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Introduction
As a geologist I'm fascinated by our wonderfully complex yet amazingly organised planet. I particularly enjoy the challenges earth scientists face when integrating and synthesising diverse and multi-scale datasets. I have a broad range of research interests and skills, including volcanology, digital outcrop acquisition and analysis, remote sensing, three-dimensional modelling and structural mapping. 🌋 www.samthiele.science
Current institution
Helmholtz-Zentrum Dresden-Rossendorf | HZDR
Institute Freiberg for Resource Technology
Current position
PostDoc Position
Skills and Expertise
Education
May 2019 - Nov 2019
Monash University (Australia)
Structural Volcanology
Awards & Achievements
Scholarship · May 2016
Westpac Future Leader Scholarship
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Helmholtz-Zentrum Dresden-Rossendorf
Monash University (Australia)
Helmholtz-Zentrum Dresden-Rossendorf
Menoufia University
Helmholtz-Zentrum Dresden-Rossendorf
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Helmholtz-Zentrum Dresden-Rossendorf
Monash University (Australia)
Helmholtz-Zentrum Dresden-Rossendorf
Menoufia University
Helmholtz-Zentrum Dresden-Rossendorf
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Projects (1)
Modelling conduit processes - intrepretation of monitoring data
Integration of different monitoring data (seismic, gas flux, effusion rates, explosion energy, ash caracteristics, petrology etc.) to understand behaviour of magma in conduit at Volcán de Colima. What controls transition between different styles of eruption?
Research
Research Items (32)
While uncrewed aerial vehicles are routinely used as platforms for hyperspectral sensors, their application is mostly confined to nadir imaging orientations. Oblique hyperspectral imaging has been impeded by the absence of robust registration and correction protocols, which are essential to extract accurate information. These corrections are especially important for detecting the typically small spectral features produced by minerals, and for infrared data acquired using pushbroom sensors. The complex movements of unstable platforms (such as UAVs) require rigorous geometric and radiometric corrections, especially in the rugged terrain often encountered for geological applications. In this contribution we propose a novel correction methodology, and associated toolbox, dedicated to the accurate production of hyperspectral data acquired by UAVs, without any restriction concerning view angles or target geometry. We make these codes freely available to the community, and thus hope to trigger an increasing usage of hyperspectral data in Earth sciences, and demonstrate them with the production of, to our knowledge, the first fully corrected oblique SWIR drone-survey. This covers a vertical cliff in the Dolomites (Italy), and allowed us to distinguish distinct calcitic and dolomitic carbonate units, map the qualitative abundance of clay/mica minerals, and thus characterise seismic scale facies architecture.
Efficient, socially acceptable and rapid methods of exploration are required to discover new deposits and enable the green energy transition. Sustainable exploration requires a combination of innovative thinking and new technologies. Hyperspectral imaging (HSI) is a rapidly developing technology and allows for fast and systematic mineral mapping, facilitating exploration of the Earth's surface at various scales on a variety of platforms. Newly available sensors allow data capture over a wide spectral range, and provide information about the abundance and spatial location of ore and pathfinder minerals in drill-core, hand samples and outcrops with mm to cm precision. Conversely, the complex geometries of the imaged surfaces affect the spectral quality and signal-to-noise ratio (SnR) of HSI data at these very narrow spatial samplings. Additionally, the complex mineral assemblages found in hydrothermally altered ore deposits can make interpretation of spectral results a challenge. In this contribution, we propose an innovative approach that integrates multiple sensors and scales of data acquisition to help disentangle complex mineralogy associated with lithium and tin mineralisation in the Uis pegmatite complex, Namibia. We train this method using hand samples and finally produce a three-dimensional (3D) point cloud for mapping lithium mineralisation in the open pit. We were able to identify and map lithium-bearing cookeite and montebrasite at outcrop scale. The accuracy of the approach was validated by drill-core data, XRD analysis and LIBS measurements. This approach facilitates efficient mapping of complex terrains, as well as important monitoring and optimisation of ore extraction. Our method can easily be adapted to other minerals relevant to the mining industry.
The digitization and automation of the raw material sector is required to attain the targets set by the Paris Agreements and support the sustainable development goals defined by the United Nations. While many aspects of the industry will be affected, most of the technological innovations will require smart imaging sensors. In this review, we assess the relevant recent developments of Machine Learning for the processing of imaging sensor data. We first describe the main imagers and the acquired data types as well as the platforms on which they can be installed. We briefly describe radiometric and geometric corrections as these procedures have been already described extensively in previous works. We focus on the description of innovative processing workflows and illustrate the most prominent approaches with examples. We also provide a list of available resources, codes, and libraries for researchers at different levels, from students to senior researchers, willing to explore novel methodologies on the challenging topics of raw material extraction, classification, and process automatization.
The widespread application of drones and associated miniaturization of imaging sensors has led to an explosion of remote sensing applications with very high spatial and spectral resolutions. The 3-D ultrahigh-resolution digital outcrop models created using drones and oblique imagery from ground-based sensors are now commonly used in the academic and industrial sectors, while the generation of spatially accurate models has been greatly facilitated by the development of computer vision tools, such as structure from motion, and the correction of spectral attributes to achieve material reflectance measurements remains challenging. Following the development of a topographical correction toolbox (mephysto), we now propose a series of new tools that can leverage the detailed geometry captured by digital outcrop models to correct for illumination effects caused by oblique viewing angles and the interaction of light with complex 3-D surfaces. This open-source code is integrated into hylite, a python toolbox for the full 3-D processing and fusion of digital outcrop models with hyperspectral imaging data. We validate the performance of our novel method using a case study at an open-pit mine in Tharsis, Spain, and demonstrate the importance of accurate illumination corrections for quantitative spectral analyses. Significantly, we show that commonly applied spectral analysis techniques can yield erroneous results for data corrected using current state-of-the-art approaches. Our proposed method ameliorates many of the issues with these established approaches.
Field observations and unmanned aerial vehicle surveys from Caldera Taburiente (La Palma, Canary Islands, Spain) show that pre-existing dykes can capture and re-direct younger ones to form multiple dyke composites. Chill margins suggest that the older dykes were solidified and cooled when this occurred. In one multiple dyke example, an 40Ar/39Ar age difference of 200 kyr was determined between co-located dykes. Petrography and geomechanical measurements (ultrasonic pulse and Brazilian disc tests) show that a microscopic preferred alignment of plagioclase laths and sheet-like structures formed by non-randomly distributed vesicles give the solidified dykes anisotropic elastic moduli and fracture toughness. We hypothesize that this anisotropy led to the development of margin-parallel joints within the dykes, during subsequent volcanic loading. Finite element models also suggest that the elastic contrast between solidified dykes and their host rock elevated and re-oriented the stresses that governed subsequent dyke propagation. Thus, the margin-parallel joints, combined with local concentration and rotation of stresses, favored the deflection of subsequent magma-filled fractures by up to 60° to form the multiple dykes. At the edifice scale, the capture and deflection of active intrusions by older ones could change the organization of volcanic magma plumbing systems and cause unexpected propagation paths relative to the regional stress. We suggest that reactivation of older dykes by this mechanism gives the volcanic edifice a structural memory of past stress states, potentially encouraging the re-use of older vents and deflecting intrusions along volcanic rift zones or toward shallow magma reservoirs.
