Active Seismic Data Research Articles

Feasibility of Identifying Shale Sweet Spots by Downhole Microseismic Imaging

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic data. In this study, an alternating method is proposed to determine microseismic event locations, 3D P-wave velocity, and Poisson’s ratio models with data recorded from downhole monitoring arrays. The method combines the improved 3D traveltime tomography, which inverts P and S arrivals for 3D P-wave velocity and Poisson’s ratio structures simultaneously, and a 3D grid search approach for event locations in an iterative fashion. The traveltime tomography directly inverts the Poisson’s ratio structure instead of calculating the Poisson’s ratios from P- and S-wave velocities (i.e., Vp and Vs) that are inverted by conventional traveltime tomography separately. The synthetic results and analysis suggest that the proposed method recovers the true Poisson’s ratio model reasonably. Additionally, we apply the method to a field dataset, which indicates that it may help delineate the reservoir structure and identify potential shale sweet spots.

Active and passive seismic data integration for delineating the near-surface velocity models at Ras Samadai area, Red Sea-Egypt

Active and passive seismic data integration for delineating the near-surface velocity models at Ras Samadai area, Red Sea-Egypt

Near-surface characterization using shear-wave resonances: A case study from offshore Svalbard, Norway

Shear-wave (S-wave) resonances are typically observed when the surficial marine sediments over a rock substrate have relatively low S-wave velocities. We observe these phenomena using ocean-bottom fiber-optic distributed acoustic sensing (DAS) in two subsea fiber-optic telecommunication cables in Svalbard, Norway. Strong seismic energy from sufficiently large earthquakes is required to trigger and enhance the multiple-order modes of S-wave resonances. Here, we use the interpreted S-wave resonance frequencies of the first two modes to determine the thickness and the S-wave velocity of the near-surface low-velocity layer (LVL) beneath the seafloor. In addition, we use existing active P-wave seismic reflection data to determine the LVL thickness and to help build a more accurate S-wave velocity model from the S-wave resonance frequencies. The estimated S-wave velocity varies laterally within the LVL formation. Here, we find that the sediments or deposits with high S-wave velocity presented in the estimated LVL model agree with the distribution of some glacigenic sediments and landforms deposited in the survey area. Therefore, S-wave resonances measured by ocean-bottom DAS can be used to characterize the corresponding near-surface LVLs.

Coastal Groundwater Bodies Modelling Using Geophysical Surveys: The Reconstruction of the Geometry of Alluvial Plains in the North-Eastern Sicily (Italy)

The integration of various geophysical methodologies is considered a fundamental tool for accurately reconstructing the extent and shape of a groundwater body and for estimating the physical parameters that characterize it. This is often essential for the management of water resources in areas affected by geological and environmental hazards. This work aims to reconstruct the pattern and extent of two groundwater bodies, located in the coastal sectors of the North-Eastern Sicily, through the integrated analysis and interpretation of several geoelectrical, seismic and geological data. These are the Sant’Agata-Capo D’Orlando (SCGWB) and the Barcelona-Milazzo (BMGWB) Groundwater Bodies, located at the two ends of the northern sector of the Peloritani geological complex. These two studied coastal plains represent densely populated and industrialized areas, in which the quantity and quality of the groundwater bodies are under constant threat. At first, the resistivity models of the two groundwater bodies were realized through the inversion of a dataset of Vertical Electrical Soundings (VES), constrained by stratigraphic well logs data and other geophysical data. The 3D resistivity models obtained by spatially interpolating 1D inverse VES models have allowed for an initial recognition of the distribution of groundwater, as well as a rough geological framework of the subsoil. Subsequently, these models were implemented by integrating results from active and passive seismic data to determine the seismic P and S wave velocities of the main lithotypes. Simultaneous acquisition and interpretation of seismic and electrical tomographies along identical profiles allowed to determine the specific values of seismic velocity, electrical resistivity and chargeability of the alluvial sediments, and to use these values to constrain the HVSR inversion. All this allowed us to recognize the areal extension and thickness of the various lithotypes in the two investigated areas and, finally, to define the depth and the morphology of the base of the groundwater bodies and the thickness of the filling deposits.

