The paper presents a framework that adapts general-purpose Vision Foundation Models (VFMs) to seismic data denoising using Parameter‑Efficient Fine‑Tuning with Low‑Rank Adaptation (LoRA). It introduces a kurtosis‑guided unsupervised test‑time adaptation module that updates only LoRA parameters to self‑calibrate for site‑specific noise without ground truth. Experiments on exploration seismic images and DAS data demonstrate that the approach matches or surpasses domain‑specific models and generalizes well to unseen cross‑site data.
By Jiahua Zhao, Umair bin Waheed, Jing Sun, Yang Cui, Nikos Savva, Eric Verschuur
arXiv:2609.36193v1 Announce Type: new
Abstract: Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is...
By Meher Gajula, Keyla Gonzalez, Ben Lasscock, Alejandro Valenciano
arXiv:2606. 17567v1 Announce Type: new Abstract: While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting correlated error components.
By Ye Su, Jipeng Guo, Yong Liu, Xin Xu, Gangchun Zhang, Jinxin Chen, Di Wu, Longlong Zhao
DeepStratNet introduces a context‑aware coordinate regression framework for seismic horizon tracking that directly predicts time/depth coordinates at each lateral position, avoiding the need for dense segmentation masks. The lightweight regression head, combined with an LSTM for inter‑slice context and geology‑informed regularization, outperforms traditional segmentation models on a New Zealand seismic volume, especially under sparse labeling. The method also provides a built‑in quality control by capturing local geological variations through prediction variability across traces.
By Aniq Ahmad, Musham Ahmad Malik, Ahmad Mustafa, Heather Bedle
The paper investigates whether seismic foundation models can transcend being mere feature extractors by training a Mamba encoder‑decoder on 3 million waveform windows with varied self‑supervised objectives, quantization bottlenecks, and mixers. Experiments show that changing the training target redistributes performance across downstream tasks rather than improving it overall; adding masking improves few‑shot P‑wave picking but harms classification accuracy. The authors conclude that current models act as feature extractors, and that a learning target spanning seismic events—capturing the stochastic inter‑event timescale—is needed to achieve true foundation‑model capabilities.
By Laura Laurenti, David Liu, Brian Kulis, Men-Andrin Meier, Christopher Johnson
arXiv:2609.36396v1 Announce Type: cross
Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical cha...
By Yinan Cheng, Lili Zheng
The paper introduces FLARE‑T, a Transfer‑Enabled Forced Latent Autoencoder for Response Equations, which learns low‑dimensional latent dynamics from dense finite‑element simulations and calibrates them with sparse field observations. By mapping simulated sensor responses into a learned coordinate system, FLARE‑T improves multi‑depth acceleration predictions and pseudo‑acceleration spectra, reducing errors across various sensor locations and motion intensities. Evaluation on a layered‑soil centrifuge test and the Lotung field array demonstrates that FLARE‑T achieves comparable accuracy with different source models, indicating less reliance on precise prior calibration.
By Yi Zhu, Su Chen, Xiaojun Li
arXiv:2609.22654v1 Announce Type: cross
Abstract: Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. W...
By Brigham Halverson, Sharmistha Guha, Jessica Bernard, Rajarshi Guhaniyogi
arXiv:2606. 15377v1 Announce Type: cross Abstract: Inaccurately labeled training data, or "label noise", poses a significant threat to the integrity of supervised machine learning models.
By Sen Li, Xu Yang, S. Mostafa Mousavi, Anye Cao, Keting Fan, Yaoqi Liu, Changbin Wang, Qiang Niu
arXiv:2609.24126v1 Announce Type: cross
Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...
By Xuhui Liu, Lili Zheng
arXiv:2606. 10069v3 Announce Type: replace Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package.
By Wei Quan, Denise Gorse
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay