Features In Seismic Data Research Articles

Intelligent fault prediction with wavelet-SVM fusion in coal mine

Fault prediction in coal mining is crucial for safety, and recent technological advancements are steering this field towards supervised intelligent interpretation, moving beyond traditional human-machine interaction. Currently, support vector machine (SVM) predictions often rely on seismic attribute data; however, the poor quality of some fault data characteristics hampers their predictive capability. To localize the fault based on original seismic data and improve SVM prediction we propose the W-SVM algorithm, which integrates wavelet transform and SVM. Through wavelet transform, we localize fault features in seismic data, which are then used for SVM prediction. Validation using real data confirms the feasibility of the W-SVM approach. The W-SVM model successfully identifies 34 known faults. Beyond achieving high prediction accuracy, the model exhibits improved stability and generalization. The difference among the evaluation metrics for training, validation, and testing is within 5%. Moreover, this study localizes the response of faults through wavelet transform, simplifies the dataset preparation process, improves computational efficiency, and increases overall applicability. This advancement further promotes the development of intelligent identification of faults in coal mines.

Deep carbonate gas reservoir sweet spot identification with seismic data based on dual-factor control of sedimentary facies and fault system

Deep carbonate reservoirs are attractive targets for gas development. These reservoirs are deeply buried, and commonly possess strong heterogeneity and poor seismic data quality, making the identification of favorable production areas (“sweet spots”) challenging. Furthermore, sedimentary facies and fault systems markedly impact reservoir quality, and identifying these features in seismic data is also crucial for sweet spot identification. To solve these problems, we propose a dual-factor-controlled sweet spot identification method with two steps. First, sedimentary facies and faults are identified separately at different seismic scales using different attributes by the steerable pyramid (SP) method. The SP method decomposes the original seismic data into high-frequency and low-frequency data. The amplitude attributes from high-frequency data are used to identify sedimentary facies, and coherence attributes based on low-frequency data are used to characterize the fault systems. Second, after separately identifying the sedimentary facies and faults, the two attribute volumes are merged together to identify reservoir sweet spots. The results are verified by using well production data. The results of a field study in the Dengying Formation deep carbonate reservoir in the central Sichuan Basin, China, indicate that reservoir sweet spots are primarily developed in ideal sedimentary facies along strike-slip fault systems. Sedimentary facies generally control the type and distribution of reservoirs, whereas strike-slip fault systems control the migration and accumulation of gas. In addition, the fault systems serve as karst channels that further improve the reservoir properties. The proposed dual-factor method might help to maximize exploration potential in deep carbonate reservoirs with similar settings.

Exploring the formation of Tangará sill: A single-pulse intrusion feeding CAMP lava flows in Parecis Basin, Brazil

The Central Atlantic Magmatic Province (CAMP) stands out as a distinctive large igneous province on the Earth. Due to the extensive outcrop area of CAMP and its complex geological setting, the links between the extrusive and intrusive units are not fully understood. In the intracratonic Parecis Basin located in Central Brazil, the CAMP rocks are represented by mafic lava flows at its south (Tapirapuã Formation) and western edges (Anari Formation). Despite the availability of substantial geophysical, petrological and geochronological data, the understanding of CAMP feeder system in this region remains unclear. In this study, our focus is on investigating the Tangará sill, a dolerite intruded into Triassic sedimentary rocks in the South region of the Parecis Basin. Our aim is to contribute to a better understanding of the plumbing system in this area by investigating the Tangará sill and its potential links to the lava flows at the surface. The sill is identified as a tabular feature in seismic data, with a discernible base and top that extend for about 100 km from northeast to southwest. The Tangará sill was drilled by the 6-ANP-MT-02 stratigraphic well, revealing a continuous igneous intrusion with a thickness of 180 m (at depths ranging from 470 to 650 m). The dolerite intrusion is composed by plagioclase, clinopyroxene, opaque minerals and mesostasis (quartz + feldspar intergrowth and apatite). It can be divided into five segments: the upper and basal margins consist of fine-grained dolerite, the central portion comprises coarse-grained gabbro, which includes pegmatoid zones. The central portion is separated from the margins by two sections of medium-grained dolerite. The dolerite is tholeiitic, with low TiO2 content (<2 wt% TiO2) and low La/Yb ratio (average of 4.4). Geochemical and petrographic data indicate that the intrusion was formed by a single magma pulse and underwent differentiation through plagioclase and pyroxene dominated crystal fractionation. Two samples dated by 40Ar/39Ar geochronology using laser step-heating technique, yield plateau ages ranging from 204.0 ± 0.4 Ma to 202.0 ± 0.5 Ma. Sr-Nd-Pb isotopic data suggest a lithospheric mantle source with possible crustal contamination. Our findings suggest an emplacement model in which a thick single-pulse magma intruded the Triassic red beds. Along a NE-SW section, the sill is truncated by faults along the southern border of the basin, near the mapped lava flows of the Tapirapuã Formation. This suggests that the magma propagated from the northeast to the southwest along the sedimentary strata and fed the Tapirapuã volcanism. This example highlights a single-pulse sill feeding lava flows of the Central Atlantic Magmatic Province (CAMP).

