Seismic Exploration Data Research Articles

Fg-ORKA: fast and gridless reconstruction of moving and deforming objects in multidimensional data

Abstract Identifying and tracking objects over multiple observations is a frequent task in many applications. Traffic monitoring requires the tracking of vehicles or pedestrians in video data and geophysical exploration relies on identifying seismic wave fronts from data of multiple sensors, only to mention two examples. In many cases, the object changes its shape or position within the given data from one observation to another. Vehicles can change their position and angle relative to the camera while seismic waves have different arrival times, frequencies, or intensities depending on the sensor position. This complicates the task at hand.

In a previous work, the authors presented a new algorithm to solve this problem - Object reconstruction using K-approximation (ORKA). This algorithm is hindered by two conflicting limitations: the tracked movement is limited by the sampling grid while the complexity increases exponentially with the resolution. We introduce an iterative variant of the ORKA algorithm that is able to overcome this conflict. We also give a brief introduction on the original ORKA algorithm. Knowledge of the previous work is thus not required.

We give theoretical error bounds and a complexity analysis which we validate with several numerical experiments. Moreover, we discuss the influence of different parameter choices in detail. The results clearly show that the iterative approach can outperform ORKA in both accuracy and efficiency. On the example of video processing we show that the new method can be applied where the original algorithm is too time and memory intensive. Furthermore, we demonstrate on seismic exploration data that we are now able to recover much finer details on the wave front movement then before.

Мониторинг развития Дуплетного объекта взрыва газа С22 на полуострове Ямал по данным дистанционного зондирования Земли

The paper presents a comprehensive study of the C22 gas blowout Doublet object formed in 2023 in the central part of Yamal peninsula carried out with the use of remote sensing (RS) data and unmanned aerial vehicle (UAV). The object is situated 12.7 km south of the Bovanenkovo field. The uniqueness of the C22 crater lies in its location — 230 m from a similar, widely-known C2 object exploded in 2012, which is why the C22 object is named ‘Doublet’. Based on RS data the researchers have determined that within August 23 and September 3, 2023 the C22 permafrost heaving mound (PHM) exploded. According to ArcticDEM data for the period 2011-2023, the features of changes in the size of C22 PHM have been studied, including an abnormally high average growth rate of 44 cm/year three years before its explosion, which can serve as one of the criteria for identifying explosive PHM objects. The UAV data of May 14, 2024 has resulted in building a 3D model, which displays a partially snow-covered cavity in a ground ice massif. The depth of the cavity from the crater parapet is at least 28.5 m, the diameter of the crater neck is about 13 m. In the lower part, the cavity widens to 26 m and additionally extends by at least 8 m in the NNW direction at an azimuth of about 341°, which is consistent with the direction of the faults of the Bovanenkovo field identified by seismic exploration data. The bottom of the cavity has an elliptical shape with the size from 26×33 m to 26×41 m. The study is interdisciplinary and is included in the list of critical technologies for the prevention and reduction of risks of natural and man-made emergencies.

What Extra Information Can Be Provided by Multi-Component Seismic Data: A Case Study of 2D3C Prospecting of a Copper–Molybdenum Mine in Inner Mongolia, China

With the decrease in shallow mineral reserves, deep mineral resources have become the focus of exploration. Seismic exploration, renowned for its deep penetration and high spatial resolution and precision, stands as a primary technique in geophysical exploration. In comparison to traditional P-wave seismic exploration, multi-component seismic techniques offer the advantage of simultaneously acquiring P-wave and S-wave data, overcoming the limitations of single P-wave impedance in predicting lithology and enabling high-precision imaging of subsurface structures. Constrained by field survey costs, the reflection seismic illumination is lower and results in a poor signal-to-noise ratio of multi-component seismic data in metallic ore exploration, which poses great challenges in imaging converted S-waves. Based on the seismic and geological characteristics of metallic ores, this study conducts imaging research on metallic ore models through synthetic data and field multi-component seismic data from a copper–molybdenum mine in Inner Mongolia, China. The emphasis is given to PS-wave pre-stack time migration based on precisely sorting the commonly converted point so as to explore the feasibility and technical advantages of multi-component seismic exploration in metal mines. Synthetic data and field data testing demonstrate that PS-wave imaging contains more abundant structural and lithological information compared to PP-waves, indicating promising prospects for the application of multi-component seismic data in metallic ore exploration.

MASS-WASTING INDUCED SHIFTS OF THE BASE OF THE GAS HYDRATE STABILITY ZONE: EXAMPLES FROM THE OFFSHORE NIGER DELTA BASIN, NIGERIA

Mass-wasting episodes lead to significant changes in the pressure and temperature regimes within shallow marine sediments and induce variations in the thickness of the gas hydrate stability zone. These variations are marked by vertical migrations of bottom-simulating reflections that typically mark the base of the hydrate zone. Using exploration seismic data from the Offshore Niger Delta, this study presents two examples involving the vertical migration of the base of the gas hydrate stability zones induced by mass-wasting events. The first is related to a slope failure surface along the forelimb of an active thrust-cored anticline with considerable bathymetric relief. Sediment removal has induced both a decrease in overburden pressure and an incursion of cooler temperature flux in strata in the anticline and led to a migration of the base of the gas hydrate stability zone to deeper levels. The second case is related to a recent erosional event that has led to sediment removal along a seafloor channel course. Evidence of ongoing migration of the base of the gas hydrate stability zone to deeper levels is provided by the lateral termination of a BSR along channel wall, its absence beneath the channel bottom and its apparent downward bend close to the channel fringes. Apart from unroofing hydrate systems and releasing free gas into the ocean and possibly the atmosphere, mass-wasting events on the seafloor, represent significant drivers influencing the formation of gas hydrates in shallow subsea sediments and the thickness and depths of the gas hydrate stability zone.

Technology of Mapping of Volcanic-Sedimentary Strata in a Complexly Dislocated Terrigenous Structure according to Seismic and Magnetic Exploration Data (Pechora Sea)

Technology of Mapping of Volcanic-Sedimentary Strata in a Complexly Dislocated Terrigenous Structure according to Seismic and Magnetic Exploration Data (Pechora Sea)

The 3D Crustal Structure in the Epicentral Region of the 1980, Mw 6.9, Southern Apennines Earthquake (Southern Italy): New Constraints From the Integration of Seismic Exploration Data, Deep Wells, and Local Earthquake Tomography

AbstractWe present the first 3D crustal model of the epicentral region of the 1980, Mw 6.9, normal‐faulting Irpinia earthquake (southern Italy) determined by jointly interpreting the CROP‐04 deep seismic profile, a grid of commercial seismic lines, deep exploration wells, and a high‐resolution Local Earthquake Tomography. Despite numerous seismotectonic surveys and source studies of the background seismicity recorded by dense networks, a complete 3D geological model of the mid‐upper crust was still lacking in the region. The architecture of the Neogene fold‐and‐thrust belt is also debated, with competing thin‐ and thick‐skinned tectonic interpretations. We use the 3D geological model derived from subsurface exploration data to interpret the upper crustal tomographic velocities in terms of rock physical properties, while Vp and Vp/Vs anomalies provide inferences on the deep structural setting down to 12 km depth. We find that a thick‐skinned deformation style allows explaining the geometry of Pliocene fold‐and‐thrust systems deforming the Apulian carbonates but also deeper Permo‐Triassic metasediments and the Paleozoic crystalline femic basement. Inherited compressional structures and lithological heterogeneities control background seismicity occurring at two crustal levels. Fluid‐driven shallow seismicity (<4–6 km) concentrates in a high‐Vp/Vs wedge of fractured, brine‐saturated Mesozoic stiff rocks delimited by the 1980 earthquake faults. Deep seismicity (9–14 km) clusters instead within the low‐Vp/Vs crystalline basement underneath the Apulian carbonate ramp‐anticlines. Commercial seismic data allow us to identify the Irpinia Fault, the main fault ruptured by the 1980 earthquake, reinforcing its previous interpretations as an immature structure with subtle geological and geophysical evidence.

