SmartGeo/Eiagrid portal (Guido Satta, CRS4)

SmartGeo/Eiagrid portal

SmartGeo

Gestore di Risorse Condivise per Analisi di Dati e Applicazioni Ambientali

In-field optimization of seismic data acquisition by real-time subsurface

imaging using a remote GRID computing

environment.


Overview

The Grida3 project

The basic concept of EIAGRID

The EIAGRID portal

Data Examples

Conclusions

Aggregating Infrastructure for Environmental Solutions

The great environmental challenges make necessary expertise from different disciplines and a strong integration of the activities, based on

  Advanced information and communication technologies   New problem solving paradigms

Organizations of federated entities (i.e. peripheral organs) with common interests and objectives in the management and monitoring of the environment and the territory must be in condition to set up shared knowledge-based systems and provide high value-added activities leading to

  Industrial products   Advanced services

tuned to implement environmental quality systems.

Real Collaborations and Virtual Organizations

Working Group 2: monitoring, planning and sustainable water resource management

Working Group 3: information systems for the analysis of integrated environmental and territorial data

Working Group 1: short term prediction of extreme events

A Grid is an infrastructure that allows the integrated and collaborative use of virtualized resources §  Data servers §  Computational servers §  Connecting networks §  Numerical applications §  Information systems owned and managed by one or more organizations

The virtual organization acts as the Grid provider while each partner becomes the recipient of the Grid services

Grida3, Gestore di Risorse Condivise per Analisi di Dati e Applicazioni Ambientali (MIUR Prog. N. 1433/2006)

A problem-solving grid platform for the integration, through a computing portal, of §  resources for

§  communication §  computation §  data storage §  visualization

§  simulation software §  instrumentation §  human know-how in Environmental Sciences

TECHNOLOGIES

Infrastructure User Interfaces Secure access

APPLICATIONS

GIS

Meteorology

Hydrology

Site Remediation

Geophysical Imaging

Numerical applications

Grid computing somehow appears as natural extension of distributed parallel computing

Code porting and new products development are focused to the domain of advanced SW technology for the intensive use of geographically distributed resources

With Grida3 applications, no disruption of algorithmic or mathematical nature is expected, at least at the moment

Data-dominated applications

Data acquisition costs are tremendously reduced

Environmental sciences are characterized by very large volumes of heterogeneous data generated by modern recording digital apparatus

Datasets are not always usable by traditional prediction solvers (PDEs and ODEs) without a preliminary conceptualization phase

Use of clever metadata, a describer designed to infer relationships within data collections and validate new model hypothesis

Data conceptualization

Need to develop SW tools for empirical analysis in environmental sciences based on geographical information systems, Integrating qualitative and quantitative data

Grouping data by relationships that may not be clearly visible among collections of field data

Looking for evidence that would support or refute the implication of a theory

Problems consequently arise at the level of data collection, retrieval and analysis

Most of the non-trivial knowledge extraction is based on heuristics operating on distributed databanks:

Data mining discovery - Ontology-based knowledge representations

Groundwater: Modeling, Management and Planning

Groundwater application §  GIS (input&output) §  Pre-processing §  Solver and Optimizer §  Post-processing §  Visualization

Application developer

Site 1

Data grid infrastructure SRB/iRODS

Compute grid infrastructure via Genius/EnginFrame

Site 2

Data integration and problem solving §  WEB collaborative environment §  Customizable analysis tools §  Problem solving driven by physical models §  Web GIS (solver output, field data, maps…) §  Decision Support System (DSS)

Environmental engineer

Groundwater: Modeling, Management and Planning

Site 3

Data grid infrastructure SRB/iRODS

Compute grid infrastructure via Genius/EnginFrame

Environmental manager

Collaborative problem-solving platform as a decision support system §  Interactive simulation tools based on physics §  Web GIS environment for data

§  Storage §  Retrieval §  Rendering

§  Analysis and decision instruments for §  Management §  Planning §  Costs evaluation

