Passive Seismic Data Research Articles

Radon-domain acoustic and elastodynamic interferometric redatuming of VSP data

Summary The virtual source method (VSM) is a useful tool for imaging and monitoring below complex and time-varying overburden. When it is applied to VSP geometries, the redatumed virtual source data often suffer from artifacts and aliasing due to the violation of theoretical acquisition conditions and partial focusing of virtual multiples, which further degrade seismic imaging quality. While the conventional Radon-domain VSM (R-VSM) mitigates these issues to some extent, it is still possible to improve upon the imaging. This study develops a simple and effective high-resolution Radon-domain VSM (HR-VSM) to effectively address these issues, offering superior noise and artifact suppression compared to traditional VSM and R-VSM. HR-VSM shows a strong performance across a wide range of VSP configurations and data types, especially in applications to multi-component and passive seismic data, demonstrating its versatility and effectiveness in seismic exploration and showing potential for use in earthquake seismology. To extend subsurface illumination, we combined correlation-type and convolution-type HR-VSM for automatically redatuming virtual surface seismic profile (SSP) shot gathers with a complete receiver array covering all surface source locations, without the additional pre-processing steps typically required by VSM. It was also tested on reverse VSP (RVSP) synthetic active data and passive seismic data, proving effective in constructing virtual shot gathers and showing potential for extending its use to earthquake data and ambient-noise seismology. Moreover, HR-VSM can perform elastodynamic interferometric redatuming, enabling the redatuming of pure P-P and pure P-SV waves from multi-component VSP data. We applied HR-VSM to redatum virtual single well profile (SWP) data containing fault-related P-P and P-SV reflections from VSP data, which were further used for fault imaging. Finally, HR-VSM was used to generate virtual crosswell data with receivers in both boreholes, eliminating the need for downhole sources. The obtained virtual crosswell containing direct waves, as well as P-P and P-SV reflections, enable constructing a velocity model and imaging the structure between wells.

Passive Seismology: Lightweight and Rapid Detection of Arctic Subsea and Sub‐Aquatic Permafrost

AbstractLow sea levels during the last Ice Age exposed millions of square kilometers of Arctic shelves which have been subsequently submerged, creating subsea permafrost. In onshore settings, permafrost can also exist beneath water bodies such as coastal lagoons, rivers, and thermokarst lakes. We explored passive seismology as a method for mapping unfrozen sediment thickness above subsea and sub‐aquatic permafrost. We present passive seismic data collected with the Mobile Ocean Bottom Seismic Instrument (MOBSI) from the Beaufort Sea near Tuktoyaktuk in Canada, Ivashkina Lagoon on the Bykovsky Peninsula, as well as a lake and river in the Lena Delta, Siberia, Russia. We use borehole data and frost probe measurements to identify permafrost‐related H/V measurement peaks and calibrate shear wave velocities for frequency‐to‐depth conversion. We employ the shortest path and maximum signal amplitude to connect peaks and generate geological profiles. The MOBSI detected the ice‐bonded permafrost table beneath the Beaufort Sea, as well as beneath a Siberian lake and lagoon. At Tuktoyaktuk, an ocean bottom seismometer revealed a 5% scatter about the peak frequency for three‐minute time windows and over 8 hr of recording time. With peak frequencies ranging from 4.9 ± 0.2 Hz to 27.6 ± 1.4 Hz, the depth to subsea permafrost ranged from 1.4 ± 0.1 m bsl at the shoreline to 14.0 ± 0.4 m bsl 240 m offshore. Given an accurate shear wave velocity, our findings highlight that MOBSI deployment times as short as 3 min are adequate for detecting Arctic subsea and sub‐aquatic permafrost.