Algorithm for automatic extraction of tectonic fault planes from the resulting probability cubes of machine learning models

Seismic exploration is an integral part of the oil and gas industry when studying the geological structure of deposits. Extracting planes of tectonic disruptions is one of the most challenging tasks in seismic exploration, involving the interpretation of seismic information and lacking universal solutions. This problem underscores the relevance of developing and researching corresponding methods. The article introduces a new algorithm for the automatic extraction of such planes from probability cubes obtained as a result of machine learning model predictions. The algorithm’s features include: 1) data smoothing to reduce noise, 2) clustering points based on their characteristics, 3) extraction of contour points and determination of plane boundaries. The algorithm was implemented and tested on synthetic and real seismic exploration data. The test results confirmed the high efficiency of the algorithm compared to existing approaches implemented in specialized industry software packages. The proposed solution allows automating the interpretation process of seismic data, aimed at obtaining information about the shapes and orientations of planes of tectonic disruptions. This, in turn, aids in well drilling planning and determining the strategy for the extraction and development of deposits.

Seismic data AVO analysis in time frequency domain using synchroextracting transform

Time-frequency analysis is a crucial tool for processing and interpreting seismic data in hydrocarbon exploration. Accurately determining the temporal and spectral characteristics is essential for applications such as reservoir characterization and fluid identification. High-resolution representation of seismic data time-frequency properties improves reliability in these applications. Recent advancements in post-processing techniques aim to enhance conventional methods. The synchroextracting transform (SET) is an innovative approach that combines a post-processing procedure with the short-time Fourier transform. It retains time-frequency information associated with spectral components while eliminating excessive energy dispersed in the time-frequency domain. This study evaluates the effectiveness of SET for seismic data time-frequency analysis, comparing it with contemporary post-processing techniques using synthetic signals and field seismic data. Results show that SET provides higher resolution for distinct signal components and effectively reduces energy dispersion. SET exhibits advantages such as robustness against random noise, reasonable computational cost, and offering more interpretable time-frequency information regarding attenuated amplitudes. Building on synthetic experiments, we demonstrate the capability of SET to generate high-resolution single frequency sections of seismic data, facilitating accurate analysis of amplitude variation with offset (AVO) in the time-frequency domain.

Comparison of surface-wave techniques to estimate S- and P-wave velocity models from active seismic data

Abstract. The acquisition of seismic exploration data in remote locations presents several logistical and economic criticalities. The irregular distribution of sources and/or receivers facilitates seismic acquisition operations in these areas. A convenient approach is to deploy nodal receivers on a regular grid and to use sources only in accessible locations, creating an irregular source–receiver layout. It is essential to evaluate, adapt, and verify processing workflows, specifically for near-surface velocity model estimation using surface-wave analysis, when working with these types of datasets. In this study, we applied three surface-wave techniques (i.e., wavelength–depth (W/D) method, laterally constrained inversion (LCI), and surface-wave tomography (SWT)) to a large-scale 3D dataset obtained from a hard-rock site using the irregular source–receiver acquisition method. The methods were fine-tuned for the data obtained from hard-rock sites, which typically exhibit a low signal-to-noise ratio. The wavelength–depth method is a data transformation method that is based on a relationship between skin depth and surface-wave wavelength and provides both S- and P-wave velocity (Vs and Vp) models. We used Poisson's ratios estimated through the wavelength–depth method to constrain the laterally constrained inversion and surface-wave tomography and to retrieve both Vs and Vp also from these methods. The pseudo-3D Vs and Vp models were obtained down to 140 m depth over an area of approximately 900 × 1500 m2. The estimated models from the methods matched the geological information available for the site. A difference of less than 6 % was observed between the estimated Vs models from the three methods, whereas this value was 7.1 % for the retrieved Vp models. The methods were critically compared in terms of resolution and efficiency, which provides valuable insights into the potential of surface-wave analysis for estimating near-surface models at hard-rock sites.

