Electric field variations measured continuously in free air over a conductive thin zone in the tilted Lias-epsilon black shales near Osnabrück, Northwest Germany
“…Comparison. Model Parameters Gurk et al 6 Mehanee (2022) 8 Present study K (mV m ) 450 (mV) 11783.60 (mV m) 46527 (mV m ) (m) 10–23 19.9 23 (m) 251.73 – 250 ( ) 95 − 99 .2 − 97 q thin sheet 1.0 (horizontal cylinder) 1.2 (quasi horizontal cylinder) …”
Section: Numerical Examplesmentioning
confidence: 93%
“… Figure 10 The Osnabr ck self-potential anomaly, Germany. Location of the survey area (star) north of Osnbrück, and isolines of Vitrinite Reflectance of the maturity map (taken from Gurk et al 6 with permission from Elsevier). NL: Netherlands, B: Belgium.…”
Section: The Methodsmentioning
confidence: 99%
“…4 and Table 1 . Figure 12 Osnabr ck self-potential anomaly, Germany: Two-dimensional electromagnetic inversion results (taken from Gurk et al 6 with permission from Elsevier). Figure 13 The Suleymankoy self-potential anomaly, Turkey.…”
Section: Numerical Examplesmentioning
confidence: 99%
“… Osnabr ck self-potential anomaly, Germany: Two-dimensional electromagnetic inversion results (taken from Gurk et al 6 with permission from Elsevier). …”
Section: Numerical Examplesmentioning
confidence: 99%
“…A self-potential anomaly near the Osnabr ck area (Fig. 10 ), Northwest Germany 6 has been carried out to trace a graphite anomaly that has a quasi-vertical form in the Lias-epsilon shales. Gurk et al 6 found a significant single self-potential anomaly of roughly − 600 mV (Fig.…”
We describe a rapid imaging approach for the interpretation of self-potential data collected along profile by some geometrically simple model of cylinders and spheres. The approach calculates the correlation coefficient between the analytic signal (AS) of the observed self-potential measurements and the AS of the self-potential signature of the idealized model. The depth, electric dipole moment, polarization angle, and center are the inverse parameters we aim to extract from the imaging approach for the interpretative model, and they pertain to the highest value of the correlation coefficient. The approach is demonstrated on noise-free numerical experiments, and reproduced the true model parameters. The accuracy and stability of the proposed approach are examined on numerical experiments contaminated with realistic noise levels and regional fields prior to the interpretation of real data. Following that, five real field examples from geothermal systems and mineral exploration have been successfully analyzed. The results agree well with the published research.
“…Comparison. Model Parameters Gurk et al 6 Mehanee (2022) 8 Present study K (mV m ) 450 (mV) 11783.60 (mV m) 46527 (mV m ) (m) 10–23 19.9 23 (m) 251.73 – 250 ( ) 95 − 99 .2 − 97 q thin sheet 1.0 (horizontal cylinder) 1.2 (quasi horizontal cylinder) …”
Section: Numerical Examplesmentioning
confidence: 93%
“… Figure 10 The Osnabr ck self-potential anomaly, Germany. Location of the survey area (star) north of Osnbrück, and isolines of Vitrinite Reflectance of the maturity map (taken from Gurk et al 6 with permission from Elsevier). NL: Netherlands, B: Belgium.…”
Section: The Methodsmentioning
confidence: 99%
“…4 and Table 1 . Figure 12 Osnabr ck self-potential anomaly, Germany: Two-dimensional electromagnetic inversion results (taken from Gurk et al 6 with permission from Elsevier). Figure 13 The Suleymankoy self-potential anomaly, Turkey.…”
Section: Numerical Examplesmentioning
confidence: 99%
“… Osnabr ck self-potential anomaly, Germany: Two-dimensional electromagnetic inversion results (taken from Gurk et al 6 with permission from Elsevier). …”
Section: Numerical Examplesmentioning
confidence: 99%
“…A self-potential anomaly near the Osnabr ck area (Fig. 10 ), Northwest Germany 6 has been carried out to trace a graphite anomaly that has a quasi-vertical form in the Lias-epsilon shales. Gurk et al 6 found a significant single self-potential anomaly of roughly − 600 mV (Fig.…”
We describe a rapid imaging approach for the interpretation of self-potential data collected along profile by some geometrically simple model of cylinders and spheres. The approach calculates the correlation coefficient between the analytic signal (AS) of the observed self-potential measurements and the AS of the self-potential signature of the idealized model. The depth, electric dipole moment, polarization angle, and center are the inverse parameters we aim to extract from the imaging approach for the interpretative model, and they pertain to the highest value of the correlation coefficient. The approach is demonstrated on noise-free numerical experiments, and reproduced the true model parameters. The accuracy and stability of the proposed approach are examined on numerical experiments contaminated with realistic noise levels and regional fields prior to the interpretation of real data. Following that, five real field examples from geothermal systems and mineral exploration have been successfully analyzed. The results agree well with the published research.
This study introduces a rapid and efficient inversion algorithm designed for the interpretation of self-potential responses originating from mineralized and ore sources and hydrothermal activity, specifically addressing spherical, vertical, and horizontal cylindrical structures. The algorithm leverages local wavenumber and correlation imaging techniques to enhance accuracy in modeling. The correlation factor (Cf value) is crucial in this approach, calculated as the correlation between the local wavenumber of the measured self-potential field and that of the computed field. The algorithm identifies the maximum correlation Cf value (CF-max) as indicative of the optimal true model parameters. To validate the proposed algorithm, it was applied to three theoretical examples—one with contamination from regional background and another with multiple sources with and without different types of noises (random Gaussian and white Gaussian noises). Additionally, the approach was tested on three distinct real field cases related to mining, ore investigation and hydrothermal activity in India, Germany and USA. Through a comprehensive analysis of results from theoretical and real-world scenarios, including comparisons with different available data and literature information, the study concludes that the method is effective, applicable to multiple sources, accurate, and does not necessitate prior knowledge of the source shape. This algorithm presents a promising advancement in the field of self-potential interpretation for mineral exploration and geothermal exploration.
This paper describes the use of the particle swarm optimization (PSO) method for interpreting observed self-potential anomalies measured along a profile. First, the technique applies the second moving average to the observed self-potential data in order to eradicate the possible influence of the regional anomaly (up to the third-order polynomial effect) via the filter of consecutive window lengths (s-values) and to calculate the residual anomaly. Following that, the PSO method is applied to the residual response to infer the source parameters: amplitude coefficient (K), depth (z), polarization angle (θ) and the shape factor (q) of the underlying buried target. The technique has been applied to three different theoretical and two field examples from the USA and Turkey. Comparisons have shown that the source parameters retrieved from the technique described here are in good agreement with the available geologic and geophysical information.
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