Wave Travel Times Research Articles

Integrated Bayesian Approach for Station Location and Orientation in Marine Active-Source Seismic Studies

In marine active-source seismic studies wherein ocean bottom seismographs are deployed via free-fall from a ship, accurately determining the location and orientation of the instrument poses challenges, especially when relying solely on the travel times of water-borne waves from air gun shots to the station, particularly with a limited distribution of shots. In this study we develop and investigate the utility of a novel methodology that integrates both travel time and waveform polarization data of acoustic waves to enhance the precision of station location. Utilizing polarization data has the potential to significantly reduce location uncertainty, particularly in the across-line direction, and provides a valuable estimate of the orientation of the horizontal channels. Employing Bayesian inference allows for mapping the relative likelihood of station position, considering tradeoffs due to shot geometry and several embedded assumptions. We explore the utility of particle motion polarization information to enhance instrument location and discuss various factors that come into play when employing polarization measurements.

A Novel Fault Location Method Based on ICEEMDAN-NTEO and Ghost-Asf-YOLOv8

Background: The rapid growth of distribution grids and the increase in load demand have made distribution grids play a crucial role in urban development. However, distribution networks are prone to failures due to multiple events. These faults not only incur high maintenance costs, but also result in reduced productivity as well as huge economic losses. Therefore, accurate and fast fault localization methods are very important for the safe and stable operation of distribution systems. Methods: Firstly, the Ghost-Asf-YOLOv8 network is employed to assess the three-phase fault voltage travelling waveforms at both ends of the line, determine the temporal range of the fault occurrence, and differentiate its line mode components. Subsequently, the ICCEMDAN algorithm is employed to decompose the line mode components, thereby yielding the IMF1 components. The key feature information is then enhanced through the application of NTEO. Finally, the Ghost-Asf-YOLOv8 network is employed to further narrow down the time range of the initial traveling wave head, thereby enabling the calculation of the fault location and the determination of the traveling wave arrival time. Results: Experiments are conducted based on the simulation data of the constructed hybrid line model, and the comparison experiments between the TEO algorithm and the NTEO algorithm are conducted, which show that the NTEO has good noise immunity when applied to fault localization. In addition, the proposed ICCEMDAN-NTEO method is also compared with the fault localization methods based on DWT and HHT, and the results show that the method has high accuracy. Finally, the light weighted YOLOv8 model captures the traveling wave time quickly and accurately to compensate for the shortcomings of the visualization data. Conclusion: This work presents a novel fault localization method that integrates traditional and artificial intelligence techniques, offering rapid detection and minimal localization error.

Modal and Wave Propagation Analysis of Vibration Tests on a Laboratory Building Model Before and After Damage

Weakened structural stiffness is often a consequence of building damage, particularly after severe events such as earthquakes, where compromised structural performance can pose significant risks. To prevent immediate structural failure, an early warning system is essential, which requires inspection of local components. This research aims to achieve that by exploring the wave propagation analysis method, specifically seismic interferometry. Previous studies have applied this method to building structures, treating them as homogeneous layers of grouped floors. By analyzing the wave travel time along the height of these layers, the fundamental period of the building was estimated. However, this approach did not account for local damage or the variability of structural components, similar to the limitations of vibration‐based damage detection methods, which mainly identify global changes. Thus, the goal of this paper is to improve structural health monitoring by examining the sensitivity of wave screening, bridging the gap between nondestructive testing and vibration‐based damage detection. A half‐scale, seven‐story building model, characterized by vertical stiffness irregularity and transverse plan asymmetry, was tested in a laboratory setting. Two vertical sensor arrays were placed near corner columns of different sizes, representing both strong and weak structural areas. These arrays recorded floor accelerations in three directions. The study confirmed the effectiveness of wave propagation analysis for detecting damage along the sensor arrays before and after the earthquake. A transmissibility damage indicator was used to correlate changes in wave velocity, providing a quantitative assessment of damage levels along the wave propagation path.

Shear Wave Travel Time Prediction using Well Log Filtering and Machine Learning

Shear wave travel time (also known as Delta-T Shear and commonly abbreviated as DTS) is an important parameter in petroleum for exploration, production, and characterization of borehole stability. Direct measurement of DTS is often limited by high costs and a constraint of geography, making machine learning (ML) predictive approaches necessary. This study aims to explore the effectiveness of ML models in predicting DTS, emphasizing the importance of data preprocessing techniques to improve prediction accuracy. Preprocessing techniques include Yeo-Johnson transformation to handle non-normality, outlier elimination using z-score, and data smoothing using the Savitzky-Golay filter and median filter. Incorporating smoothing techniques can fill important gaps in some existing studies and may improve the performance of machine learning models in predicting DTS, particularly in situations with limited or noisy data. Four ML models were tested in this study, namely Linear Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), with performance evaluation based on metrics RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R2 (coefficient of determination). The results showed that the RF model produced the best performance with RMSE of 9.41, MAE of 6.35, and R2 of 0.90 in scenarios with Yeo-Johnson transformation, outlier elimination, and smoothing techniques using a median filter with a window size of 5.

Inversion of Fracture Weakness Parameters Based on the 3CVSP Data—Part II: ORT Media Composed of A Single Fracture Set in the VTI Background Media

The inversion of fracture parameters from seismic data is a key issue in predicting fractured reservoirs. When a set of vertical fractures developed in a background medium of laminated shales or thin interbedded sandstone and mudstone with VTI (transversely isotropic media with a vertical symmetry axis) characteristics, this medium is a typical ORT (orthotropic) medium. In such a medium, due to the presence of fractures, the PS wave in the original VTI medium splits into two types of waves: PS1 and PS2 waves (fast and slow shear waves). To use the travel times of these two waveforms to invert the fracture weakness parameters, we derive approximate formulas for the travel times of PS1 and PS2 waves recorded in a Vertical Seismic Profile (VSP) for horizontally layered media. These approximate formulas are obtained using rational form approximation methods for short offsets with respect to the shot position. This approximation transforms the calculation of travel time from the complex phase velocity domain (the function of the two horizontal slownesses) to the group velocity domain (the function of shot point coordinates). The results from numerical errors analysis indicate that even with a near offset assumption, the approximated formula has high accuracy for offsets close to the depth of the target layer. By utilizing the travel times of PS1 and PS2 waves picked up by velocity under isotropic conditions, combined with undetermined anisotropic parameters and rational form approximations, we establish an objective function for inversion. The inversion results from different azimuths indicate that the accuracy of the inverted anisotropic parameters has different azimuthal sensitivities. The inversion method is applied to field multi-directional 3CVSP data and the results confirm the effectiveness of the method.

Near field tsunamis on volcanic islands: blueprint for risk management using Stromboli as a test bed

At Stromboli, coastline populations are exposed to near-field tsunami generated by flank collapses. Landslides on the Sciara del Fuoco can generate waves arriving in the village in less than a few minutes. Due to the hazard posed on Stromboli by tsunami, we have focused our research on answering three key questions in terms of mitigation: (1) How can a tsunami be detected and characterized in real-time? (2) What will be the likely event scenario and on-island impact of the resulting tsunami? (3) When and where will the tsunami arrive, and what is the evacuation capacity? To this end, we have convolved a method to assess escape times from tsunami-exposed coastal areas, with wave travel times and inundation assessments output from numerical simulations. Here we review the local situation in terms of hazard, risk and mitigation measures, and assess progress to date in preparing for tsunami on Stromboli to explore a blueprint for management actions at any near-field tsunami-exposed community.

Use of molybdenum as a pressure calibrant in a cubic press and sound velocity measurement of tungsten to 5 GPa

ABSTRACT The pressure limit of the conventional cubic press is normally below 6 GPa, and the suitable pressure calibrant of the fixed-point method is scarce. In this study, we developed a new method for pressure determination using an acoustic travel time approach. Based on the reported ultrasonic travel times of polycrystalline molybdenum under high pressure in literature, an acoustic gauge of polycrystalline molybdenum was derived. The shear wave travel times of molybdenum were measured at high pressures and utilized for the pressure calibration in a cubic press. The pressures derived from this new travel time method are in excellent agreement with those from the fixed-point method. Subsequent compressional and shear wave velocities of polycrystalline tungsten were measured up to about 5 GPa at room temperature to further verify this method. This travel time method for pressure calibration is valuable for the sound velocity measurement of other materials in an offline laboratory.

