Exploration of Psychological Resilience during a 25-Day Endurance Challenge in an Extreme Environment

Psychological resilience is the ability to use personal qualities to withstand pressure, consisting of the interaction between the individual and the environment over time. It is essential when operating in extreme environments which are typically characterised by a complex combination of stressors with increased elements of risk and adversity. Psychological resilience has never been investigated “live” (e.g., in the moment) throughout the duration of an extreme endurance challenge, despite anecdotal accounts of the need for resilience to successfully function in such environments. The aim of the study was to explore psychological resilience with challenge team members (n = 4, mean age = 46.0 years) involved in a 25-day extreme endurance challenge. The object of the challenge was to ‘TAB’ (Tactical Advance to Battle, fast marching with weighted packs) 100 peaks in the UK in 25 days and complete long-distance bike rides between base camps. A mixed-methods approach with a focus on qualitative methods was utilised. Specifically, individual reflective video diaries (n = 47) and focus groups (n = 4) were completed and analysed using interpretative phenomenological analysis (IPA). At the same time, the 10-item Connor Davidson Resilience Scale was employed to measure resilience, which highlighted the individualised and dynamic nature of resilience. Two superordinate themes were identified from the video diaries and focus groups, namely, the identification of the stressors within extreme environments and strategies to maintain functioning. Stressors were split into subordinate themes of significant and every day, and collectively, they created a cluster effect which contributed to pressure associated with operating in these environments. Challenge team members employed various strategies to maintain functioning, including using a challenge mindset to positively appraise pressure as a challenging learning experience. Further research should continue to develop an understanding of how participants completing challenges within extreme environments utilise and develop personal qualities to maintain functioning.

Obstacle Capability of an Air-Ground Amphibious Reconnaissance Robot with a Planetary Wheel-Leg Type Structure

According to the requirements of the reconnaissance robot for the ability to adapt to a complex environment and the in-depth study of the obstacle climbing mechanisms, a planetary wheel-leg-combined mechanism capable of adapting to complex terrains is proposed. According to the proposed planetary wheel-leg-combined mechanism, the land part of the air-ground amphibious reconnaissance robot is designed. Considering the obstacle and fast marching performance, four groups of combined wheel-leg mechanisms are adopted in the land bank. Under the action of three kinds of obstacles, the stability and the movement ability of the robot are analyzed by using the static method. The parameter model of the reconnaissance robot is built by a virtual prototype dynamics software MSC.ADMAS. The kinematic characteristic curves of each component and the whole prototype are obtained, which provides a theoretical basis for the design and numerical calculation of the robot structure. Finally, the climbing ability tests of the reconnaissance robot prototype verify the reliability and practicability of the body structure of the reconnaissance robot.

Application Of Finite Difference Eikonal Solver For Traveltime Computation In Forward Modeling And Migration

Traveltime is one of the propagating wave’s components. As the wave propagates further, the traveltime increases. It can be computed by solving wave equation of the ray path or the eikonal wave equation. Accurate method of computing traveltimes will give a significant impact on enhancing the output of seismic forward modeling and migration. In seismic forward modeling, computation of the wave’s traveltime locally by ray tracing method leads to low resolution of the resulting seismic image, especially when the subsurface is having a complex geology. However, computing the wave’s traveltime with a gridding scheme by finite difference methods able to overcomes the problem. This paper aims to discuss the ability of ray tracing and fast marching method of finite difference in obtaining a seismic image that have more similarity with its subsurface model. We illustrated the results of the traveltime computation by both methods in form of ray path projection and wavefront. We employed these methods in forward modeling and compared both resulting seismic images. Seismic migration is executed as a part of quality control (QC). We used a synthetic velocity model which based on a part of Malay Basin geology structure. Our findings shows that the seismic images produced by the application of fast marching finite difference method has better resolution than ray tracing method especially on deeper part of subsurface model.

Multi UAV Coverage Path Planning in Urban Environments

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints , calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

