Subsurface Imaging Research Articles

Near-surface resistivity imaging using resistivity Tomography in some most impacted areas due to the 2018 Earthquake in North Lombok Island

The Lombok earthquake in July 2018 caused considerable damage to public facilities and residential areas. Most of the houses in the north of the island of Lombok were damaged by the 6.9 magnitude earthquake. We want to know what parameters cause houses and buildings in North Lombok not to experience a severe liquefaction process. We used near-surface tomographic resistivity imaging to characterize the subsurface of North Lombok to map the subsurface of the most affected areas in North Lombok. The geological condition of northern Lombok is the most vulnerable area to liquefaction due to volcanic loose sand layers, shallow groundwater, and thick deposits. However, almost all subsurface images from resistivity tomography from North Lombok show limestone layers at shallow depths, which may play an important role as a protective layer from liquefaction phenomena in this area.

Seismic imaging of mine tunnels by ambient noise along linear arrays

Seismic imaging using ambient noise has been widely used to image subsurface structures. The spatial autocorrelation (SPAC) method using ambient noise recorded by seismic arrays can image subsurface velocity structures at various scales. In addition, an approximation of zero-offset reflection responses of the structure can be achieved by station autocorrelations of the ambient noise. In this study, to detect an abandoned mine tunnel that is expected to have low velocity anomalies, we jointly apply the SPAC method and station autocorrelation method along linear arrays on the surface above the tunnel. One hundred and thirty-six nodal seismometers were deployed linearly along three lines with station intervals of 1.5 and 5 m, respectively. The ambient seismic noise was continuously recorded for two hours by these stations. Through the SPAC analysis, the Rayleigh wave dispersion curves for the frequency band of 3 Hz to 15 Hz were extracted from the ambient noise, from which the 2D near-surface shear-wave velocity models were estimated. A low velocity zone is obvious at a depth of about 60–100 m in the velocity profile along line 2. In addition, clear diffraction signals can be seen at similar positions on station autocorrelation profile along line 3, which is very close to line 2. This anomalous area is interpreted to be the abandoned tunnel, which is further verified by drilling. Our study shows that the combination of SPAC and station autocorrelation methods can be effective in near surface exploration.

Comparison of The Electrode Configuration of The Resistivity Geoelectric Method for Landslide Analysis on Highway Suban, Bandar Lampung

Jalan Raya Suban Bandar Lampung is located on the physiography of Bukit Barisan which is influenced by the movement of the Sumatran Fault, causing morphological conditions in the form of hills and steep slopes. This condition triggers the occurrence of disasters, one of which is landslides. In this study, the lithology of subsurface rocks and slides in the landslide-prone zone was examined using the Wenner-Schlumberger and Wenner-Alpha configuration geoelectrics. The Wenner-Schlumberger configuration has deeper penetration but lower resolution subsurface images, while the Wenner-Alpha configuration has less deep penetration but lower resolution surface images. The use of these two configurations is to take advantage of the advantages and reduce the disadvantages of each configuration in landslide analysis. The measurement results in the Wenner-Schlumberger and Wenner-Alpha configurations identified that the upper layer consists of weathered rock associated with clay tuff and sandy tuff with a resistivity of 1 m – 95.6 m, the second layer is clay sand from the Campang Formation with a resistivity value of 95.6 m – 250 m and the third layer is a mixture of breccia and igneous rock from the Tarahan Formation with a resistivity value of more than 250 m. The clay-sand layer was identified as the slip plane in the study area

Imaging subsurface structure over the Xiadian fault using P waves extracted from urban traffic noise

