Enhanced Inversion for Distributed Acoustic Sensing: A Robust Approach with HOLp-OGS Regularization.

Wenhua Xu, Jingye Li, Yaning Wu, Weiheng Geng, Bangbang Gao, Lei Han

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Wenhua XuState Key Laboratory of Petroleum Resources and Prospecting, College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China.

Jingye LiState Key Laboratory of Petroleum Resources and Prospecting, College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China.

Yaning WuCNOOC China Limited, Tianjin Branch, Tianjin 100083, China.

Weiheng GengCollege of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, China.

Bangbang GaoState Key Laboratory of Petroleum Resources and Prospecting, College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China.

Lei HanState Key Laboratory of Petroleum Resources and Prospecting, College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, China.

Funding

National Natural Science Foundation of China 42530806the R&D Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications 2022DQ0604-04

6 · The paper itself

Abstract

Conversion from distributed acoustic sensing (DAS) measurements to geophone-equivalent data is important for integrating DAS into conventional seismic workflows. This is because most established seismic-processing algorithms are designed for particle-velocity or acceleration data, whereas DAS measures strain or strain rate. Recovering geophone-equivalent particle velocity from DAS strain-rate measurements requires inversion of a gauge-length-dependent spatial-difference operator, which can amplify measurement noise, particularly in field data with low signal-to-noise ratios (SNRs). Existing single-regularization methods often trade noise attenuation against waveform fidelity and the preservation of weak coherent events. To address these limitations, we propose an inverse reconstruction framework combining high-order

Indexed as

DAS-to-geophone conversiondistributed acoustic sensinghigh-order Lp quasi-normoverlapping group sparsity regularization

Identifiers

PMID42451293

DOI10.3390/s26134051

PMCPMC13363983

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.