Subsurface Properties Research Articles

CO 2 geological storage potential in the Croatian part of the Pannonian Superbasin

In the continental part of Croatia, oil and gas have been discovered in more than 60 locations. Most reservoirs are in the Neogene/Quaternary basin infill, but the largest ones are found in the basement rocks. Decades of exploration have left a significant vintage dataset and substantial knowledge of the subsurface geology and reservoir properties. Since 2005, research efforts have been redirected towards CO 2 geological storage capacity estimates, and the first results were obtained relatively quickly. All identified potential storage objects – deep saline aquifers, depleted hydrocarbon fields and oil reservoirs that are suitable for CO 2 enhanced oil recovery (CO 2 -EOR) – can be regarded as prospective. The deep saline aquifers are defined on a regional scale and their estimated capacity is the highest (2584 Mt), but this is ‘theoretical capacity’ that can only be used to outline the total resource base. The storage capacity estimated for the selected 14 depleted hydrocarbon fields sums to 143.6 Mt, and this is a viable capacity that might be developed as soon as a particular field stops with production, but the most promising storage objects are the seven fields that have reservoirs suitable for CO 2 -EOR operations. The most recent models have revealed that seven active EOR fields operating in parallel between 2025 and 2040 could store up to 1.2 Mt CO 2 a −1 , while producing c. 1.2 Mt a −1 of additional oil. This is very high for a country with only 5.5 Mt CO 2 a −1 in point sources.

Deep-Learning-Based Amplitude Variation with Angle Inversion with Multi-Input Neural Networks

Deep-learning-based (DL-based) seismic inversion has emerged as one of the state-of-the-art research areas in exploration geophysics with the development of artificial intelligence technology. Due to its good portability and high computational efficiency, this method has emerged as a data-driven approach for estimating subsurface properties. However, most of the current DL-based methods rely solely on seismic data, lacking the incorporation of prior information. In addition, these methods are usually performed trace-by-trace, resulting in insufficient horizontal constraints. These limitations make traditional methods less robust, particularly when dealing with high noise levels or limited data. To address these challenges, we propose a multi-input deep learning network for pre-stack inversion, which combines data-driven and model-driven approaches for optimization. The proposed method separately extracts features from the model and data, merging them to improve feature utilization. Moreover, by adopting a 2-D training unit, rather than a trace-by-trace approach, the method improves the horizontal continuity of the results. Tests on synthetic and real seismic data confirmed the robustness and improved stability of the proposed method, even under challenging conditions. This dual-driven approach significantly enhances the reliability of seismic inversion.

Geostatistical Inversion of Frequency-Domain Electromagnetic Data for Near Surface Modelling

The detailed characterization of near-surface deposits is important for both environmental and economic reasons. These shallow subsurface systems can be very complex and heterogenous due to natural dynamics and anthropogenic interferences. Modeling techniques based exclusively on direct sampling generate limited informed three-dimensional models of the near-surface. Geophysical methods provide valuable and additional information to model the spatial distribution of the near-surface for locations where direct observations are not available. From this set of methods, frequency-domain electromagnetic induction (FDEM) has been successfully applied to image complex near-surface deposits. Yet, predicting the spatial distribution of relevant subsurface properties from geophysical data, and the integration of direct observations, is not straightforward. It requires solving a challenging geophysical inversion problem. Geostatistical modelling tools have been effectively applied to couple direct observations with geophysical data such as seismic reflection. We propose an iterative geostatistical FDEM inversion method able to integrate data from direct measurements of the near-surface with surface loop-loop FDEM measurements to simultaneously predict high-resolution models of electrical conductivity and magnetic susceptibility, and their associated uncertainty. The iterative geostatistical inversion method is based on stochastic sequential simulation and co-simulation as model perturbation and update techniques. The iterative optimization is based on the local data misfit between observed and simulated FDEM data, weighted by the sensitivity of the acquisition equipment. The proposed method is first demonstrated for a synthetic landfill data set created based on real data collected at a mine tailing disposal site in Portugal, and on a real data set collected at a site with archaeological features in Knowlton, UK. The results show the ability of the proposed method to accurately predict and characterize the spatial distribution of electrical conductivity and magnetic susceptibility down to the depth of interest while reproducing the recorded FDEM data.

Impacts of groundwater flow on borehole heat exchangers: Lessons learned from Estonia

The production of geothermal energy relies on subsurface properties. In low-enthalpy geothermal regimes, thermal energy extracted with a borehole heat exchanger (BHE) is the subsurface heat conducted from the rock and grout to the BHE fluid. Estonian geology provides a good opportunity to investigate how groundwater flow impacts BHE heat production. The hundreds of metres thick sedimentary rock sequence holds four main aquifers. We modelled single BHEs with lengths of 400 m, 500 m and 1000 m and a BHE field of ten 400-m wells at three sites across Estonia. Then, we parametrized different hydraulic conditions to compare how the groundwater flow velocity impacts the thermal energy yield of the systems.Our results indicate that if groundwater flow increases above 0.03 m/day, it increases the total energy yield from 3 % to 20 %. Thermogeological parameters, such as subsurface temperature and thermal conductivity, affect the thermal energy yield of the wells more than the modelled groundwater flow. Our study provides an estimate of the thermal energy yield from different BHE types and the detailed sensitivity analysis. We conclude that geothermal energy is a significant renewable energy resource for Estonia and areas with similar geology and climate.

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

Research on lunar regolith of the Chang'E-4 landing site: An automated analysis method based on deep learning framework

On January 3, 2019, the Chang'E-4 lander successfully landed within the Von Kármán crater, located in the South P ole-Aitken Basin (SPA) on the farside of the Moon (45.5°S, 177.6°E), marking the first soft landing on the lunar farside. The lander, equipped with the Lunar Penetrating Radar (LPR) system, aimed to provide insights into the structure and evolution of the Moon. Previous research often relied on manually identifying hyperbolic features to analyze the lunar shallow subsurface properties. This inefficient approach may lead to subjective biases, resulting in unstable outcomes. This research constructed an automatic analysis framework by integrating the Swin Transformer with a 3D velocity spectrum, which is then applied to analyze the properties of the Chang'E-4 LPR data. The experimental results indicate that the framework achieved a precision of 98.9 % and a recall of 96.7 % in hyperbolic feature identification, with an F1 of 0.9782 and AP of 94.8 %. Additionally, it has been experimentally validated that the framework can accurately invert hyperbolic features' two-way travel time and velocity. Finally, the framework is applied to analyze the lunar shallow subsurface structure and properties within the landing area of the Chang'E-4 mission.

Markov chain Monte Carlo methods applied to the stochastic inversion of 1D viscoelastic parameters

Abstract The characterization of the subsurface profile properties that are susceptible to liquefaction during earthquakes is of great importance in accident prevention. Field data can be used in a stochastic inverse problem to estimate the material properties using Markov chain Monte Carlo Methods (McMC) that incorporates prior knowledge about the unknown parameters as well as the data available. Here, we numerically study the Random Walk (RW) and Differential Evolution (DE) variations of the Metropolis algorithm in the context of seismic modeling considering the viscoelastic wave equation. The algorithms are tested with data from the 1987 Superstition Hills earthquake recorded at the Wildlife Site. The results show the effectiveness and accuracy of these algorithms, with emphasis to DE which reached convergence earlier compared to RW.

Evidence of Subsurface Control on the Coevolution of Hillslope Morphology and Runoff Generation

AbstractTopography is a key control on runoff generation, as topographic slope affects hydraulic gradients and curvature affects water flow paths. Simultaneously, runoff generation shapes topography through erosion, affecting landscape morphology over long timescales. Previous modeling efforts suggest that subsurface hydrological properties, relative to climate, are key mediators of this relationship. Specifically, when subsurface transmissivity and water storage capacity are low, (a) saturated areas and storm runoff should be larger and more variable, and (b) hillslopes shorter and lower relief, assuming other geomorphic factors are held constant. However, it remains uncertain whether subsurface properties can exert such strong controls on emergent properties in real landscapes. We compared emergent hydrological function and topography in two watersheds with very similar climatic and tectonic history, but very different subsurface properties due to contrasting bedrock lithology. We found that hillslopes were systematically shorter and saturated areas more dynamic at the lower transmissivity site. To test whether these features could be the result of coevolution between topography, hydrological function, and subsurface properties, we estimated all parameters of a coupled groundwater‐landscape evolution model for each site. Limitations were revealed in the model's ability to reproduce aspects of morphology and hydrologic behavior, however, model results suggested differences in hillslope length and variably saturated area between the sites could be explained by differences in subsurface properties, and not by differences in geomorphic process rates alone. This work demonstrates one way subsurface hydrology can profoundly affect landscape evolution.