SUMMARY The imaging of subsurface structures is an essential task in subsurface engineering projects; it provides information regarding the locations of active faults and layer boundaries. Among the methods available for imaging of subsurface structures, the body wave imaging method using urban traffic noise has recently attracted attention because it permits continuous measurement at low cost in urban areas. However, because the urban traffic noise signal used for imaging on the engineering scale has characteristics that differ from the ambient noise used on the crustal scale, the conventional crustal-scale data processing workflow should be modified through systematic data analysis. In this study, traffic noise sources were systematically analysed using field data obtained over the Xiadian fault in Hebei province, China. The traffic noise signals were recorded in various patterns because of diverse incoming directions and show marked amplitude changes depending on time of recording. The overlapping signals originating from opposite directions generate spurious events and noise in the seismic interferometry images; constant processing parameters cannot respond to the large amplitude changes. In this study, to remove surface waves with markedly changing amplitude, we applied actively varying threshold values to each set of traces using the moving average of amplitude changes within the trace. In addition, the signals originating from diverse directions were separated into negative and positive slopes through the f–k filter; the interference generated by overlapping signals was minimized by applying data processing (e.g. median filtering and high amplitude removal) separately to the negative and positive slopes of each simultaneously acquired trace gather. Due to the modified data processing workflow, most spurious events were successfully suppressed in the final stacked image compared with those produced using the conventional data processing workflow, and reflections were imaged more clearly. Fault spatial locations and layer boundary depth variation in the final image obtained by the modified processing workflow were similar to those reported in previous studies.

Morphostratigraphy of barrier spits and beach ridges at the east margin of Salgada Lagoon (Southeast Brazil)

At wave-dominated coasts, it is common the presence of coastal barriers that can be of different typologies and whose dynamics can influence the formation of lagoon systems. Along southeastern and south Brazil the dynamics involving barrier-lagoon evolution is related to wave and aeolian process associated with sea-level behavior. For a better understanding about the relationship regarding to coastal process, barriers, and lagoons the application of geophysical techniques enables to identify morphostratigraphic characteristics improving the acknowledgment of the main processes forming these morphologies. In this study, a 1.4 km long common-offset GPR profile, in cross-shore direction, was surveyed in order to obtain the internal depositional architecture of the sandy coastal barrier to the east of Salgada lagoon, located in the wave-dominated Paraiba do Sul river delta, in southeastern Brazil. The GPR allows to describe morphostratigraphic characteristics imprinted by sediment dynamics and how it is related to the lagoon system formation. Eight radarfacies were identified, grouped according to the coastal barrier pattern, divided in barrier spit and beach ridge, and sediment transport characteristics. Five radarfacies comprise the barrier spit environment and three the beach ridge environment. The barrier spit environment radarfacies showed different characteristics of the portions of the spit sequence (initial, medium or distal), and along with its subsurface imaging angle (parallel, oblique or transversal to the spit growth direction) providing highly detailed data regarding those barrier types. This recognition allowed the interpretation of a previous spit, approximately 3 m deep, located under another one that overlapped it. Also, spit accretion records were found, which occur due to the seaward exposure to high-angle wave action. Meanwhile, the beach ridge environment radarfacies revealed the typical pattern of progradation due to swash processes, and also the occurrence of berm ridge accretion and overtopping/overwashing processes. The integrated analyses using the subsurface data indicated that the evolution of the targeted coastal barriers and the lagoon can be divided into four stages. The first stage is the most landward barrier spits growth, where Salgada lagoon was beginning its closing process. This is followed by the second stage with the continuity of barrier spits growth but with changes in spits orientation. The third stage represents an erosive phase, with a major erosive truncation that probably eroded a significant area of the spits. At the last stage suggested there was a subsequent accumulation phase, marked by the formation beach ridges. Besides that, it is also important to recognize that the development of coastal barriers of different types at the site showed an alignment variability of paleocoastlines. While barrier spits revealed variation in their orientation, beach ridges have kept their alignment more constant. This fact occurred independently of erosive episodes. Furthermore, the GPR profile acquisition strategy made it possible to investigate the spit from different perspectives, which helped with the interpretation of deeper radarfacies.

