Seismic surface waves that were likely converted from incident body waves were used to detect a 3 m deep tunnel using two novel processing methods. In data acquired at a tunnel test site, a unique forward-propagating wave (traveling away from the tunnel and seismic source) was identified as an early-arriving surface wave converted at the tunnel from an incident body wave. To our knowledge, our research

Ultradeep carbonate reservoirs have high temperatures and pressures, complex pressure/tectonic stress settings, and complex pore structures. These conditions make their seismic detection and characterization difficult, particularly if the signal-to-noise ratio is low, as is the case in most situations. Moreover, the high risk of deep-drilling exploration makes it impractical to carry out normal logging

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT)

Sedimentary rocks are often heterogeneous porous media inherently containing complex distributions of heterogeneities (e.g., fluid patches and cracks). Understanding and modeling their frequency-dependent elastic and adsorption behaviors is of great interest for subsurface rock characterization from multiscale geophysical measurements. The physical parameter of dynamic volumetric strain (DVS) associated

Reservoir integrity stewardship accompanying carbon capture and sequestration considers fluid extraction and reinjection as a risk-mitigating method against overpressuring that could lead to caprock damage and ensuing CO2 leakage. Crosswell electromagnetics offers a technically viable monitoring method with the spatial volume coverage necessary for reservoir-encompassing pressure management. However

We have developed a memory and operation-count efficient 2.5D inversion algorithm of electrical resistivity (ER) data that can handle fine discretization domains imposed by other geophysical (e.g, ground penetrating radar or seismic) data. Due to numerical stability criteria and available computational memory, joint inversion of different types of geophysical data can impose different grid discretization

We have combined electrical resistivity tomography (ERT), geologic information from boreholes and outcrops, and hydrogeologic data to investigate field-scale fault-zone cementation of the Loma Blanca Fault in the Rio Grande Rift. We have collected electrical resistivity data from 16 transects and geologic samples from 29 boreholes (completed as groundwater wells to 30 m depth) across and around the

Accurate removal of surface-related multiples remains a challenge in shallow-water cases. One reason is that the success of surface-related multiple estimation (SRME)-related algorithms is sensitive to the quality of the near-offset reconstruction. When it comes to a larger missing gap and a shallower water bottom, the state-of-the-art near-offset gap construction method — the parabolic Radon transform

During the past few decades, fiber-optic-based distributed acoustic sensing (DAS) has emerged as an affordable, easy-to-deploy, reliable, and noninvasive technique for high-resolution seismic sensing. We have determined that fiber deployments dedicated to near-surface seismic applications, commonly used for the detection and localization of voids, can be used effectively with conventional processing

I have examined the possibility of using a 3D modeling algorithm in studying tunnel detectability using the electromagnetic gradiometer (EMG) response. A detailed comparison of different source-receiver configurations reveals that the EMG response is stronger for a configuration when the transmitter and receiver are orthogonal to each other than the parallel configurations. Orthogonal configurations

The multilayer nature of airport pavement structures is susceptible to the generation of voids at the bonding parts of the structure, which is also called interlayer debonding. Observations have shown that the thickness of the resulting voids is usually at the scale of millimeters, which makes it difficult to inspect. The efficient and accurate characteristics of ground penetrating radar (GPR) make

Reduced-rank filtering is a common method for attenuating noise in seismic data. Because conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio (S/N) is very low or when data contain high levels of isolate or coherent noise. Therefore, we have developed a novel and robust reduced-rank filtering method

Seismic data are often incomplete due to equipment malfunction, limited source and receiver placement at near and far offsets, and missing crossline data. Seismic data contain redundancies because they are repeatedly recorded over the same or adjacent subsurface regions, causing the data to have a low-rank structure. To recover missing data, one can organize the data into a multidimensional array or

Eikonal solvers have important applications in seismic data processing and inversion, the so-called image-guided methods. To this day, in image-guided applications, the solution of the eikonal equation is implemented using partial-differential-equation solvers, such as fast-marching or fast-sweeping methods. We have found that alternatively, one can numerically integrate the dynamic Hamiltonian system

Least-squares migration (LSM) is an effective technique for mitigating blurring effects and migration artifacts generated by limited data frequency bandwidth, incomplete coverage of geometry, source signature, and unbalanced amplitudes caused by complex wavefield propagation in the subsurface. Migration deconvolution (MD) is an image-domain approach for LSM that approximates the Hessian operator using

Surface-related-multiple wavefields constitute redundant information in conventional migration and can often be difficult to attenuate. However, when used for migration, multiple wavefields can improve subsurface illumination. Unfortunately, the process of imaging using multiples involves the management of crosstalk, which largely restricts its application. Crosstalk causes phantom images formed by

