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Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people’s safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper. This research was funded by project Georisk, “Advances in rockfall quantitative risk analysis (QRA) incorporating developments in geomatics (GeoRisk)”, grant number PID2019-103974RB-I00, funded by MCIN/AEI/10.13039/501100011033, the Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación of Spain. The APC was covered by Remote Sensing. Peer Reviewed Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura Objectius de Desenvolupament Sostenible::13 - Acció per al Clima

Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models.

El 25 de agosto de 1804 un fuerte terremoto sacudió la región del Campo de Dalías (Almería) llegando a sentirse con una intensidad EMS de IX (Mw ~6,4). En este estudio se muestran evidencias que sugieren una relación entre este episodio sísmico y la falla de Llano del Águila. Se trata de una falla normal sub vertical de dirección NW-SE y paralela a la falla de Loma del Viento, situada a unos 3 km al sur. Para la caracterización de la falla de Llano del Águila se ha llevado a cabo una nueva interpretación geomorfológica de los depósitos aluviales cuaternarios que se ven atravesados por la traza de la falla a lo largo del área de estudio. Se han identificado cuatro generaciones de abanicos aluviales provenientes de la Sierra de Gádor, y dos secciones de falla a escala cartográfica (Cantera Este y Rambla de la Maleza). La interpretación geomorfológica se basa en el análisis de fotografías aéreas históricas. Debido a la intensa antropización de la zona, los modelos digitales del terreno actuales no son útiles. Para solventar esta limitación se procesó un modelo digital de elevaciones mediante fotogrametría usando las fotos aéreas del vuelo interministerial (1977). El análisis de escarpes de falla mediante perfiles topográficos medidos en el nuevo modelo de elevaciones proporciona un salto vertical de 6,3 ± 1,9 m para la sección de la Cantera Este y de 12,1 ± 1,9 m para la sección de la Rambla de la Maleza. Estas interpretaciones han sido verificadas en el campo donde además se adquirieron nuevos datos sobre la cinemática de la falla. Todo ello ha permitido estimar la tasa de deslizamiento neta de cada sección: 0,016 ± 0,002 y 0,10 ± 0,02 mm/año para la sección de la Cantera Este y 0,031 ± 0,002 – 0,19 ± 0,.02 mm/año para la Rambla de la Maleza, respectivamente para los últimos 126 y 781 ka (Pleistoceno Medio). A partir de la longitud total de la traza de la falla se puede estimar mediante relaciones empíricas una magnitud máxima potencial de 6,59 ± 0,19.

Seismic sensors installed in areas prone to rockfalls provide a continuous record of the phenomenon, allowing real-time detection and characterization. Detection of small scale rockfalls (< 0.001 m3), that might be precursors of larger events, can be worthwhile for early warning systems of rockfalls. However, seismic signals are closely dependent on the characteristics of the event and on the geotechnical characteristics of the ground, making the detection of small rockfalls complex and requiring detailed in-situ analyzes. For this reason, an experiment was carried out on the UB experimental site (Puigcercós Cliff, Catalonia, NE Spain) on 6th-7th of June 2013, where 21 rocks with volumes ranging from 0.0015 m3 to 0.0004 m3 were thrown from the top of the cliff (200 m long and 27 m high) and the seismic signals were registered with three 3D short period seismic sensors located at different distances from the rock wall: 57 m, 67 m, and 107 m.The recorded seismic signals have a frequency content between 10-30 Hz, and the duration of the peak amplitudes varied between 0.3 and 0.6 s. Based on these characteristics, different phases of the dynamics of the rockfalls were identified, including main impacts, rebounds, flights, rolling and final stop of the events. The furthest station recorded the lowest frequency and amplitude values, limiting our ability to detect those blocks smaller than 0.0015 m3. Comparing the results with the nearest station, seismic attenuation phenomena is detectable even at distances of 50 m.After the experiment, a permanent seismic station was installed in the area, at 107 m from the cliff. Using LiDAR and 2D imagery monitoring, two naturally triggered rockfalls were identified on 30th and 31st August 2017 (0.28 m3 and 0.25 m3 respectively). Based on the results from the experiment and an automatic detection system, these main events and prior minor events have been found in the continuous seismic records of this permanent station. The characteristics of these natural detachments differ partially from the artificially triggered rockfalls during the experiment, since the geometry of the seismic signals is different. The observed shapes of the natural detachments are similar to that of granular flows, much more continuous than the sharp shapes that were observed in the isolated blocks of the experiment. This shows the possibility of incorporating seismic stations for the automatic detection and initial characterization of rockfalls and its effectiveness in detecting frequencies of occurrence.In order to evaluate the possibility of estimating rockfall volumes, diverse energy ratios (Es/Ep) were calculated. However, precise volume estimation is not possible. Nevertheless, the combination of seismic data with LiDAR and photographic techniques allows accurate new volume calculations of rockfalls to be incorporated progressively into the study of rockfalls.ACKNOWLEDGMENTS: The authors would like to acknowledge the financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, Spanish MINEICO. We are also thankful to Origens UNESCO Global Geopark.

