TerraceM-2: A Matlab® Interface for Mapping and Modeling Marine and Lacustrine Terraces

Jara‐Muñoz, Julius; Melnick, Daniel; Pedoja, Kévin; Strecker, Manfred R.

doi:10.3389/feart.2019.00255

Cited by 29 publications

(19 citation statements)

References 66 publications

(100 reference statements)

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“…where SLP is the topographic slope and the roughness, defined as the standard deviation of the slope calculated within a 3*3 roving window (Frankel and Dolan, 2007;Jara-Muñoz et al, 2019). Slope and roughness are normalized and linearly combined to generate the SCM isolating flat and smooth platform surfaces from the rough and steep areas that represent erosional features such as valleys and cliffs.…”

Section:  Surface Classification Methodsmentioning

confidence: 99%

“…The shoreline angle is commonly covered by colluvial sediments and may be estimated from the intersection between the terrace paleo‐platform and the paleo‐cliff. To measure shoreline angles we use the software TerraceM (Jara‐Muñoz et al, 2016, 2019) that uses swath profiles and linear regressions to estimate the shoreline angle position. We use the maximum distribution of elevations from swath profiles to reconstruct the original terrace morphology, devoid of the effects of river incision.…”

Section: Methodsmentioning

confidence: 99%

“…The surface classification model (SCM) provides spatial estimation on the distribution of terraced surfaces and is based on the slope and roughness of the topography (Bowles & Cowgill, 2012). It is defined by the equation:

SCM = ((), SLP / \max ((), SLP)) * 0.5 + ((), std ((), SLP) / \max ((), std ((), SLP))) * 0.5

where SLP is the topographic slope and the roughness, defined as the standard deviation of the slope calculated within a 3 * 3 roving window (Frankel & Dolan, 2007; Jara‐Muñoz et al, 2019). Slope and roughness are normalized and linearly combined to generate the SCM isolating flat and smooth platform surfaces from the rough and steep areas that represent erosional features such as valleys and cliffs.…”

Section: Methodsmentioning

confidence: 99%

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Variable Quaternary Uplift Along the Southern Margin of the Central Anatolian Plateau Inferred From Modeling Marine Terrace Sequences

et al. 2020

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The southern margin of the Central Anatolian Plateau (CAP) records a strong uplift phase after the early Middle Pleistocene, which has been related to the slab break-off of the subducting Arabian plate beneath the Anatolian microplate. During the last 450 kyr the area underwent an uplift phase at a mean rate of ~3.2 m/kyr, as suggested by Middle Pleistocene marine sediments exposed at ~1500 meters above sea level. These values are significantly higher than the 1.0-1.5 m/kyr estimated since the Late Pleistocene, suggesting temporal variations in uplift rate. To estimate changes in uplift rate during the Pleistocene we studied the marine terraces along the CAP southern margin, mapping the remnants of the platforms and their associated deposits in the field, and used the TerraceM software to identify the position and elevation of associated shoreline angles. We used shoreline angles and the timing of Quaternary marine sedimentation as constrains for a Landscape Evolution Model that simulates wave erosion of an uplifting coast. We applied random optimization algorithms and minimization statistics to find the input parameters that better reproduce the morphology of CAP marine terraces. The best-fitting uplift rate history suggests a significative increase from 1.9 to 3.5 m/kyr between 500 and 200 kyr, followed by an abrupt decrease to 1.4 m/kyr until the present. Our results agree with slab break-off models, which suggest a strong uplift pulse during slab rupture followed by a smoother decrease.

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Section:  Surface Classification Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SCM = ((), SLP / \max ((), SLP)) * 0.5 + ((), std ((), SLP) / \max ((), std ((), SLP))) * 0.5

Section: Methodsmentioning

confidence: 99%

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Variable Quaternary Uplift Along the Southern Margin of the Central Anatolian Plateau Inferred From Modeling Marine Terrace Sequences

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“…To detect the submerged fossil rocky-shore platforms and seacliffs, various methods were combined. First, we developed a Surface Classification Model (SCM) using TerraceM R (Jara- Muñoz et al, 2019;Figures 3A,B). The SCM linearly combines the slope and the roughness of the terrain to discriminate different geomorphic elements of submerged marine terraces.…”

Section: Morphometric Analysismentioning

confidence: 99%

Submerged Marine Terraces Identification and an Approach for Numerical Modeling the Sequence Formation in the Bay of Biscay (Northeastern Iberian Peninsula)

et al. 2020

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Submerged sequences of marine terraces potentially provide crucial information of past sea-level positions. However, the distribution and characteristics of drowned marine terrace sequences are poorly known at a global scale. Using bathymetric data and novel mapping and modeling techniques, we studied a submerged sequence of marine terraces in the Bay of Biscay with the objective to identify the distribution and morphologies of submerged marine terraces and the timing and conditions that allowed their formation and preservation. To accomplish the objectives a highresolution bathymetry (5 m) was analyzed using Geographic Information Systems and TerraceM R . The successive submerged terraces were identified using a Surface Classification Model, which linearly combines the slope and the roughness of the surface to extract fossil sea-cliffs and fossil rocky shore platforms. For that purpose, contour and hillshaded maps were also analyzed. Then, shoreline angles, a geomorphic marker located at the intersection between the fossil sea-cliff and platform, were mapped analyzing swath profiles perpendicular to the isobaths. Most of the submerged strandlines are irregularly preserved throughout the continental shelf. In summary, 12 submerged terraces with their shoreline angles between approximately: −13 m (T1), −30 and −32 m (T2), −34 and 41 m (T3), −44 and −47 m (T4), −49 and 53 m (T5), −55 and 58 m (T6), −59 and 62 m (T7), −65 and 67 m (T8), −68 and 70 m (T9), −74 and −77 m (T10), −83 and −86 m (T11) and −89 and 92 m (T12).Nevertheless, the ones showing the best lateral continuity and preservation in the central part of the shelf are T3, T4, T5, T7, T8, and T10. The age of the terraces has been estimated using a landscape evolution model. To simulate the formation and preservation of submerged terraces three different scenarios: (i) 20-0 ka; (ii) 128-0 ka; and (iii) 128-20 ka, were compared. The best scenario for terrace generation was between 128 and 20 Ka, where T3, T5, and T7 could have been formed.

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“…Marine terraces -geomorphic markers of past relative sea-level positions 39 -are ubiquitous along the coast of central Chile 32 . We mapped terraces at Pichilemu using a LiDAR DTM and the TerraceM-2 software 40 (Fig. 3A highstand e.g.…”

Section: Off-fault Tectonic Geomorphology: Deformed Marine Terracesmentioning

confidence: 99%

The cryptic seismic potential of blind faults revealed by off-fault geomorphology, Pichilemu, Chile.

Jara‐Muñoz

Melnick

Socquet

et al. 2020

Preprint

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In seismically-active regions, mapping capable faults and estimating their recurrence time is the first step to assess seismic hazards. Fault maps are commonly based on geologic and geomorphic features evident at the surface; however, mapping blind faults and estimating their seismic potential is challenging because on-fault diagnostic features are absent. Here, we study the Pichilemu Fault in coastal Chile, unknown until it generated a M7.0 earthquake in 2010. The lack of evident surface faulting suggests a partly-hidden blind fault. Using off-fault deformed marine terraces, we estimate a slip-rate of 0.42 ± 0.04 m/ka, which when integrated with deformation estimated from satellite geodesy during the 2010 earthquake suggests a 2.5 ± 0.25 ka recurrence time for M6.6-6.9 extensional earthquakes. We propose that extension is associated with stress changes during megathrust earthquakes and accommodated by sporadic slip during upper-plate earthquakes. Our results have implications for assessing the seismic potential of cryptic faults along seismically-active coasts.

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TerraceM-2: A Matlab® Interface for Mapping and Modeling Marine and Lacustrine Terraces

Cited by 29 publications

References 66 publications

Variable Quaternary Uplift Along the Southern Margin of the Central Anatolian Plateau Inferred From Modeling Marine Terrace Sequences

Variable Quaternary Uplift Along the Southern Margin of the Central Anatolian Plateau Inferred From Modeling Marine Terrace Sequences

Submerged Marine Terraces Identification and an Approach for Numerical Modeling the Sequence Formation in the Bay of Biscay (Northeastern Iberian Peninsula)

The cryptic seismic potential of blind faults revealed by off-fault geomorphology, Pichilemu, Chile.

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