Determination of the VS profile at a “noisy” industrial site via active and passive data: The critical role of Love waves and the opportunities of multicomponent group velocity analysis

To define the procedures necessary to unambiguously define the subsurface model, a comprehensive set of active and passive seismic data was collected in an industrial area characterized by an extremely high level of background microtremors. Passive data are recorded to define three observables: the dispersion curve of the vertical component of Rayleigh waves via miniature array analysis of microtremors, the Love-wave dispersion curve via extended spatial autocorrelation, and the horizontal-to-vertical spectral ratio (HVSR). Active data used for the holistic analysis of surface waves are extracted from data recorded through a hybrid acquisition procedure accomplished with only two 3C geophones used to simultaneously define the HVSR at two points. Defined observables are combined according to three different approaches: the joint analysis of Rayleigh waves and HVSR, the joint analysis of Rayleigh and Love waves together with the HVSR, and the joint analysis of multicomponent group velocities together with the HVSR and Rayleigh-wave particle motion (RPM) curves. In agreement with the theory, data indicate that, in general, surface-wave modeling cannot be performed considering modal dispersion curves: dispersion obtained from passive data needs to be modeled considering the effective curve, whereas group velocity obtained from active data can be analyzed using the full velocity spectrum technique. Results indicate that joint inversion of Rayleigh-wave dispersion and HVSR does not necessarily ensure the correctness of the obtained S-wave velocity ([Formula: see text]) profile and that Love waves represent a key observable to fully constrain an unambiguous inversion procedure. However, the joint analysis of multicomponent group velocity spectra (from active multicomponent single-offset data) together with the HVSR and RPM curves is a further efficient way to obtain robust [Formula: see text] profiles through the active and passive data obtained by a single 3C geophone.

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.

A method for denoising active source seismic data via Fourier transform and spectrum reconstruction

Denoising is a critical step in signal processing. We develop a method for random noise reduction in active source seismic data using spectrum reconstruction. Two methods are developed for modifying the observed data’s amplitude spectrum: one substitutes it with the source wavelet’s amplitude spectrum, whereas the other involves multiplying the source wavelet’s amplitude spectrum with the observed data’s amplitude spectrum. By reconstructing the modified amplitude spectrum while preserving the observed data’s phase spectrum, noise suppression is achieved. Extensive testing with theoretical models, synthetic shot gathers, and field data indicate a notable improvement in the signal-to-noise ratio (S/N) compared with the traditional band-pass filtering method. This method proves particularly effective for enhancing the S/N in the context of active source wide-angle seismic data used in offshore structural studies, eliminating the need for data segmentation based on offset, and thereby improving processing efficiency. Our method relies solely on a single complete cycle of the source wavelet, making it a purely data-driven solution. It has broad applications in processing active source or controlled source data with consistent source wavelets, including but not limited to seismic exploration, acoustic detection, and signal denoising in various ground-penetrating radars used on Mars, the moon, and earth.

Modelling and inferring fracture curvature from borehole GPR data: A case study from the Bedretto Laboratory, Switzerland

AbstractFracture curvature has been observed from the millimetre to the kilometre scales. Nevertheless, characterizing curvature remains challenging due to data sparsity and geometric ambiguities. As a result, most numerical models often assume planar fractures to ease computations. To address this limitation, we present a novel approach for inferring fracture geometry from travel‐time data of electromagnetic or seismic waves. Our model utilizes co‐kriging interpolation of control points in a three‐dimensional surface mesh to simulate fracture curvature effectively, resulting in an unstructured triangular grid. We then refine the fracture surface into a structured grid with equidistant elements so that both small‐scale heterogeneities and large‐scale curvature can be modelled. To constrain the fracture geometry, we perform a deterministic travel‐time inversion to optimally place these control points. We validate our methodology with synthetic data and address its limitations. Finally, we infer the geometry of a large (more than 200 m) fracture observed in single‐hole ground‐penetrating radar field data. The fracture surface closely agrees with borehole televiewer observations and is also constrained far from the boreholes. Our modelling approach can be trivially adapted to multi‐offset ground‐penetrating radar or active seismic data.