Enhanced digital outcrop models attributed with hyperspectral reflectance data, or hyperclouds, provide a flexible, three-dimensional medium for data-driven mapping of geological exposures, mine faces or cliffs. This approach allows the collection of spatially contiguous information on exposed mineralogy and so provides key information for understanding mineralising processes, interpreting 1-D drillhole data, and optimising mineral extraction. In this contribution we present an open-source python workflow, hylite, for creating hyperclouds by seamlessly fusing geometric information with data from a variety of hyperspectral imaging sensors and applying necessary atmospheric and illumination corrections. These rich datasets can be analysed using a variety of techniques, including minimum wavelength mapping and spectral indices to accurately map geological objects from a distance. Reference spectra from spectral libraries, ground or laboratory measurements can also be included to derive supervised classifications using machine learning techniques. We demonstrate the potential of the hypercloud approach by integrating hyperspectral data from laboratory, tripod and unmanned aerial vehicle acquisitions to automatically map relevant lithologies and alterations associated with volcanic hosted massive sulphide (VHMS) mineralisation in the Corta Atalaya open-pit, Spain. These analyses allow quantitative and objective mineral mapping at the outcrop and open-pit scale, facilitating quantitative research and smart-mining approaches. Our results highlight the seamless sensor integration made possible with hylite and the power of data-driven mapping approaches applied to hyperclouds. Significantly, we also show that random forests (RF) trained only on laboratory data from labelled hand-samples can be used to map outcrop scale data.
Field observations and unmanned aerial vehicle surveys from Caldera Taburiente (La Palma, Canary Islands, Spain) show that pre-existing dykes can capture and re-direct younger ones to form multiple dyke composites. Chill margins suggest that the older dykes were solidified and cooled when this occurred. In one multiple dyke example, an 40Ar/39Ar age difference of 200 kyr was determined between co-located dykes. Petrography and geomechanical measurements (ultrasonic pulse and Brazilian disc tests) show that a microscopic preferred alignment of plagioclase laths and sheet-like structures formed by non-randomly distributed vesicles give the solidified dykes anisotropic elastic moduli and fracture toughness. We hypothesise that this anisotropy led to the development of margin-parallel joints within the dykes, during subsequent volcanic loading. Finite element models also suggest that the elastic contrast between solidified dykes and their host rock elevated and re-oriented the stresses that governed subsequent dyke propagation. Thus, the margin- parallel joints, combined with local concentration and rotation of stresses, favoured the deflection of subsequent magma- filled fractures by up to 60° to form the multiple dykes. At the edifice scale, the capture and deflection of active intrusions by older ones could change the organisation of volcanic magma plumbing systems and cause unexpected propagation paths relative to the regional stress. We suggest that reactivation of older dykes by this mechanism gives the volcanic edifice a structural memory of past stress states, potentially encouraging the re-use of older vents and deflecting intrusions along volcanic rift zones or towards shallow magma reservoirs.
The feedback between dyke and sill intrusions and the evolution of stresses within volcanic systems is poorly understood, despite its importance for magma transport and volcano instability. Long-lived ocean island volcanoes are crosscut by thousands of dykes, which must be accommodated through a combination of flank slip and visco-elastic deformation. Flank slip is dominant in some volcanoes (e.g., Kilauea), but how intrusions are accommodated in other volcanic systems remains unknown. Here we apply digital mapping techniques to collect > 400,000 orientation and aperture measurements from 519 sheet intrusions within Volcán Taburiente (La Palma, Canary Islands, Spain) and investigate their emplacement and accommodation. We show that vertically ascending dykes were deflected to propagate laterally as they approached the surface of the volcano, forming a radial dyke swarm, and propose a visco-elastic model for their accommodation. Our model reproduces the measured dyke-aperture distribution and predicts that stress accumulates within densely intruded regions of the volcano, blocking subsequent dykes and causing eruptive activity to migrate. These results have significant implications for the organisation of magma transport within volcanic edifices, and the evolution and stability of long-lived volcanic systems.
The feedback between dyke and sill intrusions and the evolution of stresses within volcanic systems is poorly understood, despite its importance for magma transport and volcano instability. Long-lived ocean island volcanoes are crosscut by thousands of dykes, which must be accommodated through a combination of flank slip and visco-elastic deformation. Flank slip is dominant in some volcanoes (e.g., Kilauea), but how intrusions are accommodated in other volcanic systems remains unknown. Here we apply digital mapping techniques to collect >400,000 orientation and aperture measurements from 519 sheet intrusions within Volcán Taburiente (La Palma, Canary Islands, Spain) and investigate their emplacement and accommodation. We show that vertically ascending dykes were deflected to propagate laterally as they approached the surface of the volcano, forming a radial dyke swarm, and propose a visco-elastic model for their accommodation. Our model reproduces the measured dyke-aperture distribution and predicts that stress accumulates within densely intruded regions of the volcano, blocking subsequent dykes and causing eruptive activity to migrate. These results have significant implications for the organisation of magma transport within volcanic edifices, and the evolution and stability of long-lived volcanic systems.
