Seismic Imaging Research Articles

Application of natural language modeling techniques in natural gas segmentation in seismic reflection images

Application of natural language modeling techniques in natural gas segmentation in seismic reflection images

Reverse-time migration of mobile marine vibrator data

Marine vibrators are viable alternatives to seismic air guns in ocean-bottom acquisition due to their less adverse impact on the environment, their ability to generate lower frequency content, and their suitability for simultaneous acquisition. However, mobile marine vibrators introduce a unique set of processing and imaging challenges for ocean-bottom acquisition, the main examples of which are the Doppler effect and the time-dependent source-receiver offsets, which are not features of conventional marine acquisitions. Standard seismic data processing and imaging techniques assume stationary sources and are not fully suitable for mobile marine vibrator data without modifications. Not accounting for source motion in seismic imaging introduces phase change and mispositioning of imaged structures. Further, ignoring source-motion effects in imaging workflow may lead to the estimation of inaccurate migration velocity models or imply the use of incorrect migration velocity, e.g., in seismic-to-well ties. We develop a reverse-time migration (RTM) approach that accounts for the source-motion effects and is capable of producing accurate subsurface images, closely matching stationary acquisition and imaging results. Synthetic examples illustrate the ability of the proposed method to construct accurate subsurface RTM images even for source velocities higher than those commonly used in typical marine acquisitions. Our approach is not limited to imaging mobile marine vibrator data in ocean-bottom acquisition, but is also applicable to mobile streamer data using marine vibrators or seismic air-gun sources.

Ambient seismic noise imaging of a tailings dam internal structure

Several tailings dam failures have been reported over the past few decades, raising questions about the stability of these structures. Over the past 30 years, there have been more severe failures of tailings dams, which have resulted in damage to the environment, fatalities and severe socio-economic issues. It is often impossible to predict when tailings dams could fail, so there is an urgent need to develop cost-effective methods for monitoring their stability and preventing such catastrophic events. Recent advancements in techniques related to ambient seismic noise have the potential to introduce new approaches to subsurface imaging and monitoring. In this study, we investigated the potential of using ambient seismic noise to monitor the interior wall of a tailings dam. We used 20 3C short-period geophones to record ambient noise over 3 days continuously. We set up the geophones at the Harmony Gold Mine tailings dam in Welkom, South Africa, along a survey profile approximately 100 m long. Seismic interferometry was used from the recorded data to retrieve empirical Green’s functions. We computed the dispersion curves’ inversion to determine the dam wall’s shear wave velocity at different depths. The calculated shear wave velocity cross-sections revealed a region of reduced velocity within the dam wall, situated between 2 m and 10 m beneath the surface. This zone of low velocity can be a sign of water-saturated material or other subsurface anomalies, which might jeopardise the dam’s structure.

Foreword to New developments in passive seismic imaging and monitoring

Foreword to New developments in passive seismic imaging and monitoring

Impact of natural fractures on hydraulic fracture propagation in Denver-Julesburg Basin: Insights from a decade of research

The Denver-Julesburg (DJ) Basin is a key region for unconventional oil and gas extraction, particularly from the Niobrara and Codell formations. This study synthesizes findings from a decade of research conducted in three different geographic areas of the basin as part of the industry consortium Reservoir Characterization Project at Colorado School of Mines. The research explores how natural fractures affect hydraulic fracture propagation, leveraging geophysical data sets including 3D seismic imaging, image logs, microseismic monitoring, and crosswell distributed strain sensing. Key observations reveal significant variations in hydraulic fracture orientations and propagation speeds between the Niobrara and Codell formations, which are the main targets for horizontal drilling and stimulation. High natural fracture densities and orientations notably influence the propagation of hydraulic fractures in the Niobrara Formation, deviating them from the regional maximum horizontal stress (SHmax) directions, while hydraulic fractures in Codell consistently align with SHmax where lower natural fracture densities are observed. The study also highlights the critical role of stress anisotropy and zipper fracturing sequences in dictating hydraulic fracture behavior. Additionally, a marked negative correlation between natural fracture density and hydraulic fracture propagation speed underlines the fracture network complexity added by natural fractures. These findings advocate for a comprehensive natural fracture characterization and tailored fracturing strategies to optimize well placement and completions. The implications for DJ Basin development from this study are profound, suggesting that understanding local geomechanics and fracture networks can significantly enhance hydrocarbon recovery. Future work should focus on more precise assessments of hydraulic fracture interactions with natural fracture systems to refine operational strategies further.