Using convolutional neural networks to develop starting models for near-surface 2-D full waveform inversion

SUMMARY Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change near-surface (<30 m) site characterization by enabling the recovery of high-resolution (metre-scale) 2-D/3-D maps of subsurface elastic material properties. Yet, FWI results are quite sensitive to their starting model due to their dependence on local-search optimization techniques and inversion non-uniqueness. Starting model dependence is particularly problematic for near-surface FWI due to the complexity of the recorded seismic wavefield (e.g. dominant surface waves intermixed with body waves) and the potential for significant spatial variability over short distances. In response, convolutional neural networks (CNNs) are investigated as a potential tool for developing starting models for near-surface 2-D elastic FWI. Specifically, 100 000 subsurface models were generated to be representative of a classic near-surface geophysics problem; namely, imaging a two-layer, undulating, soil-over-bedrock interface. A CNN has been developed from these synthetic models that is capable of transforming an experimental wavefield acquired using a seismic source located at the centre of a linear array of 24 closely spaced surface sensors directly into a robust starting model for FWI. The CNN approach was able to produce 2-D starting models with seismic image misfits that were significantly less than the misfits from other common starting model approaches, and in many cases even less than the misfits obtained by FWI with inferior starting models. The ability of the CNN to generalize outside its two-layered training set was assessed using a more complex, three-layered, soil-over-bedrock formation. While the predictive ability of the CNN was slightly reduced for this more complex case, it was still able to achieve seismic image and waveform misfits that were comparable to other commonly used starting models, despite not being trained on any three-layered models. As such, CNNs show great potential as tools for rapidly developing robust, site-specific starting models for near-surface elastic FWI.

Nanomechanical subsurface characterisation of cellulosic fibres

AbstractThe mechanical properties of single fibres are highly important in the paper production process to produce and adjust properties for the favoured fields of application. The description of mechanical properties is usually characterised via linearized assumptions and is not resolved locally or spatially in three dimensions. In tensile tests or nanoindentation experiments on cellulosic fibres, only mechanical parameter for the whole fibre, such as elastic modulus or hardness, is usually obtained. To obtain a more detailed mechanical picture of the fibre, it is crucial to determine mechanical properties in depth. To this end, we discuss an atomic force microscopy-based approach to examine stepwise the local stiffness as a function of indentation depth via static force-distance curves. To our knowledge, we are the first authors to apply this method cellulosic fibres. The method was applied to linter fibres (extracted from a finished paper sheet) as well as to natural raw cotton fibres to better understand the influence of the pulp treatment process in paper production on the mechanical properties. Both types of fibres were characterised in dry and wet conditions with respect to alterations in their mechanical properties. The used stepwise analysis method of the force-distance curves allowed subsurface imaging of the fibres. It could be revealed how the walls in the fibre structure protects the fibre against mechanical loading. Via a combined 3D display of the mapped topography and the fitted elastic moduli in z-direction, a spatially resolved mechanical map of the fibre interior near the surface could be established. Additionally, we labelled the fibres with different carbohydrate binding modules tagged with fluorescent proteins to compare the AFM results with fluorescence confocal laser scanning microscopy imaging. Nanomechanical subsurface imaging in combination with fluorescent protein labelling is thus a tool to better understand the mechanical behaviour of cellulosic fibres, which have a complex, hierarchical structure.Graphical abstract

Deep learning inversion of Rayleigh-wave dispersion curves with geological constraints for near-surface investigations

SUMMARY With the emergence of massive seismic data sets, surface wave methods using deep learning (DL) can effectively obtain shear wave velocity (Vs) structure for non-invasive near-surface investigations. Previous studies on DL inversion for deep geophysical investigation have a reference model to generate the training data set, while near-surface investigations have no model. Therefore, we systematically give a set of training data set generation processes. In the process, we use both prior information and the observed data to constrain the data set so that the DL inversion model can learn the local geological characteristics of the survey area. Because the space of inverted Vs models is constrained and thus narrowed, the inversion non-uniqueness can be reduced. Furthermore, the mean squared error, which is commonly used as loss function, may cause a poor fitting accuracy of phase velocities at high frequencies in near-surface applications. To make the fitting accuracy evenly in all frequency bands, we modify the loss function into a weighted mean squared relative error. We designed a convolutional neural network (CNN) to directly invert fundamental-mode Rayleigh-wave phase velocity for 1-D Vs models. To verify the feasibility and reliability of the proposed algorithm, we tested and compared it with the Levenberg–Marquardt (L-M) inversion and neighbourhood algorithm (NA) using field data from the Lawrence experiment (USA) and the Wuwei experiment (China). In both experiments, the inverted Vs models by CNN are consistent with the borehole information and are similar to that from existing methods after fine tuning of model parameters. The average root mean squares errors (RMSEs) of the CNN, NA and L-M methods are also similar, except in the Lawrence experiment, the RMSE of CNN is 17.33 m s−1 lower than previous studies using the L-M method. Moreover, the comparison of different loss functions for the Wuwei experiment indicates that the modified loss function can achieve higher accuracy than the traditional one. The proposed CNN is therefore ideally suited for rapid, repeated near-surface subsurface imaging and monitoring under similar geological settings.