Fault zone architecture and hydraulic properties characterization using earth tide analysis

Fault zone architecture and hydraulic properties, which determine subsurface fluid flow and storage, have received long-term research attention from hydrogeologists. Current field experimental techniques and numerical simulation methods are limited by the scale and complexity of the investigations and are inadequate for deep subsurface fault zones. Estimating the internal rock properties and structures of fracture zones requires a combination of methods from many different fields. In recent years, groundwater tidal response methods have been widely used to characterize subsurface hydrogeomechanical properties because of their advantages of being in situ and low cost. In this study, tidal analysis was employed for a well within the Blue Ridge Fault Zone, and we integrated the fracture zone properties revealed by a single well. Well W-03 revealed an aquifer with an overall permeability of ∼ 10–14 m2 and hydraulic diffusivity of 0.24 m2⋅s−1. The fault damage zone had a permeability of 10–14 ∼ 10–13 m2 while the host rock had a permeability of 10–16 ∼ 10–14 m2. In addition, we found that the heterogeneity of specific storage affects the assessment of poroelasticity parameters, we offer a method to estimate these parameters without relying on specific storage, yielding porosity estimates ranging from 0.05 to 0.06, aligning with field test results. Furthermore, the estimated dip and strike of the fault zone were also consistent with the outcrop and borehole observations.

Investigating Subsurface Properties of the Shallow Lunar Crust Using Seismic Interferometry on Synthetic and Recorded Data

AbstractIn the past few years, the remarkable progress of commercially operated spacecrafts, the success with reusable rocket engines, as well as the international competition to explore space, has led to a substantial acceleration of activities in the design and preparation of ambitious future lunar missions. In the search for ice and/or cavities imaging the shallow subsurface structure is of vital importance. Hereby, previous studies have shown that seismic interferometry is a promising method to investigate the subsurface properties from passive lunar data. In this study, we want to evaluate the potential of this method further by examining the required duration of seismic measurements and the influence of scattering on the Green's function retrieval. Therefore, we applied seismic interferometry to both measured Apollo 17 data and synthetic data. Our findings indicate that, under optimal conditions, a few hours of data are sufficient when using the method of time‐scaled phase‐weighted stack (ts‐PWS). However, this strongly depends on the inter‐station distance, the orientation toward the principal noise sources, and the timing of the measurement during the lunar cycle. Additionally, we were able to reproduce the measured data using numerical simulations in 2D. The synthetic results show that scattering effects clearly influence the Green's function extraction, especially for larger station distances.

Enhancing wetting and adhesion of stainless steel (SS304) surfaces with dielectric barrier discharge (DBD) plasma jet treatment

Enhancing the adhesion of coatings to metallic surfaces can be achieved through decontamination and increased surface energy. Among the various techniques used for functionalizing metallic surfaces, such as chemical etching, laser etching, flame treatment, and ultraviolet radiation; dielectric barrier discharge (DBD) plasma stands out as a promising option. It offers low-power, cost-effective, room-temperature operation and provides treatment with minimal alteration of subsurface properties. This study investigates using a DBD plasma jet to increase the treatment area, surface wettability, organic decontamination, and paint adhesion on stainless steel (SS304). The impact of different plasma treatments and aging times on the liquid contact angles and corresponding surface energies are experimentally examined. The results indicate that the surface energy of SS304 increases with treatment time. X-ray photoelectron spectroscopy (XPS) measurements are carried out to correlate this increase in surface energy to an increase in oxide bonds and decontamination of organic species, resulting in surface activation. However, it is observed that the plasma-treated surfaces exhibit no significant alterations in surface morphology, as confirmed through analyses of surface roughness and micro-hardness. Cross-hatch adhesion tests are carried out to demonstrate that the plasma surface treatment results in a significant improvement in surface adhesion to external coatings. Furthermore, the study evaluates the impact of DBD plasma jet treatment speed and dose on coating adhesion, providing valuable insights for making a trade-off between treatment quality and energy efficiency. The findings could extend the applicability of the DBD plasma jet to various areas, including anti-fogging surfaces, water harvesting, paints and coatings, and organic decontamination of surfaces.

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.