Improving subsurface structural interpretation in complex geological settings through geophysical imaging and machine learning

This study employs seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) to assess subsurface geological conditions along the proposed Porsgrunn Highway in Norway. The primary objective is to analyze SRT and ERT tomograms to identify subsurface geological structures. However, interpreting tomograms is often limited by smoothed boundaries and reduced resolution. To address these challenges, we apply k-means clustering, a machine learning technique that groups data based on similarities in physical properties, to post-process the geophysical tomograms. This study pioneers the use of k-means clustering to interpret tomograms from SRT and ERT data in complex geological settings. We first evaluate the effectiveness of clustering techniques using numerical modeling for two geological scenarios: a horizontally layered case and a layered case with undulation and a fault structure. Utilizing automated methods (Elbow and Silhouette), we objectively determine the optimal number of clusters for each geophysical tomogram. Subsequently, we compare the performance of the k-means clustering algorithm with subjective expert interpretations and the Laplacian edge detection method. Borehole data validate the clustering results and confirm the effectiveness of optimal cluster selection techniques. The findings of this study demonstrate that k-means clustering significantly enhances the detection of geological structures by establishing clearer boundaries and minimizing noise interference, enabling more accurate fault zone delineation. Compared to traditional edge detection and subjective interpretation methods, k-means clustering offers a systematic and objective approach that improves consistency and reliability across diverse geological settings. Moreover, its automated classification of geophysical data into meaningful clusters enables efficient analysis of large datasets. This study underscores the value of integrating machine learning techniques with geophysical methods such as SRT and ERT to improve interpretability and accurately identify subsurface geological structures, particularly in fault zone identification.

Prospection of faults on the Southern Erftscholle (Germany) with individually and jointly inverted refraction seismics and electrical resistivity tomography

As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that influences hydrological and geological conditions on a local as well as regional level. The study area is located in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Since the tectonic structures were located by past studies based on a sparse foundation of geological data, the positions include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements. Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. In addition to compiling individual resistivity and velocity models for all deduced measurements, ERT and SRT datasets were cooperatively inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The cooperative inversions do not improve the geophysical models qualitatively, since the individually inverted datasets already provide results of good quality and resolution.

Geoscientist-driven machine learning-assisted fault interpretation: A case study from Atlanta Field to demonstrate the impact of fault labeling on the fault prediction cube

The energy industry is undergoing a digital transformation, leveraging cloud capabilities and artificial intelligence technology to overcome the challenges of conventional geoscience workflows. Machine learning (ML) methodologies have been developed and applied to various geophysical and geologic workflows, such as seismic processing, imaging, seismic interpretation, and petrophysical analysis. The traditional seismic interpretation approach requires manual interpretation of faults line by line at a fixed increment. Consequently, depending on the geologic complexity and size of the area of interest, the traditional approach can be a time-consuming and repetitive task. We focus on applying ML to seismic data for fault interpretation using a 2D convolutional neural network methodology to accelerate the fault interpretation process. This approach has the potential to significantly reduce the time and effort required to interpret faults, depending on the seismic data quality and geologic complexity, which is demonstrated by our study in the Atlanta Field, a postsalt asset in Brazil's northern Santos Basin. The basin has high structural complexity due to the salt tectonics, which required additional labeling interpretation to capture different fault patterns and train the algorithm to predict faults within the seismic volume. Additionally, the study provides the impact of the initial fault interpretation label on the ML result, such as in the fault extension and fault dip. Overall, the ML approach reduced the total effort from two weeks to four days, representing a time improvement of 60% from interpretation to structural framework workflow.