Calculation of normal and leaky modes for horizontal stratified models based on a semi-analytical spectral element method

SUMMARY The dispersion curves of surface waves controlled by normal modes have been widely used for the retrieval of subsurface structures. However, only the S-wave velocity structures can be retrieved in most cases because normal modes are primarily sensitive to S-wave velocities. Compared with normal modes, leaky modes, which depend on subsurface structures as well and are more sensitive to P-wave velocities, are rarely applied for subsurface imaging. Besides the difficulties of extracting leaky modes from field data, the calculation of leaky modes also prevents the practical application because traditional methods need to search the leaky-mode roots in the complex frequency-wavenumber domain and thus suffer from root skipping. Recently, some new observation methods support the extraction of multi-order leaky mode dispersion, an effective method for calculating leaky modes is consequently required for further investigation. In this paper, a semi-analytical spectral element method (SASEM) is proposed to solve for the normal and leaky modes of elastic waves propagating in a stratified model with a half-space substrate. The transparent boundary condition and semi-infinite element method are used to model the elastic wavefields in the half-space substrate. Then, a linear eigenvalue problem is derived for the modal calculation. Through simple eigenvalue decomposition, we can obtain the solutions of both normal and leaky modes stably and efficiently without any prior estimations, which makes SASEM very friendly to forward modelling and inversion. Several numerical tests were performed to verify the effectiveness of SASEM, as well as to demonstrate its features of high accuracy and no root skipping. Besides the models composed of several homogeneous layers, SASEM was applied to a vertically inhomogeneous offshore model to demonstrate its wide applicability. Analyses on the oscillations of the solved modes show that the leaky modes differ from the normal modes because of the increasing wavefields in the half-space. Moreover, modal analyses confirm that a part of the leaky modes (guided-P modes) is more dependent on the P waves, whereas the other modes are primarily determined by the S waves. Consequently, the effective calculation of leaky modes makes it possible to constrain the P-wave velocity using leaky-mode dispersion curves.

Comparison of ground-based and airborne transient electromagnetic methods for mapping glacial and permafrost environments: Cases from McMurdo Dry Valleys, Antarctica

The transient electromagnetic (TEM) method is a non-invasive geophysical tool well-suited for subsurface imaging in cold and polar regions, where common targets are associated with strong contrasts in electrical resistivity. By imaging the electrical properties of the subsurface, the TEM methods can discriminate between geological units such as frozen ground (permafrost), fresh/saline groundwater systems, and bedrock/glacier ice. In this study, we compare TEM data acquired with ground-based and airborne TEM systems. We demonstrate the mapping capabilities of these two approaches in high latitude polar environments with datasets from Taylor Glacier, Lake Vanda, and Canada Glacier in the McMurdo Dry Valleys of Antarctica. The results show a high consistency between the airborne and ground-based TEM data, both with a high resolution and a deep penetration depth down to hundreds of meters due to the resistive background material, which makes both approaches capable of mapping hydrological systems and identifying the base of glaciers. The airborne TEM approach offers an unmatched spatial data coverage in difficult terrain and a far improved lateral resolution compared to the static ground-based system. The ground-based TEM system offers the possibility for using larger transmitter coils and longer stacking times and therefore has potential for reaching deeper penetration depths. The ground-based TEM approach is hence a valuable tool that can provide consistent imaging results while also being far more accessible in terms of cost and field logistics compared to an airborne TEM campaign.