Multiphase flow modelling of gas migration from a hypothetical integrity-compromised petroleum well in the peace region of North-eastern British Columbia, Canada

Well-integrity failure occurs in a small subset of petroleum wells, resulting in release of fugitive gas into intersected geologic formations. Released fugitive gas from geoenergy systems is a growing environmental concern that can contaminate groundwater aquifers and emit greenhouse gases (GHGs) to atmosphere. Currently, the roles of well-cement quality and properties of intersected geologic formations on the environmental outcomes of well-integrity failure is poorly understood. To advance understanding, we numerically modelled a hypothetical fugitive methane release from a petroleum well intersecting the Sunset Paleovalley aquifer system in Northeast British Columbia, Canada. We simulate a 10-year release and migration of fugitive gas into a two dimensional, two-phase, two-component advective flow field with the subsurface properties informed by field and laboratory data. We evaluate the effects of cement quality, gas release depth, and geologic heterogeneity on fugitive-gas containment or emission by defining and/or evaluating three key numbers: a) emission-retention ratio (ERR), b) well integrity index (WII), and c) fugitive gas mobility ratio (MR) over relevant spatiotemporal scales. We show that ERR and WII capture the bifurcated impacts of fugitive gas from petroleum wells, including groundwater contamination and atmospheric emissions. A WII close to one reduces vertical fugitive-gas migration along the well bore, fosters lateral migration into intersected geologic materials and significantly limits GHG emissions to atmosphere. MR and ERR values show fugitive-gas migration and fate are primarily controlled by the casing annulus cement quality, particularly when fugitive gas is released at shallow depths. We conclude that the quality of petroleum-well cement is among the parameters controlling the migration pathways, impacts, and fate of fugitive-gas release.

Cross‐equalization for time‐lapse sparker seismic data

AbstractTime‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.

Joint PP and PS AVA inversion using an acceleration algorithm and a multi-trace strategy

Abstract Exploration utilizing converted wave (PS) technology has garnered considerable interest in recent years owing to its ability to provide distinct insights into subsurface properties compared to compressional waves (PP). PS wave is particularly advantageous for accurately determining S-wave velocity (${V}_S$) and bulk density ($\rho $), whereas PP wave is better suited for assessing P-wave velocity (${V}_P$). Thus, theoretically, joint PP and PS AVA inversion can yield precise results for ${V}_P$, ${V}_S$, and $\rho $. However, a critical step before joint inversion is PP and PS registration, which is prone to causing lateral discontinuity in the inversion outcomes. To mitigate this issue, we present a simultaneous multi-trace joint inversion method. The method can improve the lateral consistency of inversion results by imposing lateral continuity constraints on the elastic parameters being inverted. Nevertheless, this simultaneous multi-trace inversion method inherently increases computational burden. To enhance algorithmic efficiency without sacrificing inversion precision, we implement an acceleration strategy. Synthetic and real data examples demonstrate that the proposed method offers remarkable performance in enhancing computational efficiency and lateral continuity compared to the conventional methods.

Finite-difference time-domain method of ground penetrating radar images for accurate estimation of subsurface pipe properties

The increasing length of subsurface pipe causes overlapping, accumulation, and occasionally the old pipe layout is not also available. Consequently, accidents, damages, time delays, and financial losses occur during construction of new structures or installation of new pipes. Therefore, depth, radius, material of the existing pipe, and map of pipe are indispensable for knowing proper construction planning. In this article, an algorithm is proposed to estimate the properties of subsurface pipes and show 3D maps. Using this algorithm, the radius of the field pipes was estimated with 83, 67, and 89 % accuracy and depth with 95, 95, and 98 % accuracy. The effect of pipe radius should be considered to assess the pipe depth with higher accuracy. The material of the field pipe was successfully determined using the evaluated relative permittivity. A 3D map of the field pipe was developed by applying the tracing algorithm and linear regression on estimated depth.

Improving the Performance of the Machining Process by Using Ultra‐Advanced Tools in a Clean Turning of Inconel 686 Using the Minimum Quantity Lubrication Method

ABSTRACTThe high tensile strength and high resistance of nickel‐based superalloy 686 against high temperatures and corrosion rates have made it a widely used in important applications such as the aerospace industry, high pollution‐ and corrosion‐resistance equipment manufacturing and petrochemical industry. Therefore, the machining of this advanced alloy with its unique properties is extremely important and can be challenging. Significant increase in input parameters levels, reduction of machining costs, improvement of surface and subsurface properties and clean production are among the issues that should be considered in dealing with Inconel 686 turning operations. Simultaneous application of advanced tools such as polycrystalline diamond (PCD) and polycrystalline cubic boron nitride (PCBN) and optimised minimum quantity lubrication (MQL) method and evaluating the results obtained with a wide range of output parameters related to machining process performance and tribological properties can be proposed as an innovation and a solution to this problem in this article. This study analyses several output parameters with different speeds and feeds to evaluate the effect of cutting insert type on machining process performance and tribological properties. The output parameters include tool wear, residual stress, cutting zone temperature, surface smoothness, machining forces and workpiece surface defects. The results indicated that using the optimised MQL method reduces the size of lubricant droplets and increases the surface covered by cooling. With these changes, the performance of the machining process and the parameters related to the surface integrity increase significantly. Among the parameters associated with the performance of the machining process, the PCD tool reduces the cutting zone temperature by 23%, the tool wear by 19% and the machining forces by 18% compared to the PCBN tool. In the parameters related to surface integrity, this method reduces the residual stress by 19% and the surface roughness by 9% compared to the PCBN tool. From the production index perspective, the PCD tool can significantly increase the cutting speed and feed rate, reducing production time and costs.