Quality control measures for seismic exploration data acquisition in front of Longmen Mountain

In front of Longmen Mountain, there is a mountainous area with large terrain elevation differences and dense vegetation. The coupling between detectors in limestone and gravel exposed areas and the surface layer is poor. The completion of blasting wells in gravel areas, as well as the layout and burial of seismic survey lines and detectors in mountainous areas, pose difficulties in seismic exploration. The article dissects the factors that affect the quality of collected data, such as shot point measurement, shot point drilling, detector burial, mountainous survey line layout, and interference source control. Targeted measures to enhance GPS measurement signals, gravel area shot well drilling, and mountainous survey line layout are proposed to effectively improve the quality of collected data and work efficiency, which can provide reference for seismic exploration in mountainous areas.

First Arrival Picking of Exploration Seismic Data Using Pseudo-coloring and Color Image Segmentation

First Arrival Picking of Exploration Seismic Data Using Pseudo-coloring and Color Image Segmentation

Sparse prior-net: A sparse prior-based deep network for seismic data interpolation

Seismic data interpolation plays a crucial role in obtaining dense and regularly sampled data, contributing to improving the quality of seismic data in seismic exploration. Sparsity-promoting methods use a two-step iteration to gradually recover missing traces, by exploiting the sparsity representation of seismic data in transform domains, such as Fourier, wavelet, and curvelet transform, within the framework of the projection onto convex sets (POCS). In the first step, the missing traces are restored by applying the thresholding shrinkage to the transform coefficients. In the second step, the observed data are inserted into the updated result. However, this method relies on a preselected transform and lacks the capability to adaptively capture sparse representations. In addition, determining the optimal threshold parameters can pose difficulties. These limitations yield unsatisfactory reconstruction results. To address this issue, we propose a novel approach called sparse prior-based seismic interpolation network (SP-net) that combines the sparsity-promoting method with a deep neural network. Unlike traditional end-to-end networks, our proposed neural network integrates the widely used POCS method into its architecture, enabling automatic learning of the sparse transform, and threshold parameters from the training data set. By combining the merits of the sparsity-promoting techniques and data-driven deep-learning approaches, SP-net achieves enhanced adaptability and more accurate interpolation results. Through experiments conducted on synthetic and field seismic data, we demonstrate the effectiveness of our proposed method.

Recognition of small faults in coal fields based on multi-scale seismic curvature attributes fusion

In order to meet the needs of intelligent development of coal mines in China for transparency of geological conditions, identifying small faults with a drop of about 3 m has become one of the important geological tasks in structural interpretation. However, the accuracy of conventional 3D seismic exploration data interpretation methods for detecting small faults is still low. On the basis of introducing the basic principles of S-transform time-frequency analysis, principal component analysis, and RGB fusion, this paper proposes a method for identifying small faults in coal fields using multi-scale seismic curvature attribute fusion. The method uses S-transform to perform time frequency analysis to obtain seismic data volumes with multiple frequencies and seismic data volumes at different frequencies correspond to different scales of underground geological information portrayal. Perform spectral analysis on seismic data, determine parameters such as the dominant frequency and frequency bandwidth of seismic signals, and extract the maximum positive curvature attributes of seismic data volumes at different frequencies. Then the principal component analysis (PCA) method is used to analyze the seismic attributes of different frequency seismic data, the GRB fusion method is used to fuse the first three principal components. The application results of actual seismic data show that the results of multi-scale seismic curvature attribute fusion have obvious advantages in identifying small faults, and can improve the accuracy and interpretation accuracy of small faults in seismic data.

Connecting the Iberian Range with the Basque-Cantabrian Basin: The structure of the Sierra de Atapuerca (Spain)

We present a structural model of the Sierra de Atapuerca which constitutes the transition zone between the Iberian Range and the southwestern Basque Cantabrian Basin, NW Spain, where the Sierra de Atapuerca is located. The interpretation integrates both subsurface well and seismic hydrocarbon exploration data with surface geology. According to our interpretation, the Sierra de Atapuerca consists of a pop-up basement involved structure and is part of a NW-SE transpressive strike-slip band formed by high-angle faults segmented by transfer faults with a NE-SW direction. All this strike-slip alignment is located in the northern area of the Duero Basin and forms the footwall of the Duero North Thrust, which has transported the Folded Band and the Burgalesa Platform over this area thanks to a Triassic detachment, in a thin-skinned tectonics context. The Rojas and NW Demanda tectonic arcs represent the structural terminations of the Basque-Cantabrian Basin and Iberian Range domains, respectively, with the Sierra de Atapuerca located between both. The current structure of this zone is the result of an Alpine tectonic inversion of a previous Mesozoic extensional configuration, inherited from late Hercynian faults.