Reconstructing seismic data using the accelerated linearized Bregman method and iterative soft thresholding algorithm

Seismic data reconstruction plays a crucial role in seismic exploration and imaging, facilitating accurate subsurface characterization and geological interpretation. In this study, we propose a novel approach for reconstructing seismic data using the accelerated linearized Bregman method (ALBM) in conjunction with the iterative soft thresholding algorithm (ISTA). This combined method, referred to as ALBM+ISTA, aims to exploit the complementary strengths of ALBM’s accelerated convergence properties and ISTA’s sparsity-promoting capabilities. The proposed approach is evaluated through theoretical simulations and case studies, wherein specific quantitative metrics such as signal-to-noise ratio (SNR), structural similarity index, root mean square error, peak signal-to-noise ratio, and edge preservation index are employed to assess reconstruction quality and accuracy. Results demonstrate that ALBM+ISTA offers improved performance in terms of noise suppression, structural fidelity, and edge preservation compared to existing reconstruction techniques. Furthermore, the method exhibits adaptability to diverse seismic acquisition geometries and noise levels, making it a promising tool for enhancing seismic data processing in various geological science applications.

The Inherited Crustal Structure and Lithospheric Thermal Field Beneath the Sea of Marmara (NW Türkiye): Observations From 3D Gravity Modeling and Seismic Tomography Analysis

AbstractThe North Anatolian Fault (NAF) extends for over 1,000 km across Türkiye and poses significant seismic hazard in the region. The Main Marmara Fault (MMF) segment of the NAF in the Sea of Marmara (NW Türkiye), exhibits along‐strike segmentation in its interseismic strain accumulation. Constraining the lithospheric configuration below the MMF is critical to understand its segmentation and assessing seismic hazard in the area. We present a new 3D lithospheric‐scale density of the Sea of Marmara, that combines gravity modeling and seismic tomography analysis. Using forward and inverse gravity modeling with free‐air gravity data and available constraints of geological units we derived the intra‐crustal density structure. Shear‐wave velocity tomography models provided insights into the temperature and density configuration of the uppermost mantle, and the geometry of the 1330°C isotherm. Our results highlight significant crustal density variations: lower‐density crust in the Sakarya Zone and Strandja Massif, and denser crust below the Istanbul Zone, which overlies a relatively hotter lithospheric mantle. This lithospheric configuration reflects both ongoing tectonic processes and inheritance from past geological events, including the drifting of the Istanbul Zone crustal block and the signature of past subduction events. The extent of the Istanbul Zone denser crust spatially correlates with the locked segment of the MMF. The bimaterial nature of the fault segment likely influences its interseismic and coseismic behavior. The denser, stiffer Istanbul Zone crust would promote interseismically locked conditions in contrast to the adjacent, more compliant crustal block and could result in asymmetric rupture with a preferred directivity.

Feeding system beneath active volcanoes in central part of Iturup Island (Kuril Arc) inferred from local earthquake tomography

Iturup is the largest island of the Kuril Arc with more than 20 Holocene volcanoes of which 9 considered active. Here we investigate the central part of the island where we deployed in 2022–2023 a portable network of 12 seismic stations. The data of this network together with several permanent stations in surrounding islands were used to identify almost 300 events and to perform seismic tomography based on the picked arrival times of the P and S seismic waves. A challenging problem was that most of the events were located outside the network, and we performed careful analysis to examine the actual capacity of inversion with such data to recover seismic velocity structures below the network. In the resulting model, we found a dominating high-velocity anomaly below the central part of the study area, which is bounded by zones of low velocities and high Vp/Vs ratio collocated with two active volcano complexes (Chirip to the north and Ivan Grozny to the south). Below the third volcano, Baransky, we observe a change of the Vp/Vs ratio from high at large depths to low at shallow depth, indicating the process of degassing, which is supported by strong fumarolic activity and hydrothermal manifestations around this volcano. At depths of more than 20 km, the feeding paths from Baransky and Ivan Grozny volcanoes seem to be connected in one anomaly representing a common magma source below the center of the island. This seems to be a common feature observed below several volcanic islands, such as Tenerife and El Hierro, where the high-velocity rigid core in a central part is surrounded by low-velocity flows associated with recent volcanic manifestations.