Geomechanical Interferometry: Theory and Application to Time-Lapse Interferometric Synthetic Aperture Radar Data for Separating Displacement Signal Between Overburden and Reservoir Sources

Summary Interferometric synthetic aperture radar (InSAR) data provide a measurement of the Earth’s surface displacements to monitor reservoir stresses, fluid pressure, and volume changes. However, the InSAR measurements may suffer from poor sensitivity and resolution. To improve the sensitivity of the InSAR data and localize the effects of the near-surface overburden, we employ a Green’s function retrieval (GFR) approach that uses time-lapse InSAR data. In this work, we derive the equations and compute the sensitivity between InSAR displacements caused by the reservoir changes with respect to observation points (i.e., virtual sources) at the surface. We present this method with time-lapse InSAR data from an oil field in the San Joaquin Valley to demonstrate the improved resolution of the GFR-InSAR measurements for subsurface imaging and continuous reservoir monitoring with applications to development, production, and subsurface integrity.

Seasonal Impact on 3D GPR Performance for Surveying Yedoma Ice Complex Deposits

Ground-penetrating radar (GPR) is a popular geophysical method for imaging subsurface structures with a resolution at decimeter scale, which is based on the emission, propagation, and reflection of electromagnetic waves. GPR surveys for imaging the cryosphere benefit from the typically highly resistive conditions in frozen ground, resulting in low electromagnetic attenuation and, thus, an increased penetration depth. In permafrost environments, seasonal changes might affect not only GPR performance in terms of vertical resolution, attenuation, and penetration depth, but also regarding the general complexity of data (e.g., due to multiple reflections at thaw boundaries). The experimental setup of our study comparing seasonal differences of summertime thawed and winter- and springtime frozen active layer conditions above ice-rich permafrost allows for estimating advantages and disadvantages of both scenarios. Our results demonstrate major differences in the data and the final GPR image and, thus, will help in future studies to decide about particular survey seasons based on the GPR potential for non-invasive and high-resolution investigations of permafrost properties.

3D Migration Depth Focus Velocity Analysis of Hand-Held Ground Penetrating Radar

Hand-held ground penetrating radar (GPR) systems have been widely applied to landmine detections during recent decades. The accuracy of an imaging result by migration for a hand-held GPR is strongly related to the accuracy of subsurface velocity distribution obtained from multi offset data. For shallow targets like landmines, the hyperbolas are usually not distinct in 2D slices and are masked by the surface reflections. In this article, we propose a 3D migration depth focus velocity analysis method for hand-held GPRs to estimate the background velocity of the subsurface. This method is performed based on the images generated by migrations. The objective function is defined as the proportion of the target on the depth slice containing the target. After migrating a GPR radargram with different velocities, the background velocity, which minimizes the objective function, can be determined by comparing the imaging results by migration using different velocities. To test the proposed method, we apply this procedure to experimental GPR data collected with an advanced landmine imaging system (ALIS) in the laboratory. Subsequently, the velocity of the background is obtained, 3D diffraction migration with the obtained velocity achieves subsurface imaging with high quality. The accurate position and depth of the target are obtained from the optimal migration image.

