Flat slab deformation caused by interplate suction force

We image the structure at the southern end of the Peruvian flat subduction zone, using receiver function and surface wave methods. The Nazca slab subducts to ~100 km depth and then remains flat for ~300 km distance before it resumes the dipping subduction. The flat slab closely follows the topography of the continental Moho above, indicating a strong suction force between the slab and the overriding plate. A high-velocity mantle wedge exists above the initial half of the flat slab, and the velocity resumes to normal values before the slab steepens again, indicating the resumption of dehydration and ecologitization. Two prominent midcrust structures are revealed in the 70 km thick crust under the Central Andes: molten rocks beneath the Western Cordillera and the underthrusting Brazilian Shield beneath the Eastern Cordillera

Structure of the Los Angeles Basin from ambient noise and receiver functions

A velocity (V_s) and structure model is derived for the Los Angeles Basin, California based on ambient-noise surface wave and receiver-function analysis, using data from a low-cost, short-duration, dense broad-band survey (LASSIE) deployed across the basin. The shear wave velocities show lateral variations at the Compton-Los Alamitos and the Whittier Faults. The basement beneath the Puente Hills–San Gabriel Valley shows an unusually high velocity (∼4.0 km s^(−1)) and indicates the presence of schist. The structure of the model shows that the basin is a maximum of 8 km deep along the profile and that the Moho rises to a depth of 17 km under the basin. The basin has a stretch factor of 2.6 in the centre grading to 1.3 at the edges and is in approximate isostatic equilibrium

Rethinking Implicit Neural Representations for Vision Learners

Implicit Neural Representations (INRs) are powerful to parameterize continuous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution, and image generation. The questions on how to explore INRs to high-level tasks and deep networks are still under-explored. Existing INRs methods suffer from two problems: 1) narrow theoretical definitions of INRs are inapplicable to high-level tasks; 2) lack of representation capabilities to deep networks. Motivated by the above facts, we reformulate the definitions of INRs from a novel perspective and propose an innovative Implicit Neural Representation Network (INRN), which is the first study of INRs to tackle both low-level and high-level tasks. Specifically, we present three key designs for basic blocks in INRN along with two different stacking ways and corresponding loss functions. Extensive experiments with analysis on both low-level tasks (image fitting) and high-level vision tasks (image classification, object detection, instance segmentation) demonstrate the effectiveness of the proposed method

Imaging the Earth with Ambient Noise and Earthquakes

In this thesis, I develop the velocity and structure models for the Los Angeles Basin and Southern Peru. The ultimate goal is to better understand the geological processes involved in the basin and subduction zone dynamics. The results are obtained from seismic interferometry using ambient noise and receiver functions using earthquake- generated waves. Some unusual signals specific to the local structures are also studied. The main findings are summarized as follows: (1) Los Angeles Basin The shear wave velocities range from 0.5 to 3.0 km/s in the sediments, with lateral gradients at the Newport-Inglewood, Compton-Los Alamitos, and Whittier Faults. The basin is a maximum of 8 km deep along the profile, and the Moho rises to a depth of 17 km under the basin. The basin has a stretch factor of 2.6 in the center decreasing to 1.3 at the edges, and is in approximate isostatic equilibrium. This "high-density" (~1 km spacing) "short-duration" (~1.5 month) experiment may serve as a prototype experiment that will allow basins to be covered by this type of low-cost survey. (2) Peruvian subduction zone Two prominent mid-crust structures are revealed in the 70 km thick crust under the Central Andes: a low-velocity zone interpreted as partially molten rocks beneath the Western Cordillera – Altiplano Plateau, and the underthrusting Brazilian Shield beneath the Eastern Cordillera. The low-velocity zone is oblique to the present trench, and possibly indicates the location of the volcanic arcs formed during the steepening of the Oligocene flat slab beneath the Altiplano Plateau. The Nazca slab changes from normal dipping (~25 degrees) subduction in the southeast to flat subduction in the northwest of the study area. In the flat subduction regime, the slab subducts to ~100 km depth and then remains flat for ~300 km distance before it resumes a normal dipping geometry. The flat part closely follows the topography of the continental Moho above, indicating a strong suction force between the slab and the overriding plate. A high-velocity mantle wedge exists above the western half of the flat slab, which indicates the lack of melting and thus explains the cessation of the volcanism above. The velocity turns to normal values before the slab steepens again, indicating possible resumption of dehydration and ecologitization. (3) Some unusual signals Strong higher-mode Rayleigh waves due to the basin structure are observed in the periods less than 5 s. The particle motions provide a good test for distinguishing between the fundamental and higher mode. The precursor and coda waves relative to the interstation Rayleigh waves are observed, and modeled with a strong scatterer located in the active volcanic area in Southern Peru. In contrast with the usual receiver function analysis, multiples are extensively involved in this thesis. In the LA Basin, a good image is only from PpPs multiples, while in Peru, PpPp multiples contribute significantly to the final results