Dykes are the principal magma transport pathways in many ocean island volcanoes and, after solidification, these intrusions form abundant geomechanical discontinuities. However, their spatial and temporal distribution is poorly understood due to limited exposure in most volcanic environments. As such, their cumulative influence on the magnitude and distribution of stress in volcanic edifices remains unexplored. This thesis aims to address these knowledge gaps by applying novel digital outcrop techniques to constrain the distribution of sheet-intrusions (dykes and sills) in the exposed interior of Volcán Taburiente (La Palma, Canary Islands, Spain) and by modelling their mechanical influence during edifice growth and eventual collapse. A least-cost-path solver was applied to semi-automatically map >500 intrusions in digital outcrop models created using unmanned aerial vehicle photogrammetry. A Bayesian method was developed to constrain intrusion orientations and apertures at ~10 cm intervals along 64 km of mapped intrusion contacts, resulting in ~400,000 measurements. These show that the dykes form a radial swarm with a dominant N–S axis. A Maxwell visco-elastic model suggests that the 2–10% bulk strain induced by the intrusions could have been accommodated visco- elastically, without sliding on a basal detachment or volcanic flank spreading. The model also reproduces the observed dyke aperture distribution, and implies that increased compressive stress due to dyke swarm emplacement could have caused the locus of volcanism to migrate. The mechanical significance of solidified dykes was also investigated. Multiple-dykes, observed in the field and mapped using the digital outcrop models, are shown to have formed long after the first set of intrusions solidified. It is proposed that the elastic contrast between stiff dykes and compliant host rock led to the formation of pervasive margin-parallel joint sets that captured second generation magma-driven fractures to form multiple-dykes. Such reactivation of older dykes can change the organisation of a magma plumbing system by redirecting dykes along rift zones or towards older vents and magma chambers. Finally, it is suggested that dykes form networks of stiff inclusions that are analogous to fibres in reinforced composite materials. These load-bearing frameworks can potentially influence volcano stability. Observations of faults confined to dykes indicate that stress concentration occurs within dykes where they crosscut weak layers, causing them to fail despite their greater strength. Micromechanical analyses and fibre bundle models show that this process has implications for the development of gravitational instabilities; stress redistribution following dyke failure can trigger a cascading weakening process that causes catastrophic failure with limited precursor seismicity. A major implication of this thesis is that dyke networks should be considered when evaluating volcano stability.
During eruptive activity of andesitic stratovolcanoes, the extrusion of lava domes, their collapse and intermittent explosions are common volcanic hazards. Many lava domes grow in a preferred direction, in turn affecting the direction of lava flows and pyroclastic density currents. Access to active lava domes is difficult and hazardous, so detailed data characterizing lava dome growth are typically limited, keeping the processes controlling the directionality of extrusions unclear. Here we combine TerraSAR-X satellite radar observations with high-resolution airborne photogrammetry to assess morphological changes, and perform finite element modeling to investigate the impact of loading stress on shallow magma ascent directions associated with lava dome extrusion and crater formation at Volcán de Colima, México. The TerraSAR-X data, acquired in ~1-m resolution spotlight mode, enable us to derive a chronology of the eruptive processes from intensity-based time-lapse observations of the general crater and dome evolution. The satellite images are complemented by close-range airborne photos, processed by the Structure-from-Motion workflow. This allows the derivation of high-resolution digital elevation models, providing insight into detailed loading and unloading features. During the observation period from Jan-2013 to Feb-2016, we identify a dominantly W-directed dome growth and lava flow production until Jan-2015. In Feb-2015, following the removal of the active summit dome, the surface crater widened and elongated along a NE-SW axis. Later in May-2015, a new dome grew toward the SW of the crater while a separate vent developed in the NE of the crater, reflecting a change in the direction of magma ascent and possible conduit bifurcation. Finite element models show a significant stress change in agreement with the observed magma ascent direction changes in response to the changing surface loads, both for loading (dome growth) and unloading (crater forming excavation) cases. These results allow insight into shallow dome growth dynamics and the migration of magma ascent in response to changing volcano summit morphology. They further highlight the importance of detailed volcano summit morphology surveillance, as changes in direction or location of dome extrusion may have major implications regarding the directions of potential volcanic hazards, such as pyroclastic density currents generated by dome collapse.