The facies distribution in time and space of sedimentary successions is controlled by a complex interplay between physical, chemical and biological processes, which are nowadays difficult to construe from the geological record. Numerical models constitute a valuable tool to identify and quantify such controlling factors permitting a reliable 3D extrapolation and prediction of stratigraphic and facies architectures beyond outcropping rock strata. This study assesses the roles of three controlling parameters being carbonate production rate, relative sea-level changes and terrigenous clastic sediment supply, on the evolution of an Aptian carbonate system. The SIMSAFADIM-CLASTIC, a 3D process-based sedimentary-stratigraphic forward model, was used for this evaluation. The carbonate succession modelled crops out in the western Maestrat Basin (E Iberia), and corresponded to a platform-to-basin transition comprising three depositional environment-related facies assemblages: platform top, slope and basin. Testing of geological parameters in forward modelling results in a wide range of possible 3D geological scenarios. The documented distribution of facies and sequence-stratigraphic framework combined with a virtual outcrop model were used as a reference to perform geometric (quantitative) and architectural and stacking pattern (qualitative) research by model-data comparison. The time interval modelled spans 1450 ky. The best-fit simulation run characterizes and quantifies (1) relative sea-level fluctuations recording five different genetic types of deposit (systems tracts) belonging to two depositional sequences as expected from field-data analysis, (2) a rate of terrigenous clastic sediment input ranging between 0.5 and 2.5 gr/s, and (3) a mean autochthonous carbonate production maximum rate of 0.08 m/ky. Furthermore, the quantitative and qualitative sensitivity tests carried out highlight that the fluctuation of relative sea level exerted the main control on the resulting stratigraphic and facies architectures, whereas the effect of inflowing terrigenous clastic sediment is less pronounced. Facies assemblages show different sensitivities to each parameter, being the slope carbonates more sensitive than the platform top facies to inflowing fine terrigenous sediments. On slope depositional settings, siliciclastic input also controls stratal stacking patterns and the dimensions of the carbonate bodies formed. The final 3D model allows to spot architectural features such as stacking patterns that can be misinterpreted by looking at the resulting record in the outcrop or by using other 2D approaches, and facilitates the comprehension of reservoir connectivity highlighting the occurrence of initial disconnected regressive platforms, which were later connected during a transgressive stage.

Rockfall detection is a crucial procedure in the field ofgeology, which helps to reduce the associated risks.Currently, geologists identify rockfall events almostmanually utilizing point cloud and imagery data ob-tained from different caption devices such as TerrestrialLaser Scanner (TLS) or digital cameras. Multitemporalcomparison of the point clouds obtained with thesetechniques requires a tedious visual inspection to iden-tify rockfall events which implies inaccuracies that de-pend on several factors such as human expertize and thesensibility of the sensors. This paper addresses this issueand provides an intelligent framework for rockfall eventdetection for any individual working in the intersectionof the geology domain and decision support systems.The development of such an analysis framework pre-sents major research challenges and justifies exhaustiveexperimental analysis. In particular, we propose an in-telligent system that utilizes multiple machine learningalgorithms to detect rockfall clusters of point cloud data.Due to the extremely imbalanced nature of the problem,aplethoraofstateoftheart resampling techniques ac-companied by multiple models and feature selectionprocedures are being investigated. Various machine learning pipeline combinations have been examinedand benchmarked applying wellknown metrics to beincorporated into our system. Specifically, we developedmachine learning techniques and applied them to ana-lyze point cloud data extracted from TLS in two distinctcase studies, involving different geological contexts: thebasaltic cliff of Castellfollit de la Roca and the con-glomerate Montserrat Massif, both located in Spain. Ourexperimental results indicate that some of the abovementioned machine learning pipelines can be utilized todetect rockfall incidents on mountain walls, with ex-perimentally validated accuracy.