Higher-mode ambient-noise Rayleigh waves in sedimentary basins

We show that higher modes are an important component of high-frequency Rayleigh waves in the cross-correlations over sedimentary basins. The particle motions provide a good test for distinguishing and separating the fundamental from the first higher mode, with the fundamental mode having retrograde and the first higher mode having prograde motion in the 1–10 s period of interest. The basement depth controls the cut-off period of the first higher mode, which coincides with a rapid increase (over period) in the particle-motion ellipticity or H/V ratio of the fundamental mode. The strong higher mode we observed is not only due to the low-velocity sedimentary layer but also due to the noise sources with significant radial component such as the basin edge scattering. It is important to correctly identify the mode order when inverting the dispersion curves because misidentifying the higher mode as fundamental will lead to an anomalous high V_(SV) velocity

Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks

Adversarial attacks on a convolutional neural network (CNN) -- injecting human-imperceptible perturbations into an input image -- could fool a high-performance CNN into making incorrect predictions. The success of adversarial attacks raises serious concerns about the robustness of CNNs, and prevents them from being used in safety-critical applications, such as medical diagnosis and autonomous driving. Our work introduces a visual analytics approach to understanding adversarial attacks by answering two questions: (1) which neurons are more vulnerable to attacks and (2) which image features do these vulnerable neurons capture during the prediction? For the first question, we introduce multiple perturbation-based measures to break down the attacking magnitude into individual CNN neurons and rank the neurons by their vulnerability levels. For the second, we identify image features (e.g., cat ears) that highly stimulate a user-selected neuron to augment and validate the neuron's responsibility. Furthermore, we support an interactive exploration of a large number of neurons by aiding with hierarchical clustering based on the neurons' roles in the prediction. To this end, a visual analytics system is designed to incorporate visual reasoning for interpreting adversarial attacks. We validate the effectiveness of our system through multiple case studies as well as feedback from domain experts.Comment: Accepted by the Special Issue on Human-Centered Explainable AI, ACM Transactions on Interactive Intelligent System

Locating a scatterer in the active volcanic area of Southern Peru from ambient noise cross-correlation

We report on a strong scatterer of seismic energy in the 5–10 s period range located in the volcanic arc of Southern Peru. It is superficially like an active noise source in that it produces a continuous signal that arrives earlier than the inter-station surface wave in the noise cross-correlations. However, it is clearly determined to be a scatterer based on the coda arrivals observed in the cross-correlations, and the fact that it scatters waves from earthquake sources. We model the scatterer as a cylinder approximately 5 km in diameter with a shear wave velocity 30 per cent lower than the background velocity. It is likely to exist at the depth of 5–10 km, and is located at 71.6°W/16.1°S with an error of 10 km, which is near the inactive volcano Nevado Chachani and the active volcano El Misti which recently erupted in 1985

MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images

As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria