Vertical Data Research Articles

Quantification of hydrodynamic model sea level bias utilizing deep learning and synergistic integration of data sources

This study demonstrates the use of machine learning strategies to examine and quantify the bias that often exists in sea level data from hydrodynamic models. The sea level bias is considered to consist of two components: (i) hydrodynamic modelling errors due to numerical modelling limitations, and (ii) a bias related to the difference between vertical datums. The goal is to accurately quantify these components, enabling the determination of absolute dynamic topography from coastal to offshore areas. The method is tested in the Baltic Sea employing a synergy of hydrodynamic models, tide gauges, and satellite altimetry.Firstly, a multivariate deep neural network approach inspired by WaveNet is used to identify and quantify hydrodynamic modelling errors. A wrapper-type sequential feature elimination algorithm identifies seven relevant variables out of the initially considered sixteen for training the deep leaning model in the Baltic Sea region. The model is trained using sixteen tide gauge records. As a result, the model predicts hydrodynamic modelling errors with a root mean squared error of 3.2 cm and 3.4 cm, and an R-Squared value of 0.82 and 0.77 for the training and test sets, respectively. Comparing the predicted and observed errors reveals localized areas where other sea level dynamics, such as seiches in the Gulf of Riga, may be of interest but were not incorporated into the deep learning model. Secondly, once the hydrodynamic modelling errors are quantified, the method allows for the determination of the vertical reference bias by comparing known and reliable observations, such as tide gauge and satellite altimetry data. The vertical reference bias is calculated to be 18.1 ± 2.9 cm.The method significantly improves the accuracy of dynamic topography, resulting in an average root mean squared error of 4.1 cm compared with satellite altimetry and a correlation of 0.98 compared with tide gauges. This approach presents a novel way to integrate modelled and observed dynamic topography using machine learning techniques for enhancing our understanding and its applications.

Identifying and characterising trapped lee waves using deep learning techniques

Trapped lee waves, and resultant turbulent rotors downstream, present a hazard for aviation and land‐based transport. Though high‐resolution numerical weather prediction models can represent such phenomena, there is currently no simple and reliable automated method for detecting the extent and characteristics of these waves in model output. Spectral transform methods have traditionally been used to detect and characterise regions of wave activity in model and observational data; however, these methods can be slow and have their limitations. Machine‐learning (ML) techniques offer a new and potentially fruitful method of tackling this problem. We demonstrate that a deep‐learning model can be trained to accurately recognise and label coherent regions of lee waves from vertical velocity data on a single level from a high‐resolution numerical weather prediction (NWP) model. Using transfer learning, wave characteristics (wavelength, orientation, and amplitude) can be extracted from the trained segmentation model. The use of synthetic wave fields with prescribed wave characteristics makes this transfer learning possible without the need to characterise real complex wave fields. Addition of noise to the synthetic data makes the models more robust when applied to more complex and noisy NWP data. The collection of trained models produced provides a valuable tool to investigate the prevalence and nature of lee wave activity, as well as a new way for forecasters to detect resolved waves. The deep‐learning model was more capable and quicker at detecting and characterising lee waves than a spectral technique was. This work is just one example of how already established ML techniques can be used to detect and characterise complex weather phenomena from NWP model output and observational data, and how the careful use of synthetic data can reduce the requirements for large volumes of hand‐labelled training data for ML models.

S-wave velocity structure and Vp/Vs ratios beneath Liyuexi Trough, Nansha Block, the South China Sea: Implications for crustal lithology and tectonic nature of the southern continental margin