The Pont de Montanyana outcrop exposes strata of the lower Eocene Montllobat Formation deposited in alluvial plains and in meandering river systems of the Tremp-Graus piggyback basin (Southern Pyrenees, Spain). This article focuses on the characterisation of the outcrop using conventional descriptions (photomosaics, correlated stratigraphic logs and palaeocurrent data) together with 3D interpretations of digital outcrop models. The architectural elements identified include lateral-accretion deposits, channel fills (meandering, chute and distal alluvial-fan types), overbank sheets (wings and crevasse splay deposits) and floodplain fines. The channelised deposits were collectively classified, using the width (W) and thickness (T) ratio, as ribbons (W/T ≤ 15) to narrow sheets (W/T ≤ 100). Variations in sandstone/mudstone ratio (between 20% and more than 90% of sandstone) and in the internal architecture of the meandering channel fills suggest deposition in the abandoned channel at different times of the cut-off process and at different locations in relation to the position of the active channel. A high-resolution 3D facies model of the upper stratigraphic interval was constructed, containing point-bar deposits and an associated channel fill. The model was subsequently populated with petrophysical data representative of point-bar reservoirs. Flow simulations based on a water-injection development strategy were carried out. The flow behaviour and the distribution of the bypassed oil are determined by the high degree of internal heterogeneity in the meandering channelised deposits. This heterogeneity is produced by the facies variability, the inclined bedding of the lateral-accretion deposits, the lithology of the channel fill, the downstream fining along the bend and by the change in the orientation of the lateral-accretion surfaces due to expansion and rotation of the point bar. The moveable oil was effectively drained from the sandstone-dominated channel-fill body. However, more than 27% of the moveable oil remained trapped in the uppermost part of the lateral-accretion deposits.

Tight fractured sandstones of the Devonian Huamampampa Formation are associated with large gas discoveries in the sub-Andean fold-and-thrust belt of southern Bolivia. A LIDAR-based fracture characterization of the Abra del Condor backlimb anticline, a structural-stratigraphic analog, is used as the basis for a fracture stratigraphy determination. Fracture characterization using LIDAR is integrated with outcrop scanlines and is framed by stratigraphy and structural positions within this thrust-related anticline. SEFL software was used to process LIDAR data, dividing the outcrop by orientations. A workflow to extract modeled fracture planes and their associated orientations, lengths, and heights results in five fracture sets, partially validated by fracture outcrop scanlines. Multiple virtual scanlines are used to measure fracture intensity, identify fracture stratigraphic units, and define fracture-associated parameters of abundance and size distribution. Our LIDAR-based fracture characterization indicates a distribution of fracture intensities according to their structural position, decreasing from the hinge to the backlimb. From the five fracture sets identified, one set of orthogonal fractures dominates. Moreover, most of the fractures are contained or bounded within their fracture stratigraphic units and calculated fracture spacing ratio and the fracture space index show a nonexistent relation between fracture spacing and the fracture stratigraphic unit thickness.

Digital elevation models (DEM) were generated from oblique stereo-images acquired with a handheld digital camera. Two model scenarios are considered. Firstly, at local outcrop scale, with easy access, and distances between camera and outcrop varying between c. 40 m and c. 120 m, a very dense and high resolution point cloud was produced. The quality of the point cloud was evaluated against a terrestrial laser scan derived model of the same outcrop. The deviation between the two datasets varies between 0.02 m and 0.09. This is negligible for most geological purposes and illustrates the potential of using terrestrial photogrammetry at local outcrop scale as an alternative to lidar generated elevation data. Secondly, the method is explored at a regional scale, where a set of oblique stereo-images of a remotely located steep inaccessible mountain cliff was collected from a helicopter at a distance of c. 2–5 km under challenging and unfavourable conditions. The quality of the point cloud was evaluated a...

Permeability hosted in andesitic lava flows is dominantly controlled by fracture systems, with geometries that are often poorly constrained. This paper explores the fracture system geometry of an andesitic lava flow formed during its emplacement and cooling over gentle paleo-topography, on the active Ruapehu volcano, New Zealand. The fracture system comprises column-forming and platy fractures within the blocky interior of the lava flow, bounded by autobreccias partially observed at the base and top of the outcrop. We use a terrestrial laser scanner (TLS) dataset to extract column-forming fractures directly from the point-cloud shape over an outcrop area of ∼3090 m2. Fracture processing is validated using manual scanlines and high-resolution panoramic photographs. Column-forming fractures are either steeply or gently dipping with no preferred strike orientation. Geometric analysis of fractures derived from the TLS, in combination with virtual scanlines and trace maps, reveals that: (1) steeply dipping column-forming fracture lengths follow a scale-dependent exponential or log-normal distribution rather than a scale-independent power-law; (2) fracture intensities (combining density and size) vary throughout the blocky zone but have similar mean values up and along the lava flow; and (3) the areal fracture intensity is higher in the autobreccia than in the blocky zone. The inter-connected fracture network has a connected porosity of ∼0.5 % that promote fluid flow vertically and laterally within the blocky zone, and is partially connected to the autobreccias. Autobreccias may act either as lateral permeability connections or barriers in reservoirs, depending on burial and alteration history. A discrete fracture network model generated from these geometrical parameters yields a highly connected fracture network, consistent with outcrop observations. peer-reviewed 2019-06-03

The analysis of the three-dimensional rockfall scar geometry provides clues for the understanding of the failure mechanisms acting on cliffs, of the conditioning factors, and on the frequency of the events. In this paper, a supervised step-by-step methodology is presented for establishing the statistical magnitude–frequency relation of rockfall scar volumes, using a point cloud from Terrestrial Laser Scanner (TLS) data. The methodology includes a procedure for identifying discontinuity surfaces, calculating the areas of those which are exposed, and the height of rockfall scars. In the estimation of the rockfall scar volume a key issue is the consideration of the minimum spacing of the discontinuity sets to differentiate between step-path surfaces and undulated ones. Having obtained the distributions of both the basal area and height of the scar across the slope, the volume of the rockfall scars is calculated stochastically by multiplication of these two parameters by means of a Monte Carlo simulation. Both distributions of the basal area and of the rockfall scar volume are found to be power-law, with the exponent b ranging from 0.9 to 1.2. The relation obtained might be used as a first approach of rockfall magnitude–frequency curves in large cliffs.

Terrestrial laser scanning is an effective method for digitally capturing outcrops, enabling them to be visualized, analyzed, and revisited in an office environment without the limitations of fieldwork (such as time constraints, weather conditions, outcrop accessibility, repeatability, and poor resolution of measurements). It is common practice in geological interpretation of digital outcrops to use visual identification and manual digitization of pointsets or polylines in order to characterise geological features using 3D CAD-like modules. Other recent and less generic approaches have focused on automated extraction of geological features by using segmentation methods, mostly based on geometric parameters derived from the point cloud, but also aided by attributes captured from the outcrop (intensity, RGB). This paper presents a workflow for the supervised and automated identification and reconstruction of near-planar geological surfaces that have a three-dimensional exposure in the outcrop (typically bedding, fractures, or faults enhanced by differential erosion). The original point cloud is used without modifications, and thus no decimation, smoothing, intermediate triangulation, or gridding are required. The workflow is based on planar regressions carried out for each point in the point cloud, enabling subsequent filtering and classification to be based on orientation, quality of fit, and relative locations of points. A coarse grid preprocessing strategy is implemented to speed up the search for neighboring points, permitting analysis of multimillion point clouds. The surfaces identified are organized into classes according to their orientations and regression quality parameters. These can then be used as seeds for building outcrop reconstructions or further analyzed to investigate their characteristics (geometry, morphology, spacing, dimensions, intersections, etc.). The workflow is illustrated here using a synthetic example and a natural example from a limestone outcrop, in which surfaces corresponding to bedding and three fault orientations were reconstructed.

