Earth-Model Discrimination Method
Introduced is a general (single station, all-type earthquakes; no pre- or post-processing) **method that enables discrimination among geophysical earth models** by establishing if and when there exist high direct functional correlation values between the oscillations of the earth gravity field taken at a model's low eigenfrequencies, and the earth seismicity expressed in seismic energies and seismic magnitudes.
Classically in geophysics - like in many sciences - the Fourier Spectral Analysis (and its derivatives) that requires evenly spaced input data, is used. On the other hand, the [Gauss-Vaniček Spectral Analysis][1] (pronounce *van-knee-check*), being a least squares spectral fit, treats records with gaps as well as fully populated series. The former technique yields power spectra from evenly sampled data, whilst the latter one estimates both variance- and power-spectra from virtually any numerical record.
The direct functional correlation values between diurnal average magnitudes of the earth gravity field and all medium-to-large earthquakes from one decade are proposed as a tool for discriminating among earth models. If **high functional correlation values** can be obtained using this method, a tested **earth model is to be considered a successful one**.
The functional correlation values computed from variance-spectra are shown to possess an **absolute high for periods of 821 s**, which could be used as a refinement constraint in earth-model design. The functional correlation values are higher when seismic energies are used rather than seismic magnitudes, as well as when variance-spectra are used rather than power-spectra.
The method is unique in its rigor, since it enables assessments of earth models from single-station (gravity) measurements, by using all global earthquakes above certain strength, i.e., regardless of their type, faulting mechanism, etc., and without preprocessing or post-processing to enhance and correct either raw gravity data or their spectra. As such, the method serves as a **basis for a definition of different discriminatory criteria**.
[1]: https://en.wikipedia.org/wiki/Least-squares_spectral_analysis#The_Van%C3%AD%C4%8Dek_method
Classically in geophysics - like in many sciences - the Fourier Spectral Analysis (and its derivatives) that requires evenly spaced input data, is used. On the other hand, the [Gauss-Vaniček Spectral Analysis][1] (pronounce *van-knee-check*), being a least squares spectral fit, treats records with gaps as well as fully populated series. The former technique yields power spectra from evenly sampled data, whilst the latter one estimates both variance- and power-spectra from virtually any numerical record.
The direct functional correlation values between diurnal average magnitudes of the earth gravity field and all medium-to-large earthquakes from one decade are proposed as a tool for discriminating among earth models. If **high functional correlation values** can be obtained using this method, a tested **earth model is to be considered a successful one**.
The functional correlation values computed from variance-spectra are shown to possess an **absolute high for periods of 821 s**, which could be used as a refinement constraint in earth-model design. The functional correlation values are higher when seismic energies are used rather than seismic magnitudes, as well as when variance-spectra are used rather than power-spectra.
The method is unique in its rigor, since it enables assessments of earth models from single-station (gravity) measurements, by using all global earthquakes above certain strength, i.e., regardless of their type, faulting mechanism, etc., and without preprocessing or post-processing to enhance and correct either raw gravity data or their spectra. As such, the method serves as a **basis for a definition of different discriminatory criteria**.
[1]: https://en.wikipedia.org/wiki/Least-squares_spectral_analysis#The_Van%C3%AD%C4%8Dek_method
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