Integrating earthquake-based passive seismic methods in mineral exploration: Case study from the Gerolekas bauxite mining area, Greece

As the global need for aluminum constantly rises, bauxite is considered to be a critical mineral, and the mining industry is in search of new and effective exploration solutions. In this context, we design and implement a purely earthquake-based passive seismic survey at the Gerolekas bauxite mining site in Greece. It is a very difficult exploration setting, characterized by rough topography, limited accessibility, and a very complex geotectonic regime. We gather a passive seismic data set consisting of four months of continuous recordings (May to August 2018) from 129 stand-alone 3C seismological stations. We then analyze this data set and extract 848 microearthquakes that will serve as sources for the application of local earthquake tomography (LET) and transient-source seismic interferometry (TSI) by autocorrelation. We apply LET to estimate the 3D P- and S-wave velocity models of the subsurface below the study area and TSI by autocorrelation to retrieve the zero-offset virtual reflection responses below each of the recording stations. The velocity models provide a relatively coarse image of a previously completely unexplored part of the mining concession, whereas the higher-resolution virtual reflection imaging illuminates in detail the different interfaces. We also reprocess three lines of legacy active seismic data that were shot in 2003, using the LET P-wave velocity model for depth migration, and confirm the improvement of seismic imaging. Finally, we evaluate the obtained results using well data and jointly interpret them, extracting useful information on the expected target depths and indicating that earthquake-based passive seismic techniques can be an innovative and environmentally friendly option for mineral exploration.

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

Shallow S wave velocity profile from active source seismic data at the Apollo 14 landing site based on virtual multichannel analysis of surface waves

We used active source seismic data at the Apollo 14 landing site to investigate the shallow S wave velocity structure (from the surface to a depth of 10 m) of the Moon. We applied seismic interferometry to data from shots located between two geophones to construct a virtual shot gather for multichannel analysis of surface waves (MASW). We included continuous wavelet transform processing in the MASW analysis to improve the signal-to-noise ratio of the data before estimating the S wave velocity VS profile. The resultant profile showed gradual increase in S wave velocity from the surface (VS = ∼50 m/s) to a depth of 6 m (VS = ∼100 m/s), followed by a harder layer with S wave velocity of 110 m/s. This study demonstrated that future extraterrestrial active seismic surveys can be accomplished faster, with simpler field geometry, and using fewer shots and receivers than past missions, thus reducing the payload and cost of future exploration missions.

Inverse Q-Filtering as a Tool for Seismic Resolution Enhancement: A Case Study from the Carpathian Foredeep

In this study, we analyze the quality factor- Q derived from the Vertical Seismic Profiling (VSP) and sonic log data that was performed in the DS-2 borehole located in the Carpathian Foredeep. We use the obtained quality factor distributions in the inverse Q-filtering (IQF) procedure during active seismic data processing. Another very important and, unfortunately, subjective in nature parameter is the gain function applied to seismic data while performing the IQF. To calculate Q from VSP data we use an iterative approach to the spectral ratio method where we use several shallowest receivers as reference. We observe high variability of the obtained Q values, depending on the reference receiver, however, depth distribution of Q was consistent regardless To increase reliability of the obtained values, an additional method of Q measurements is incorporated, i.e., acoustic travel time dispersion method. We show that incorporating inverse Q-filtering to active seismic data processing flow allows to enhance the vertical resolution of 2D seismic data.

