Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)

Kaftan, Ilknur; Şalk, Müjgan; Şenol, Yavuz

doi:10.1016/j.jappgeo.2011.09.017

Cited by 24 publications

(6 citation statements)

References 28 publications

(26 reference statements)

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“…It is therefore useful for finding buried geological objects and structures, such as igneous intrusions as a heat source of geothermal system; and some faults that usually control the existance of geothermal manifestation [12][13][14]. The measurement of gravity on land and in the air using airborne gravity for geothermal exploration has rapidly grown [15].…”

Section: Gravity Measurementmentioning

confidence: 99%

The Effect of Gravity Measurement Distribution Points on Interpretation of Gravity Data in the Gunung Endut Geothermal Prospect Area, Indonesia

Supriyanto

Rokhmatuloh

Sobirin

et al. 2018

GEOMATE

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ABSTRACT:The geothermal prospect located at Gunung Endut was investigated in 1980s based on reconnaissance surveys, primarily on geological and geochemical aspects. Then in 2006 a gravity measurement with 247 stations was carried out. However, due to the distribution of the gravity station was less than optimal, interpretation of gravity data was not suitable with the geological situation that support geothermal system. To ensure this, we have carried out a new gravity survey with 134 random stations distribution but remained concentrated on hot spring locations. Data from both measurements were processed by the same treatment starting from pre-processing with some standar corrections to obtain complete Bouguer anomaly, then continue by filtering process to separate regional and residual gravity anomaly using Butterworth filter in a wavenumber domain. Residual gravity anomaly map obtained by random position of stations is more resemble to the geological features developed in the prospect area. The contrast boundaries of the residual gravity anomaly value coincide with the fault-line structures recorded in the surface geological data. This result is better when compared with previous measurements. This study shows that there is a significant influence of the distribution pattern of the gravity stations which affects the conformity with the geological data. Based on the new interpretation result, the structural features in the prospect area are characterized by the complex fault structures that intersected each other mainly close to the surface manifestations. This intersection area is expected to be an enhanced permeability zone so that the hydrothermal fluid can flow to the surface. The interpretation of new gravity data combined with the results of geochemical analysis can be used for more understanding of the geothermal system beneath Gunung Endut prospect area.

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Section: Gravity Measurementmentioning

confidence: 99%

The Effect of Gravity Measurement Distribution Points on Interpretation of Gravity Data in the Gunung Endut Geothermal Prospect Area, Indonesia

Supriyanto

Rokhmatuloh

Sobirin

et al. 2018

GEOMATE

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“…Accordingly, applicability of the AI techniques in the form of artificial neural network, machine/deep learning, evolutionary algorithms, and hybrid structures in producing the predictive 3D subsurface models have been highlighted [46][47][48][49][50][51]. Due to characterized features in creating transferable solutions and learnability from high-level data attributes [52] the feasibility of AI techniques in geothermal modeling [53,54] and compared performance by field prospecting methods [55,56] have been notified in several studies dealing with predicting the location of hot spot structures [57][58][59][60], estimating the temperature distribution [61,62], and potential of geothermal production associated with geological data [63,64].…”

Section: Introductionmentioning

confidence: 99%

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

et al. 2022

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Mapping the subsurface temperatures can efficiently lead to identifying the geothermal distribution heat flow and potential hot spots at different depths. In this paper, an advanced adaptive multitask deep learning procedure for 3D spatial mapping of the subsurface temperature was proposed. As a result, predictive 3D spatial subsurface temperatures at different depths were successfully generated using geolocation of 494 exploratory boreholes data in Catalonia (Spain). To increase the accuracy of the achieved results, hybridization with a new modified firefly algorithm was carried out. Subsequently, uncertainty analysis using a novel automated ensemble deep learning approach for the predicted temperatures and generated spatial 3D maps were executed. Comparing the accuracy performances in terms of correct classification rate (CCR) and the area under the precision–recall curves for validation and whole datasets with at least 4.93% and 2.76% improvement indicated for superiority of the hybridized model. According to the results, the efficiency of the proposed hybrid multitask deep learning in 3D geothermal characterization to enhance the understanding and predictability of subsurface spatial distribution of temperatures is inferred. This implies that the applicability and cost effectiveness of the adaptive procedure in producing 3D high resolution depth dependent temperatures can lead to locate prospective geothermally hotspot active regions.

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“…coordinate transformation, tide modelling, gravity field modelling, orbit determination, digital terrain height estimation, crustal deformation, GNSS error modelling, etc.) makes it important computational tool (Tierra and De Freitas, 2005;Kaftan et al, 2011;Liao et al, 2012;Salim et al, 2015;Lei et al, 2015;Okwuashi and Ndehedehe, 2015;Huang et al, 2016;Razin and Voosoghi, 2017;Gullu and Narin, 2019). Therefore, motivated by the successful application of ANN, the main focus of this study is to explore the potential of integrating Total Least Squares (TLS) and Radial Basis Function Neural Network (RBFNN) in coordinate transformation process.…”

Section: Introductionmentioning

confidence: 99%

Empirical study on the integration of total least squares and radial basis function neural network for coordinate transformation

Kumi-Boateng

Ziggah

2020

GJSTD

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Total Least Squares (TLS) is noted to be a solution approach to solving several geodetic problems. The method has the ability to estimate unknown quantities that are useful for many geodetic applications. Hence, the main objective of this study was to improve the estimation performance of TLS via Radial Basis Function Neural Network (RBFNN) in coordinate transformation. This hybrid approach called TLS-RBFNN was applied to Ghana geodetic reference network, which has a coverage area of 79857 km2 representing 33.5% of the total land mass (238540 km2). A comparative performance analysis of TLS, RBFNN and TLS-RBFNN was carried out using Root Mean Square Horizontal Error (RMSHE) and Standard Deviation (SD). Based on the testing results, it was found that the TLS-RBFNN improved the transformation accuracy of RBFNN and TLS by 20.2% and 37.3% based on the RMSHE. In addition, it was observed that the TLS-RBFNN improved the transformation precision based on SD by 0.37% and 8.52%, respectively. Furthermore, the Bayesian Information Criterion (BIC) applied confirmed the superiority of the hybrid approach than using TLS and RBFNN as independent transformation methods. Consequently, the hybrid approach is recommended for enhanced coordinate transformation results in Ghana geodetic reference network.

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Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)

Cited by 24 publications

References 28 publications

The Effect of Gravity Measurement Distribution Points on Interpretation of Gravity Data in the Gunung Endut Geothermal Prospect Area, Indonesia

The Effect of Gravity Measurement Distribution Points on Interpretation of Gravity Data in the Gunung Endut Geothermal Prospect Area, Indonesia

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

Empirical study on the integration of total least squares and radial basis function neural network for coordinate transformation

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