Dykes are the most common means of magma transport in basaltic volcanoes, so knowledge of dyke propagation paths is critical for volcanic hazard analyses. Some dykes contain internal chill margins and/or compositional variation that suggest they formed from two or more temporally separate dyking events. These multiple-dykes have been studied from a geochemical perspective to explore fractionation in magma chambers, but literature on the mechanics of their formation is lacking. It is commonly assumed that multiple-dykes form either because (1) the initial dyke did not have time to solidify completely before the subsequent injection, or (2) the solidified dyke (or its margin) is weaker than the host rock it intrudes. In this contribution we present an analysis of exceptionally well-exposed multiple-dykes on the island of La Palma (Canary Islands, Spain) that appear not to have formed by either of these mechanisms. Dykes in the study area are basaltic and variably vesiculated. They are crosscut either by 5-15 cm spaced cooling joints or 1-10 cm spaced and remarkably persistent margin-parallel joints (MPJs). Internal contacts within the dykes show distinct glassy chill margins up to 1 cm thick, suggesting that they comprise multiple intrusions and that the exterior (older) dyke had cooled prior to subsequent intrusions. Dyke interactions were observed along cliff faces, where younger intrusions intersect and are reoriented along older ones by as much as 60°; the older dykes clearly provide preferential propagation pathways. This observation is counterintuitive, as dykes at this location crosscut comparatively weak and compliant phreatomagmatic tuff, scoria and matrix-rich volcanic breccia. As such, we propose that the multiple-dykes formed due to the mechanical contrast between solidified older dykes and the host-rock. Linear-elastic models suggest that the stiffness contrast will result in significantly larger stress within the dykes than the more-compliant host rocks under volcanic and gravitational loading, and that this stress will be rotated towards parallelism with the dyke. As a result, a multiple-dyke is formed as subsequent intrusions tend to be deflected along the dyke contact (if the dyke has a weak margin) or within the dyke itself due to the stress rotation. Geomechanical tests also show that the dykes have anisotropic elastic properties, tensile strength and fracture toughness, probably due to pervasive flow fabrics defined by aligned plagioclase lathes. This anisotropy will exaggerate the stress rotation and encourage formation of MPJs during volcano inflation/deflation cycles, which in turn will further enhance the anisotropy and multiple-dyke formation. At a large scale, the geomechanical discontinuities that solidified dykes create give volcanic edifices a structural memory of past stress-states. Deflection of active dykes along these discontinuities will cause intrusions to become misoriented with respect to current stresses and hence potentially unexpected propagation paths. Re-activation of older dykes also has implications for the organisation of the magma plumbing system, encouraging re-use of older vents and directing dykes along volcanic rift-zones or towards shallow magma reservoirs.
Measurement of structure orientations is a key part of structural geology. Digital outcrop methods provide a unique opportunity to collect such measurements in unprecedented numbers, and are becoming widely applied. However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. Here we present a Bayesian approach to plane fittingthat can use data extracted from digital outcrop models to constrain the orientation of structures and their associated uncertainty. We also describe a moving-window search algorithm that exploits this Bayesian formulation to estimate local structure orientations for segmented structures. These methods are validated on synthetic datasets for which both the structure orientation and associated uncertainty is known. Finally, we implement the method in the point cloud analysis package CloudCompare and use it to estimate the orientation and thickness of dykes exposed in cliffs on the island of La Palma (Spain).The results highlight the potential of this method to generate structural data at unprecedented spatial resolution, while simultaneously characterising the associated uncertainties.