Fault Detection via 2.5D Transformer U-Net with Seismic Data Pre-Processing

Seismic fault structures are important for the detection and exploitation of hydrocarbon resources. Due to their development and popularity in the geophysical community, deep-learning-based fault detection methods have been proposed and achieved SOTA results. Due to the efficiency and benefits of full spatial information extraction, 3D convolutional neural networks (CNNs) are used widely to directly detect faults on seismic data volumes. However, using 3D data for training requires expensive computational resources and can be limited by hardware facilities. Although 2D CNN methods are less computationally intensive, they lead to the loss of correlation between seismic slices. To mitigate the aforementioned problems, we propose to predict a 2D fault section using multiple neighboring seismic profiles, that is, 2.5D fault detection. In CNNs, convolution layers mainly extract local information and pooling layers may disrupt the edge features in seismic data, which tend to cause fault discontinuities. To this end, we incorporate the Transformer module in U-net for feature extraction to enhance prediction continuity. To reduce the data discrepancies between synthetic and different real seismic datasets, we apply a seismic data standardization workflow to improve the prediction stability on real datasets. Netherlands F3 real data tests show that, when training on synthetic data labels, the proposed 2.5D Transformer U-net-based method predicts more subtle faults and faults with higher spatial continuity than the baseline full 3D U-net model.

Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data

The resolution of seismic data determines the ability to characterize individual geological structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for improving the resolution of seismic data. However, the basic assumption of SSI is that the strong reflectivity of the formation is sparse, which may not be a reasonable fit for weak thin-layer reflections. In this study, we propose a deep learning-based method to reconstruct high-resolution seismic data by combining information from the longitudinal reflectivity distribution and lateral geological structure features in the field data. A U-shaped network that fuses residual block and attention mechanism is used to learn the relationship between low resolution and high resolution. In addition, we use a hybrid loss function that combines <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\ell _{1}}$ </tex-math></inline-formula> loss and structural similarity (SSIM) loss to optimize the network parameters for better distinguishing the geometrical features characterized by structural amplitude changes. To train the network, we adopt a workflow to automatically generate numerous 2-D low-resolution data and their corresponding high-resolution data. In this workflow, the prior information, such as statistical reflectivity distribution of well logs and structural features of the data are considered. The synthetic data and field data tests show that our method can work well compared to the traditional method even though only a few well logs are available. Especially in the field data example, our method recovers thin layers better and yields laterally more consistent high-resolution results.

A computational approach for 3D modeling and integration of heterogeneous geo-data

This paper tackles the volumetric representation of geophysical and geotechnical data, gathered during exploration surveys of the subsoil; in particular, we focus on the modeling and analysis of underwater deposits. The creation of a 3D model as support to geological interpretation has to take into account the heterogeneity of the input data, coming from offshore acquisition campaigns. Some data are massive, but cover the domain unevenly, e.g., along dense differently spaced lines, while others are very sparse, e.g., borehole locations with soil sampling and CPTU (Piezocone Penetration Test) locations.A automatic process is presented to generate the subsurfaces and volume defining a sub-seabed deposit, starting from the identification of relevant morphological features in seismic data. In particular, simplification and refinement based on geostatistics have been applied to generate regular 2D meshes from strongly anisotropic data, in order to improve the quality of the final 3D tetrahedral mesh. Furthermore, we also use geostatistics to predict geotechnical parameters from local surveys and estimate their distribution on the whole domain: in this way the 3D model will include relevant geological features of the deposit and allow extrapolating different geotechnical information with associated uncertainty. The volume characterization and its 3D inspection will support geological analysis and planning of future engineering activities. The developed methodology has been tested on two real case studies.