Multicriteria evaluations for sand production potentials: a case study from a producing oil field in the Niger Delta Basin (Nigeria)

Geomechanical evaluation of three wells in an onshore Niger Delta Oil Field in which some reservoirs have histories of free water production was carried out to predict their potential for sand production. The mechanical characteristics of the reservoirs, such as the Poisson ratio (v), shear modulus (G), elastic modulus (E), bulk modulus (Kb), and bulk compressibility, were calculated using petrophysical parameters such as s-velocity (Vs), p-velocity (Vp), gamma ray index (IGR), the volume of shale (Vsh), porosity (), and pseudo factor (q) derived from wireline logs (Cb). Five geomechanical techniques—porosity, acoustic wave travel time, sand production index, Schlumberger sand production index, and shear modulus to bulk compressibility ratio (G/Cb) were used to forecast the potential for sand production in the reservoirs. The porosity values varied from 14 to 27%, which is below the threshold value of 30% for sand production. The acoustic wave travel time ranged from 81.59 to 89.91s/ft, which is below the threshold value of 104s/ft for sand production. The threshold value for the sand production index was < 2.9 ×106psi. The values for the sand production index fell between 11x 106psi and 12.4 x 106psi, which is below the limits for the onset of sanding. The G/Cb values in the reservoirs across the three wells fell between 1.15 x 1012psi2 and 1.43 x 1012psi2 while the threshold value for the Schlumberger sand production index fell between 1.15 x 1012psi2 and 1.43 x 1012psi2. Only the Schlumberger criterion predicted the potential for likely sand production.

Construction and experimental verification of wave velocity model for source location in goaf overlying rock strata

The geological structure of the goaf overlying rock is complex, a consequence of coal mining that has modified the original stratified structure of the sedimentary strata. To enhance the accuracy of microseismic source location in such intricate geological formations, a wave velocity model for the “three zones” goaf was constructed based on natural divisions within the strata using Snell's law and assuming a homogeneous medium. The model took into account the effects of rock deformation and fracture development, enabling the derivation of formulas for microseismic wave propagation path and travel time calculation. Additionally, the concept of equivalent wave velocity was defined. An indoor simulation test using similar materials was conducted to establish a geological model of the goaf. By comparing the errors between the theoretical and measured values of equivalent wave velocity, assessing the locating effects before and after implementing the wave velocity model of the goaf, and verifying the feasibility of the model, it was demonstrated that establishing a wave velocity model based on the characteristics of the strata structure was crucial for improving the accuracy of the microseismic source location. Notably, as the propagation path of microseismic waves in the goaf increased, the equivalent wave velocity decreased. The wave velocity structure in the goaf exhibited nonuniformity, with the relative error between the theoretical and measured values of equivalent wave velocity being limited to 10 %. The incorporation of this established wave velocity model into the location method resulted in a substantial 58.57 % increase in locating accuracy.

Deep‐Learning Phase‐Onset Picker for Deep Earth Seismology: PKIKP Waves

AbstractBody waves traversing the Earth's interior from a seismic source to receivers on the surface carry rich information about its internal structures. Their travel time measurements have been widely used in seismology to constrain Earth's interior at the global scale by mapping the time anomaly along their ray paths. However, picking the travel time of global seismic waves, suitable for studying Earth's fine‐scale structures, requires highly skilled personnel and is often fairly subjective. Here, we report the development of an automatic picker for PKIKP waves traveling through the inner core (IC), especially nearly along Earth's diameters, based on the latest advances in supervised deep learning. A convolutional neural network (CNN) we develop automatically determines the PKIKP onset on vertical seismograms near its theoretical prediction of cataloged earthquakes. As high‐quality manual onset picks of global seismic phases are limited, we employ a scheme to generate a synthetic supervised training data set containing 300,000 waveforms. The PKIKP onsets picked by our trained CNN automatic picker exhibit a mean absolute error of ∼0.5 s compared to 1,503 manual picks, comparable to the estimated human‐picking error. In an integration test, the automatic picks obtained from an extended waveform data set yield a cylindrically anisotropic IC model that agrees well with the models inferred from manual picks, which illustrates the success of this pilot model. This is a significant step closer to harvesting an unprecedented volume of travel time measurements for studying the IC or other regions of the Earth's deep interior.