Active seismic surveys based on distributed acoustic sensing in near-surface active fault exploration: A test in Datong Basin, North China

The exploration of active faults provides a basis for earthquake disaster prevention and migration. Active seismic survey is generally considered to be one of the most effective active fault exploration techniques because of its high resolution and deep penetration. However, this technique always yields low-resolution seismic data of a near-surface active fault. This limitation can be improved with the increase of sensor density with conventional nodal seismometers, but this is an expensive approach for active fault exploration. Distributed Acoustic Sensing (DAS) seems to be an ideal technique to replace traditional nodal seismometer surveys because it has the advantages of high-density sensors, many channels, and low cost. However, the applicability of active seismic surveys via DAS in the exploration of active faults remains unknown. Here, we designed an active seismic survey test in the Datong Basin, North China, focusing on a graben marked by well-developed normal faults in and around the basin. We selected a buried active fault identified in our previous active seismic profile, and DAS and nodal seismometers were deployed over a possible site of the active fault to conduct a comparative active seismic survey using a tamping machine. The comparisons with data recorded by three-component nodal seismometers demonstrate that DAS can also provide effective near-surface active fault information, and the imaging results also showed that the combination of conventional telecommunication fibre-optic cable and active seismic data processing techniques can be effectively used for near-surface active fault exploration, which is helpful in determining the true location of an active fault.

A look at the blind Kumamoto experiment: combining active and passive seismic observations to avoid Rayleigh-wave mode misidentification

We present our pathway through participation in the blind Kumamoto exercise, particularly the Step 1 of site characterization. The combination of passive and active seismic imaging techniques is used to image the velocity profile beneath the KUMA site. The estimation of the broadband Rayleigh wave dispersion curve is based on cross-correlations of ambient seismic noise and analysis of active seismic shots. We calculate correlations from the entire time series (only vertical components) of each seismic array after classical pre-processing of ambient noise data. Then, a passive seismic section is constructed using all available stations pairs and stacking the cross-correlation traces with similar interstation distances. The obtained passive seismic section is analyzed using a high-resolution Radon transform to obtain the dispersion image of Rayleigh waves traveling through the KUM-LL, KUM-M and KUM-SM arrays. Then, the information is merged and interpolated to obtain the final broadband dispersion curve. In addition, active source seismic data are used with the high-resolution Radon technique to constrain the model at shallow depths (< 30 m). Then, a broadband dispersion image is constructed with significant energy from 0.9 Hz to 45 Hz. The final dispersion curve is inverted using the non-linear neighborhood algorithm. Using just the fundamental mode Rayleigh wave, a first model with normal velocity variation in depth is obtained that corresponds well with the preferred model provided by the organizing committee. The addition of a mHVSR curve in a joint inversion better constrains the deeper part of the model (> 1 km). After comparison of the submitted dispersion curve to the theoretical dispersion curve for the preferred model (Step 4 of the blind test), the authors note that there was a clear misinterpretation in the fundamental mode of their submitted results, especially at frequencies higher than 5 Hz. Using both fundamental (only visible in the passive data set) and first overtones of Rayleigh waves (only visible in the active seismic data set) a refined velocity model could have been inferred, but we decided to keep our first submitted result. This detailed interpretation should be further studied as dispersion images from forward and backward hammer shots are quite different, which may indicate strong variations in the geometry and/or shear-wave velocities of the first meters of the subsurface.Graphical

Understanding Subsurface Fracture Evolution Dynamics Using Time‐Lapse Full Waveform Inversion of Continuous Active‐Source Seismic Monitoring Data

AbstractPredicting the behavior, geometry, and flow properties of subsurface fractures remains a challenging problem. Seismic models that can characterize fractures usually suffer from low spatiotemporal resolution. Here, we develop a correlative double‐difference time‐lapse full waveform inversion of continuous active source seismic monitoring data for determining high‐spatiotemporal‐resolution time‐lapse Vp models of in‐situ fracture evolution at a shallow contamination site in Wyoming, USA. Assisted by rock physics modeling, we find that (a) rapidly increasing pore pressure initializes and grows the fracture, increasing the porosity slightly (from ∼13.7% to ∼14.6%) in the tight clay formation, thus decreasing Vp (∼50 m/s); (b) the fluid injection continues decreasing Vp, likely through the introduction of gas bubbles in the injectate; and (c) final Vp reductions reach over ∼150 m/s due to a posited ∼4.5% gas saturation. Our results demonstrate that high‐resolution Vp changes are indicative of mechanical and fluid changes within the fracture zone during hydrofracturing.

Tomographic evidences for hydraulic fracturing induced seismicity in the Changning shale gas field, southern Sichuan Basin, China

Seismic activity near the shale gas fields in the Sichuan Basin of China has risen significantly in the past few years. Due to the lack of industrial stimulation data to the public, it is difficult to directly link the surge in seismicity near the shale gas fields with hydraulic fracturing stimulation. Meanwhile, it is also challenging to better understand the mechanisms inducing/triggering seismicity in these areas. In the absence of stimulation data, understanding subsurface fluid distribution can go a long way toward addressing these problems. Therefore, in this study, we have applied the double-difference seismic tomography method and the focal mechanism tomography method to the monitoring data of a local seismic array in the Changning shale gas field to build high-resolution three-dimensional models of seismic velocity structure and pore pressure field, then used these models to characterize fluid distributions beneath the shale gas field. From the inverted models, the correlation of overpressure regions (>12 MPa) with moderate-to-high Vp/Vs (1.75-1.90) is clearly observed. With the aid of the exploration data (including well log, active and passive seismic data), the overpressure regions are interpreted to be created by pore pressure or fluid diffusion along pre-existing faults from the shale gas reservoirs, while variations of the Vp/Vs model partly indicate water- and gas-bearing rocks. By jointly analyzing the geophysical model features and earthquake spatio-temporal characteristics, we infer that the earthquakes are primarily caused by increased pore pressures on the seismogenic faults due to the presence of water and/or gas. Besides, the poroelastic stress perturbation and aseismic slip produced by hydraulic fracturing also contribute to the generation of certain earthquake swarms.

First-Break Picking Method Based on the Difference Between Multiwindow Energy Ratios

First-break picking is an important step during processing of both passive and active seismic data. Many automated algorithms have been developed to detect first-break points in large volumes of seismic data. However, it remains difficult to determine precise first-break points in seismograms with low signal-to-noise ratios. Therefore, we present a new approach based on the differences between multi-window energy ratios (DERs) that minimizes the effects of noise. First, the DER is defined and a thresholding method detecting first-break points using the DERs is proposed. Thresholding can be varied depending on the DER parameters, which ensures reliable results even if the parameters change. We use two types of seismic data to establish and verify the DER method: big data derived via global earthquake monitoring (STanford EArthquake Dataset) and ocean bottom cable (OBC) data acquired offshore of Pohang, Republic of Korea. We investigated the effects of parameter changes on the DER picking results. Good picking performance was verified under low signal-to-noise conditions and compared to conventional first-break picking methods. The DER accuracy was higher than that of conventional methods and outliers were rare.

Joint FWI of Active Source Data and Passive Virtual Source Data Reconstructed Using an Improved Multidimensional Deconvolution

Traditional full waveform inversion (FWI) highly depends on sufficient low-frequency data or a good initial model. Passive seismic data contain rich low-frequency components, and passive seismic FWI using virtual source data by seismic interferometry (SI) is a promising method. However, the distribution of passive sources in the subsurface is always inhomogeneous, which will lead to artifacts in the reconstruction results by SI using cross-correlation (CC). SI by multidimensional deconvolution (MDD) can counteract the source inhomogeneity but requires the separation of the reference wavefields, which is difficult to achieve for noise source data. To mitigate this problem, we propose an improved SI method by linear Radon transform based multidimensional deconvolution (LRTMDD). The interferometric point-spread function can be extracted more accurately and efficiently in the linear Radon domain, thus improving the reconstruction results. The passive virtual source full waveform inversion (PVSFWI) based on LRTMDD is further proposed, which can effectively use the low-frequency information in the virtual source data to invert the macroscopic velocity structures even in the case of inhomogeneous source distributions, and without the need to estimate the virtual source wavelets. A joint multi-source FWI strategy is proposed to solve the problem of missing low-frequency data suffered by active source FWI. Numerical experiments on the Marmousi model and the SEG/EAGE overthrust model show that the proposed methods can fully combine the respective advantages of multisource seismic data to stably achieve high-accuracy velocity models in the case of inhomogeneous passive source distributions and the lack of low-frequency data in active seismic data.