Unsupervised 3-D Random Noise Attenuation Using Deep Skip Autoencoder

Effective random noise attenuation is critical for subsequent processing of seismic data, such as velocity analysis, migration, and inversion. Thus, the removal of seismic random noise with an uncertainty level is meaningful. Attenuating 3-D random noise in a supervised way based on deep learning (DL) is challenging because clean labels are difficult to obtain. Therefore, it is necessary to develop an adaptive unsupervised-based method for random noise attenuation. In this article, we propose a deep-denoising unsupervised learning (DDUL) network to attenuate random noise in 2-D/3-D seismic data. A patching technique is used to split 2-D/3-D seismic data into several patches to be fed into the network, which helps to expand the number of samples for training. We use the fully symmetrical structure of the autoencoder to construct the network. In each corresponding encoder and decoder layer, skip connections are added to enhance the learning of seismic data features. We construct three blocks to extract waveform features in seismic data, i.e., encoder, decoder, and skip blocks. Among them, the skip is connected between the encoder and decoder blocks of each hidden layer. The use of multiple blocks not only improves the network&#x2019;s ability to extract seismic data features but also solves the problem of excessive training parameters caused by hidden layer stacking. Five 2-D/3-D synthetic and field seismic datasets are used to test the denoising performance of our proposed method. The denoising results demonstrate that our proposed method has good signal-preserving and noise attenuation capabilities in real-world applications.

Seismic Sparse Time-Frequency Network With Transfer Learning

Time-frequency analysis (TFA) is a powerful tool for describing time-frequency (TF) features of seismic data, such as short-time Fourier transform and S-transform. Recently, sparse time-frequency analysis (STFA) is proposed for enhancing TF readability of commonly used TFA tools. However, STFA is often solved via an optimal inverse problem with a prior regularization term, which is difficult to set in practice, where the key regularization parameters are sensitive to noise. Moreover, it often takes expensive calculation time, especially for 3D field data application. We build a deep learning based workflow for implementing STFA to obtain sparse time-frequency (STF) spectra, termed the sparse time-frequency network with transfer learning (STFNTL). We first adopt a Marmousi II reflectivity model and Ricker wavelets with different dominant frequencies to generate synthetic training data set. Then, we adopt a simplified STFA method with optimized parameters to generate synthetic training labels, i.e., sparse TF spectra. Afterward, we propose the sparse time-frequency network (STFN) based on a simplified Unet model, which is trained using synthetic training data and corresponding STF labels. Moreover, to enhance the generalization of STFN, we introduce an adaptive transfer learning strategy based on small samples of field data and their corresponding STF labels. Finally, synthetic and field data are utilized to illustrate the effectiveness and generalization ability of our proposed model.

A deep-learning method for latent space analysis of multiple seismic attributes

Seismic attributes are a well-established method for highlighting subtle features in seismic data to improve interpretability and suitability for quantitative analysis. Seismic attributes are an enabling technology in such areas as thin-bed analysis, geobody extraction, and seismic geomorphology. Seismic attributes are mathematical functions of the data that are designed to exploit geologic and/or geophysical principles to provide meaningful information about underlying processes. Seismic attributes often suffer from an “abundance of riches” because the high dimensionality of seismic attributes may cause great difficulty in accomplishing even simple tasks. Spectral decomposition, for instance, typically produces tens and sometimes hundreds of attributes. However, when it comes to visualization, for instance, we are limited to visualizing three or at most four attributes simultaneously. We have developed a deep-learning-based approach to latent space analysis. This method is superior to other methods in that it focuses upon capturing essential information rather than just focusing upon probability density functions or clusters. Our method provides a quantitative way to assess the fit of the latent space to the original data. We apply our method to a data set from Canterbury Basin, New Zealand. This data set contains a turbidite system, and it has been the subject of several other papers. We examine the goodness of fit of our model by comparing the input data to what can be reproduced, and we provide an interpretation based upon our method.