An approximation method to determine the travel time in total focusing imaging of carbon fiber-reinforced laminates

Abstract It is critical to accurately determine the propagation time in testing objects for ultrasonic array imaging using the Total Focusing Method (TFM). However, it poses challenges for the calculation of wave propagation time in carbon fiber reinforced polymer (CFRP) laminates because of the material anisotropy and inhomogeneity. This paper introduces a novel approach for computing ultrasonic wave travel time to enhance the TFM image of CFRP laminates. This method, termed the layer-by-layer acoustic travel time approximation (L-L TravelTimeAppro), considers the anisotropy within each monolayer and the heterogeneity of CFRP. It approximates the propagation path as straight throughout the material and computes the wave propagation duration utilizing the group velocity in each ply. The application of this method to TFM imaging is referred to as TravelTimeAppro-TFM. Experiments were conducted in a specially designed CFRP laminate. Subsequently, TFM imaging was processed by using propagation time calculated by isotropic, Dijkstra, and L-L TravelTimeAppro methods, respectively. The results indicate that TravelTimeAppro-TFM outperforms isotropic-TFM in terms of imaging amplitude gain with a maximum gain of 4.67 dB. Furthermore, it offers superior computational efficiency compared to Dijkstra-TFM.

Investigation of Phase Shift and Travel Time of Acoustic Waves in the Lower Solar Atmosphere Using Multiheight Velocities

We report and discuss the phase shift and phase travel time of low-frequency (ν < 5.0 mHz) acoustic waves estimated within the photosphere and photosphere–chromosphere interface regions, utilizing multiheight velocities in the quiet Sun. The bisector method has been employed to estimate seven height velocities in the photosphere within the Fe i 6173 Å line scan, while nine height velocities are estimated from the chromospheric Ca ii 8542 Å line scan observations obtained from the narrowband imager instrument installed on the Multi-Application Solar Telescope operational at the Udaipur Solar Observatory, India. Utilizing a fast Fourier transform at each pixel over the full field of view, phase shift and coherence have been estimated. The frequency and height-dependent phase shift integrated over the regions having an absolute line-of-sight magnetic field of less than 10 G indicates the nonevanescent nature of low-frequency acoustic waves within the photosphere and photosphere–chromosphere interface regions. Phase travel time estimated within the photosphere shows nonzero values, aligning with previous simulations and observations. Further, we report that the nonevanescent nature persists beyond the photosphere, encompassing the photospheric–chromospheric height range. We discuss possible factors contributing to the nonevanescent nature of low-frequency acoustic waves. Additionally, our observations reveal a downward propagation of high-frequency acoustic waves indicating refraction from higher layers in the solar atmosphere. This study contributes valuable insights into the understanding of the complex dynamics of acoustic waves within different lower solar atmospheric layers, shedding light on the nonevanescent nature and downward propagation of the acoustic waves.

Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins

The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (MTBS, 2020).Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m3/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity ck=2.619meterspersecond,m/s±0.556percent and dynamic celerity cd=3.533m/s±0.181percentandtheiruncertainties were computed. L-moments were computed to establish probability density functions (PDFs) and associated statistics for each of the at-a-section hydraulic parameters to serve as a workflow for implementing alert networks in hydrologically similar basins that lack data.Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage.The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.

Simulation of large plausible tsunami scenarios associated with the 2019 Durres (Albania) earthquake source and adjacent seismogenic zones