Preliminary Model of P-Wave Tomography Beneath Central Java using FMTOMO

The tectonic setting of Java Island is mainly controlled by the collision of Indo-Australian plate subducting the Eurasian plate. The high collision activity of Eurasian and Indo-Australian plates often causes megathrust earthquakes and the rise of arc magmatism that includes volcanic eruption. This study aims to determine the tectonic pattern beneath Central Java based on P-wave tomography inversion. We used the fast-marching method as ray tracing and subspace inversion to image subsurface velocity model to a depth of 150 km. The data used in this study are catalogue events data derived from a temporary seismometer network MERAMEX installed around central Java and DOMERAPI installed surround Mt. Merapi and Mt. Merbabu. We also include events collected from the International Seismological Centre. In total, we processed 563 earthquake events to illustrate velocity structures under central Java. The checker-board model shows that good resolutions can be identified at shallow depth, including offshore south Java contributed from Ocean Bottom Seismometer data. In vertical axis, good resolution models can be expected down to a depth 150 km following rich events from the Benioff zone. Current P wave model show a distinct low velocity zone under Mt Merapi that can be seen down to a depth of 40 km, suggesting a possible separated deep magma reservoir. To the south of Mt Merapi area also shows a low-velocity band that may be related with the southern mountain arc. Additionally, the northern part of Mt. Merapi displays a band of strong low-velocity anomaly to the East and West with the anomaly in the Eastern Part seems to have a deeper extension to a depth of ~50 km. We related this anomaly with Merapi Lawu Anomaly and Kendeng basin. Our results show a similar result with the previous tomography models in this region.

Initial Result of P Wave Tomography Model in Sunda-Banda Arc Transition using FMTOMO

Our study area is located near island Sumbawa, Sumba, Flores, West Timor, Indonesia and East Timor, popularly known as Sunda-Banda arc transition zone. The tectonic setting is mainly controlled by the movement of the oceanic lithosphere Indo-Australian plate subducting the Eurasian plate and Northward migration of Australian continental lithosphere into western Banda-arc in the region of Flores, Sumba and Timor island. We tried to image velocity structure beneath these regions using regional events and tomography inversion model. We collected 5 years of regional events from the Indonesian Agency of Meteorology, Climatology and Geophysics. In total, we reserved 3186 events recorded on 29 stations. For data processing, we used fast marching method as ray tracing between sources and receiver. We then employed subspace inversion as the tomography procedure to estimate the best velocity model representing the tectonic model in the region. Hypocenter data distribution is concentrated on shallow parts of the region and along the Benioff zone down to a maximum depth of 400 km. One of challenge of this study is that although events are abundance, the stations used are mostly located onshore and does not extend in the south-north direction that leads us to under determined problem in the inversion process. However, checker-board models show most our target area can be retrieved to its initial model with sign of smearing effects shown start from a depth of 50 km. After six iteration and optimized selection of damping and smoothing parameters, we observed low velocity anomaly under Bali, Lombok, Sumba, East Nusa Tenggara at shallow depth that may be related with volcanic activity. Deeper low anomaly can also be seen that may be related with partial melting process. A band of fast velocity is clearly seen that goes deepen to the north depicting subducting slabs own to a depth of 300 km. We also observed a possible of fast velocity in the northern part of our stations at shallow depth that we believe may represent the back arc thrust.

An Efficient Deep Learning-Based Workflow Incorporating a Reduced Physics Model for Subsurface Imaging in Unconventional Reservoirs

We present a novel deep learning-based workflow incorporating a reduced physics model that can efficiently visualize well drainage volume and pressure front propagation in unconventional reservoirs in near real-time. The visualizations can be readily used for qualitative and quantitative characterization and forecasting of unconventional reservoirs. Our aim is to develop an efficient workflow that allows us to ‘see’ within the subsurface given measured data, such as production data. The most simplistic way to achieve the goal will be to merely train a deep learning-based regression model where the input consists of some measured data, and the output is a subsurface image, such as pressure field. However, the high output dimension that corresponds to spatio-temporal steps makes the training inefficient. To address this challenge, an autoencoder network is applied to discover lower dimensional latent variables that represent high dimensional output images. In our approach, the regression model is trained to predict latent variables, instead of directly constructing an image. In the prediction step, the trained regression model first predicts latent variables given measured data, then the latent variables will be used as inputs of the trained decoder to generate a subsurface image. In addition, fast marching-method (FMM)-based rapid simulation workflow which transforms original 2D or 3D problems into 1D problems is used in place of full-physics simulation to efficiently generate datasets for training. The capability of the FMM-based rapid simulation allows us to generate sufficient datasets within realistic simulation times, even for field scale applications. We first demonstrate the proposed approach using a simple illustrative example. Next, the approach is applied to a field scale reservoir model built after the publicly available data on the Hydraulic Fracturing Test Site-I (HFTS-I), which is sufficiently complex to demonstrate the power and efficacy of the approach. We will further demonstrate the utility of the approach to account for subsurface uncertainty. Our approach, for the first time, allows data-driven visualization of unconventional well drainage volume in 3D. The novelty of our approach is the framework which combines the strengths of deep learning-based models and the FMM-based rapid simulation. The workflow has flexibility to incorporate various spatial and temporal data types.