By processing the vertical component data of OBS2019–2, a wide angle seismic line situated adjacent to the Liyuexi Trough in the Nansha Block of the South China Sea (SCS), we obtained a crustal model of P-wave velocity structure beneath the line and acquired information about the deep crustal structure in the Liyuexi Trough region. However, the lithology remains to be fully constrained. This paper presents the results of processing the horizontal component data of OBS2019–2 to obtain the S-wave velocity structure model and Vp/Vs ratios. The results show that the Vp/Vs ratios of sedimentary layer range from 1.90 to 4.60, while the corresponding S-wave velocities range from 0.4 to 1.8 km/s. As for the crust, the Vp/Vs ratios fall between 1.70 and 1.89, while the S-wave velocities range between 2.9 and 4.2 km/s. The lithological compositions of oceanic crust from top to bottom may consist of basalt, diabase, and gabbro. In contrast, the lithologies of continental crust transition from felsic to mafic downwards, including andesite, granodiorite, granite, greenschist, and anorthosite. The rock compositions of the Continent-Ocean Transition (COT) region are intermediate between the continental and oceanic crusts, exhibiting transitional features. The rock composition of the high-velocity layer (HVL) in the lower crust may consist of amphibolite, and the presence of magma intrusions and underplating in this region could be associated with the Zhongnan-Liyue Fault. Compared to the Dongsha Block, the lithologies of the Liyue Block and Zhongsha Block are more similar, indicating a higher possibility of conjugation between the Liyue Block and the Zhongsha Block.

Force plate coverings significantly affect measurement of ground reaction forces.

The purpose of this study was to carry out a material test to investigate the effect of different force plate coverings on vertical and horizontal ground reaction force and derived parameters. Four surface conditions were analysed; bare plate, vinyl, sportflex, and astroturf on a Kistler force plate. Vertical data were collected by dropping a 2 kg rigid, textured medicine ball from a low (61 cm) and a high (139 cm) height. Horizontal data were collected using a custom-built, rigid, metal pendulum device. A one-way ANOVA revealed a significant main effect of surface on peak force and rate of force development for high height, low height, and horizontal force conditions (all p<0.001), with effect sizes in the post-hoc analysis being mostly large to very large. Interestingly, sportflex yielded the highest vertical but the lowest horizontal ground reaction forces. This study showed the use of current force platform coverings had a significant effect on peak force and rate of force development measurements during a standardised testing procedure. Future research should try to obtain rate of force development values that more closely replicate aspects of human performance during standardised testing procedures. Also further investigate the effect of the different surfaces on ground reaction forces during human movement.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Short-time frequency-domain method for truly nonlinear dynamic ground response analysis: The equivalent-nonlinear approach

Evaluating soil nonlinearity during cyclic loading is one of the most significant challenges in ground response analysis, especially when dealing with the inverse problem of deconvolution. Different schemes have already been developed for dynamic ground response analysis, both in the time and the frequency domain. The most accurate method to account for soil nonlinearity is the nonlinear dynamic analysis in the time domain. This approach is based on nonlinear constitutive models capable of accurately simulating highly nonlinear problems like soil liquefaction. However, the time-domain analysis is suitable only for the convolution analysis to define the ground motion at the free surface of a soil deposit from the bedrock motion. The frequency-domain analysis is the most common solution for the inverse problem called deconvolution, which is used to define the bedrock motion from the free surface ground motion. A well-known approach developed in the frequency domain for ground response analysis is the equivalent-linear method (EQL). This approach adopts an iterative procedure to define elastic shear modulus and damping ratio compatible with the induced strain level. Still, it presents some limitations, especially for highly nonlinear soil response, due to the use of strain-compatible but constant soil properties. This article presents a new scheme to conduct truly nonlinear dynamic analysis in the frequency domain based on the new concept of the short-time transfer function. Unlike the EQL method, which uses a constant transfer function, the proposed approach, called the “Equivalent-Nonlinear” method (EQNL), defines a soil transfer function evolving in time, depending on the shear stress and strain demands. The EQNL method approximates the response of a nonlinear system as an incrementally changing viscoelastic system and could represent a valuable tool for nonlinear deconvolution. This article shows the analytical formulation and the first set of validations of the EQNL approach, with detailed comparisons with the EQL and NL methods and vertical array data. These comparisons show the potentialities of the EQNL approach to reproduce the results of the nonlinear dynamic analysis. The EQNL approach has been implemented in MATLAB, and the source code is provided as supplementary material for this article. A more comprehensive validation is underway, aiming to better characterize the limitations and the capabilities of the method.