Measurement of structure orientations is a key part of structural geology. Digital outcrop methods provide a unique opportunity to collect such measurements in unprecedented numbers, and are becoming widely applied. However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. Here we present a Bayesian approach to plane fitting that can use data extracted from digital outcrop models to constrain the orientation of structures and their associated uncertainty. We also describe a moving-window search algorithm that exploits this Bayesian formulation to estimate local structure orientations for segmented structures. These methods are validated on synthetic datasets for which both the structure orientation and associated uncertainty is known. Finally, we implement the method in the point cloud analysis package CloudCompare and use it to estimate the orientation and thickness of dykes exposed in cliffs on the island of La Palma (Spain), which were captured using an unmanned aerial vehicle (UAV) and digital photogrammetry. The results highlight the potential of this method to generate structural data at unprecedented spatial resolution, while simultaneously characterising the associated uncertainties.
The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost functions to rapidly interpolate structural features between manually defined control points in point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3-D) and two-dimensional (2-D) datasets, including high-resolution aerial imagery, digital outcrop models, digital elevation models (DEMs) and geophysical grids. We demonstrate the algorithm with four diverse applications in which we extract (1) joint and contact patterns in high-resolution orthophotographs, (2) fracture patterns in a dense 3-D point cloud, (3) earthquake surface ruptures of the Greendale Fault associated with the Mw7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (lidar) data, and (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the consistency of the interpretation process while retaining expert guidance and achieves significant improvements (35–65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.
The island of La Palma (Spain) is well known for large collapse events, the most recent of which removed a significant portion of the Cumbra Nueva edifice at ca. 550 ka. Erosion of this collapse scar has formed extensive cliffs within Caldera de Taburiente, exposing thousands of dykes in an approximately radial swarm. The role of these dykes in the edifice instability and eventual failure remains largely unexplored. We present UAV-based 3D modelling, mapping and field observations of the dykes within Caldera de Taburiente, where near-continuous exposure provides an ideal field locality to investigate the distribution of dykes within volcanic edifices and their potential effect on edifice mechanics. Our results highlight the large number of dykes involved, illustrate their relationships to the collapse geometry and provide quantification of key geotechnical parameters (e.g., fracture intensity and dyke spacing). Furthermore, observations of internal dyke-parallel joint sets and multiple-dykes with internal glassy chill margins in weak host rocks provide structural evidence for local stress concentration within solidified dykes, and hence a broader contribution to edifice mechanics and deformation than has previously been recognised. We suggest that solidified dykes with different mechanical properties to their hosts have the potential to significantly affect volcanic edifice strength, and hence stability. If dykes behave in a more rigid fashion than their surroundings, they will act to support the edifice weight, " channelling " and locally concentrating gravitational stresses. This effect can improve edifice strength, allowing steeper slopes. Conversely, it could also provide a potentially catastrophic weakening mechanism because progressive hydrothermal alteration or strain softening (due to fracturing or eventual dyke truncation) reduces the ability of dykes to support a load. Interactions between dykes and edifice hydrogeology are also likely to have significant implications for instability formation and development.
Two centuries ago William Smith produced the first geological map of England and Wales, an achievement that underlined the importance of mapping geological contacts and structures as perhaps the most fundamental skill set in earth science. The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost-functions to rapidly extract and measure structural features from point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3D) and two-dimensional (2D) datasets including high-resolution aerial imagery, virtual outcrop models, digital elevation models (DEMs) and geophysical grids. We demonstrate the algorithm with four diverse applications, where we extract: (1) joint and contact patterns in high-resolution orthophotographs; (2) fracture patterns in a dense 3D point cloud; (3) earthquake surface ruptures of the Greendale Fault associated with the Mw7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (LiDAR) data, and; (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the objectivity and consistency of the interpretation process while retaining expert-guidance, and achieves significant improvements (35–65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.
