A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Spatially-aware model improves earthquake hazard assessment accuracy.
Study constructs balanced datasets for seismic failure prediction.
Study uses machine learning to optimize seismic design parameters.
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wi…
Deep learning model improves seismic rock property estimation.
This study improves uncertainty quantification in seismic inversion.
We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation bet…
Seismic inversion method uses GAN to improve efficiency and accuracy.
Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints - the lack of which might lead to undesirable results. To overcome this issue, we have deve…
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context,…
Optimizes seismic monitoring networks using Bayesian OED.
Reconstruction of seismic data with missing traces is a long-standing issue in seismic data processing. In recent years, rank reduction operations are being commonly utilized to overcome this problem, which require the rank of seismic data to be a prior. However, the rank of field data is unknown; usually it requires m…
In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing. Here, we propose a model to leverage the deep-learning model for this task. Rather than directly applying an existing de-noising model from ordinary images to th…
Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for thes…
New method uses CNN for seismic inversion uncertainty quantification.
This document describes an approach to the problem of predicting dangerous seismic events in active coal mines up to 8 hours in advance. It was developed as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. The solutions presented consist of ensembles of various pred…
A new method uses denoising diffusion models to improve seismic data interpolation.
Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks, and underlies most seismicity catalogs produced around t…
Automated quality control for seismic data reduces human labor and time.
Research on unique continuation principles in medical and seismic imaging.
SAGE generates subsurface velocity models from sparse well logs and seismic images.
Study combines VICReg and TNC for better encoding of non-stationary seismic signals.
Active learning method optimizes seismic fragility curve estimation.
Deep learning calibrates CO2 storage formations from seismic and well data.
Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of construction details on the structural performance of installations under se…
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs a…
Physics-consistent method improves seismic inversion accuracy.
Develops a method to estimate average hazard under non-proportional hazards without relying on proportional hazards assumption.
Bayesian deep learning improves seismic imaging uncertainty.
Study explains mortgage burnout using Cox hazard models.
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using sparsity optimization…
We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are explored to augment velocity model building workflows during processing the 3D seismic volume in salt-prone environments. Specifically, a neural…
Study uses machine learning to predict nonlinear seismic brace behavior.
New method estimates hazard ratios without bias in observational studies.
Scalable approach for high-dimensional dynamical systems with noise filtering and parameter estimation.
Expert-guided model improves seismic compliance monitoring.
The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.
New model predicts weekly earthquakes with better tail risk assessment.
Novel approach to compute hazard ratios from observational studies using SCMs and backdoor adjustment.
We present an application of deep generative models in the context of partial-differential equation (PDE) constrained inverse problems. We combine a generative adversarial network (GAN) representing an a priori model that creates subsurface geological structures and their petrophysical properties, with the numerical so…
Unified model estimates landslide hazard combining susceptibility, intensity, and frequency.
Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion in extremely large-scale applications, such as seismic data interpolation. In this paper, we consider matrix completion formulations designed to hit a targ…
EdgeLite detects hazardous supermarket floors, improving safety.
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in d…
Recent work on follow the perturbed leader (FTPL) algorithms for the adversarial multi-armed bandit problem has highlighted the role of the hazard rate of the distribution generating the perturbations. Assuming that the hazard rate is bounded, it is possible to provide regret analyses for a variety of FTPL algorithms f…
Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Older techniques for such picking include interpolation of control po…
New method disentangles sources of different timescales in planetary seismic data.
QSurv models survival data without discretization, achieving high accuracy.