Implementation of least-square constrain inversion method of geoelectrical resistivity data Wenner-Schlumberger for investigation the characteristic of landslide

Numerous studies have been conducted on an inversion method, focus on constraining factor,singuler value, speed of convergence. However, the result of inversion is not unique and bivalent. In this research, we optimize of Least-Square constrain by using damping factor. Th is method used for interpretation of the volumes and rock structure in Malalak Distric of Agam West Sumatra. This is undertaken because Malalak districts of Agam West Sumatra that passed by highway Padang and Bukittingi is a frequent area of landslide. Furthermore, the frequency of the landslide depends on the type of rock and the angle of the slope . The depth of the slide surface can be predicted by using the least squares inversion constrain method of Geoelectric Resistivity. Landslides resulted in disruption of transportation between the city of Padang and another district in Sumatra. Based on the above, to determine the rock's structure, the depth and tilt angle of the slide surface in Malalak districts Agam West Sumatra has to take place. Data obtained through Geoelectrical exploration using with automatic resistivitymeter equipment. Constrains were obtained using the Marquat inversion method. The result of the research is first, the damping factor for structures which have wide range resistivity is 0.02 and the smallest damping factor is 0.015. Second, the rock structure in Malalak of Agam consists of clay, sandstone, andesite, and limestone and dolomite. Implementation this research can be used to develop mitigation of landslide deserter.

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Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model

International Journal of Geophysics ◽

10.1155/2015/134834 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11

Author(s):

A. Stanley Raj ◽

D. Hudson Oliver ◽

Y. Srinivas

Keyword(s):

Fuzzy Logic ◽

Soft Computing ◽

Subsurface Layer ◽

Subtractive Clustering ◽

Model Parameters ◽

Layer Model ◽

Inference System ◽

Resistivity Data ◽

Data Inversion ◽

Geoelectrical Resistivity

Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzyC-means clustering, fuzzyK-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.

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Seepage Zone Identification at Sutami Dam by Means of Geoelectrical Resistivity Data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/75/1/012011 ◽

2017 ◽

Vol 75 ◽

pp. 012011

Author(s):

Sunaryo ◽

A. Susilo

Keyword(s):

Resistivity Data ◽

Geoelectrical Resistivity

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Predicting the movements of permanently installed electrodes on an active landslide using time-lapse geoelectrical resistivity data only

Geophysical Journal International ◽

10.1111/j.1365-246x.2010.04760.x ◽

2010 ◽

Vol 183 (2) ◽

pp. 543-556 ◽

Cited By ~ 48

Author(s):

Paul B. Wilkinson ◽

Jonathan E. Chambers ◽

Philip I. Meldrum ◽

David A. Gunn ◽

Richard D. Ogilvy ◽

...

Keyword(s):

Time Lapse ◽

Resistivity Data ◽

Active Landslide ◽

Geoelectrical Resistivity

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Zeeman-Doppler imaging: old problems and new methods

Proceedings of the International Astronomical Union ◽

10.1017/s1743921309031469 ◽

2008 ◽

Vol 4 (S259) ◽

pp. 633-644 ◽

Cited By ~ 9

Author(s):

Thorsten A. Carroll ◽

Markus Kopf ◽

Klaus G. Strassmeier ◽

Ilya Ilyin

Keyword(s):

Radiative Transfer ◽

Principal Component ◽

Magnetic Surface ◽

Least Square ◽

Doppler Imaging ◽

Inversion Method ◽

Reconstruction Process ◽

Physical Constraints ◽

Non Linear ◽

Transfer Method

AbstractZeeman-Doppler Imaging (ZDI) is a powerful inversion method to reconstruct stellar magnetic surface fields. The reconstruction process is usually solved by translating the inverse problem into a regularized least-square or optimization problem. In this contribution we will emphasize that ZDI is an inherent non-linear problem and the corresponding regularized optimization is, like many non-linear problems, potentially prone to local minima. We show how this problem will be exacerbated by using an inadequate forward model. To facilitate a more consistent full radiative transfer driven approach to ZDI we describe a two-stage strategy that consist of a principal component analysis (PCA) based line profile reconstruction and a fast approximate polarized radiative transfer method to synthesize local Stokes profiles. Moreover, we introduce a novel statistical inversion method based on artificial neural networks (ANN) which provide a fast calculation of a first guess model and allows to incorporate better physical constraints into the inversion process.

