Related papers: surfQuake: A new Python toolbox for the workflow p…
Motivation: Recent advances in single-cell analysis have introduced new computational challenges. Researchers often need to use multiple analysis tools written in different programming languages while managing version conflicts between…
We present SPIKE (Spectrometry Processing Innovative KErnel), an open-source Python package dedicated to Fourier spectroscopies. It provides basic functionalities such as apodisation, a complete set of Fourier transforms, phasing for NMR),…
Survival analysis, a foundational tool for modeling time-to-event data, has seen growing integration with machine learning (ML) approaches to handle the complexities of censored data and time-varying risks. Despite these advances,…
MyShake harnesses private/personal smartphones to build a global seismic network. It uses the accelerometers embedded in all smartphones to record ground motions induced by earthquakes, returning recorded waveforms to a central repository…
CyberShake (CS) is a high-performance computing workflow for Probabilistic Seismic Hazard Assessment (PSHA) developed by the Statewide California Earthquake Center. Here, we employ CS to generate a set of 2103 fault ruptures and simulate…
Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is…
Unlike their line-based counterparts, surface-based techniques have yet to be thoroughly investigated in flow visualization due to their significant placement, speed, perception, and evaluation challenges. This paper presents SurfPatch, a…
This work extends the VirtualQuake earthquake simulation framework to incorporate the effects of fluid injection on fault stability and induced seismicity. Reworking VirtualQuake into a system using stress point sources, instead of…
Seismic phase association is an essential task for characterising seismicity: given a collection of phase picks, identify all seismic events in the data. In recent years, machine learning pickers have lead to a rapid growth in the number of…
Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical…
We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and…
Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing, but in general cannot be directly measured. We show that fault friction can be determined at any…
The increasing availability of free high-resolution earth observation data covering any point on the globe every few days led to the emergence of new remote sensing tools that can manipulate the very large volumes of data generated by those…
The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning…
Data cubes are used for analyzing large data sets usually contained in data warehouses. The most popular data cube tools use graphical user interfaces (GUI) to do the data analysis. Traditionally this was fine since data analysts were not…
Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major…
This paper presents a seismic activity simulator that models the effects of fault lines on surface pressure. This project uses C programming to create a fully interactive learning resource intended to educate users on the mechanics of…
In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes, our application identifies potential earthquakes by…
The association of seismic wave arrivals with causative earthquakes becomes progressively more challenging as arrival detection methods become more sensitive, and particularly when earthquake rates are high. For instance, seismic waves…
This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient descent…