We present an analysis of the hazards of potential earthquake-generated tsunamis along the Albanian–Adriatic coast. The study adopts a case study approach to model plausible tsunamigenic events associated with the 2019 Mw 6.4 Durres (Albania) earthquake source zone. The approach combines current findings on regional tectonics and scenario-based calculations of potential tsunami impacts. The study’s goal is to analyse the propagation of tsunami waves generated by identified seismogenic sources (namely ALCS002 [Lushnje] and ALCS018 [Shijak]) and determine the tsunami risk assessment for Durres City on the Albanian–Adriatic coast. The sources can generate earthquakes with maximum moment magnitudes of Mw 7.5 and Mw 6.8, which are likely to trigger tsunamis that could cause significant impacts in the region. The modelling is performed deterministically with the NAMI DANCE numerical code, including scenarios associated with the largest plausible earthquake. The model integrates bathymetry and topography datasets of large and medium resolutions. Each tsunami scenario simulation is based on the solution of the non-linear shallow water equations used to generate maximum positive wave amplitudes (water elevation), travel time, and tsunami inundation maps. In Durres City, modelling indicates that medium-sized waves could reach up to 2.5 m inland, posing a significant danger to the city’s low-lying areas. The most substantial tsunami waves are expected to impact the area within the first 10 to 20 min. Combining inundation maps and information on exposed assets allows for identifying areas where damages can be expected. In terms of human impact, a preliminary analysis shows that the study area is prone to tsunami threat, with more than 138,000 inhabitants living in vulnerable urban areas of Durres City by 2036. The model’s capacity to capture details related to the presence of buildings is limited due to constraints posed by the resolution of bathymetry and topography datasets available during this study. If refined with high-resolution bathymetry and topography datasets, our results can be considered a backbone for exposure and resilience assessment features to be integrated into preparedness or new urban development plans.

Determination of Earthquake Depths Using Data of Cross-Sectional and Areal Deep Seismic Studies in Siberia

Abstract ––Information on the distribution of earthquake hypocenters for many seismically active zones of Siberia remains insufficient, which is associated with sparse networks of seismological observations. The paper presents the results of determining the earthquake depths in several seismogenic areas in the Altai-Sayan region, Cisbaikalia, Transbaikalia, and Yakutia using the travel time of longitudinal refracted waves from the Mohorovičič discontinuity (Pn waves) from earthquakes and the recently obtained information about the deep structure of these regions. The depth determination algorithm is tested using data from aftershocks of large earthquakes occurring in Tuva in 2011 and 2012 (ML = 6.7 and 6.8), recorded both by the regional seismological network and by a local group of seismic stations. Different methods are applied to reveal that some aftershock depths have a close match, including those for the main shocks – the Tuva-1 and Tuva-2 earthquakes. Another good match of earthquake depths is obtained using Рn waves with materials from regional and detailed studies of the Baikal branch of the Geophysical Survey of the Russian Academy of Sciences in the Muyakan activation site in the Baikal rift zone. The resulting data complement the information on the hypocenter of the main shock and confirm the change of the Muyakan activation cluster from large depths to shallow ones since the onset of activation in 2014. New information on the earthquake depths using Рn waves is obtained in Yakutia along the border of the largest Eurasian and Okhotsk tectonic plates. It is revealed that they decrease to 6–12 km as compared to higher depths of 20–30 km in adjacent areas. The resulting series of new information on the distribution of earthquake hypocenters using Рn waves is extremely important mostly because it indicates the possibility of identifying and redefining earthquake hypocenters using previous seismological observations in seismically active zones of Siberia.

Shear wave anisotropy response characteristics of laminated shale and its correction methods

Laminated shale exhibits strong anisotropic characteristics in horizontal wells, significantly affecting the application of shear wave travel time data. Using equivalent medium theory and the elastic wave equation, we constructed a physical model for laminated shale, simulating the impact of lamination angle and lamination density on shear wave velocity and shear wave anisotropy coefficient. We established the relationship between the shear wave anisotropy coefficient and the cosine of the lamination angle. The simulation results indicate that shear wave velocity decreases with an increase in lamination angle and density, while the shear wave anisotropy coefficient gradually decreases with an increase in lamination angle and initially increases and then decreases with an increase in lamination density. Moreover, it exhibits a power exponential relationship with the cosine of the lamination angle. After correction, the minimum horizontal principal stress and fracture pressure calculated from shear wave data show an average relative error reduction of 6.72% and 7.77%, respectively, compared to measured data. This demonstrates that the correction method effectively eliminates the impact of laminated shale anisotropy on shear waves.