We have introduced a Bayesian neural network in quantitative log prediction studies with the goal of improving the petrophysical characterization and quantifying the uncertainty of model predictions. Neural network (NN) methods are gaining popularity in the petrophysics and geophysics communities; however, uncertainty quantification in model predictions is often neglected in the available literature

Stress-sensitive permeability (SSP) influences gas well productivity and is a crucial element influencing gas reservoir development. SSP for high-pressure fractured gas reservoirs with an initial reservoir pressure of more than 20 MPa has never been comprehensively evaluated to the best of our knowledge. SSP experiments with special procedures were designed by adopting the variable confining pressure

With the expanding size of 3D seismic data, manual seismic interpretation becomes time-consuming and labor-intensive. For automating this process, recent progress in machine learning, in particular the convolutional neural network (CNN), has been introduced into the seismic community and successfully implemented for interpreting seismic structural and stratigraphic features. In principle, such automation

The irregular interface model setting side by side two dense homogeneous media has found many applications in gravity-data exploration such as for petroleum and gas in sedimentary basins, groundwater resources in buried paleochannels, characterization of abandoned landfills, and variable regolith-depth mapping. Despite its simplicity and wide range of applicability, the determination of the interface

Aligned fractures/cracks in rocks are a primary source of elastic anisotropy. In an azimuthally anisotropic formation surrounding a borehole, shear-waves (S-waves) split into fast and slow waves that propagate along the borehole and are recorded by a borehole logging tool. However, when the formation has conjugate fractures with orthogonal strike directions, the azimuthal anisotropy vanishes. Hence

Monitoring the shear modulus of formations around boreholes is of interest for various applications, ranging from near-surface investigation to reservoir monitoring. Downhole logging tools and borehole seismic are common techniques applied to measure and characterize formation properties. These methods rely on transmitted and reflected waves to retrieve the rock properties. Wave modes traveling along

Seismic methods are an affordable and effective way of studying the subsurface for mineral exploration. With the goal of testing new technologies for mineral exploration in highly challenging mining areas, in early 2019, an innovative seismic survey was conducted at the Neves-Corvo mine, south Portugal. We have focused on the data and results from the surface array data, whereas other work deals with

To evaluate and upscale the feasibility of using exploration tunnels in an operating mine for active-source seismic imaging, a seismic experiment was conducted at the Neves-Corvo mine, in southern Portugal. Four seismic profiles were deployed in exploration drifts approximately 650 m beneath the ground surface, above the world-class Lombador volcanogenic massive sulfide deposit. In addition to the

The prediction of dispersion curves is an essential part of understanding borehole acoustic modes. Traditional dispersion curve calculation requires finding zeros of the determinant of the characteristic matrix of a borehole system. This approach is subject to interpretational ambiguity because the determinant can be scaled arbitrarily. We have developed an alternative approach based on the reciprocal

Sparse-spike deconvolution (SSD) is an important method for seismic resolution enhancement. With the wavelet given, many trace-by-trace SSD methods have been proposed for extracting an estimate of the reflection-coefficient series from stacked traces. The main drawbacks of trace-by-trace methods are that they neither use the information from the adjacent seismograms nor do they take full advantage

Neural networks hold substantial promise for automating various processing and interpretation tasks. Yet, their performance is often suboptimal compared with standard but more closely guided approaches. The lack of performance is often attributed to poor generalization, in particular if fewer training examples are provided than free parameters that exist in the machine-learning algorithm. In this case

Thermoelasticity is important in seismic propagation due to the effects related to wave attenuation and velocity dispersion. We have applied a novel finite-difference (FD) solver of the Lord-Shulman thermoelasticity equations to compute synthetic seismograms that include the effects of the thermal properties (expansion coefficient, thermal conductivity, and specific heat) compared with the classic

Least-squares migration is an advanced imaging technique capable of producing images with improved spatial resolution, balanced illumination, and reduced migration artifacts; however, the prohibitive computational cost poses a great challenge for its practical application. We have incorporated the beam methodology into the implementation of Kirchhoff time modeling/migration and developed a fast common-offset

Least-squares reverse time migration (RTM) has become the method of choice for quantitative seismic imaging. The main drawback of such a scheme is that it requires many migration/modeling cycles. The convergence of least-squares RTM can be accelerated by using a suitable preconditioner. In the context of an extended domain in variable-density acoustic media, the pseudoinverse Born operator is the recommended