Plumbing systems and associated seafloor fluid seepages in deep-water Nigeria: Factors controlling their architecture and cyclic evolution

A range of plumbing systems has been identified in deep-water Nigeria by interpreting multi-scale seismic data sets (reprocessed 3D exploration seismic reflection data and 2D very high-resolution near-bottom seismic reflection data) as well as sedimentological and geochemical data obtained from a long sediment core recovered by the seafloor drill rig MARUM-MeBo70. The plumbing systems are characterized by diverse fault networks and lie above tectonic features including thrust/fold structures and strike-slip faults linked to gravity-driven deformation caused by underlying over-pressured shales. The plumbing systems are associated with fluid seeping structures at the seafloor such as pockmarks, mud volcanoes and seafloor zonations colonized by living benthic macrofauna typical of active fluid flow. The comparison of seismic stratigraphic sequences with climate and environmental proxies determined for sediments including lithology, element ratios such as Ca/Fe and Zr/Rb, sedimentation rates, and planktonic foraminifera δ18O records shows a control of short-term (0.1–0.4 Myr) and long-term (around 1 Myr) sedimentary cycles. During short-term fluctuations, the recurrence of fluid seeping structures is controlled by the lithology (coarse-grained versus fine-grained sediments) linked to glacial-interglacial fluctuations and a monsoon regime. Conditions favorable for fluid flow are related to the deposition of coarse-grained layers, glacial periods, sea-level lowstands, and low sedimentation rates. We hypothesize that cyclic gravity-driven sediment deformations controlled by glacial-interglacial and long-term variations in accommodation and sedimentation, have led to the cyclic evolution of the plumbing systems and associated seeping structures since the Plio-Pleistocene.

Detailed Geoseismic Structure of the Riphean Deposits in Buolkalakh–Lena Area Based on Deep Drilling and Seismic Exploration Data

Abstract —The work considered an integrated approach to the study of deep-lying Riphean strata of the Buolkalakh–Lena region, which have certain prospects in terms of oil and gas potential. The method of revision and synthesis of acoustic and density logs is described in detail using the example of wells drilled in 80–90 for the purpose of correct reference to seismic sections. It has been shown that the Khaipakh, Debengdin, Arymas, Kyutingdinsky, and Sygynakhtakh formations of the Early and Middle Riphean eras overlook the pre-Permian surface in the middle part of the Lena–Anabar trough. The region also developed older Riphean deposits that are not exposed on the Olenek uplift and are not studied by deep drilling. The prospects for the oil and gas content of Riphean strata in the region are mainly associated with carbonate reservoirs formed in the top of the Riphean sequence and cropping out on the pre-Permian erosive surface, the most promising rocks are those constituting large carbonate platforms that were partially eroded and subjected to processes of hypergenesis and karst formation.

Multiple seismic attributes fusion approach with support vector regression and forward simulation for sand body prediction and sedimentary facies interpretation — A case of the X gas field in Xihu Sag

Marine exploration and production play a vital role in petroleum industries. It is difficult to acquire sufficient well data in marine settings, so seismic data become the most important interpretation data in research. In general, the seismic data in marine exploration have low quality because of the deep depth from the surface. To partly address this, the support vector regression (SVR) algorithm is proposed to fuse multiple seismic attributes for sand thickness prediction. First, we use forward modeling to establish virtual wells for improving the training data set. Second, we select the optimal attributes by correlation analysis. Third, we apply the SVR algorithm to learn the relationship between seismic attributes and sand thickness. Fourth, we use the SVR model to predict the sand thickness between wells by calculating a fused attribute. The results indicate that the fusion attribute with SVR has a higher correlation coefficient with sand thickness than the original attributes by statistical method. The approach can be widely used for improving the seismic interpretation quality of the research area with few wells.

