Modeling Terrains and Subsurface Geology

Natali, Mattia; Lidal, Endre M.; Parulek, Július; Viola, Ivan; Patel, Daniel

doi:10.2312/conf/eg2013/stars/155-173

Cited by 10 publications

(4 citation statements)

References 78 publications

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“…A second approach is physically-based simulation, using models derived from physical geography [11,38,39]. Some methods in this category synthesize terrain by running simulations at interactive rates [4,5,41,53], and others through the use of high level evaluations [12,40]. While these methods produced more realistic terrain than the procedural methods, most approaches rely on models of hydraulic and thermal (difusive) erosion, ignoring other inluential forces that shape terrain including glaciers, earthquakes, tectonic uplift, weather patterns, and animal and human interference.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Realism in Example-based Terrain Synthesis

Scott

Dodgson

2022

ACM Trans. Appl. Percept.

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We report two studies that investigate the use of subjective believability in the assessment of objective realism of terrain. The first demonstrates that there is a clear subjective feature bias that depends on the types of terrain being evaluated: our participants found certain natural terrains to be more believable than others. This confounding factor means that any comparison experiment must not ask participants to compare terrains with different types of feature. Our second experiment assesses four methods of example-based terrain synthesis, comparing them against each other and against real terrain. Our results show that, while all tested methods can produce terrain that is indistinguishable from reality, all also can produce poor terrain; that there is no one method that is consistently better than the others; and that those who have professional expertise in geology, cartography or image analysis are better able to distinguish real terrain from synthesised terrain than the general population but those who have professional expertise in the visual arts are not.

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Section: Related Workmentioning

confidence: 99%

Evaluating Realism in Example-based Terrain Synthesis

Scott

Dodgson

2022

ACM Trans. Appl. Percept.

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“…The estimated GSI range was used to obtain the Hoek-Brown envelopes for rock mass [71]. The expected GSI range (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) for the residual state [70] was used to obtain the Hoek-Brown envelopes characterizing the landslide itself (Table 5, Figure S4). The microstructure influence on the strength decreased with the GSI, resulting in a similar behavior at the residual state for both the examined basalts (Table 5).…”

Section: Rocksmentioning

confidence: 99%

“…When working with a large amount of geophysical and geological data describing rock environments at different depths, data integration in the form of 3D geomodels represents a key task to visualize and analyze diverse surface and subsurface information as well as comprehend their spatial relationships. 3D geomodelling applications include, amongst others, structural modeling [22,23], reservoir characterization (petroleum engineering), and hydrogeological engineering [24]. Applications in the field of geohazard assessment are less frequent; however, geomodels of large landslides possibly enhance the understanding of these complex sites and represent valuable input information for further numerical or physical computations [19,25,26].…”

Section: Introductionmentioning

confidence: 99%

New Insights into the Internal Structures and Geotechnical Rock Properties of the Giant San Andrés Landslide, El Hierro Island, Spain

et al. 2023

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The San Andrés landslide on El Hierro (Canary Islands) represents a rare opportunity to study an incipient volcanic island flank collapse with an extensive onshore part. The presented research improves the knowledge of the internal structure and rock characteristics of a mega-landslide before its complete failure. The investigation combines multiple geophysical measurement techniques (active and passive seismic) and remotely sensed, high spatial resolution surveys (unmanned aerial vehicle) with in situ and laboratory geotechnical descriptions to characterize the rock properties inside and outside the San Andrés landslide. The available geophysical and geological data have been integrated into 3D geomodels to enhance their visual interpretation. The onshore geophysical investigations helped detect the possible San Andrés landslide sliding surfaces at depths between 320 m and 420 m, with a rather planar geometry. They also revealed that rocks inside and outside of the landslide had similar properties, which suggests that the previous fast movements of the landslide did not affect the bulk properties of the displaced rocks as the failure chiefly occurred along the weakened sliding plane. Uniaxial strength tests on basalt rocks further indicate a high variability and spatial heterogeneity of the rock strength properties due to the different types of volcanic rocks and their texture. The new information on the rock properties and structural setting of the San Andrés landslide can now be used to develop realistic geotechnical slope models of the onshore part of the flank collapse that are possibly applicable for slope stability or deformation calculations. It will also help assess related hazards marked by a low occurrence probability and a high impact potential.

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“…A second approach is physically-based simulation, using models derived from physical geography [19,20,21]. Some methods in this category synthesise terrain by running simulations at interactive rates [22,23,24,25], and others through the use of high level evaluations [26,27]. While these methods produced more realistic terrain than the procedural methods, the use of simplified models limits the realism of the terrain that they can produce.…”

Section: Physically-based Simulation For Terrain Synthesismentioning

confidence: 99%