Strain calculations of active tectonic blocks in northeastern Venezuela from GNSS analysis

International audienceWe study translation, rotation, and strain of active tectonic blocks in Northeastern Venezuela from the Global Navigation Satellite System (GNSS) observations. Since the installation of the geodetic network in 2003, one of the goals was to place at least three observation sites at each tectonic block to study the deformation of each one. Based on this premise, we define at least seven blocks: Bergantín and Caripe blocks south of the El Pilar Fault (EPF), and Cariaco Gulf, Land bridge, Paria, North Peninsula, and Margarita Island blocks north of the EPF. Our preferred block modeling shows angular rotations from 0.02 to 0.29° Ma−1. It is known that the EPF concentrates the active deformation in this region of the Caribbean-South American plate boundary. However, the existent rotation could accommodate part of the motion. The strain rate tensors (SRT) indicate NW-SE compression and NE-SW extension for the western blocks. To the east, the 4-Land bridge block keeps the NW-SE compression but shows a decrease in the extensional component. The 5-Paria blocks show a complete inversion in the sense of semi-axis. Additionally, we evaluate the possibility of different motions in Margarita block calculating translational vector, rotational velocity, and strain from three GNSS sites at each side thereof. Our results show remarkable similarities for the Macanao Peninsula and Eastern Margarita Island, pointing to both belonging to a single block

Localized Afterslip at Geometrical Complexities Revealed by InSAR After the 2016 Central Italy Seismic Sequence

International audienceThe Mw 6.5 Norcia earthquake occurred on 30 October 2016, along the Mt Vettore fault (Central Apennines, Italy), it was the largest earthquake of the 2016-2017 seismic sequence that started 2 months earlier with the Mw 6.0 Amatrice earthquake (24 August). To detect potential slow slip during the sequence, we produced Interferometric Synthetic Aperture Radar (InSAR) time series using 12- to 6-day repeat cycles of Sentinel-1A/1B images. Time series indicates that centimeter-scale surface displacements took place during the 10 weeks following the Norcia earthquake. Two areas of subsidence are detected: one in the Castelluccio basin (hanging wall of the Mt Vettore fault) and one in the southern extent of the Norcia earthquake surface rupture, near an inherited thrust. Poroelastic and viscoelastic models are unable to explain these displacements. In the Castelluccio basin, the displacement reaches 13.2 +/- 1.4 mm in the ascending line of sight on 6 January 2017. South of the Norcia earthquake surface rupture (a zone between the Norcia and Amatrice earthquakes), the postseismic surface displacements affect a smaller area but reach 35.5 +/- 1.7 mm in ascending line of sight by January 2017 and follow a logarithmic temporal decay consistent with postseismic afterslip. Our analysis suggests that the structurally complex area located south of the Norcia rupture (30 October) is characterized by a conditionally stable frictional regime. This geometrical and frictional barrier likely halted rupture propagation during the Amatrice (24 August) and Norcia (30 October) earthquakes at shallow depth (<3-4 km)

Pleistocene slip rates on the Boconó fault along the North Andean Block plate boundary, Venezuela

International audienceThe Boconó fault is a strike-slip fault lying between the North Andean Block and the South American plate which has triggered at least five M w > 7 historical earthquakes in Venezuela. The North Andean Block is currently moving toward NNE with respect to a stable South American plate. This relative displacement at ~12 mm yr À1 in Venezuela (within the Maracaibo Block) was measured by geodesy, but until now the distribution and rates of Quaternary deformation have remained partially unclear. We used two alluvial fans offset by the Boconó fault (Yaracuy Valley) to quantify slip rates, by combining 10 Be cosmogenic dating with measurements of tectonic displacements on high-resolution satellite images (Pleiades). Based upon a fan dated at >79 ka and offset by 1350-1580 m and a second fan dated at 120-273 ka and offset by 1236-1500 m, we obtained two Pleistocene rates of 5.0-11.2 and <20.0 mm yr À1 , consistent with the regional geodesy. This indicates that the Boconó fault in the Yaracuy Valley accommodates 40 to 100% of the deformation between the South American plate and the Maracaibo Block. As no aseismic deformation was shown by interferometric synthetic aperture radar analysis, we assume that the fault is locked since the 1812 event. This implies that there is a slip deficit in the Yaracuy Valley since the last earthquake ranging from ~1 to 4 m, corresponding to a M w 7-7.6 earthquake. This magnitude is comparable to the 1812 earthquake and to other historical events along the Boconó fault

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

Fault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

Characterization of normal fault scarp using convolutional neural network: application to Mexico