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Non linear inversion of gravity gradients and the GGI gradiometer

Open Geosciences ◽

10.2478/s13533-011-0041-3 ◽

2011 ◽

Vol 3 (4) ◽

Cited By ~ 2

Author(s):

Manik Talwani

Keyword(s):

Iterative Procedure ◽

Least Square ◽

Inversion Method ◽

Gravity Gradients ◽

Linear Inversion ◽

Non Linear ◽

Best Fitting ◽

The Difference ◽

Differential Curvature ◽

Fitting Model

AbstractAll gradiometers currently operating for exploration in the field are based on Lockheed Martin’s GGI gradiometer. The working of this gradiometer is described and a method for robust non linear inversion of gravity gradients is presented. The inversion method involves obtaining the gradient response of a trial body consisting of vertical rectangular prisms. The inversion adjusts the depth to the tops or bases of the prisms. In the trial model all the prisms are not required to have the same area of cross section or the same density (which can also be allowed to vary with depth). The depth to the tops and bottoms of each prism can also be different. This response is compared with the observed values of gradient and through an iterative procedure, the difference is minimized in a least square sense to arrive at a best fitting model by varying the position of the tops or bottoms of the prisms. Each gradient can be individually inverted or one or more gradients can be jointly inverted. The method is extended to invert gravity values individually or jointly with gradient values. The use of Differential Curvature, a quantity which is directly obtained by current gradiometers in use and which is an invariant under a rotation in the horizontal plane, is emphasized. Synthetic examples as well as a field example of inversion are given.

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High-resolution spectropolarimetry of κ Cet: A proxy for the young Sun

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314001902 ◽

2013 ◽

Vol 9 (S302) ◽

pp. 142-143

Author(s):

J. D. do Nascimento ◽

P. Petit ◽

M. Castro ◽

G. F. Porto de Mello ◽

S. V. Jeffers ◽

...

Keyword(s):

Magnetic Field ◽

High Resolution ◽

Atmospheric Model ◽

Longitudinal Component ◽

Least Square ◽

Doppler Imaging ◽

Inversion Method ◽

Radial Magnetic Field ◽

Field Geometry ◽

The Magnetic Field

Abstractκ1 Cet (HD 20630, HIP 15457, d = 9.16 pc, V = 4.84) is a dwarf star approximately 30 light-years away in the equatorial constellation of Cetus. Among the solar proxies studied in the Sun in Time, κ1 Cet stands out as potentially having a mass very close to solar and a young age. On this study, we monitored the magnetic field and the chromospheric activity from the Ca II H & K lines of κ1 Cet. We used the technique of Least-Square-Deconvolution (LSD, Donati et al. 1997) by simultaneously extracting the information contained in all 8,000 photospheric lines of the echelogram (for a linelist matching an atmospheric model of spectral type K1). To reconstruct a reliable magnetic map and characterize the surface differential rotation of κ1 Cet we used 14 exposures spread over 2 months, in order to cover at least two rotational cycles (Prot ~9.2 days). The Least Square deconvolution (LSD) technique was applied to detect the Zeeman signature of the magnetic field in each of our 14 observations and to measure its longitudinal component. In order to reconstruct the magnetic field geometry of κ1 Cet, we applied the Zeeman Doppler Imaging (ZDI) inversion method. ZDI revealed a structure in the radial magnetic field consisting of a polar magnetic spot. On this study, we present the fisrt look results of a high-resolution spectropolarimetric campaign to characterize the activity and the magnetic fields of this young solar proxy.

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PERSAMAAN PERCEPATAN TANAH MAKSIMUM GEMPA TEKTONIK UNTUK KAWASAN SUMATERA BARAT

10.31227/osf.io/y2ad3 ◽

2019 ◽

Author(s):

Akmam Akmam

Keyword(s):

Physical Characteristics ◽

Least Square ◽

Inversion Method ◽

Local Magnitude ◽

Ground Acceleration ◽

Earthquake Energy ◽

West Sumatra ◽

Region Data ◽

Fundamental Research

Characteristic of earthquake in a region can be studied through its physical characteristics, such as magnitude and earthquakes energy. Energy which is produced by earthquake can be predicted from magnitude. So far, a lot of research produces different equation to predict of earthquake energy that is depending on zone of earthquake occurred. The purpose of descriptive fundamental research was to find equation for ground acceleration which is presented by local magnitude earthquake for West Sumatra region. Data sounder for research were collected from BMG Padang Panjang which was recorded from 1982 to 2008. Data are analyzed by using least square inversion method. Equation ground acceleration is log αac = 0.451Io +0.146 ML + 0.744 log ∆ - 2.644, which local magnitude is ML = 0.413I0 – 0.785 Log H + 5.334 and intensity of the earthquake in the epicenter is Io = I + 3 log (Δ/H) + 0.01 (ΔH).