Advanced Imaging Techniques Provide New Near Surface Details in 3D Seismic Exploration Data and an Application to New Energy-Related Offshore Shallow Subsurface Investigations

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Seismic Reflection Profiles Reveal the Ore-Controlling Structures of Carlin-Style Gold Deposits in Lannigou Gold Fields, Southwestern Guizhou, China

AbstractThe Carlin-style Lannigou gold deposit is hosted in Triassic flysch in the Nanpanjiang basin in southwestern Guizhou Province, southern China. This study is the first to use seismic exploration data to elucidate the structural architecture of the Lannigou Carlin-style gold deposit. We use regional seismic reflection data to show that gold orebodies are controlled by faults that link with the regional Lannigou-Weihuai fault, and we use this new insight to determine the relationship between mineralization and inverted basin faults to constrain the structural controls of this gold system.A grid of seven seismic sections covering the Lannigou gold fields is combined with field geologic data to reveal two distinct structural patterns. Seismic data reveals that the inverted, E-dipping Lannigou-Weihuai fault is shallowly penetrating (less than 2 km) in the north and increasingly penetrates the pre-Devonian basement rocks to the south, where the fault can be imaged to depths greater than 5 to 7 km. Basement-penetrating faults link the metamorphic basement and overlying basin successions that include potential source rocks for oil generation, interpreted gas reservoirs, and gold deposits. Therefore, these deep-seated faults are important channels for the migration of ore fluids, especially for the transport of hydrocarbon gas, which may have served as an important chemical trap for gold mineralization.Seismic data also reveals the W-dipping Qiaoluo fault that bounds the inverted Qiaoluo half-graben. Fault crosscutting relationships reveal an extensional episode from the Late Paleozoic to the early Anisian age of the Middle Triassic epoch (i.e., the depositional age of the second member of the Middle Triassic Xuman Formation), which is overprinted by an episode of middle Anisian basin inversion during deposition of the third member of the Xuman Formation. This inversion occurred at ca. 248 to 246 Ma. Based on the ore-controlling constraints and previous geochronological data, we consider that the Lannigou gold deposit was formed in the Late Triassic to Early Jurassic during syndepositional inversion of existing basement-penetrating basinal faults in a foreland basin. The first-order faults in the above structural belts, such as the Lannigou-Weihuai fault and the blind Qiaoluo fault, are most favorable targets for further exploration of undiscovered gold orebodies. This study demonstrates that seismic reflection is a powerful tool to reveal deep structures at varying scales from mineral deposits to sedimentary basins.

Convolution neural network application for first‐break picking for land seismic data

ABSTRACTAn automatic and robust algorithm for the first‐break picking is necessary to build the near‐surface velocity model. We propose the algorithm based on a convolution neural network. The introduced first‐break picking strategy and neural network architecture are suited for processing large volumes of seismic exploration data with reasonable computational resources. To develop an optimal neural network topology and architecture, extensive testing was performed. We compared several architectures of neural networks, including one‐ and two‐dimensional approaches. Our tests justify that the one‐dimensional approach (trace‐by‐trace processing) provides the most reliable results in the case of first‐break travel‐time variations typical of complicated near‐surface structures. This study demonstrates that the four‐layered neural network trained on 5,000 traces is enough for robust first‐break picking. The algorithm is evaluated on two land‐acquisition field datasets from West Siberia with a total used size of about 7 million traces. The first dataset is used for training, and the second one is used only for testing. For both datasets, the error between the original and the predicted first breaks is not more than three samples for 95% of traces. The final evaluation is done by a comparison of seismic stacks to prove the benefits of the approach and its robustness for offsets over 600 m. Finally, the influence of choosing the locations for the training dataset is discussed, and a strategy for using the proposed approach in production work is introduced.