International audienceFault markers in the landscape (scarps, offset rivers) are records of fault activity. The geomorphological characterization of these markers is currently a time-consuming step with expert-dependent results, often qualitative and with uncertainties that are difficult to estimate. To overcome those issues, we are developing a bayesian supervised machine learning method using convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScLearn. From a topographic profile the implemented, ScLearn is able to automatically give the scarp heigth with an uncertainty, and to show the area of the profile containing the scarp. We apply ScLearn for the characterization of normal active faults in the Trans-Mexican Volcanic Belt. From this specific case study, we will explore the progress (computation time, accuracy, uncertainties) that machine learning methods bring to the field of morphotectonics, as well as the current limits (such as bias)

36Cl exposure dating of post-glacial features along the Mt Vettore Fault (Central Apennines, Italy) constraining fault slip rate and last glacial advance.

International audience&lt;p&gt;In the Central Apennines (Italy), up to now, no absolute dating directly based on the moraines has been carried out to constrain glacial oscillation. However, climatic constrains are often used in the Central Apennine to estimate long term (&gt; 10 ka) fault slip rate. In addition slip rate assessments based on offset morphotectonic markers on the main branches of fault systems and encompassing several seismic cycles (&gt; 10 ka) are sparse. This is particularly true for the Monte Vettore-Monte Bove fault system which triggered the 2016-2017 seismic sequence. We thus provide new assessment for the vertical slip rates along the Mt Vettore-Mt Bove fault system. &amp;#160;Offset measurements were made using a 5-cm resolution DEM obtained through a drone survey and constrain a fault scarp height of 15.5 &amp;#177; 1.4 m and a cumulative offset of 32-40.5 m. Samples were collected from the Valle Lunga terminal moraine at 1710 m asl and yield &lt;sup&gt;36&lt;/sup&gt;Cl exposure ages of 12.7 + 2.2/-1.9 ka while the flat, abraded surface located on top of the tectonic scarp yield &lt;sup&gt;36&lt;/sup&gt;Cl exposure ages of 23.4 + 5.3/-4.3 ka. Assuming the offset started to accumulate when climate conditions allow its preservation, thus once the surface was abandoned, we constrain a vertical slip rate of 1.2 &amp;#177; 0.2 mm/yr along the master branch of the Mt Vettore normal fault. &amp;#160;This rate is higher than the ones previously obtained from trenches along secondary splays of the Mt Vettore-Mt Bove and on the Norcia fault systems. Besides, the yielded chronology for the last glacial maximum in that area at ~23 ka is in good agreement with the timing previously proposed for the LGM in the Apennines.&lt;/p&gt

36Cl exposure dating of glacial features to constrain the slip rate along the Mt. Vettore Fault (Central Apennines, Italy)

International audienceThe Central Apennines are among the most seismically active regions in Italy. This region is affected by an extension accommodated by active normal faults. Those faults with high seismogenic potential hosted Mw > 6 shallow damaging earthquakes, such as the Mw 6.5 event of Oct. 30, 2016. Although fault slip rates are crucial to seismic hazard assessments, in the Central Apennines, slip rate assessments encompassing several seismic cycles (> 10 ka) are sparse. This is particularly true for the Mt. Vettore-Mt. Bove Fault system that ruptured during the mainshocks of the 2016–2017 seismic sequence. In this study, we present new geochronological constraints of offset geomorphological markers along the northern portion of the fault system on the Mt. Porche Fault segment using in-situ produced 36Cl cosmogenic nuclides. Offset measurements were made using a 5-cm resolution DEM obtained through a drone survey and constrained a fault scarp height of 15.5 ± 1.4 m and a maximal cumulative offset estimated between 32 and 40.5 m. Samples were collected from the Valle Lunga terminal moraine at 1710 m asl and yielded 36Cl exposure ages of 12.7 + 2.2/−1.9 ka, while the abraded surface, located on top of the tectonic scarp yielded 36Cl exposure ages of 23.4 + 5.3/−4.3 ka. Using the fault scarp height and the exposure age of this abraded surface, we constrained a minimum vertical fault slip rate of 0.7 + 0.2/−0.1 mm/yr. Assuming the offset started to accumulate when climate conditions allowed the scarp preservation, we constrained a maximal vertical slip rate of 1.2 ± 0.2 mm/yr along the main fault of the Mt. Vettore-Mt. Bove Fault system. This rate is higher than those previously obtained from trenches along secondary branches of the Mt. Vettore-Mt. Bove Fault system. Besides, the yielded chronology for the last glacial maximum in that area at ~23 ka is in good agreement with the timing previously proposed for the LGM in the Apennines