Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

409
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
409
Aliasing01:18

Aliasing

296
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
296
Upsampling01:22

Upsampling

360
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
360
Sampling Theorem01:15

Sampling Theorem

872
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
872
Downsampling01:20

Downsampling

311
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
311
Bandpass Sampling01:17

Bandpass Sampling

287
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
287

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ground penetrating radar observations of ancient large-scale deltaic structures in Jezero crater, Mars.

Science advances·2026
Same author

Exo-Geoscience Perspectives Beyond Habitability.

Space science reviews·2026
Same author

Water Versus Land on Temperate Rocky Planets.

Space science reviews·2026
Same author

Seismic evidence for a highly heterogeneous martian mantle.

Science (New York, N.Y.)·2025
Same author

Induced allopatry as main mechanism explaining trap catch reduction in low dose mating disruption trials on the strawberry pest Acleris comariana (Lepidoptera: Tortricidae).

Pest management science·2025
Same author

Extensive Secondary Cratering From the InSight Sol 1034a Impact Event.

Journal of geophysical research. Planets·2024

Related Experiment Video

Updated: Oct 18, 2025

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

9.5K

A Reconstruction Algorithm for Temporally Aliased Seismic Signals Recorded by the InSight Mars Lander.

David Sollberger1, Cedric Schmelzbach1, Fredrik Andersson1

  • 1Institute of Geophysics ETH Zürich Zürich Switzerland.

Earth and Space Science (Hoboken, N.J.)
|October 1, 2021
PubMed
Summary
This summary is machine-generated.

Scientists developed a new algorithm to reconstruct high-frequency seismic signals from Mars. This method enhances data from the InSight lander

Keywords:
Marsaliasingseismic explorationsignal processingsignal reconstruction

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.6K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.1K

Related Experiment Videos

Last Updated: Oct 18, 2025

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

9.5K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.6K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.1K

Area of Science:

  • Planetary Science
  • Seismology
  • Signal Processing

Background:

  • NASA's InSight lander deployed a seismometer and hammering probe on Mars for heat flow measurements.
  • Hammering generated seismic signals, but their high frequencies exceeded the seismometer's 100 Hz sampling rate (Nyquist frequency).

Purpose of the Study:

  • To develop an algorithm for reconstructing seismic signals beyond the classical Nyquist frequency limit.
  • To improve the resolution of shallow subsurface imaging using seismic data from Mars.

Main Methods:

  • Exploited repeated, gradually varying hammering signals and differential sub-sampling due to unsynchronized timing.
  • Applied a sparsity constraint in a modified Radon transform domain.
  • Developed an algorithm to reconstruct high-frequency seismic signals and reduce noise.

Main Results:

  • Successfully reconstructed high-frequency hammering signals at very high resolution using synthetic and actual Martian data.
  • Demonstrated the algorithm's capability to overcome classical sampling limitations.
  • Showcased noise reduction in recorded seismic data.

Conclusions:

  • The proposed algorithm enables reconstruction of seismic signals beyond the Nyquist frequency.
  • This advancement allows for higher-resolution imaging of Mars' shallow subsurface.
  • The method enhances the scientific return from the InSight mission's seismic data.