JAMES BUTTLE REVIEW: Interflow, subsurface stormflow and throughflow: A synthesis of field work and modelling

AbstractInterflow, throughflow and subsurface stormflow are interchangeable terms that refer to the lateral subsurface flow above a restricting layer of lower hydraulic conductivity that occurs during and following storm events. Interflow (used here) is a more dominant process in steeper catchments with high infiltration capacity soils overlying a more impermeable soil or geologic layer. Interflow as a runoff process was first recognised in the early 1900s, yet hydrologists still struggle to predict its occurrence, persistence, importance, interaction with other streamflow generation processes, and potential to connect to valleys and streams during and following storms. We review the history of interflow research and address some of the challenges in understanding its role in runoff production. We argue that characterising the controls on interflow initiation and occurrence relies on detailed field observations of subsurface properties, which exist only in limited experimental settings. This data shortcoming contributes to our inability to predict interflow or determine its contribution to streamflow more broadly. There remain many opportunities to advance our understanding of interflow that include both modelling and experimental or observational approaches in hydrology.

The qP- and qSV-wave separation in 2D vertically transversely isotropic media and their applications in elastic reverse time migration

Compressional and shear wave separation is an important step in anisotropic elastic reverse time migration (ERTM). With separated wavefields, we can generate images between different wave types and reveal more subsurface physical properties, as well as remove unwanted crosstalk and improve image quality. Traditional Helmholtz decomposition based on divergence and curl operations for isotropic media cannot be directly extended to vertically transversely isotropic (VTI) media. Wave-mode separation methods, similar to the nonstationary spatial filter and Poynting vector, cannot preserve the phase and amplitude information of the original coupled wavefield, thus limiting their application in ERTM. Currently, the anisotropic wavefield decomposition methods include the wavenumber-domain approach, the low-rank approximation, and other separation approaches based on different approximations, e.g., weak or elliptical anisotropy. These methods are either computationally intensive or involve large separation errors, especially in models with strong heterogeneities and anisotropy. The VTI wavefield separation operator is essentially defined in a mixed space-wavenumber domain. We introduce scalar operators to transfer operations from this mixed space-wavenumber domain to the space domain. The local wave propagation direction in the scalar operators is estimated using the Poynting vector, which is highly computationally efficient in the space domain. When we apply the wave separation method to ERTM, we not only obtain the vectorized qP and qSV waves but also retrieve the scalar qP wavefield. The scalar and vector wavefields preserve the amplitude and phase information in the original coupled wavefield. For anisotropic ERTM, we suggest using the scalar imaging condition to generate the PP image and the magnitude- and sign-based vector imaging condition to produce the PS image, both having higher image accuracy than the dot-product imaging condition. Numerical examples are used to validate our anisotropic wavefield separation method and the related ERTM workflow.

Ray and Halo Impact Craters on Ganymede: Fingerprint for Decoding Ganymede's Crustal Structure

AbstractImpact craters are a unique tool not only for inferring ages of planetary surfaces and examining geological processes, but also for exploring subsurface properties. We use ejecta blankets as proxies to obtain insights into the subsurface characteristics and the vertical stratification of Ganymede's icy crust. We investigated 36 prominent ray and halo craters using images acquired during the Voyager, Galileo, and Juno spacecraft missions. These craters exhibit diverse characteristics, including dark rays, bright rays, or their combination, in both continuous and discontinuous patterns as well as dark and bright halos. Dark halo craters (DHCs) have the smallest radial extents of their dark ejecta deposits, while dark ray craters (DRCs) have the largest. DRCs in dark terrain suggest a thickness of less than ∼2 km based on their excavation depths. DRCs and DHCs craters located in light terrain (LT) reveal significant heterogeneity in the uppermost portions of icy crust at various locations. DRCs and DHCs in the LT require the presence of at least one layer of dark material. This is the case if the LT is formed by tectonic rifting and graben formation. In contrast, if the LT is formed by tectonic spreading, bright halo and ray craters are expected to form.