Edge detection and location of seismic image based on PCNN

The discontinuous features in seismic data usually correspond to different geological edge information. Effective processing of these seismic data can help us make correct geological interpretations. In this paper, we apply coherent slices to detect and locate the boundary of fault polygons and propose a method for boundary detection and localization of seismic images based on PCNN. In image edge detection, the basic PCNN needs to adjust many parameters. This paper aims at this problem to improve the basic PCNN model, we simplify the feedback input and pulse input, only retain the external input stimulation and the connection domain external neuron stimulation, reduce the parameters, and simplify the calculation. Optimize parameters such as internal activity link coefficients, dynamic thresholds, and cycle times to improve interpretation efficiency and accuracy. Experiments show that the method used in this paper has good practicability and can effectively realize the fault polygons detection and positioning of coherent slices.

3D seismic fault detection using the Gaussian process regression, a study on synthetic and real 3D seismic data

Faults are natural tectonic events as planar features along which rock units are moved. The patterns of these planes on seismic sections include linear or curvilinear features that are called edge in image processing, where amplitudes sharply change. In seismic data, these edge features made by fault traces are usually detected by seismic attributes, which need complex mathematical calculation such as a dip-steered cube. In this study, we introduce faults as global anomalies which disturb the normal interaction of seismic reflectors. Here, the normal interaction means the absence of intensive changes in reflectors trend in seismic sections. Fault detection as a global anomaly is done by the Gaussian process regression model (GPR), which is a nonparametric probabilistic model based on Bayesian statistics supporting noisy (Gaussian noise) and sparse features in data. In data mining, anomaly detection identifies items or events that do not match an expected pattern in a dataset. Global anomalies are sparse and affect a wide range of normal trends of data which are also held by fault features in seismic data. In this study, the GPR-based anomaly detection algorithm was implemented on the 3D seismic data of the Gulf of Mexico containing normal growth faults and a salt dome to detect salt boundary and fault traces in seismic data. In this respect, the reflections from rock units and fault features were taken into account as normal interactions and global anomalies, respectively, because faults disrupted the normal trend of reflectors in seismic sections. To detect fault locations, the location of a voxel in voxel grid where it is a part of fault in seismic data, after smoothing the seismic data, a Gaussian process (GP) model was trained on seismic data, attempting to describe the seismic amplitude data as a multivariate Gaussian model. However, GP regression fails to describe the seismic data at fault locations. Thus, the failure of the GP in the regression step was analyzed to separate the probable fault points, highlighted by calculating the variance of the GPR results. Finally, the detected probable fault points were improved and separated from background results by implementing a consistent reconstruction morphological algorithm. The results were validated using a similar structural index method, mean square error, and power signal to noise ratio indices in comparison with interpreted faults, implying the superiority of the proposed method in comparison with seismic attributes. The faults detected by the proposed method have the most structural similarity to faults interpreted. This similarity has improved by 22% compared to used attributes.

Reservoir characterisation method with multi-component seismic data by unsupervised learning and colour feature blending

ABSTRACTMulti-attribute analysis of multi-component seismic data can provide abundant information on seismic hydrocarbon reservoirs. It can exploit the different responses of compressional and shear waves to the reservoir, in conjunction with colour blending and fusion technology applied to seismic images, to enable the human eye to better discriminate features in seismic data, thereby improving the accuracy of reservoir characterisation. The integration of multi-component data and sophisticated visualisation techniques helps to reduce the number of possible models of the reservoir. Thus, we designed a reservoir prediction method based on unsupervised learning and colour feature blending. First, a large number of compressional and shear wave seismic attributes were extracted using cluster analysis to conduct unsupervised learning to optimise the attributes. Then, using the different responses of compressional and shear waves to oil and gas, and an understanding of rock physics, three types of composite attribute were constructed to highlight oil and gas anomalies by multi-component seismic attributes. Finally, the three composite attributes were transformed to the colour space by a first-order linear transformation and RGB colour blending. Applying this scheme to reservoir prediction shows that unsupervised learning and colour blending techniques could help the human eye perceive geological anomalies, highlight common hydrocarbon characteristics, reduce differences and decrease interpretation ambiguity. The prediction results are essentially consistent with actual data and can be used to predict favourable exploration areas.