Structure-from-motion (SfM) algorithms greatly facilitate the generation of 3-D topographic models from photographs and can form a valuable component of hazard monitoring at active volcanic domes. However, model generation from visible imagery can be prevented due to poor lighting conditions or surface obscuration by degassing. Here, we show that thermal images can be used in a SfM workflow to mitigate these issues and provide more continuous time-series data than visible counterparts. We demonstrate our methodology by producing georeferenced photogrammetric models from 30 near-monthly overflights of the lava dome that formed at Volcán de Colima (Mexico) between 2013 and 2015. Comparison of thermal models with equivalents generated from visible-light photographs from a consumer digital single lens reflex (DSLR) camera suggests that, despite being less detailed than their DSLR counterparts, the thermal models are more than adequate reconstructions of dome geometry, giving volume estimates within 10% of those derived using the DSLR. Significantly, we were able to construct thermal models in situations where degassing and poor lighting prevented the construction of models from DSLR imagery, providing substantially better data continuity than would have otherwise been possible. We conclude that thermal photogrammetry provides a useful new tool for monitoring effusive volcanic activity and assessing associated volcanic risks.
Documentation for the CloudCompare plugin which implements our shortest-path based fracture identification tool in a usable fashion. The plugin is now bundled with Cloud Compare (from v. 2.9 alpha)
UAV-based photogrammetric and LiDAR techniques provide high resolution 3D point clouds and ortho-rectified photomontages that can capture surface geology in outstanding detail over wide areas. Automated and semi-automated methods are vital to extract full value from these data in practical time periods, though the nuances of geological structures and materials (natural variability in colour and geometry, soft and hard linkage, shadows and multiscale properties) make this a challenging task. We present a novel method for computer assisted trace detection in dense point clouds, using a lowest cost path solver to “follow” fracture traces and lithological contacts between user defined end points. This is achieved by defining a local neighbourhood network where each point in the cloud is linked to its neighbours, and then using a least-cost path algorithm to search this network and estimate the trace of the fracture or contact. A variety of different algorithms can then be applied to calculate the best fit plane, produce a fracture network, or map properties such as roughness, curvature and fracture intensity. Our prototype of this method (Fig. 1) suggests the technique is feasible and remarkably good at following traces under non-optimal conditions such as variable-shadow, partial occlusion and complex fracturing. Furthermore, if a fracture is initially mapped incorrectly, the user can easily provide further guidance by defining intermediate waypoints. Future development will include optimization of the algorithm to perform well on large point clouds and modifications that permit the detection of features such as step-overs. We also plan on implementing this approach in an interactive graphical user environment.