This case study deals with a rock face monitoring in urban areas using a Terrestrial Laser Scanner. The pilot study area is an almost vertical, fifty meter high cliff, on top of which the village of Castellfollit de la Roca is located. Rockfall activity is currently causing a retreat of the rock face, which may endanger the houses located at its edge. TLS datasets consist of high density 3-D point clouds acquired from five stations, nine times in a time span of 22 months (from March 2006 to January 2008). The change detection, i.e. rockfalls, was performed through a sequential comparison of datasets. Two types of mass movement were detected in the monitoring period: (a) detachment of single basaltic columns, with magnitudes below 1.5 m3 and (b) detachment of groups of columns, with magnitudes of 1.5 to 150 m3. Furthermore, the historical record revealed (c) the occurrence of slab failures with magnitudes higher than 150 m3. Displacements of a likely slab failure were measured, suggesting an apparent stationary stage. Even failures are clearly episodic, our results, together with the study of the historical record, enabled us to estimate a mean detachment of material from 46 to 91.5 m3 year−1. The application of TLS considerably improved our understanding of rockfall phenomena in the study area.

© 2015, Springer-Verlag Berlin Heidelberg. Erosional processes (natural or anthropogenic) may partly destroy the relatively small-sized volcanic edifices characteristic of monogenetic volcanic zones, leaving their internal structure well exposed. Nevertheless, the study of these outcrops may be extremely challenging due to restricted accessibility or safety issues. Digital representations of the outcrop surface have been lately used to overcome such difficulties. Data acquired with terrestrial laser scanning instruments using Light Detection and Ranging technology enables the construction of such digital outcrops. The obtained high-precision 3-D terrain models are of greater coverage and accuracy than conventional methods and, when taken at different times, allow description of geological processes in time and space. Despite its intrinsic advantages and the proven satisfactory results, this technique has been little applied in volcanology-related studies. Here, we want to introduce it to the volcanological community together with a new and user-friendly digital outcrop analysis methodology for inexperienced users. This tool may be useful, not only for volcano monitoring purposes, but also to describe the internal structure of exposed volcanic edifices or to estimate outcrop erosion rates that may be helpful in terms of hazard assessment or preservation of volcanic landscapes. We apply it to the Croscat volcano, a monogenetic cone in the La Garrotxa Volcanic Field (Catalan Volcanic Zone, NE Spain), quarrying of which leads to a perfect view of its interior but restricts access to its uppermost parts. Croscat is additionally one of the most emblematic symbols of the La Garrotxa Volcanic Field Natural Park, and its preservation is a main target of the park administration., AG is grateful for her Juan de la Cierva post-doctoral grant (JCI-2010-06092) and her Ramón y Cajal contract (RYC-2012-11024). SBC acknowledges the JAE-Doc postdoctoral personal grant program of CSIC (JAEDoc_09_01319). This work was partly financed by projects CGL2010-21968-C02-01, CGL2010-18609 and CGL2013-40828-R of the Spanish Ministry of Science and Innovation.

Outcrop studies can be substantially improved by use of photorealistic digital outcrop models. A method is presented that has been tested for decametre to hectometre long outcrops. The models have accuracy and resolution around one decimeter and the models are conveniently scaled and oriented, allowing for comparison to geological data that were directly acquired in the field. The gear involved is lightweight and low cost when compared to LIDAR. The geologist can backpack this gear, decide acquisition on the flight, and finish the computer processing back in the base camp or in the office. The experiments have been carried in a series of Spanish outcrops that represent a variety of depositional settings: alluvial fan, fluvial fan, point bar deposits, fan-delta, and deepwater. The output models will be discussed in in terms of their quality by report to LIDAR data.