Understanding basement fracture architecture in Padra Field, South Cambay Basin, India, through full-azimuth imaging

Detection and characterization of fractures in reservoirs is of great importance for maximizing hydrocarbon productivity and recovery efficiency. Fractures play an important role in the producibility of unconventional reservoirs such as basement reservoirs. Basement reservoirs are typically found within metamorphic and igneous rock underlying a sedimentary basin, where faulting and tectonic uplift has led to creation of a fracture network. For fracture characterization, integration of information from seismic and nonseismic data such as cores and/or formation microimaging (FMI) logs is essential. Various seismic attributes such as coherency and curvature that are derived from reflection seismic data have been used for more than a decade to detect faults and fractures. In advanced seismic fracture detection technology, automatic fault extraction (AFE) from diffraction seismic data (discontinuity volume) more effectively detects finer scale features in seismic data. We demonstrate the utility of this methodology with an application to seismic data from the Padra Field, South Cambay Basin, India, where the basaltic Deccan Trap forms the basement, and hydrocarbons are produced from basement fractures. Diffraction imaging was applied during processing of the full-azimuth 3D-3C seismic data that cover this field. Using wavefield decomposition in the subsurface local angle domain, separate reflection (specular) and diffraction data volumes were produced. The high-resolution specular stack data imaged a prominent reflector well below the trap top, which is not visible in conventional seismic reflection data. Diffraction stack data also provided higher resolution fault definition and enhanced imaging of spatially consistent geological discontinuities. Subsequent application of the AFE technique to diffraction-imaged data yielded sharp and crisp definition of faults and fractures. We also performed velocity variation with azimuth analysis of 3D angle-azimuth reflection gathers to generate a fracture orientation map. Both sets of results were validated by fractures detected in FMI logs from wells in the field.

Identification of shallow geohazard channels based on discontinuity seismic attributes in the South Caspian Sea

Identification of geomorphological features in seismic data is a key element of seismic interpretation. Channels in the shallow subsurface are potential geohazards. At deeper levels, they can be the actual targets for (horizontal) drilling. Either way, it is important to optimally delineate these features prior to well location positioning and drilling. We have studied a poststack 3D seismic data from the South Caspian Sea featuring shallow channels that are considered potential geohazards for drilling operations. In the first step, we attenuate the acquisition footprints along the inline direction using a geostatistics approach based on factorial kriging. To better visualize channels in the presence of stratigraphic dips, we create a dense set of horizons using an inversion-based flattening algorithm. In the next step, we compare various discontinuity attributes such as semblance, similarity, curvature, and the relatively new attribute based on the multiscale and multidirectional shearlet transformation to determine which one best images our features of interest. Curvature attributes clearly image channel levies (positive curvature) and channel centers (negative curvature). Lateral changes in the curvature magnitude infer sedimentation from the north. Similarity, semblance, and shearlet transform attributes also successfully delineate channel edges, but these attributes do not contain additional geologic information. In the final step, we qualitatively analyze channel thickness variations by the red-green-blue blending of three spectral components based on short window Fourier transforms.

A compound method for random noise attenuation

ABSTRACTRandom noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio.

Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data

ABSTRACTAutomatic feature detection from seismic data is a demanding task in today's interpretation workstations. Channels are among important stratigraphic features in seismic data both due to their reservoir capability or drilling hazard potential. Shearlet transform as a multi‐scale and multi‐directional transformation is capable of detecting anisotropic singularities in two and higher dimensional data. Channels occur as edges in seismic data, which can be detected based on maximizing the shearlet coefficients through all sub‐volumes at the finest scale of decomposition. The detected edges may require further refinement through the application of a thinning methodology. In this study, a three‐dimensional, pyramid‐adapted, compactly supported shearlet transform was applied to synthetic and real channelised, three‐dimensional post‐stack seismic data in order to decompose the data into different scales and directions for the purpose of channel boundary detection. In order to be able to compare the edge detection results based on three‐dimensional shearlet transform with some famous gradient‐based edge detectors, such as Sobel and Canny, a thresholding scheme is necessary. In both synthetic and real data examples, the three‐dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise.