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Practical Implementation of Multiple Attenuation Methods on 2D Deepwater Seismic Data : Seram Sea Case Study

BULLETIN OF THE MARINE GEOLOGY ◽

10.32693/bomg.32.1.2017.365 ◽

2017 ◽

Vol 32 (1) ◽

Cited By ~ 1

Author(s):

Tumpal Bernhard Nainggolan ◽

Deny Setiady

Keyword(s):

Seismic Data ◽

Input Data ◽

Complex Structure ◽

Least Square ◽

Inversion Method ◽

Practical Implementation ◽

Proper Solution ◽

Multiple Attenuation ◽

Multiple Elimination ◽

Water Bottom

Some deepwater multiple attenuation processing methods have been developed in the past with partial success. The success of surface multiple attenuation relies on good water bottom reflections for most deepwater marine situations. It brings the bigger ability to build an accurate water bottom multiple prediction model. Major challenges on 2D deepwater seismic data processing especially such a geologically complex structure of Seram Sea, West Papua – Indonesia are to attenuate surface related multiple and to preserve the primary data. Many multiple attenuation methods have been developed to remove surface multiple on these seismic data including most common least-squares, prediction-error filtering and more advanced Radon transform.Predictive Deconvolution and Surface Related Multiple Elimination (SRME) method appears to be a proper solution, especially in complex structure where the above methods fail to distinguish interval velocity difference between primaries and multiples. It does not require any subsurface info as long as source signature and surface reflectivity are provided. SRME method consists of 3 major steps: SRME regularization, multiple modeling and least-square adaptive subtraction. Near offset regularization is needed to fill the gaps on near offset due to unrecorded near traces during the acquisition process. Then, isolating primaries from multiples using forward modeling. Inversion method by subtraction of input data with multiple models to a more attenuated multiple seismic section.Results on real 2D deepwater seismic data show that SRME method as the proper solution should be considered as one of the practical implementation steps in geologically complex structure and to give more accurate seismic imaging for the interpretation.Keywords : multiple attenuation, 2D deepwater seismic, Radon transform, Surface Related Multiple Elimination (SRME). Banyak metode atenuasi pengulangan ganda dikembangkan pada pengolahan data seismik dengan tingkat keberhasilan yang rendah pada masa lalu. Keberhasilan dalam atenuasi pengulangan ganda permukaan salah satunya bergantung pada hasil gelombang pantul pada batas dasar laut dan permukaan pada hampir seluruh survei seismik laut. Hal tersebut menentukan keakuratan dalam membuat model prediksi pengulangan ganda dasar laut dan permukaan air. Tantangan utama dalam pemrosesan data seismik 2D laut dalam khususnya struktur geologi kompleks seperti Laut Seram, Papua Barat – Indonesia adalah pada kegiatan menekan pengulangan ganda permukaan sekaligus mempertahankan data primer. Beberapa metode yang dikembangkan untuk menghilangkan pengulangan ganda permukaan pada data seismik seperti least-square, filter prediksi kesalahan dan transformasi Radon.Dekonvolusi Prediktif dan Metode Surface Related Multiple Elimination (SRME) digunakan sebagai solusi yang baik pada struktur kompleks dimana metode-metode lain gagal untuk memisahkan perbedaan kecepatan interval data primer dan pengulangan ganda. Metode tersebut tidak membutuhkan informasi bawah permukaan selain parameter sumber dan reflektivitas permukaan. Metode SRME terdiri dari 3 tahapan utama : regularisasi SRME, pemodelan pengulangan ganda dan pengurangan adaktif least-square. Regularisasi near offset diperlukan untuk mengisi kekosongan pada near offset yang disebabkan oleh adanya sejumlah tras terdekat yang tidak terekam selama akuisisi. Pemodelan maju digunakan untuk memisahkan data primer dan pengulangan ganda kemudian inversi dengan pengurangan input data dengan model multiple.Hasil pada data seismik 2D laut dalam menunjukkan bahwa metode SRME layak diterapkan sebagai salah satu pengembangan metode atenuasi multiple permukaan serta untuk meningkatkan akurasi data seismik terutama untuk struktur geologi kompleks.Kata kunci : peredaman pengulangan ganda (multiple), seismik 2D laut dalam, transformasi Radon, Surface Related Multiple Attenuation (SRME).

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