Field assessment of elastic full-waveform inversion of combined accelerometer and distributed acoustic sensing data in a vertical seismic profile configuration

Seismic data are a significant facilitator for monitoring in carbon capture and sequestration projects, providing high-resolution images of fluid migration, using, for example, full-waveform inversion (FWI). Distributed acoustic sensing (DAS), a relatively novel technology for wavefield sampling, is well suited for this type of monitoring. Using noninvasive optical fibers, DAS allows for dense spatial sampling along the entire length of the wellbore, without disrupting operations. Permanently installed in the wellbore, typically behind casing, DAS offers highly repeatable and dense sampling of the transmitted wave modes crucial to seismic monitoring of injected carbon dioxide ([Formula: see text]). However, the DAS data consist of measurements of strain along the tangent of the fiber and therefore do not transfer directly to conventional FWI algorithms. Incorporation of DAS data in their native strain (or strain-rate) form in standard FWI algorithms, requires changing the definition of the receiver sampling operator to use geometric information about the fiber to supply tangential strain measurements to the FWI residual. The theoretical developments are applied to invert field vertical seismic profile data acquired with DAS fiber and accelerometers at a [Formula: see text] sequestration site in Newell Country, Alberta. Our method incorporates DAS data and accelerometer data in one objective function and allows us to tune the relative importance we wish to place on each data set. This method also transfers to noncollocated sensors, for example, surface-deployed geophones and borehole fiber. The inverted models contain features expected from the geology of the field site, and data modeled in the inverted models compare favorably with the field data for these sensor types. The models are derived from data acquired prior to [Formula: see text] injection, representing baseline models for future time-lapse studies planned at the field research station.

Polarization Method-Based Research on Magnetic Field Data Associated with Earthquakes in Northeast Asia Recorded by the China Seismo-Electromagnetic Satellite

Previous earthquake polarization (as the ratio of vertical and horizontal components) studies using geomagnetic data were all performed with ground data. The advantage of satellite data is that it is not limited by geography. Therefore, in this work, we tried to select 12 typical earthquakes in Northeast Asia with Ms &gt; 5.0 and an epicenter depth ≤ 40 km within the longitude 105° E–145° E and latitude 38° N–58° N ranges from December 2018 to January 2023 for analysis by using the satellite data of the high-precision magnetometer (HPM) payload onboard the China Seismo-Electromagnetic Satellite (CSES) for the first time in a quiet magnetic environment. The geomagnetic three-component vector data were investigated, and the minimum study period was divided into 10 s intervals. Fourier transform was performed to obtain 0.01–0.2 Hz geomagnetic three-component dynamic spectra, and the time series of the polarization (as the ratio of vertical and horizontal components) data was then obtained. The average value of the polarization data over four years was used to obtain the time series of the polarization perturbation amplitude, after which joint research was conducted. The results showed that (1) earthquakes with larger magnitudes are more likely to exhibit anomaly perturbations recorded by satellites; (2) among all earthquakes with anomalies, the horizontal east–west component perturbation is the largest, the vertical component perturbation is the smallest, and the east–west component may be the dominant component in seismic anomaly observations; (3) the applicability of the polarization method to space-based earthquake-related data is limited; (4) the perturbation amplitude of polarization data can be used as a reference for extracting seismic anomalies; and (5) ion velocity Vx data from the plasma analyzer package (PAP) can be considered to approximately verify the physical mechanism of the anomaly perturbation of the horizontal component in the ionospheric magnetic field, and the two kinds of data (PAP and HPM) can be combined in seismic prediction research.

Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, China

The study of sedimentary microfacies of horizontal wells is important for improving oil recovery using horizontal well technology. Vertical well data alone do not provide accurate enough information to determine the sedimentary microfacies of horizontal wells. Therefore, a comprehensive method combining the data of both horizontal and vertical wells was established to identify sedimentary microfacies of horizontal wells and applied to a single horizontal well in the Daqing oilfield in China’s Songliao Basin. The results identified the study area as a delta sedimentary environment, mainly subdivided into four microfacies types: a distributary channel, the main overbank sand, the overbank sand, and an interdistributary bay. The criteria for identifying each sedimentary microfacies were established. Among them, the criteria for identifying distributary channels include a natural gamma value continuously less than 90 API; a resistivity value continuously greater than 11 Ω·m; a logging curve, which is typically bell-shaped or box-shaped with very high amplitude and amplitude difference; a mainly siltstone lithology; and a total hydrocarbon content (Tg) continuously greater than 3%. The variations in the two types of channel boundaries (narrowing of the channel boundary and reverse extension of the bifurcated channel boundary) were corrected. The research results can provide guidance for the efficient development of favorable reservoirs in oilfields using horizontal well technology.

Relationship Between Kinetic and Kinematic Measures of the Countermovement Jump and National Weightlifting Performance

Abstract Purpose The association between vertical jump and weightlifting performance have been well established often using discrete performance measures such as jump height or peak power which provides little insight into temporal strategies. The purpose of this investigation was to identify currently unassessed temporal countermovement jump metrics and their reliability and relationship to national weightlifting performance. Methods A total of 42 national weightlifting athletes (n = 30 females and 12 males), were recruited for this study. Countermovement jump was measured using a force plate following a national and international competition. Vertical ground reaction force and time data were analyzed using a custom script where temporal metrics relating to specific phases of the jump were extracted. Snatch, Clean and Jerk and Total weightlifting performance was obtained following each competition. Reliability of jump metrics were determined using coefficient of variation and interclass correlation coefficient. A Spearman’s Rho, non-parametric bivariate correlation was used to determine the relationship between the jump metrics and weightlifting performance. Results From a total of 15 metrics, 13 were deemed reliable, with propulsive impulse showing the greatest level of reliability. Correlational analysis showed strong to very strong (r = 0.676–0.817) relationships between all absolute measures of weightlifting performance and propulsive impulse for both women and men. Conclusion This novel finding suggests that practitioners may wish to use propulsive impulse as it may provide more insight into changes of force capabilities following training. Additionally, it may also be used as a talent identification tool given its strong relationship to performance.

Complete perception self-attention network for weak seismic signal recovery in distributed acoustic sensing vertical seismic profile data

Distributed acoustic sensing (DAS) is a new technology for acquiring seismic data with high spatial resolution at low cost. Furthermore, in real downhole seismic exploration, DAS can receive some weak signals reflected from deep and thin layers. Unfortunately, some real downhole seismic data received by DAS often are characterized by low quality. Specifically, in real DAS records, desired signals with weak energy often are contaminated by some new noise not presented in seismic data received by conventional electronic geophones. Due to the characteristics of seismic data, such as frequency band aliasing, low signal-to-noise ratio, and complex noise wavefield, existing linear or nonlinear denoising methods based on information processing theories cannot effectively eliminate this complex and multitype noise. Recently, the deep-learning method has been regarded as a powerful tool for background noise attenuation in seismic data. Most of the existing deep-learning methods are concerned with local features and ignore the global features that can be used to enhance their performance further. To simultaneously extract global and local features, we design a novel complete perception self-attention network (CP-SANet) based on the transformer framework and apply it to the denoising of downhole DAS records. The network embeds a transformer module into multilevel encoder-decoder framework. Depth-wise convolution is applied to enhance the local perception capability. Given the transformer’s requirement for a large amount of data, we specifically design abundant seismic data samples using formation models with different parameters. The noise in the data sets is obtained from actual field DAS data. The effectiveness and feasibility of CP-SANet are verified on synthetic and field DAS records. All of the experimental results prove its satisfactory performance compared with some classical and network methods.

Novel Compact Polarized Martian Wind Imaging Interferometer

The Mars Atmospheric Wind Imaging Interferometer offers several advantages, notably its high throughput, enabling the acquisition of precise and high vertical resolution data on the temperature and wind fields in the Martian atmosphere. Considering the current absence of such an Interferometer, this paper introduces a novel Mars wind field imaging interferometer. In analyzing the photochemical model of O2 (a1Δg) 1.27 μm molecular airglow radiation in the Martian atmosphere and considering the impact of instrument signal-to-noise ratio (SNR), we have chosen an optical path difference (OPD) of 8.6 cm for the interferometer. The all-solid-state polarized wind imaging interferometer is miniaturized by incorporating two arm glasses as the compensation medium in its construction, achieving the effects of field-widening and temperature compensation. Additionally, an F-P Etalon is designed to selectively filter the desired three spectral lines of O2 dayglow, and its effect is evaluated through simulations. The accuracy of the proposed compact Mars polarized wind imaging interferometer for detecting Mars’ wind field and temperature field has been validated through rigorous theoretical derivation and comprehensive computer simulations. The interferometer boasts several advantages, including its compact and small size, static stability, minimal stray light, and absence of moving parts. It establishes the theoretical, technological, and instrumental engineering foundations for future simultaneous static measurement of Martian global atmospheric wind fields, temperature fields, and ozone concentrations from spacecraft, thereby significantly contributing to the dataset for investigating Martian atmospheric dynamics.