A fast complex domain-matching pursuit algorithm and its application to deep-water gas reservoir detection

The main emphasis of exploration and development is shifting from simple structural reservoirs to complex reservoirs, which all have the characteristics of complex structure, thin reservoir thickness and large buried depth. Faced with these complex geological features, hydrocarbon detection technology is a direct indication of changes in hydrocarbon reservoirs and a good approach for delimiting the distribution of underground reservoirs. It is common to utilize the time–frequency (TF) features of seismic data in detecting hydrocarbon reservoirs. Therefore, we research the complex domain-matching pursuit (CDMP) method and propose some improvements. First is the introduction of a scale parameter, which corrects the defect that atomic waveforms only change with the frequency parameter. Its introduction not only decomposes seismic signal with high accuracy and high efficiency but also reduces iterations. We also integrate jumping search with ergodic search to improve computational efficiency while maintaining the reasonable accuracy. Then we combine the improved CDMP with the Wigner–Ville distribution to obtain a high-resolution TF spectrum. A one-dimensional modeling experiment has proved the validity of our method. Basing on the low-frequency domain reflection coefficient in fluid-saturated porous media, we finally get an approximation formula for the mobility attributes of reservoir fluid. This approximation formula is used as a hydrocarbon identification factor to predict deep-water gas-bearing sand of the M oil field in the South China Sea. The results are consistent with the actual well test results and our method can help inform the future exploration of deep-water gas reservoirs.

Seismic data filtering using non-local means algorithm based on structure tensor

AbstractNon-Local means algorithm is a new and effective filtering method. It calculates weights of all similar neighborhoods’ center points relative to filtering point within searching range by Gaussian weighted Euclidean distance between neighborhoods, then gets filtering point’s value by weighted average to complete the filtering operation. In this paper, geometric distance of neighborhood’s center point is taken into account in the distance measure calculation, making the non-local means algorithm more reasonable. Furthermore, in order to better protect the geometry structure information of seismic data, we introduce structure tensor that can depict the local geometrical features of seismic data. The coherence measure, which reflects image local contrast, is extracted from the structure tensor, is integrated into the non-local means algorithm to participate in the weight calculation, the control factor of geometry structure similarity is added to form a non-local means filtering algorithm based on structure tensor. The experimental results prove that the algorithm can effectively restrain noise, with strong anti-noise and amplitude preservation effect, improving PSNR and protecting structure information of seismic image. The method has been successfully applied in seismic data processing, indicating that it is a new and effective technique to conduct the structure-preserved filtering of seismic data.

The effect of colour blindness on seismic interpretation

Colour perception is an intrinsic part of visual cognition and affects how we understand the images that we see. Colour is now a fundamental part of how we display seismic reflectivity and attribute data, but not everyone sees colour in the same way. Colour deficiencies (or colour blindness) alters our perception of an image, and this study investigates whether colour deficiency also alters the interpretation of geological features in seismic data.

The facies architecture of submarine basaltic volcanoes and their effects on fluid flow

Volcanic-affected hydrocarbon basins commonly contain a variety of intrusive and extrusive volcanic rocks. All extrusive facies are ultimately sourced from volcanoes. Importantly, volcanoes link the extrusive components to the underlying magmatic plumbing system; features which may act as subsurface conduits and baffles for fluid flow. Volcanoes also provide insights into the timing of both intrusive and extrusive activity, thus helping constrain hydrothermal and contact metamorphic processes associated with magma intrusion. However, in comparison to the intrusive components of volcanic systems, the criteria for recognising these important features in seismic data are less well known. In addition, the facies of which volcanoes are commonly composed are poorly characterised from well and seismic data. In this study we use a combination of 3D seismic data, well data and field analogues to detail the architecture of submarine basaltic volcanoes constructed in the Bass Basin, offshore southern Australia. These volcanoes are Miocene in age and were emplaced in a thermally subsiding rift basin. Our studies indicate that the volcanoes are composed of volcaniclastic rock such as hyaloclastite and pyroclasts, produced during effusive activity and magma-water interaction. These facies present a range of drilling complications, and may act as either seals, reservoirs or migration pathways. After their eruption, the volcanoes were encased in a sequence of claystones, and continued to focus subsurface fluid flow and sediment recycling for 20 Myr after their extinction. We conclude that basaltic volcanoes are important components of volcanic-affected basins. This study can be used to help recognise basaltic volcanoes in other data sets, and provide insights into their impacts on petroleum system.