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

Session 5. Oral Presentation for: A feasibility study of Boree Salt body mapping in the Adavale Basin using passive seismic data

Session 5. Oral Presentation for: A feasibility study of Boree Salt body mapping in the Adavale Basin using passive seismic data

A feasibility study of Boree Salt body mapping in the Adavale Basin using passive seismic data

Hydrogen plays a pivotal role in the global energy transition and may require underground storage. So far salt cavern storage is the only proven technology for underground hydrogen storage. The Boree Salt in the Adavale Basin, mostly at depths from 1 to 2.5 km and up to 550 m thick, consists predominantly of halite and is deemed suitable for hydrogen storage. However, current maps are inadequate. Recently passive seismic data (ambient noise) have received much interest for subsurface imaging. The main signal from passive data is surface waves (usually below 2 Hz). The capability of surface waves for the Boree Salt body mapping is examined. Parameters of seismic sensor spacing, the dominant frequencies of the surface waves, and data noise levels are all considered. It is demonstrated that surface waves from ambient noise can map the Boree Salt bodies with a survey distance of ~40 km. Between frequencies of 0.12 and 0.25 Hz, results from the latter have better resolution because of a shorter wavelength. Moving to higher frequencies of 0.5 and 1 Hz, however, the resolution becomes worse, because the depth sensitivity of surface waves moves to the shallower part of the model with increasing frequencies, rendering them incapable of effectively probing the targeted depths. For signal/noise ratio above five, station spacing can be as large as 1 km without compromising quality. Therefore, cost-effective and environmentally friendly passive seismic data can be a good alternative to the traditional active-source data for deep salt body imaging.

Development of a novel seismic acquisition system based on fully autonomous ocean bottom nodes

It is now established that ocean bottom node (OBN) surveys provide superior seismic data compared to traditional towed streamers, but deployment of the nodes is time-consuming and expensive. As an alternative, we are developing a novel seismic acquisition system that utilises a fleet of fully autonomous underwater vehicles (AUVs) as OBNs. The objective is to eliminate the reliance on remotely operated vehicles (ROVs) or ropes for deployment, making ocean bottom seismic acquisition significantly more efficient and cost-effective. To succeed, several criteria need to be met, including recording of good geophysical data, system affordability, low power consumption, units’ ability to act autonomously (e.g. steer, land, listen, re-position, surface) and to respond to remote commands (e.g. cease activity, return), a failure recovery protocol, automated deployment and retrieval, and system-wide communications. The ability to record good seismic data was assessed in October 2022, when we performed active-source seismic field trials in the UK. Five AUVs were deployed adjacent to commercial nodes (all units carried identical geophysical payloads) and a series of 2D lines were acquired at varying orientations. Analysis of the data confirmed that our units achieved satisfactory coupling with the seabed. The unit’s in-water capabilities were assessed during multiple field trials in 2022 and 2023, offshore UK and Australia. Tests consisted of simulating seismic acquisition cycle (flights, landings, take-offs and repositioning) where it was verified that missions were reliably executed, including landing within 10 m from pre-planned locations and recording good quality passive seismic data.

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.

Sediment thickness across Australia from passive seismic methods

SUMMARY Around the world the Earth’s crust is blanketed to various extents by sediment. For continental regions, knowledge of the distribution and thickness of sediments is crucial for a wide range of applications including seismic hazard, resource potential and our ability to constrain the deeper crustal geology. Excellent constraints on the sediment thickness can be obtained from borehole drilling or active seismic surveys. However, these approaches are expensive and impractical in remote continental interiors such as central Australia. Recently, a method for estimating the sediment thickness using passive seismic data, the collection of which is relatively simple and low cost, was developed and applied to seismic stations in South Australia. This method uses receiver functions, specifically the time delay of the P-to-S converted phase generated at the sediment-basement interface, relative to the direct-P arrival, to generate a first-order estimate of the thickness of sediments. In this work, we expand the analysis to the vast array of over 1500 seismic stations across Australia, covering an entire continent and numerous sedimentary basins that span the entire range from Precambrian to present day. We compare with an established yet separate method to estimate the sediment thickness, which utilizes the autocorrelation of the radial receiver functions to ascertain the two-way traveltime of shear waves reverberating in a sedimentary layer. Across the Australian continent the new results match the broad pattern of expected sedimentary features based on the various geological provinces. We are able to delineate the boundaries of many sedimentary basins, such as the Eucla and Murray Basins, which are Cenozoic, and the boundary between the Karumba Basin and the mineral rich Mount Isa Province. Contrasts in seismic delay time across these boundaries are upwards of 0.4 $\, \mathrm{s}$. The delay signal is found to diminish to <0.1 $\, \mathrm{s}$ for older Proterozoic basins, likely due to compaction and metamorphism of the sediments over time. As an application of the method, a comparison with measurements of sediment thickness from local boreholes allows for a straightforward predictive relationship between the delay time and the cover thickness to be defined. This offers future widespread potential, providing a simple and cheap way to characterize the sediment thickness in underexplored areas from passive seismic data.