Improving receiver function imaging with high-resolution Radon transform

SUMMARY The receiver function (RF) method is one of the most widely used passive seismic approaches for imaging subsurface structures from shallow sedimentary basins to the deep Earth’s interior. RF is typically computed with a deconvolution operation that enables separating structural response from source–time function embedded in teleseismic wavefields. However, seismic recordings of temporary arrays are often contaminated with strong random noise and even amplitude bias due to poor installation conditions. Both factors can cause instability to deconvolution and severely degrade the accuracy, thereby the imaging quality, of the RF method. This underscores the importance of effective denoising algorithms in RF processing. In this study, we explore the application of high-resolution Radon transform (RT) to improve the conventional RF imaging workflow. Contrary to the commonly implemented post-processing (i.e. after deconvolution) noise suppression schemes, we introduce the RT to data pre-processing (i.e. before deconvolution). This method seeks a sparse representation of teleseismic wavefields in the τ–p domain by iteratively solving a least-squares minimization problem with the conjugate gradient algorithm. Synthetic test with a 2-D step-Moho model shows that non-linear phase arrivals including incoherent noise and diffraction energy are effectively removed in resulting RFs, with the signal-to-noise ratio increased by as much as ∼8 dB. Real data experiments using the Hi-CLIMB network in the Tibetan Plateau demonstrate the superior performance of the proposed workflow in regularizing the wavefield and improving the coherence of converted phases across the recording array. Consequently, imaging results of common conversion point stacking using a single and a group of teleseismic events both recover subtle converted phases from the Moho and potential lithospheric discontinuities that are otherwise obscured by noise arising from the conventional processing workflow. This study highlights the necessity of wavefield regularization in the RF method and calls for improved data processing techniques in array-based seismic imaging.

MCMC Method of Inverse Problems Using a Neural Network—Application in GPR Crosshole Full Waveform Inversion: A Numerical Simulation Study

Ground-penetrating radar (GPR) crosshole tomography is widely applied to subsurface media images. However, the inadequacies of ray methods may limit the resolution of crosshole radar images, since the ray method is a type of high-frequency approximation. To solve this problem, the full waveform method is introduced for GPR inversion. However, full waveform inversion is computationally expensive. In this paper, we introduce a trained neural network that can be evaluated very quickly to replace a computationally intensive forward model. Additionally, the forward error of the trained neural network can be statistically analyzed. We demonstrate a methodology for a full waveform inversion of crosshole ground-penetrating radar data using the Markov chain Monte Carlo (MCMC) method. An accurate forward model based on Maxwell’s equations is replaced by a quickly trained neural network. This method achieves a high computation efficiency, which is four orders of magnitude faster than the accurate forward model. The inversion result of the synthetic waveform data shows a good performance of the trained neural network, which greatly improves the calculation efficiency.

Imaging Subsurface Structures at Fast Eroding Coastal Areas in Northern Bengkulu Using 2D Seismic MASW Method

Imaging Subsurface Structures at Fast Eroding Coastal Areas in Northern Bengkulu Using 2D Seismic MASW Method

Trends in volcano seismology: 2010 to 2020 and beyond

Volcano seismology has been fundamental to our current understanding of crustal magma migration and eruption. The increasing availability of portable seismic networks with the creative use of seismic sources and ambient noise has led to a better understanding of the volcanic structure of many volcanoes and is producing increasingly detailed images of the volcanic subsurface. The past decade (2010-2020) has seen advances in our understanding of seismic sources under and surrounding volcanoes through precise locations, and through analysis of source mechanisms from seismic signals that are more varied and smaller in magnitude, reaching beyond traditional techniques. In tandem with continued research on fundamental physics-based understanding of volcano-seismic sources, new advances in computational analyses including machine learning methods will push our understanding of volcanic processes into the future. Incorporation of multidisciplinary geophysical observations (especially infrasound) has become commonplace, and our understanding of infrasound propagation and sources will feed back into our ability to monitor ongoing eruptions and surficial mass movements. Open-source codes will permit widespread evaluation and adoption of new methodologies for volcano-seismic analysis and inversion. Combined with quantitative and conceptual source models using improved structural constraints, these new methodologies will better characterize the range of volcano-seismic signal evolution scenarios and hold promise for creating better short-term forecasts. Finally, permanent instrumentation is available on an expanding range of volcanoes, and open data policies are increasingly making these data available to the scientific community in near real time.

