This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface ($V_{s30}$) at strong motion recording stations in T\"urkiye. $V_{s30}$ is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measurements and is therefore estimated using empirical correlations. Such correlations however are commonly inadequate in capturing complex, site-specific variability and this motivates the need for data-driven approaches. In this study, we employ a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dependencies in strong motion records. Furthermore, we explore how using different parts of the signal influence our deep learning model. Our results suggest that the hybrid approach effectively learns complex, nonlinear relationships within seismic signals. We observed that an improved P-wave arrival time model increased the prediction accuracy of $V_{s30}$. We believe the study provides valuable insights into improving $V_{s30}$ predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this repo: https://github.com/brsylmz23/CNNLSTM_DeepEQ

In volcano monitoring, effective recognition of seismic events is essential for understanding volcanic activity and raising timely warning alerts. Traditional methods rely on manual analysis, which can be subjective and labor-intensive. Furthermore, current automatic approaches often tackle detection and classification separately, mostly rely on single station information and generally require tailored preprocessing and representations to perform predictions. These limitations often hinder their application to real-time monitoring and utilization across different volcano conditions. This study introduces a novel approach that utilizes Semantic Segmentation models to automate seismic event recognition by applying a straight forward transformation of multi-channel 1D signals into 2D representations, enabling their use as images. Our framework employs a data-driven, end-to-end design that integrates multi-station seismic data with minimal preprocessing, performing both detection and classification simultaneously for five seismic event classes. We evaluated four state-of-the-art segmentation models (UNet, UNet++, DeepLabV3+ and SwinUNet) on approximately 25.000 seismic events recorded at four different Chilean volcanoes: Nevados del Chill\'an Volcanic Complex, Laguna del Maule, Villarrica and Puyehue-Cord\'on Caulle. Among these models, the UNet architecture was identified as the most effective model, achieving mean F1 and Intersection over Union (IoU) scores of up to 0.91 and 0.88, respectively, and demonstrating superior noise robustness and model flexibility to unseen volcano datasets.

As part of Probabilistic Risk Assessment studies, it is necessary to study the fragility of mechanical and civil engineered structures when subjected to seismic loads. This risk can be measured with fragility curves, which express the probability of failure of the structure conditionally to a seismic intensity measure. The estimation of fragility curves relies on time-consuming numerical simulations, so that careful experimental design is required in order to gain the maximum information on the structure's fragility with a limited number of code evaluations. We propose and implement an active learning methodology based on adaptive importance sampling in order to reduce the variance of the training loss. The efficiency of the proposed method in terms of bias, standard deviation and prediction interval coverage are theoretically and numerically characterized. Keywords: Computer experiments, probabilistic risk assessment, importance sampling, statistical learning 1. Introduction The notion of fragility curve was developed in the early 80s in the context of seismic probabilistic risk assesment (SPRA) [1, 2] or performance based earthquake engineering (PBEE) [3]. Fully documented templates are available in the elsarticle package on CTAN. Fragility curves are used in several domains: nuclear safety evaluation [4], estimation of the collapse risk of structures in seismic regions [5], design checking process [6]. Nonetheless, the use of fragility curves is not limited to seismic load but is extended to other loading sources such as wind and waves 10 [7]. For complex structures, fragility curve estimation requires a large number of numerical mechanical simulations, involving in most cases non linear computationally expensive calculations. Moreover, they should account for both the uncertainties due to the seismic demand and due to the lack of knowledge on the system itself, respectively called random and epistemic uncertainties [8, 9]. As 15 failure for a typical and reliable mechanical structure is a rare event, the crude Monte Carlo method cannot be applied because it would require too many numerical simulations to produce a sufficiently large number of failed states [10, p.27].

Machine learning could provide up an extra hour of warning time for debris flows along the Illgraben torrent in Switzerland, researchers report at the Seismological Society of America (SSA)'s 2021 Annual Meeting. Debris flows are mixtures of water, sediment and rock that move rapidly down steep hills, triggered by heavy precipitation and often containing tens of thousands of cubic meters of material. Their destructive potential makes it important to have monitoring and warning systems in place to protect nearby people and infrastructure. In her presentation at SSA, Ma?gorzata Chmiel of ETH Zürich described a machine learning approach to detecting and alerting against debris flows for the Illgraben torrent, a site in the European Alps that experiences significant debris flows and torrential events each year. Seismic records from stations located in the Illgraben catchment, from 20 previous debris flow events, were used to train an algorithm to recognize the seismic signals of debris flow formation, accurately detecting early flows 90% of the time. The machine learning system was able to detect all 13 debris flows and torrential events that occurred during a three-month period in 2020.

Five years ago, Paul Johnson wouldn't have thought predicting earthquakes would ever be possible. "I can't say we will, but I'm much more hopeful we're going to make a lot of progress within decades," the Los Alamos National Laboratory seismologist says. "I'm more hopeful now than I've ever been." The main reason for that new hope is a technology Johnson started looking into about four years ago: machine learning. Many of the sounds and small movements along tectonic fault lines where earthquakes occur have long been thought to be meaningless.

The American space agency's InSight lander appears to have detected its first seismic event on Mars. The faint rumble was picked up by the probe's sensors on 6 April - the 128th Martian day, or sol, of the mission. It is the first seismic signal detected on the surface of a planetary body other than the Earth and its Moon. Scientists say the source for this "Marsquake" could either be movement in a crack inside the planet or the shaking from a meteorite impact. Nasa's InSight probe touched down on the Red Planet in November last year. It aims to identify multiple quakes, to help build a clearer picture of Mars' interior structure.

We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimay\'{o} geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of $\mathrm{CO}_2$-rich fluids from depth, which has relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However, the extent of the RF method's accuracy is poorly understood for this $\mathrm{CO}_2$-driven geysering application. The current study aims to quantify the performance of RF-classifiers to discern the geyser state. Towards this goal, we first present the data collected from the seismometer that is installed near the Chimay\'{o} geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, seasonal trends, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth-Highpass filter and an Autoregressive method in a multi-level fashion. We show that by combining these filtering techniques, in a hierarchical fashion, leads to reduction in the noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes -- remnant noise, precursor, and eruption states. We show that the classification accuracy using RF on the filtered data is greater than 90\%.These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration.

Footfall based biometric system is perhaps the only person identification technique which does not hinder the natural movement of an individual. This is a clear edge over all other biometric systems which require a formidable amount of human intervention and encroach upon an individual's privacy to some extent or the other. This paper presents a Fog computing architecture for implementing footfall based biometric system using widespread geographically distributed geophones (vibration sensor). Results were stored in an Internet of Things (IoT) cloud. We have tested our biometric system on an indigenous database (created by us) containing 46000 footfall events from 8 individuals and achieved an accuracy of 73%, 90% and 95% in case of 1, 5 and 10 footsteps per sample. We also proposed a basis pursuit based data compression technique DS8BP for wireless transmission of footfall events to the Fog. DS8BP compresses the original footfall events (sampled at 8 kHz) by a factor of 108 and also acts as a smoothing filter. These experimental results depict the high viability of our technique in the realm of person identification and access control systems.