Enhanced subsurface edge detection using a 3D integration operator

Orientation analysis is widely employed in edge and line detection and is an important step in object detection and illumination. The direct application of a 3D integration orientation operator to a seismic image is proposed to enhance efficiency and improve delineation of subsurface edges. The primary objective in doing so is to overcome the weaknesses associated with gradient operator-based edge detections, namely, noise sensitivity due to using a fixed smoothing window and orientation bias of the gradient operators. Our study shows that the application of a 3D integration operator to a seismic data volume results in more coherent edges than the gradient-based Sobel operator. In addition, the computation efficiency improves significantly in comparison to a direct 2D integration operator.

Integration of FWI-derived reflectivity in seismic interpretation

Full-waveform inversion (FWI) processing provides an improved and higher-resolution velocity model. This study focuses on how to use FWI products in seismic interpretation. One such product is FWI-derived reflectivity (FDR), which often has better illumination than migrated seismic images. We want to go beyond structural interpretation and utilize FDR data in reservoir characterization (e.g., fault imaging, resolution, and amplitude fidelity). FWI can be performed up to the maximum frequency available in the input seismic data. However, in the case of our study area in offshore Trinidad, the FDR data set is based on acoustic FWI with frequency only up to 10 Hz. While comparing amplitude extractions from full-stack and FDR data, we observe complementary amplitude distribution. Similar complementary information is found when we decompose the data in frequency bands (higher-frequency migrated seismic data and lower-frequency FDR data). We discuss the integration of the FDR volume in seismic interpretation with data examples. We combine FDR data with full-stack seismic data in two ways to generate new attributes for reservoir mapping and to reduce vertical and lateral uncertainty.

Potential fluid migration process inferred from integrated active-source seismic imaging in the Nankai Trough subduction zone off Cape Muroto, Japan

Potential fluid migration process inferred from integrated active-source seismic imaging in the Nankai Trough subduction zone off Cape Muroto, Japan

Amplitude versus offset attribute inversion method for characterizing gas hydrate: Insights from high resolution seismic imaging and drilling results in the Shenhu area, South China Sea

AbstractAmplitude versus offset attribute inversion primarily utilizes the change in amplitude with offset to extract lithologic information of the reservoir. We performed high resolution imaging using three‐dimensional seismic data and simulated four different models of gas hydrate and free gas to optimize the selection of sensitive attributes for gas hydrate characterization. We optimized the selection of sensitive attributes for gas hydrate characterization. Our research identified the gradient parameter G as a highly sensitive attribute for characterizing gas hydrate reservoirs. By comparing theoretical models with drilling site data, we predicted the saturation variations of gas hydrate based on the amplitude of G and summarized the amplitude versus offset characteristics and sensitive attributes of gas hydrate at different saturation levels. The results offer valuable insights for identification of gas hydrate in seismic data and provide a reference for their characterization.

On the adjoint state method for the gradient computation in full waveform inversion: a complete mathematical derivation for the (visco-)elastodynamics approximation

Summary High resolution seismic imaging at all scales using full waveform inversion is now routinely used in the industry and in the academy. One key element for the success of this approach is a numerical method, named adjoint state method, originally designed for optimization problems constrained by partial differential equations, a category to which full waveform inversion belongs. This method provides an efficient way to compute the gradient of the full waveform inversion misfit function, which is the most computationally demanding task in the implementation of full waveform inversion. While well known, the complete and rigorous mathematical derivation of the adjoint state method for full waveform inversion remains missing in the scientific bibliography. The aim of this study is to remedy this lack. The derivation is performed in general settings, that is in the elastodynamics approximation, with and without considering viscosity. Through the calculus, the mechanism of the adjoint state strategy makes clear the connection between the incident and adjoint fields, especially regarding their initial and boundary conditions. The impact of introducing the viscosity is carefully analyzed. The resulting gradient formulas are analyzed and shown to be consistent with already published ones. The generic approach which is adopted also makes it possible to derive misfit function gradients with respect to other quantities than the subsurface mechanical parameters, for instance with respect to the initial or the boundary conditions, which could be of interest for specific applications where the reconstructed parameters are not only volumetric mechanical parameters but boundary parameters or initial field values.

Methane sealed due to the formation of gas hydrate system in the South China Sea

Although the release of methane into oceans and potentially the atmosphere could accelerate climate change, detailed investigations on the gas source from deep-buried strata and its migration through the gas hydrate stability zone (GHSZ) to the seafloor are limited. These studies are often hindered by the presence of diffracted waves and inaccuracies in seismic velocity models, leading to poor seismic imaging that hampers the understanding of gas sources, migration pathways, and gas hydrate accumulation. In the study, we utilize the technique of common scatter point (CSP) gathers to build an accurate velocity model and obtain high-quality images for a complex gas-hydrate and natural-gas petroleum system. The CSP processing enables the accurate migration of reflected and diffracted waves, resulting in improvements in signal-to-noise ratio and lateral resolution. The improved seismic images offer clearer visualization of various petroleum elements. Specifically, we can identify the top of the hydrate zone and base of the free gas zone within a shallow-buried hydrate system, fault geometries within the free gas zone, a middle-buried natural gas reservoir, gas chimneys as migration pathways, and the deep-buried source rock strata beneath the intrusive volcanic rocks. As a result, we reveal a joint prospect of natural-gas reservoir and gas-hydrate system in the deep-water region of the South China Sea. Our results suggest that methane in the natural gas reservoir has migrated upwardly into the hydrate system, and it is unlikely to leak into the water column.

On one minimal hyperbolic Gellerstedt operator with internal degeneracy

On one minimal hyperbolic Gellerstedt operator with internal degeneracy

Does optimality partitioning theory fail for belowground traits? Insights from geophysical imaging of a drought-release experiment in a Scots Pine forest.

We investigate the impact of a 20-yr irrigation on root water uptake (RWU) and drought stress release in a naturally dry Scots pine forest. We use a combination of electrical resistivity tomography to image RWU, drone flights to image the crown stress and sensors to monitor soil water content. Our findings suggest that increased water availability enhances root growth and resource use efficiency, potentially increasing trees' resistance to future drought conditions by enabling water uptake from deeper soil layers. This research highlights the significant role of ecological memory and legacy effects in determining tree responses to environmental changes.

A CNN-accelerated workflow for stochastic seismic property estimation

As one of the major tools in resolving the non-uniqueness challenge in subsurface interpretation and reservoir characterization, stochastic inversion from post- and pre-stack seismic data remains challenging, which not only requires heavy computational resources but also relies on intensive manual supervision. Inspired by the recent advances in deep learning particularly convolutional neural networks (CNNs) for interdisciplinary data integration, this study proposes a deep learning workflow that enables stochastic property estimation by efficiently integrating seismic images with sparse wells. It starts with sampling a set of property prior models (PPMs) from densely-measured properties at well locations and corrupting local seismic patterns with Gaussian noise. The core idea is to train a structure-guided CNN by mapping the contaminated seismic with the sampled PPMs while enforcing structural consistency to avoid overfitting in the presence of sparse wells. Finally, the baseline and uncertainty of target properties are estimated by running multiple realizations of the trained CNN. As demonstrated by three examples, with minimum efforts of CNN architecture customization according to data availability, the proposed workflow can accommodate various use cases, including rock acoustic/elastic property estimation from 3D post-/angle-stack seismic and soil geotechnical properties from 2D ultra-high-resolution seismic. In all examples, the machine predictions match seismic patterns well and are of high lateral consistency.