§  Editing of results and dissemination

Grida3, Shared Resources Manager for Environmental Data Analysis and Applications

The Grida3 portal aims at supporting problem solving and decision making by integrating

resources for communication computation data storage

software for simulation inversion visualization

and human know how into a grid computing platform for Environmental Sciences

TECHNOLOGIES

Infrastructure User Interfaces Secure access

APPLICATIONS

GIS Tools

Meteorology

Hydrology

Site Remediation

Geophysical Imaging EIAGRID

Service


Creating a grid computing environment for in-field QC and Optimization of SR/GPR data acquisition by:

1.  Providing a web-browser-based user interface easily accessible from the field

2.  On-the-fly processing of the seismic field data using a remote GRID environment

3.  Fast optimization of data analysis and imaging parameters by parallel processing of alternative workflows

The EIAGRID Portal Main Objectives


Creating a data grid environment to facilitate analysis & decision making in integrated multi-disciplinary studies by:

1.  Providing a flexible and customizable data grid management architecture using iRODS

2.  Georeferencing the data using Geo I n f o r m a t i o n S y s t e m ( G I S ) technologies

3.  Interconnecting the different types of data by mesh-generators and data crossing techniques

The EIAGRID Portal Main Objectives


Seismic reflection data acquisition

GPR data

Multi-offset GPR data: Aim: monitoring of water content and water conductivity Target depth: 0 - 5 m 2D line: length 55 m RAMAC/GPR CU II with MC4 +

4 unshielded 200 Hz antennas Number of sources: 546 Source spacing: 0.1m Number of receivers: 28 Receiver spacing: 0.2 m Maximum offset: 0.6 m


SR/GPR data: Fields of application

Environmental engineering: Ø  Detection of problematic solid-waste in dumping grounds

Ø  Control of the topography of the impermeable basement

Ø  Characterization of landslides on slopes proximal to the ground rupture

Seismic and geotechnical engineering:

Ø  Evaluation of the seismic local response

Hydrogeology: Ø  Identification of aquifer boundaries Ø  Estimation of hydrological parameters (porosity, fluid

content, etc.)

Near-surface geophysics

•  Near-surface geophysics is the use of geophysical methods to investigate small-scale features in the shallow (tens of meters) subsurface.

•  It is closely related to exploration geophysics. •  Methods used include seismic refraction and reflection,

gravity, magnetic, electric, and electromagnetic methods.

•  These methods are used for archaeology, environmental science, forensic science, military intelligence, geotechnical investigation, and hydrogeology.

•  near-surface geophysics includes the study of biogeochemical cycles.

Hydrogeophysics

•  Hydrogeophysics involves use of geophysical measurements for estimating parameters and monitoring processes that are important to hydrological studies: water resources, contaminant transport, ecological and climate investigations.

•  Improved characterization and monitoring using hydrogeophysical techniques can lead to improved management of our natural resources, understanding of natural systems, and remediation of contaminants.


Main Steps of SR/GPR Data Processing

PREPROCESSING

GEOMETRICAL PROCESSING

WAVELET PROCESSING

IMAGING Pr

inci

pal e

labo

ratio

n st

eps

Data conversion Geometry setup Trace editing Noise atenuation CMP sorting

Velocity analysis and NMO Residual static CMP stacking

Deconvolution

Seismic migration

Seismic Records Processing Phases Subsurface Image

Input System Output


Seismic reflection data processing

Seismic Records

Input System Output

Processing Phases Subsurface Image


Conventional velocity analysis Final Processing Results


Main Problem of SR/GPR acquisition:

Real-time processing is difficult and cost intensive

Ø  Acquisition parameters such as recording time, sampling interval, source strength and receiver

spacing cannot be optimized in the field

Solution: Wireless data

transmission + remote GRID computing facilities


Main Concept

25

EIAGRID Portal

1.  Web-browser-based interface accessible from the field

2.  Real-time processing using distant HPC resources

3.  Remote collaboration and acquisition controlling

Features

26

EIAGRID Portal

1.  Web-browser-based interface accessible from the field

2.  On-the-fly processing using a distant HPC resources

3.  Remote collaboration and acquisition controlling

Features

1.  Project, data and user management

2.  Simplistic toolbox for data visualization and manipulation

3.  Data-driven imaging method suited for parallel computing

Components

• Riduzione dei tempi di Elaborazione

• Ottimizzazione delle fasi di acquisizione

Obiettivi:

Abbattere i costi e trasformare la Sismica a Riflessione in una tecnica di

indagine appetibile e di Routine in campo ambientale e ingegneristico

•  CRS-Stack per la Sismica Superficiale •  Analisi di velocità completamente automatica •  Trasferimento dei dati sismici, via cellulare, dal sito al centro di calcolo; •  Elaborazione in tempo reale; •  trasferimento sezione sismica dal centro di calcolo al sito per ottimizzazione acquisizione

Implementazioni:


Remote Grid Computing

Preprocessing and Visualization using SU: Ø  Basic preprocessing steps can be applied without installing

the complex SU processing package.

Ø  Data-driven CRS imaging technology---state-of-the-art in oil exploration---enables highly automated data processing.

Imaging and RSC using CRS technology:

ü  GRID deployment using high performance computing facilities provides the necessary computing power.

Parallel processing of different Processing workflows: Ø  Cumbersome sequential optimization of processing

workflow and processing parameters speeds up drastically.


Seismic reflection data processing

Seismic Records

Input System Output

Processing Phases Subsurface Image

ZERO-OFFSET AND CMP METHODS

•  The simplest type of acquisition would be to use a single coincident source and receiver pair and profile the earth along a line

•  Such an experiment would be called a zero-offset experiment because there is no offset distance between source and receiver

•  The resulting seismic data will be single-fold because there will only be a single trace per sub-surface position.

•  The zero-offset concept is an important one and the method might be used in practise if noise could be ignored.


•  In order to overcome the noise problem and to estimate earth velocity, the method of acquisition most commonly used is the Common-Mid-Point (CMP) method.

•  The general idea is to acquire a series of traces (gather) which reflect from the same common subsurface mid-point.

•  The traces are then summed (stacked) so that superior signal-to-noise ratio to that of the single-fold stack results.

•  The fold of the stack is determined by the number of traces in the CMP gather.


•  Traces resulting from a single six-fold CMP gather depicting reflections from a single flat interface

•  The reflection from the flat interface produces a curved series of arrivals on the seismic traces since it takes longer to travel to the far offsets than the near offsets.

•  This hyperbolic curve (red line) is called the Normal Moveout curve or NMO and is related to travel time, offset and velocity

•  Before stacking the NMO curve must be corrected such that the seismic event lines up on the gather. Normal Moveout Correction.

•  The results are shown in the central portion of the figure. The moveout corrected traces are then stacked, to produce the 6-fold stack trace, which simulates the zero-offset response but with increased signal-to-noise ratio.

•  The CMP gather provides information about seismic velocity of propagation

CMP in practice

•  CMP acquisition is accomplished by firing the source into many receivers simultaneously

•  (a) a shot gather where a single shot (red) is fired into six receivers (green). A receiver is also co-located with the shot to produce a zero-offset trace. By moving the source position an appropriate multiple of the receiver spacing CMP gathers can be constructed by re-ordering the shot traces (this process is called sorting).

•  (b) shows the original shot and second shot (traces in red). In this case, the shot has moved up a distance equal to the receiver spacing. The CMP spacing is equal to half the receiver spacing.

•  (c) shows how the fold of the CMP gathers is starting to build up after six shots have been fired. At the beginning of the line the fold builds up to it's maximum of three. The fold stays at the maximum until the end of the line is reached where the fold decreases.