High-resolution velocity estimation from surface-based common-offset GPR reflection data

SUMMARY Surface-based common-offset ground-penetrating radar (GPR) reflection profiling is a popular geophysical exploration technique for obtaining high-resolution images of the shallow subsurface in a cost-effective manner. One drawback of this technique is that, without complementary borehole information in form of dielectric permittivity and/or porosity logs along the profile, it is currently not possible to obtain reliable estimates of the high-frequency electromagnetic velocity distribution of the probed subsurface region. This is problematic because adequate knowledge of the velocity is needed for accurate imaging and depth conversion of the data, as well as for quantifying the distribution of soil water content. To overcome this issue, we have developed a novel methodology for estimating the detailed subsurface velocity structure from common-offset GPR reflection measurements, which does not require additional conditioning information. The proposed approach combines two key components: diffraction analysis is used to infer the smooth, large-scale component of the velocity distribution, whereas the superimposed small-scale fluctuations are inferred via inversion of the reflected wavefield. We test and validate our method on two synthetic data sets having increasing degrees of complexity and realism before applying it to a field example from the Boise Hydrogeophysical Research Site, where independent control data in the form of neutron–neutron porosity logs are available for validation. The results obtained demonstrate the viability and robustness of the proposed approach. Further, due to its efficiency, both in terms of field effort and computational cost, the method can be readily extended to 3-D, which further enhances its attractiveness compared to multi-offset-based GPR velocity estimation techniques.

Nondestructive inspection of packaged microcircuits by aperture-type terahertz near-field imaging

A scanning aperture-type terahertz near-field imaging system is developed to perform precise nondestructive testing on packaged aluminum etched antenna arrays, and surface imaging of a flexible gold electrode array, to obtain subsurface imaging and surface imaging. The resolution of subsurface imaging is 110 and 500 μm when the packaging (polyethylene terephthalate) thickness is 50 and 200 μm respectively, and the surface imaging resolution is 6 μm at 0.11 THz. An adaptive threshold segmentation algorithm of Hilbert scan combined with wavelet transform were employed to successfully suppress the noise of the imaging results due to the change of near-field distance. We also proposed a derivative filtering extremum threshold segmentation algorithm to effectively suppress severely uneven threshold distribution of the packaged aluminum etched antenna arrays.

Simulating the stratigraphy of meandering channels and point bars of Cretaceous system using spectral decomposition tool, Southwest Pakistan: Implications for petroleum exploration

Fluvial channels (FC) and point bars sandstone (PDs) sedimentary fills form primary brilliant stratigraphic petroleum systems within the transgressive system tract (TST). They are developed during the rapid rise and subsequent standstill of sea-level, which fills the reservoirs with FC and PD. These petroleum systems are ultra-thin-bedded (UTB) due to the very small vertical thickness of TST within the complete depositional sequences. Hence, they create hurdles in the prediction of lithology and porosity for these stratigraphic traps. The continuous wavelet transform (CWT) is the only sub-surface seismic imaging tool of spectral decomposition that can resolve the thin-beds of petroleum-bearing stratigraphic traps. This method removes the tuning effects of ambiguous lithology and fluids that masks the reservoir zones, and hence, generates false bright spots. This method decomposes the conventional seismic data into its constituent frequency components without fixing the vertical time window for the stratigraphic systems. These frequency components are further used for the prediction of lithology and porosity for resolving sub-surface depositional systems. Therefore, the CWT has delineated the fluvial sandstone facies, fractures connectivity, and architecture of the fluvial depositional system during the sea-level fluctuations. The 48-Hz has imaged the regional standstill of sea level, which have accumulated the porous sedimentary facies of coarse-grained FC andPDs. A very local sea-level rise was imaged in the eastern to the central part of this petroleum system. This sea-level fluctuation has implicated that there were minor cycles of transgressive sea-level, which has developed a pure stratigraphic play. The trace envelope attribute experienced a poor correlation coefficient with R2<0.2. The 48-Hz CWT experienced a correlation coefficient of R2>0.91 for FC and PDs. The conventional inverted reservoir simulation has imaged the low-frequency variance inside ~15-31% porous zones of FC and PDs. The 48-Hz volume has imaged the laterally continuous and hydrocarbon-bearing reservoirs with >25% porosity. 48-Hz CWT model also has imaged the coarse-grained FC of aggradational meandering channel stream, which were implicated to develop during stand-still of sea-level. These sandstone lenses were enclosed at the top by progradational shale facies of the highstand system tract (HST) and bottom and lateral by the shales of the TST. This workflow serves as an analogue within the SE-Asian sedimentary Basins and similar worldwide geological basins.