A matching pursuit approach to the geophysical inverse problem of seismic traveltime tomography under the ray theory approximation

SUMMARYSeismic traveltime tomography is a geophysical imaging method to infer the 3-D interior structure of the solid Earth. Most commonly formulated as a linearized inverse problem, it maps differences between observed and expected wave traveltimes to interior regions where waves propagate faster or slower than the expected average. The Earth’s interior is typically parametrized by a single kind of localized basis function. Here we present an alternative approach that uses matching pursuits on large dictionaries of basis functions.Within the past decade the (Learning) Inverse Problem Matching Pursuits [(L)IPMPs] have been developed. They combine global and local trial functions. An approximation is built in a so-called best basis, chosen iteratively from an intentionally overcomplete set or dictionary. In each iteration, the choice for the next best basis element reduces the Tikhonov–Phillips functional. This is in contrast to classical methods that use either global or local basis functions. The LIPMPs have proven their applicability in inverse problems like the downward continuation of the gravitational potential as well as the MEG-/EEG-problem from medical imaging. Here, we remodel the Learning Regularized Functional Matching Pursuit (LRFMP), which is one of the LIPMPs, for traveltime tomography in a ray theoretical setting. In particular, we introduce the operator, some possible trial functions and the regularization. We show a numerical proof of concept for artificial traveltime delays obtained from a contrived model for velocity differences. The corresponding code is available online.

Recent Advances in Dielectric Properties-Based Soil Water Content Measurements

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements.

Heterogeneous mantle effects on the behaviour of SmKS waves and outermost core imaging

SUMMARYSeismic traveltime anomalies of waves that traverse the uppermost 100–200 km of the outer core have been interpreted as evidence of reduced seismic velocities (relative to radial reference models) just below the core–mantle boundary (CMB). These studies typically investigate differential traveltimes of SmKS waves, which propagate as P waves through the shallowest outer core and reflect from the underside of the CMB m times. The use of SmKS and S(m-1)KS differential traveltimes for core imaging are often assumed to suppress contributions from earthquake location errors and unknown and unmodelled seismic velocity heterogeneity in the mantle. The goal of this study is to understand the extent to which differential SmKS traveltimes are, in fact, affected by anomalous mantle structure, potentially including both velocity heterogeneity and anisotropy. Velocity variations affect not only a wave's traveltime, but also the path of a wave, which can be observed in deviations of the wave's incoming direction. Since radial velocity variations in the outer core will only minimally affect the wave path, in contrast to other potential effects, measuring the incoming direction of SmKS waves provides an additional diagnostic as to the origin of traveltime anomalies. Here we use arrays of seismometers to measure traveltime and direction anomalies of SmKS waves that sample the uppermost outer core. We form subarrays of EarthScope's regional Transportable Array stations, thus measuring local variations in traveltime and direction. We observe systematic lateral variations in both traveltime and incoming wave direction, which cannot be explained by changes to the radial seismic velocity profile of the outer core. Moreover, we find a correlation between incoming wave direction and traveltime anomaly, suggesting that observed traveltime anomalies may be caused, at least in part, by changes to the wave path and not solely by perturbations in outer core velocity. Modelling of 1-D ray and 3-D wave propagation in global 3-D tomographic models of mantle velocity anomalies match the trend of the observed traveltime anomalies. Overall, we demonstrate that observed SmKS traveltime anomalies may have a significant contribution from 3-D mantle structure, and not solely from outer core structure.

Joint data and model-driven simultaneous inversion of velocity and density

SUMMARY Density is an important parameter for both geological research and geophysical exploration. However, for model-driven seismic inversion methods, high-fidelity density inversion is challenging due to seismic wave traveltime insensitivity to density and crosstalk that density has with velocity. To circumvent the challenge of density inversion, some inversion methods treat density as a constant value or derive density from velocity through empirical equation. On the other hand, deep learning approaches are completely driven by data and have strong target-oriented characteristics, offering a new way to solve multiparameter coupling problems. Nevertheless, the accuracy of the inversion results of data-driven algorithms is directly related to the amount and diversity of the training data, and thus, they lack the universality of model-driven algorithms. To achieve accurate density inversion, we propose a simultaneous inversion algorithm for velocity and density that combines the advantages of data- and model- driven approaches: A neural network model (U-T), combining the U-net and Transformer architectures, is proposed to construct non-linear mappings between seismic data as inputs and the velocity and density as predictions. Next, the model-driven inversion algorithm uses the U-T prediction as the initial model to obtain the final accurate solution. In the model-driven module, envelope-based sparse constrained deconvolution is used to obtain full-band seismic data, while a variable dominant frequency full waveform inversion algorithm is used to perform multiscale inversion, ultimately leading to accurate inversion results of velocity and density. The performance of the algorithm on the Sigsbee2A and Marmousi models demonstrates its effectiveness.