Wavefield decomposition can be used to extract effective information in reverse time migration and full-waveform inversion. The wavefield decomposition methods based on the Hilbert transform (HTWD) and the Poynting vector (PVWD) are the most commonly used. The HTWD needs to save the wavefields at all time steps or introduce additional numerical simulation, which increases the computational cost. PVWD

We consider seismic amplitude variation with offset (AVO) inversion for prediction of the reservoir properties porosity and water saturation. An oil reservoir at the initial state is studied; hence, gravitational effects dominate and keep hydrocarbons from mixing with water. Histograms of observations of water saturation along wells are consequently clearly bimodal, which is challenging to model. The

Nowadays, to obtain a better understanding of dynamic time-lapse changes, frequent seismic monitoring is necessary, although it will generate a considerable cost increase. Therefore, low-cost frequent monitoring, e.g., sparse and/or nonrepeated surveys, is desired. The simultaneous inversion-based method allows the baseline and monitor parameters to communicate and compensate with each other during

Vehicle-induced vibrations provide useful signals for passive seismic exploration. Such signals are repeatable and environmentally friendly; hence, they can provide an economical way to analyze subsurface structures. We have developed a new workflow to monitor roads or railways by producing 1D subsurface S-wave velocities in real time. This workflow consists of two steps: seismic interferometry and

Low-frequency shadows are frequently interpreted as attenuation phenomena due to partial saturation with free gas. However, several researchers have argued that shadows are not necessarily a simple attenuation phenomenon because low-frequency energy must have been added or amplified by some physical or numerical process. Attenuation alone should attenuate higher frequencies, not boost lower frequencies

Quantitative estimation of rock-physics properties is an important part of reservoir characterization. Most current seismic workflows in this field are based on amplitude variation with offset. Building on recent work on high-resolution multiparameter inversion for reservoir characterization, we have constructed a rock-physics parameterized elastic full-waveform inversion (EFWI) scheme. Within a suitably

Seismic image registration is crucial for the joint interpretation of multivintage seismic images in time-lapse reservoir monitoring. Time-shift analysis is a commonly used method to estimate the warping function by creating a time-shift map, where the energy of each time-shift point in the 3D map indicates the probability of a correct registration. We have adopted a new method to obtain a high-resolution

We have developed a MATLAB-based inversion program, b-spline polynomial approximation using the differential evolution algorithm (SPODEA), to recover the concealed basement geometry under heterogeneous sedimentary basins. Earlier inversion techniques used the discretized subsurface interface topography into a grid of juxtaposed elementary prisms to estimate the basement depth of a basin. Such discretization

Machine learning, and specifically deep-learning (DL) techniques applied to geophysical inverse problems, is an attractive subject, which has promising potential and, at the same time, presents some challenges in practical implementation. Some obstacles relate to scarce knowledge of the searched geologic structures, a problem that can limit the interpretability and generalizability of the trained DL

The smoke-ring concept is a useful device for understanding how the electromagnetic fields induced in a 1D earth propagate and diffuse through a medium. Aside from facilitating a physical understanding of field propagation, the smoke-ring concept has been used to interpret behavior of vertical and radial magnetic fields at the surface and used to estimate depth of penetration for conductivity-depth

Controlled-source electromagnetic (CSEM) data recorded in industrialized areas are inevitably contaminated by strong cultural noise. Traditional noise attenuation methods are often ineffective for intricate aperiodic noise. To address the abovementioned problem, we have developed a novel noise isolation method based on the fast Fourier transform, complementary ensemble empirical mode decomposition

In geothermal exploration, magnetotelluric (MT) data and inversion models are commonly used to image shallow conductors typically associated with the presence of an electrically conductive clay cap that overlies the main reservoir. However, these inversion models suffer from nonuniqueness and uncertainty, and the inclusion of useful geologic information is still limited. We have developed a Bayesian

Detection of pore types and diagenetic features from seismic data is a major challenge for the evaluation of carbonate reservoirs in the subsurface. Based on a detailed petrographical and petrophysical analysis of carbonate rock using optical and scanning electron microscopy, mercury-injection measurements, digital image analysis, and well logs, we have determined the potential of the geophysical pore

The modern use of clandestine tunnels has antagonized military and law enforcement agencies alike over the past 50 years, not only in their simplicity and effectiveness, but also in the challenges of defending against them. Likewise, geophysicists have been confounded, finding what should be a straightforward target on paper — detecting an anomaly with often vastly different physical properties than

