun source broadcasts sound into the seabed and the reflections from the seafloor and seabed layers are measured on a long (up to ∼15 km) towed hydrophone array. In these surveys, both the shallow seabed layers (several hundred meters below the seafloor) and deeper layers (multiple kilometers below the seafloor) can significantly impact the acoustic field in the water column at close ranges. Experimental data have shown instances of sound exposure levels (SEL) at ranges between ∼8 and 15 km that exceeded levels predicted by cylindrical spreading models by nearly 15 dB. This work aims to use the abundance of seismic data available to explore the relationship between seabed and sub-seabed characteristics and the acoustic field in the water column. Inversion results that provide seabed sound speed profiles and reflection characteristics will be used to understand the seabed characteristics and its potential effects on SELs or sound pressure levels evaluated along the towed array. [Work supported by ONR.]

Imaging ocean water columns by acoustic contrast reflection signals in existing marine seismic data

Marine seismic surveys conducted for oil and gas exploration use a source-receiver system consisting of airgun sources and hydrophone receiver arrays. The collected data were commonly used to image subsurface beneath the seafloor. The surveys had also collected signals of acoustic reflections from ocean water columns due to acoustic impedance contrast by temperature and salinity. These signals can be processed to provide high-resolution images of the water-column structures such as eddies and internal waves, forming a discipline termed as seismic oceanography in the literature. Acoustic contrast reflection signals in a three-dimensional seismic survey, collected by multiple parallel receiver arrays, can even be used to explore movements of water columns {see our recent study in [Zou,etal., Ocean Sci. 17(4), 1053–1066 (2021)]}. Nevertheless, because of the low acoustic impedance contrast in oceans, these water-column reflection signals are extremely weak, e.g., about 2–3 orders weaker than the seafloor reflections. Meanwhile, these signals consist of acoustic multipath features that require an appropriate filtering of the signals (see our recent study in the study by Zou and Zhang [JASA 150(5), 3852–3860 (2021)]. Understanding of the signal features and appropriate applications of acoustic signal processing methods are inspired to improve the imaging quality.

Prehistoric settlements and rivers in the Lithuanian coastal area in the Early Holocene

The search for sites inhabited by humans of the Late Palaeolithic to Mesolithic period on the coasts of Lithuania is closely related to the coastal and underwater relicts of the Early Holocene and palaeo-watercourses. This article presents the results of coastal, underwater and seismic seabed surveys. The estuaries of the rivers of the Late Mesolithic period could have been at the present seabed level at a depth of 30 m or even deeper. The watercourse sites of the Littorina Sea stage are in shallow coastal waters. At the latitude of Šventoji, Palanga, Klaipėda, Juodkrantė and the area of the Nemunas palaeo-estuary, the seabed was explored with side-scan sonar and by diving. An artefact from the Early Neolithic period has been found in the coastal area next to Klaipėda, and underwater, at a depth of 14.5 m, a relict tree stump has been detected. Two sites at a depth of 10–12 m can be associated with the relict Danė watercourse containing the preserved fragments of relict landscapes. During marine seismic survey, the probable Smeltalė River palaeo-watercourse was detected, and three sites of the former watercourses found to the south of Klaipėda could be the traces of the Dreverna palaeo-river estuary. This area has good prospects as regards the search for Early Mesolithic period settlements. The underwater survey showed no traces of human activity. A further search for the Stone Age sites would be more promising in locations where palaeo-landscapes have survived adjacent to the palaeo-watercourses.

Effects of sub-seabed characteristics on acoustic transmission loss in seismic reflection surveys

Marine seismic reflection surveys are used to study the composition of the seabed and sub-seabed by recording reflections of the field generated by an airgun array from the seafloor with a towed horizontal streamer. The abundance of data within a single survey and the number of publicly available surveys provides substantial opportunity for data-driven acoustic analysis. Here, we consider data from surveys that were conducted off the coasts of Oregon, Washington, British Columbia, and Alaska, covering portions of the Cascadia Subduction Zone and the Queen Charlotte Fault. Throughout the experiments, data was collected at depths ranging from 200 m to 3 km, typically using a 15 km long towed streamer containing 1200 hydrophone groups with 12.5 m spacing. With these datasets, there is a breadth of opportunity for studying the impacts of various seabed features on acoustic propagation. This talk will explore the impacts of seabed and sub-seabed characteristics, bathymetry, reflectivity, etc., on acoustic transmission loss. The data from these experiments, corrected for array design impacts, will be compared directly with computational propagation models. [Work supported by ONR.]

A new tsunami hazard assessment for eastern Makran subduction zone by considering splay faults and applying stochastic modeling

ABSTRACT Tsunami hazard imposed by possible rupture of splay faults is important as it may significantly intensify tsunami heights locally. The Makran Subduction Zone (MSZ) in the northwestern Indian Ocean can generate large thrust earthquakes that could trigger significant tsunamis. In this paper, the effects of possible rupture of splay faults on the tsunami hazards of eastern MSZ are studied by developing a framework that uses stochastic earthquake rupture models and considers uncertainties related to rupture location, rupture geometry, seismic moment split ratio, earthquake slip asperity location within a fault plane, and earthquake slip heterogeneity. To quantify these uncertainties, 484 different parameter combinations of tsunami sources are considered systematically. The geometry of splay faults is developed using the most recent marine seismic surveys of the tectonic structure of the MSZ. A moment magnitude of 8.6 is considered as a scenario magnitude. The results of this study are generated in two parts, by considering average sources and stochastic sources. Results show significant local amplification of the maximum tsunami heights due to splay faults. For instance, the maximum wave height in Pasni, Pakistan can be amplified by a factor of four due to a single splay fault rupture scenario of average sources.