Photogrammetric and structure from motion (SfM) techniques are increasingly being used to monitor active lava domes (e.g. JAMES & VARLEY, 2012, DIEFENBACH et al., 2013). This study applies SfM techniques to digital single lens reflex (DSLR) and thermal images acquired during observation overflights of Volcán de Colima prior to an eruption and associated dome collapse in July 2015. The collapse triggered a pyroclastic flow which travelled ~10.7km's, threatening several ranches and the town of Quesaría. Models of the dome were constructed from DSLR and thermal images, and georeferenced by comparison with Google Earth imagery. Models built using DSLR images were found to be substantially more sensitive to degassing and poor lighting, but were of superior quality during favourable conditions. Conversely, models produced from thermal images were less detailed but more robust in non-optimal circumstances. Thermal models were constructed from most flights, while DSLR models could only be built for about 60% of the datasets. Georeferenced models were exported as triangular meshes and aligned with a pre-dome model to improve relative georeferencing, using MeshLab's iterative closest point algorithm (CIGNONI et al., 2008). Volume differences were then calculated using an implementation of the signed tetrahedron method (ZHANG & CHEN, 2001). In our application of this method, 'regions of interest' are interactively selected and the volume between a reference surface (pre-dome model) and test surface (each dome model) calculated. Hence, the volume of the dome, top portion of the main lava flow, and two reference areas (zero volume change assumed) were estimated (Fig. 1). Estimations derived from the DSLR and thermal models generally correspond, suggesting (along with low reference area volumes) that they are reasonable, though this method assumes constant underlying topography and hence likely produces underestimates. The data show that dome growth occurred in three distinct episodes. Between January and April 2013 the dome grew at a rate of ~0.05 – 0.12 m 3 /sec, slowly filling the pre-dome crater (Figure 2a). By late April the crater was overtopped and a lava flow formed on the volcano's west flank, creating a stable configuration where inflow ≈ outflow and dome growth dropped to <0.01 m 3 /sec. The second period of dome growth occurred between July and November 2014, growing at ~0.06 m 3 /sec (Fig. 2c) and forming several new flows (which accommodated most new lava). The dome then underwent a period of substantial subsidence, deflating at ~0.03 m 3 /sec, accompanied by endogenous growth from a second (easterly) vent (Figure 2c & d). Finally, the dome inflated again (at ~0.05 m 3 /sec) from May 2015, before collapsing to the south in early July. Photographic evidence suggests effusion rate may have increased dramatically in the hours preceding collapse. The geometric and thermal evolution of the lava dome was also examined using the photogrammetric dataset. Most significantly, the models indicate that the July eruption was preceded by effusion from two separate vents (Fig. 2d) and by substantial south directed bulging of the dome. These results suggest that photogrammetric monitoring provides both important insight into volcanic processes and a useful dataset for risk forecasting.
We present a novel methodology for performing experiments with subsurface structural models using a set of flexible and extensible Python modules. We utilize the ability of kinematic modelling techniques to describe major deformational, tectonic, and magmatic events at low computational cost to develop experiments testing the interactions between multiple kinematic events, effect of uncertainty regarding event timing, and kinematic properties. These tests are simple to implement and perform, as they are automated within the Python scripting language, allowing the encapsulation of entire kinematic experiments within high-level class definitions and fully reproducible results. In addition, we provide a link to geophysical potential-field simulations to evaluate the effect of parameter uncertainties on maps of gravity and magnetics. We provide relevant fundamental information on kinematic modelling and our implementation, and showcase the application of our novel methods to investigate the interaction of multiple tectonic events on a pre-defined stratigraphy, the effect of changing kinematic parameters on simulated geophysical potential fields, and the distribution of uncertain areas in a full 3-D kinematic model, based on estimated uncertainties in kinematic input parameters. Additional possibilities for linking kinematic modelling to subsequent process simulations are discussed, as well as additional aspects of future research. Our modules are freely available on github, including documentation and tutorial examples, and we encourage the contribution to this project.
We present a novel methodology for performing experiments with subsurface structural models using a set of flexible and extensible Python modules. We utilise the ability of kinematic modelling techniques to describe major deformational, tectonic, and magmatic events at low computational cost to develop experiments testing the interactions between multiple kinematic events, effect of uncertainty regarding event timing, and kinematic properties. These tests are simple to implement and perform, as they are automated within the Python scripting language, allowing the encapsulation of entire kinematic experiments within high-level class definitions and fully reproducible results. In addition, we provide a~link to geophysical potential-field simulations to evaluate the effect of parameter uncertainties on maps of gravity and magnetics. We provide relevant fundamental information on kinematic modelling and our implementation, and showcase the application of our novel methods to investigate the interaction of multiple tectonic events on a pre-defined stratigraphy, the effect of changing kinematic parameters on simulated geophysical potential-fields, and the distribution of uncertain areas in a full 3-D kinematic model, based on estimated uncertainties in kinematic input parameters. Additional possibilities for linking kinematic modelling to subsequent process simulations are discussed, as well as additional aspects of future research. Our modules are freely available on github, including documentation and tutorial examples, and we encourage the contribution to this project.
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