At the site of a water drainage shaft on the campus of Qatar University that serves as a man-made karst analog, two seismic imaging techniques were adapted to use resonant scattered waves recorded during active-source seismic surveys and during passive ambient-noise surveys. Data acquisition included two seismic transmission surveys that encompassed the shaft and a passive ambient-noise survey that

Cavities under urban roads have increasingly become a great threat to traffic safety in many cities. As a quick, effective, and high-resolution geophysical method, ground-penetrating radar (GPR) has been widely used to detect and image near-surface objects. However, the interpretation of field GPR data remains challenging. For example, it is hard to distinguish reflections caused by road cavities or

Predictive filtering (PF) in the frequency domain is one of the most widely used denoising algorithms in seismic data processing. PF is based on the assumption of linear or planar events in the time-space domain. In traditional PF methods, a predictive filter is fixed across the spatial dimension, which cannot deal with spatial variations in seismic data well. To handle the curved events, the predictive

The determination of subsurface elastic property models is crucial in quantitative seismic data processing and interpretation. This problem is commonly solved by deterministic physical methods, such as tomography or full-waveform inversion. However, these methods are entirely local and require accurate initial models. Deep learning represents a plausible class of methods for seismic inversion, which

Seismic high-resolution processing plays a critical role in reservoir target detection. As one of the most common approaches, regularization can achieve a high-resolution inversion result. However, the performance of regularization depends on the settings of the associated parameters and constraint functions. Further, it is difficult to solve an objective function with complex constraints, and it requires

Segmentation of faults based on seismic images is an important step in reservoir characterization. With the recent developments of deep-learning methods and the availability of massive computing power, automatic interpretation of seismic faults has become possible. The likelihood of occurrence for a fault can be quantified using a sigmoid function. Our goal is to quantify the fault model uncertainty

An elliptical anisotropic medium is defined as a simplified representation of anisotropy in which the anelliptic parameters are set to zero in all symmetry planes. Despite the fact that this model is rather seldom observed for real rocks, it is often used as a reference model. The P-wave equations for an elliptical anisotropic medium are well known. However, the shear wave (S-wave) equations have not

We have developed a transient electromagnetic (TEM) study in the noisy urban environment of the megacity Santiago de Chile. Our investigation characterizes the electrical conductivity structure of the Santiago Basin down to 300 m depth, providing key information about the sedimentary infill, hydrogeologic aspects, and geomorphological units. In total, 52 TEM soundings were recorded over roughly 900 km2

The semiautomatic seismic refraction supervirtual interferometry (SVI) algorithm has been developed to improve the conventional SVI method. The conventional SVI method uses convolution techniques and involves the raw trace, which reintroduces noise back into the enhanced trace. However, the semiautomatic method uses a first-arrival reference picked from a raw trace to compute the arrival times of all

We have studied seismoelectric (SE) surface-wave signals and found that they can be used to infer changes in the SE coupling properties at depth. SE surface-wave signals have much higher amplitudes than SE body-wave signals. We measured the seismic and the electrical potential or electromagnetic (EM) field along the surface of the earth. We used dispersive relative spectral amplitudes (DRSAs) that

Seismic fault surfaces are compulsory input for structure modeling that unravels the structural deformation history of the subsurface. Seismic fault attributes provide geoscientists with alternative images of faults. However, seismic fault attributes only highlight possible fault locations and do not directly provide fault surfaces that are required inputs for structural modeling. Interpreters construct

Seismic diffractions carry the signature of near-surface high-contrast anomalies and need to be extracted from the data to complement the reflection processing and other geophysical techniques. Because diffractions are often masked by reflections, surface waves, and noise, careful diffraction separation is required as a first step for diffraction imaging. A multiparameter time-imaging method is used

Detecting small-size objects is a primary challenge at archaeological sites due to the high degree of heterogeneity present in the near surface. Although high-resolution reflection seismic imaging often delivers the target resolution of the subsurface in different near-surface settings, the standard processing for obtaining an image of the subsurface is not suitable to map local diffractors. This happens

It is well known that source deghosting can best be applied to common-receiver gathers, whereas receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in the common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative

Sparse solutions of linear systems of equations are essential in many applications of seismic data processing. These systems arise in many denoising algorithms, such as those that use Radon transforms. We have developed a robust matching pursuit (RMP) algorithm for the retrieval of sparse Radon domain coefficients. The algorithm is robust to outliers and, hence, is applicable for seismic data deblending

We have developed the first experimental acoustic properties measurement during gas-hydrate formation and dissociation in crushed bituminous coal samples. We also compare the results with acoustic properties measurements during freezing and thawing water in the same samples. The results show a more complicated behavior that differs from similar experiments with sand. For the samples with adsorbed water