Hydrostratigraphy and hydrogeophysical studies to delineate fresh and saline aquifer boundaries in Lesser Cholistan of Pakistan.

The differentiation of saline water and fresh water interfaces is a key objective in ground water exploration and management. Bahawalpur is the twelfth biggest metropolitan area of Pakistan situated in south Punjab near the bank of River Sutlej and lies at 29°59'55" N latitude and 73°15'12" E longitude at an elevation of 521 ft AMSL in the Cholistan area close to the Thar abandon. The study area comprised of Lesser Cholistan experiencing acute shortage of water for inhabitants and livestock as well. The occurrence of fresh water is also challenging because of high salinity in groundwater. The present study is intended to identify hotspots of fresh groundwater zones. To achieve the goal, vertical electrical resistivity and borehole data are used to mark fresh and saline interfaces in groundwater. To achieve the results 230 vertical electric sounding were performed in the study area. A total of 3 to 5 geo-electric layers are identified with modeling along with the processing and interpretation of resistivity data. In the study area, resistivity values are classified as very high (>230 Ω-m), high (230-100 Ω-m), medium (100-40 Ω-m), low (40-20 Ω-m) and very low (<20 Ω-m). Borehole data is used to interpret subsurface lithologies and to calibrate the modeled resistivity curves. The electric resistivity data indicates that thick layers of Quaternary sediments is present in the subsurface that is primarily composed of clay, silt, sand, gravels and some kanker. Inversion technique is applied to generate 2D subsurface resistivity maps to delineate fresh and saline water zones. The generated 2D resistivity maps at variable depth above and below water table and formation resistivity maps are successfully utilized to differentiate fresh and saline water zones. The identification of a saline water aquifer within sediments rich in clay was made possible by the observation of very low resistivity measurements in the southern region. Conversely, the detection of relatively high resistivity values, coupled with the presence of sand and gravel deposits in the northern section of the lesser Cholistan area, provided compelling evidence of the existence of fresh groundwater. These findings have significant implications for the management of water resources in the region, as they provide valuable insights into the distribution and availability of groundwater resources for future use.

High‐resolution P‐ and S‐wavefield seismic investigations of a quick‐clay site in southwest of Sweden

AbstractSeismic investigations were performed at a site in the southwest of Sweden where major quick‐clay landslides have occurred in the past. Given the potential high risk of the area and the presence of medium infrastructures, the site posed a need for detailed investigations in a wide depth range and in high resolution. A high‐fold seismic survey was designed and conducted along two profiles using a 1–2 m receiver and shot spacing in order to retrieve both P‐ and S‐wavefield seismic images from vertical component data. The data were analysed by combining first‐break traveltime tomography and surface‐wave analysis as well as P‐ and S‐wavefield reflection seismic imaging. Using the first breaks, P‐wave velocity (VP) models were estimated, indicating the bedrock topography along the profiles and the sediment characteristics. The S‐wave velocity (Vs) models were estimated from the surface waves and indicated areas of low shear strength. Combined with VP and Vs models, this permits the estimation of VP/VS, a parameter that can indicate areas with high water content, significant for the detection of quick clays and possible liquefaction issues. The results are integrated with the P‐ and S‐wave reflection seismic images and compared with other geophysical investigations, such as magnetic and gravity data that were collected along the profiles.

Inversion of walkaway VSP data in the presence of lateral velocity heterogeneity

Multi-azimuth walkaway vertical seismic profiling is an established technique for the estimation of in situ slowness surfaces and inferring anisotropy parameters. Normally, this technique requires the assumption of lateral homogeneity, which makes the horizontal slowness components at depths of downhole receivers equal to those measured at the surface. Any violations of this assumption, such as lateral heterogeneity or nonzero dip of intermediate interfaces, lead to distortions in reconstructed slowness surfaces and, consequently, to errors in estimated anisotropic parameters. In this work, we relax the assumption of lateral homogeneity and discuss how to correct vertical seismic profile data for weak lateral heterogeneity. We describe a procedure of downward continuation of recorded traveltimes that accounts for the presence of both vertical inhomogeneity and weak lateral heterogeneity, which produces correct slowness surfaces at depths of downhole receivers, noticing that sufficiently dense receiver coverage along a borehole is required to separate influences of vertical and lateral heterogeneity on measured traveltimes and obtain accurate estimates of the slowness surfaces. Once the slowness surfaces are found and a desired type of anisotropic model to be inverted is selected, the corresponding anisotropic parameters, providing the best fit to the estimated slownesses, can be obtained. We invert the slowness surfaces of P-waves for parameters of the simplest anisotropic model describing dipping fractures (transversely isotropic medium with a tilted symmetry axis). Five parameters of this model, namely, the P-wave velocity V0 in the direction of the symmetry axis, Thomsen's anisotropic coefficients ϵ and δ, the tilt ν, and the azimuth β of the symmetry axis, can be estimated in a stable manner when maximum source offset is greater than half of receiver depth.

Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

AbstractThe accuracy of ionospheric models estimated by ground‐based multiple global navigation satellite system ionospheric data over regions with sparse tracking stations is not ideal. To improve the accuracy of the estimated ionospheric model, different types of ionospheric data with different combinations were employed for previous studies. However, the ionospheric observational ranges for different types of ionospheric data are not the same. In this study, the accuracy of ionospheric maps generated by ground‐based ionospheric data (ground‐based strategy) and ground‐based ionospheric data combined with data provided by other geodetic measurements normalized by the single‐layer normalization method (multi‐source strategy) were studied. The results showed that the main differences between the ionospheric models estimated by the two strategies occur for data taken over the ocean, which mainly range from −1 to 0 total electron content unit (TECU). When assessed using Jason‐3 vertical total electron content data, the mean root mean square (RMS) value of the ionospheric model estimated by the multi‐source strategy was 5.03 TECU, which is approximately 15% smaller than that estimated by the ground‐based strategy. The maximum reduction in results using the multisource strategy was approximately 25% over different latitudes compared with that of the ground‐based strategy. Furthermore, the self‐consistency evaluation method was employed for evaluation. The results showed that the RMS of the ionospheric model estimated by the multi‐source strategy was 2.41 TECU, which is 3.60% better than that of the ground‐based strategy. The maximum reduction was 15% on different days.

Relationship between body condition and vertical movement symmetry in 109 riding school horses

Abstract For riding schools, the health and function of their horses is paramount. Unfortunately, lameness and overweight are common problems in this population, and a causal relationship whereby increased body weight causes lameness is also possible. This observational field study investigated the relationship between body condition score (BCS) and motion movement asymmetries in riding school horses and also explored the relationships between BCS, movement asymmetry and subjectively scored willingness to work. Eight riding schools within 2.5 h by car from Uppsala, Sweden, were convenience-sampled and 3-24 horses from each were included in the study. Body condition was scored (scale 1-9) and vertical movement asymmetry data were collected in straight-line trot, using an inertial measurement unit system. Asymmetry parameters were calculated as the mean difference between local vertical displacement minima/maxima for the head/pelvis (HD/PD) between the two halves of the stride. A questionnaire was used to assess perceived willingness to work and energy level of the horses. The data were analysed with linear mixed models, t-tests and Mann-Whitney U-tests. A correlation was found between BCS and PDmin (), an asymmetry parameter related to hindlimb weight-bearing lameness, where PDmin increased by 0.96 mm for each unit increase in BCS. Horses with vertical movement classified as symmetric had lower BCS than horses with movement classified as asymmetric (5.4 ± 0.4 vs 5.7 ± 0.6, ). Horses classed as overweight (BCS ≥ 6) scored lower on willingness to work () and energy level (). These results indicate that the body condition of riding school horses may influence their function and the health of their locomotor apparatus. Further studies are required to identify the underlying mechanisms.

LAND SUBSIDENCE DYNAMICS IN MALAYSIA BASED ON TIME-SERIES VERTICAL DEFORMATION USING MODIFIED D-INSAR SENTINEL-1

Land subsidence in urban areas is geohazard that can be caused by tectonic movements, changes in aquifer networks, or anthropogenic activities such as excessive groundwater extraction, mining, tunnelling, and plantations. The degree of land subsidence can be monitored using time-series vertical deformation data extracted from Sentinel-1 satellite imagery using the modified D-InSAR method. This study aims to determine the land subsidence dynamics of 16 cities in Malaysia based on time-series vertical deformation data, including Kota Bahru, Kuala Terengganu, George Town and Butterworth, Alor Setar, Kangar, Ipoh, Seremban, Malacca, Kuala Lumpur, Putra Jaya, Shah Alam, Kuantan, Johor Bahru, Kinabalu, Bandar Labuan (Victoria), and Kuching. The time-series vertical deformation data used in this study were extracted between 2014-2022. Negative values of vertical deformation indicate that land subsidence is occurring, while positive values of vertical deformation are indicative of regional uplift. The overall rate of land subsidence in Malaysia is between -0.5 cm to -6.0 cm, while the average uplift rate is between +0.5 cm to +4.5 cm. An analysis of the data extracted reveals that the city that is most vulnerable to land subsidence is Johor Bahru, followed by Kuala Terengganu, Seremban, Kuala Lumpur, Shah Alam, Malacca, and Kuantan, while the city that has the lowest risk of land subsidence is Kangar. In contrast, cities that are vulnerable to regional uplift are Kinabalu and Bandar Labuan (Victoria). The results of this study can be used to guide urban planning initiatives, allowing them to consider any threats that might be posed by land subsidence.

Signal recovery and noise suppression of the Ocean‐Bottom Cable P‐component data based on improved dense convolutional network

AbstractEffective attenuation of noise in seismic data is important for high‐quality seismic imaging. Noise suppression in Ocean‐Bottom Cable data is particularly challenging. The challenge for the geophysicist is to process the individual hydrophone and vertical geophone data up to a level where they can conveniently be combined for effective multiple suppressions. In this study, we propose a deep learning‐based solution for noise attenuation and signal recovery of the P‐component of Ocean‐Bottom Cable data. To effectively attenuate complex noise, a denoising model based on dense convolutional network is proposed for Ocean‐Bottom Cable data processing. The backbone of the denoising network uses dense blocks to extract the potential features. Dense connections are applied to fuse the features at each stage to further enhance the effective information and thus improve the reconstruction of the signal. A high‐quality training set was built for the training network to ensure that the trained model was suitable for noise suppression. Synthesis and field experiments show that the proposed method can completely eliminate complex noise and recover weak signals from the P‐component data of the Ocean‐Bottom Cable data.

Geophysical Based Groundwater Potential Zone Mapping: Perspectives and Inferences from Lisana Area along the Guder River, Western Margin of the Central Main Ethiopian Rift

Vertical electrical soundings and magnetic data were used to map the groundwater aquifer system in the Lisana area along the Guder River, northeast of Hosanna town, near the western margin of the Central Main Ethiopian Rift. The surveys were used to map a potential aquifer zone and investigate the groundwater potential for the current and future development of the resource in the Lisana area. The VES data were used to map the depth of a potential aquifer zone and its distribution over the area. The magnetic survey was used to delineate contacts between lithologic units and geological structures. Two-dimensional magnetic forward modeling and one-dimensional electrical modeling were used to identify fractures and lithological units containing potential groundwater. The magnetic and electrical resistivity modeling indicated that a potential aquifer occurs at a depth range from 115m to 142m. The potential aquifer occurs within weathered and fractured ignimbrite and a pumice layer containing sandy soil. This latter lithological unit is the main water-bearing horizon.