Constraining seismic parameters with a controlled-source audio-magnetotelluric method (CSAMT)
نویسندگان
چکیده
منابع مشابه
Constraining relative source locations with the seismic coda
The relative location of seismic sources is of importance for the location of aftershocks on a fault, for the positioning of sources in repeat seismic surveys, and for monitoring induced seismicity. In this paper I show theoretically how the seismic coda can be used to infer a measure of the relative source location of two identical seismic sources from the correlation of the waveforms recorded...
متن کاملNeural Networks to Retrieve Seismic Source Parameters
The use of supervised neural networks for the estimation of seismic source parameters from SAR interferometric data is presented in this paper. The RNGCHN software allowed the generation of the input-output pairs necessary for the learning phase of the net. After being trained, the net has been tested on real measured data. The obtained results encourage future developments of such an approach.
متن کامل3-d Focusing Inversion of Csamt Data
We present a method for the solution of 3-D controlled source magnetotelluric (CSAMT) inverse problems. The inverse problem is formulated as the minimization of a Tikhonov parametric functional with a focusing stabilizer. Observed CSAMT apparent resis tivities are converted to log-anomalous apparent resistivities, which are linearly connected to anomalous currents via the integral equation. We...
متن کاملPerceptually controlled doping for audio source separation
The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the mixture a watermark, which information...
متن کاملNeural Networks to Retrieve Seismic Source Parameters by Sar Interferometry
The use of supervised neural networks for the estimation of seismic source parameters from SAR interferometric data is presented in this paper. The RNGCHN software allowed the generation of the input-output pairs necessary for the learning phase of the net. After being trained, the net has been tested on real measured data. The obtained results encourage future developments of such an approach.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Geophysical Journal International
سال: 1995
ISSN: 0956-540X,1365-246X
DOI: 10.1111/j.1365-246x.1995.tb03543.x