Machine Learning Provides Fracture Analysis, Mapping for CCUS

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214996, “Machine-Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in the Illinois Basin,” by Guoxiang Liu, SPE, US Department of Energy, and Abhash Kumar and William Harbert, SPE, Contractors, et al. The paper has not been peer reviewed. _ The monitoring of storage reservoirs to ensure safe, long-term storage of CO2 and to derisk operations and storage management is undergoing dynamic shifts, expanding opportunities for implementing innovative techniques and applications, especially for commercial-scale deployment. In the complete paper, a method with multitiered analysis has been developed to leverage advanced machine-learning (ML) techniques to process passive seismic monitoring data acquired during an injection period along with pumping/injection pressure and rate in the Illinois Basin for potential fracture and fault analysis. Introduction This study is part of the Science-Informed ML for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative funded by the US Department of Energy Carbon Storage Program. The outcomes of the SMART Initiative are science-informed, ML-based tools that can be applied at carbon storage sites throughout the nation and the world to achieve the following objectives: - Improve the ability to consolidate technical knowledge, site-specific characterization information, and real-time data in a digestible way - Enable the optimization of carbon-storage reservoirs by creating a capability for real-time forecasting of the behavior of such reservoirs - Improve the ability to understand and communicate subsurface behavior during carbon-storage operations to nonexperts Methodology In the authors’ study, a multitiered, data-driven approach was created by integrating all available measurement data to visualize fracture networks. The data sets included measurements from drilling, log and core testing, injection and downhole pressure measurements, and five vertical-pressure-monitoring gauges, providing a wealth of detail on the fracture networks. A fracture network encompasses natural fractures, induced or hydraulic fractures, and the dynamic response of such fractures, culminating in a comprehensive understanding of this system. Because of the wide range of resolutions and scales of the data sets, the fracture network was mapped into 19 distinct time windows over a 3-year injection period by analyzing pumping data, which included CO2 injection and bottomhole pressure. Passive seismic data (microseismic) were compartmentalized into these time windows, similar to the industry practice of compartmentalizing microseismic data into separate stages for high-pressure fluid- injection activities (i.e., induced fracturing). In each of these time windows, the b-value of the associated microseismic population was estimated. Furthermore, event-occurrence time and distance from the treatment well were used to identify the distinct triggering front of microseismicity likely associated with pre-existing fractures and faults within the reservoir. Seismogenic b-value and diffusivity analyses were applied to identify triggered fronts for further clustering and fracture-plane and 3D fracture-distribution analysis. The potential of several unsupervised ML algorithms was leveraged to identify the spatial clusters of microseismic events within each triggering front of individual time windows by following the work flow described in Fig. 1 of the complete paper. Furthermore, these ML techniques were implemented to determine the best-fitting surface for each spatial cluster of microseismicity to infer the directional and spatial distributions of fractures.

Impact of Lossy Compression Errors on Passive Seismic Data Analyses

Abstract New technologies such as low-cost nodes and distributed acoustic sensing (DAS) are making it easier to continuously collect broadband, high-density seismic monitoring data. To reduce the time to move data from the field to computing centers, reduce archival requirements, and speed up interactive data analysis and visualization, we are motivated to investigate the use of lossy compression on passive seismic array data. In particular, there is a need to not only just quantify the errors in the raw data but also the characteristics of the spectra of these errors and the extent to which these errors propagate into results such as detectability and arrival-time picks of microseismic events. We compare three types of lossy compression: sparse thresholded wavelet compression, zfp compression, and low-rank singular value decomposition compression. We apply these techniques to compare compression schemes on two publicly available datasets: an urban dark fiber DAS experiment and a surface DAS array above a geothermal field. We find that depending on the level of compression needed and the importance of preserving large versus small seismic events, different compression schemes are preferable.

Accurate Reconstruction of Short-Duration Passive Seismic Data With Transformer Integrating Multiscale Dense Network

Accurate Reconstruction of Short-Duration Passive Seismic Data With Transformer Integrating Multiscale Dense Network

Combining PCA and DCT for Improved Passive Seismic Data Compression

Combining PCA and DCT for Improved Passive Seismic Data Compression

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.

Dictionary Learning for Single-Channel Passive Seismic Denoising

Abstract Passive seismic denoising is mostly performed using a simple band-pass filter, which can be problematic when signal and noise share the same frequency band. More advanced passive seismic denoising methods take advantage of fixed-basis transforms, for example, the wavelet, to remove noise. Here, we present an open-source package for data-driven denoising based on adaptively learning sparse transform. Contrary to the fixed-basis transforms, the proposed method belongs to the adaptive-basis transforms. We learn the 1D features embedded in the passive seismic data from all the available waveform data sets without requiring spatial coherency in a data-driven way. Thus, the new method is flexible to apply in any passive seismic monitoring project because of its data-driven and single-channel nature when implemented. Considering the computationally expensive K-singular-value-decomposition (KSVD) in the traditional dictionary learning framework, we suggest applying a fast SVD-free dictionary learning method that can be readily applicable to process massive seismic data during passive seismic monitoring. The proposed method is applied to two synthetic data examples and three real passive seismic data sets to demonstrate its effectiveness in improving the signal-to-noise ratio, and its potential in applications like arrival picking. The open-source reproducible package can be found in the Data and Resources section.

Seismic ambient noise imaging above an underground mine: An example from Boliden Tara Mines, Ireland

We use a passive seismic data set recorded from June 2017 to July 2018 by twenty-one stations deployed in a five by five km grid to the south-west of the Boliden Tara Mines orebody (Eastern Ireland) to image the shallower part of the Carboniferous rock units. We cross-correlate one year of ambient noise between all station pairs to generate cross-correlation functions that are rich in surface waves. Then we investigate the locations of the noise sources by applying f-k cross-correlation beamforming. We find two persistent noise sources associated with oceanic microseisms and a cluster of anthropogenic activities, respectively. We undertake dispersion analysis to extract group velocity measurements in the 1–9 Hz band that we invert using the fast marching method to create Rayleigh wave velocity maps. This is followed by a wavelet-based sparsity constrained tomographic method to produce a 3-D shear wave velocity model down to 800 metres. The results highlight low and high-velocity anomalies related to the structural and stratigraphic heterogeneity within the Upper Dark limestone formation. In addition, the velocity gradient of our model agrees with the sonic log velocity profile available adjacent to the study area, suggesting that this sparse network can be used to obtain reliable broad-scale information and could also guide the design phase of more expensive and involved experiments, such as active seismic reflection imaging.

Early detection of internal erosion in earth dams: combining seismic monitoring and convolutional AutoEncoders

ABSTRACT Levees/earth dams are critical infrastructures for supplementing clean water, flood management, and energy production, prone to progressive failures due to internal erosion. Current inspection methods are unable to detect internal erosion until its exterior manifestation when it is too late to prevent the often-catastrophic failures. Therefore, finding innovative methods for the early detection of internal erosion is crucial. Despite the knowledge about the general mechanism of internal erosion, its early detection (and prevention) has remained a gap. This study introduces a novel artificial intelligence (AI) method to identify the temporal patterns within the passive seismic monitoring data, which can be associated with internal erosion initiation in earth dams. The proposed approach implements Convolutional AutoEncoders, an emerging deep-learning algorithm for anomaly detection in time-series data. Through an unsupervised learning framework, the AutoEncoders are trained using passive seismic monitoring data collected from a full-scale test embankment. In addition to the approximate initiation time, this algorithm can evaluate the initiation location by identifying the first sensors demonstrating internal erosion signs. The proposed deep learning framework combined with continuous seismic monitoring can serve as a basis for developing advanced early warning systems for internal erosion in earth dams.

Improving Surface Wave Retrieval From Traffic Noise by Deconvolution of the Decomposed Wavefield

AbstractTraffic noise is an important type of passive seismic data because it usually includes strong dispersive surface wave components and can be easily accessed. It can be used to extract virtual surface waves via seismic interferometry algorithms for the purpose of imaging subsurface shear wave velocity distribution. In this paper, we propose a scheme to improve the retrieval of surface waves from traffic noise recorded using linear arrays along traffic roads. By deconvolving the decomposed traffic noise wavefield, robust surface wave traces can be computed from a short noise record. First the far‐field component of the traffic noise recording is extracted and separated into unidirectionally propagating components. Then deconvolution interferometry is applied to these separated far‐field wavefield to extract surface wave Green's function. With this scheme, crosstalk noise and near‐field artifacts are excluded from the computation, and surface wave traces with high signal‐to‐noise ratio (SNR) are achieved using short traffic noise traces. In a synthetic test virtual surface waves estimated with the proposed method show significantly higher SNR than those computed with the conventional interferometry workflows, and matches well with simulated active source traces. A field data example with traffic noise recorded in a distributed acoustic sensing experiment also shows that surface waves estimated using the proposed methodology demonstrate higher SNR than those computed with the conventional interferometry schemes and that the virtual surface waves generated using 4 s of traffic noise demonstrate signal quality comparable to the surface waves recorded in this experiment with an active source.

The crust-mantle transition beneath Northeast China from P–wave receiver functions

Introduction: The continental lithosphere of the Northeast China Plain (NECP) is probed using P–wave receiver function analysis of passive seismic data. The NECP is much–discussed as it includes different geological provinces of varying tecto–magmatic origin and dates back from Archean to Holocene. Quantifying the tectonic and magmatic influences on the structure and composition of the lithosphere puts important constraints on evolution of NECP. For this, we explore 75 sites across NECP using receiver function analysis.Methods: A recently developed technique of inverting for 1–D S–wave velocity profiles beneath seismic stations that is based on the principles of Bayesian statistics (hierarchical transdimensional Bayesian Inversion; HTBI) is applied to receiver functions from the NECP. In addition, an improved crustal thickness–compressional to shear wave velocity ratio (H–κ) analysis was conducted to retrieve the crustal thickness and Vp/Vs ratio of the region. These estimated point measurements are integrated and systematically studied for a regional view of the current crustal architecture.Results and Discussion: We observe a laterally varying and highly complex lithosphere beneath the NECP. A shallower crust–mantle transition (≤32 km) characterises the Precambrian North China craton and Late Mesozoic–Cenozoic Songliao Basin from the adjacent Central Asian Orogenic Belt and the Changbaishan Volcanic field (35–40 km). Beneath the latter, low Vp/Vs ratios (~1.65) are obtained, whereas all other regions feature ratios in excess of 1.75. Multiple velocity gradients are observed at crustal depths within the craton, in contrast to the adjacent orogen, which indicates a higher degree of crustal complexity of the former. The width of the crust–mantle transition across the NECP is found to be mainly intermediate (2–7 km) and occasionally sharp (≤2 km). From our observations, we infer that there is a substantial difference between the eastern North China Craton’s lithospheric architecture and the rest of the NECP, with most of the NECP exhibiting more complexity than previously reported.

Mapping the soil thickness in the Main Ethiopian Rift using passive seismic data and HVSR, case for Northern water divided between the Ziway-Shala lakes basin and the Awash basin

A passive seismic survey wasconducted in the northern water dividebetween the Ziway-Shala Lakes Basin, Central Main Ethiopian Rift (CMER), and Awash basin. Previous geophysical studies in the same areahad mainly focused on mapping intermediate to deeper Earth geological structures, and little attention given to shallowest layer.In contrast, the study aimed to map these shallow geologic structures of soil layer undulations at this specific location of CMER. A rapid and non-invasive technique was employed, which involve recording three component measurements of ambient seismic noise data. The collected seismic noise data was processed and analyzed using the average of Horizontal to Vertical Spectral Ratio (HVSR) which helpsto determine resonance frequencies of the survey stations. After frequency estimatesweremadefor each station, corresponding depth estimates were obtained using the method of Nakamura.The point depth to the shallowest layer was determined for each siteand these depth valueswere gridded and mapped to show subsurface topographic undulations or thickness of topsoil cover.The result showed that the shallowest bedrock layer wasthickening northwards, toward the Awash basin.