Common‐midpoint cross‐correlation stacking tomography: A 3D approach for frequency‐dependent mapping of Rayleigh waves, group and phase velocity throughout an active seismic network

AbstractShear‐wave velocity constitutes an important characterization parameter in terms of engineering properties. Several techniques of surface wave analysis have been widely adopted for building near‐surface 1D, or even 2D, S‐wave velocity models, but comparable 3D approaches, such as the surface wave tomography (SWT) method, are usually limited to global or regional seismological studies. Moreover, a reliable 3D subsurface imaging in a complex and noisy near‐surface environment becomes difficult when using the fully automated processing techniques that are commonly suggested for the SWT method. Here, a new strategy that is well adapted to geotechnical investigations is proposed to accomplish automatically the frequency‐dependent mapping of Rayleigh waves, both for group and phase velocity, throughout an active seismic network. The specific methodology can be implemented in both 2D and 3D active seismic data acquisition schemes involving both uniform and non‐uniform layouts of receivers. It combines the advantages of a newly created common‐midpoint (CMP) cross‐correlation (CC) analysis technique, with the precision of the travel‐time tomography method that is usually used in regional seismological studies. The main analysis is based on partitioning the different wave propagation directions, weighting the CC frequency–velocity analysis around one‐wavelength and stacking the CMP dispersion images. The tomographic inversion is applied to frequency‐dependent virtual travel‐times, produced from the azimuth‐dependent results of the specific analysis, in order to generate, as output, the local dispersion curves. The output of the proposed processing method can be interpreted with any preferred inversion algorithm for group and phase velocity, either individually or jointly. Simple synthetic tests together with a 3D seismic experiment in well‐known conditions, using standard refraction and multichannel analysis of surface waves (MASW) equipment, confirmed the effectiveness of the proposed methodology in detecting both lateral and vertical S‐wave velocity variations. A ‘construction site’ case study finally highlighted the potential of the new tool in a very difficult and noisy environment.

Structural information derived from ambient noise tomography over a hydrocarbon‐producing region in the Cachar fold belt, lower Assam, northeast India

AbstractAmbient noise tomography is a powerful tool that has found increasing application in reservoir analysis and imaging. The Cachar fold belt in lower Assam, northeast India encompasses several wells under active hydrocarbon production, along with several dry wells. To overcome the lack of active seismic data over the entire fold belt, a passive seismic study was carried out to image the concealed three‐dimensional sub‐surface structures. The data were recorded from February to November 2011 by a network of 65 wideband seismometers spanning an area of about 40 × 60 km2. The data are crosscorrelated in the 2–5 s band, followed by phase‐weighted stacking to estimate noise correlation functions with surface wave signatures. The traveltimes picked from the frequency‐time analysis are utilized in a tomographic inversion for Rayleigh wave group velocities. The group velocity anomalies have a lateral resolution of ~ 3.5 × 5.5 km2 and variations of up to for each period. The group velocities are in turn inverted for S‐wave velocity distribution as a function of depth. The three‐dimensional S‐wave velocity tomograms reveal the tight anticlines and broad synclines, with high‐ and low‐velocity zones corresponding to structural highs and lows, respectively. The structural interpretation is supported for the part of the region with producing wells and covered by active seismic data, wherein the post‐stack time migrated seismic section shows anticlinal and synclinal features similar to those obtained from ambient noise tomography. The structures revealed by ambient noise tomography can help identify zones of interest to be targeted by active seismic surveys in the Cachar fold belt.