Point clouds acquired with terrestrial LIDAR are used as a digital support to accurately and precisely georeference outcrop characterizations; as well as to resolve accessibility problems, and improve outcrop characterizations. The LIDAR data allows for an efficient visualization and analysis of the outcrop in the computer, and is also useful for revisiting field data in the office or for teaching purposes. The common practice for virtual outcrop interpretation is visual identification and manual digitalization of pointsets or polylines by using 3-D CAD-like modules. Other, less generic, approaches are oriented towards the automated or semi-automated extraction of geological features, either based on the processing of intensity or other attributes of the virtual outcrop (RGB, hyperspectral) or on geometric parameters calculated from positions. In this presentation, we propose a workflow for the automatic characterization of planar surfaces (typically stratigraphic bedding or fractures) from LIDAR data. The workflow directly uses the point cloud; therefore no decimation, smoothing, intermediate triangulated or gridded surface are required; and is designed aiming to minimize user interaction to allow for a simple, fast, objective and semi-automated use. The result of the workflow is the reconstruction of planar surfaces identified in the point cloud by means of TIN surfaces, organized into families according to their orientations. These surfaces can be used as seeds for building surface-based models of the outcrop, or can be further analysed to investigate their characteristics (geometry, morphology, spacing, dimensions, intersections, etc.). The workflow is based on planar regressions carried out for each point in the point cloud. Which allow the subsequent filtering of points based on normal vector orientation, planar regression quality, relative locations of points or their relative normal vectors differences. This is aimed at individualizing planar patches with geological signification. A coarse grid search strategy is implemented to speed up neighbouring points searches and allow handling multimillion point clouds. The workflow is illustrated using synthetic and natural examples.

Outcrop-scale reconstruction of depositional geometries and facies distribution of carbonate systems improves our knowledge on their heterogeneity distribution, stacking patterns and stratal architecture. The collected data and derived models can be used as analogues for characterizing and modelling potential subsurface reservoirs. Traditional sedimentological analyses in cropping out carbonate systems have limited accuracy depending on exposure conditions, accessibility or past erosive processes. On the other hand, there is a need to complement classical sedimentological approaches with quantitative characterizations and models of sedimentary bodies. In this respect, processing of three-dimensional (3D) point clouds captured by terrestrial LIght Detection And Ranging (LIDAR) technology combined with real-time kinematic global positioning system offers to field geologists the possibility to construct virtual 3D digital outcrop models (DOMs), which allow for more accurate analyses, reconstructions and quantification of the outcropping facies distribution than conventional digital terrain models. We present a LIDAR 3D DOM of an Aptian flat-topped non-rimmed carbonate platform margin from the western Maestrat Basin (Spain). The DOM served as a departing point to perform a 3D reconstruction that shows the relationship between depositional architecture and facies distribution of the carbonate system. The reconstruction not only highlights the value of digital outcrop models to characterize virtual attributes not observable in the outcrops due to the limitations of the 2D views of the exposures, but also allows to refine outcrop-scale sequence stratigraphic analyses. In addition, the 3D sequence stratigraphic approach obtained together with the 3D facies distribution model generated can be used as an analogue for the characterization of subsurface carbonate reservoirs with similar depositional profiles. The workflow of this study followed these steps: 1) Acquisition of the outcrop 3D point data set using a ground-based terrestrial LIDAR equipped with a differential GPS; 44 overlapping scans were needed to cover the entire outcrops of the flat-topped non-rimmed carbonate system characterized, each scan has associated a high-resolution photograph. 2) Mapping stratigraphic surfaces and pseudowells describing 5 lithofacies onto each individual photograph using a CAD-based tool, the mapping is carried directly onto the photographs because manipulating the images and interpreting the details is easier than directly digitizing onto the point-cloud. 3) The features mapped onto the photographs are projected into the corresponding point-cloud in order to georeference them. 4) Locally georeferenced individual point-clouds and attached interpretations were globally georeferenced by means of the UTM coordinates of each scan. 5) The stratigraphic boundaries mapped are used reconstruct the surfaces bounding stratigraphic units. 6) Population of the internal facies distribution conditioned to the pseudowells. This methodology allows to efficiently extracting information from point clouds, and resulted in the construction of a high-resolution 3D geological model displaying the stratal architecture and facies heterogeneity of sedimentary bodies, confined within a 3D sequence stratigraphical framework.

Field trips can be improved by integration of new technologies. Among these, virtual outcrops allow for improved, fast and accurate, perception of geological features. Visualization of other complementary information, like terrain models draped with geology and static and dynamic reservoir models, are also important at providing contextual and across-discipline information. All these techniques are independent of accessibility, weather or light conditions, and together render fieldtrips optimal in terms of learning, safety, costs, and organization.