EFFECT OF DIPPING HORIZONS

•  The CMP method holds for multiple layers and the data can be moved out and stacked to produce three reflections.

•  Where dip is present it is clear that the CMP method is breaking down since the traces do not all reflect from the same mid-point location. Processing techniques such as DMO and Migration are required to accurately process CMP data acquired from dipping strata.

Common-Reflection-Surface stack

•  (generalized) stacking velocity analysis –  search for stacking operator fitting best actual reflection event –  based on coherence analysis

•  data-driven stacking with CRP trajectories –  fitting a space curve to a traveltime surface highly ambiguous, hardly applicable

•  solution: •  consider entire reflector segments

–  i. e., consider neighboring CRPs –  i. e., consider local curvature of reflector –  fitting spatial operator to traveltime surface –  three stacking parameters

Overlap of CMP illuminations






Basic idea

Observations: –  conventional stack implicitly relies on reflector continuity (this also applies to NMO + DMO correction) –  based on normal rays for offset zero –  we have band-limited data

•  Fresnel zone concept Consequences: If conventional stack works

–  there are neighboring reflection points –  they physically contribute to the wavefield at a considered CMP

Why shouldn’t we incorporate these neighboring reflection points?

CRS stack

•  Features inherited from conventional stack: –  normal ray concept –  assumption of reflector continuity –  analytical traveltime approximation (2nd order) –  coherence analysis yields stacking parameters –  stack yields simulated zero-offset section

•  Additional features: –  incorporates neighboring CMP gathers –  yields additional stacking parameters –  increases the coverage –  improves reflector continuity and S/N ratio

From CMP to CRS stacking:

Figure taken from Perroud and Tygel 2005. NMO velocity analysis for the CMP at position x = 10 m.

45

Conventional CMP-by-CMP velocity analysis:

Fig.: Mann et al. 2007

CRS-STACK 3D   Lo stacking consente di comprimere i dati, con aumento S/N,

simulando una acquisizione zero-offset (source-receiver)

  L’idea del CRS STACK e’ descrivere la propagazione d’onda mediante una geometria locale (ottica parassiale): raggi + fronti d’onda paraboloide-ellittico

Massimizzazione di una funzione peso: Semblance ( )hHKHhmHKHmmw T

zyNIPzyTT

zyNzyTT ++⎟⎟

⎠

⎞⎜⎜⎝

⎛+=

0

0

2

00

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vtthyp

2 parameters ( emergence angle & azimuth )

3 Normal Wavefront parameters ( KN,11; KN,12 ; KN22 )

3 NIP Wavefront parameters ( KNIP,11; KNIP,12 ; KNIP22 )

Definizione del problema

•  Problema di ottimizzazione: – Ricerca di 8 parametri per il fit di un’ipersuperficie in

uno spazio pentadimensinale

( )

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SC = SCmax

Legame fra Semblance e parametri sono i dati sismici

Ricerca velocità NMO

Metodi data-driven: trovare la velocità con algoritmi di ottimizzazione di funzioni di coerenza come la semblance

Basta limitare lo spazio di ricerca: due possibilità

2

222

20nmo

hyp vh

tt += ( )

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Limiti assoluti uguali per ogni punto della linea d’acquisizione time min max 0.00 1500.0 1900.0 0.10 1540.0 2200.0 0.20 1550.0 2450.0 0.30 1575.0 2800.0 0.50 1600.0 3300.0 0.70 1600.0 3800.0

Limiti relativi ad una mappa di velocità data con un valori diversi per ogni punto time min max 0.00 -30.0 +10.0 0.10 -45.0 +25.0 0.20 -45.0 +30.0 0.30 -10.0 +50.0 0.50 -20.0 +85.0 0.70 -80.0 +100.0

•  Possibiltà di ricerche ricorsive

Strategia adottata

Step III Determination of RNIP parameters

From VNMO,min , VNMO,max and γv (Step I)

and ψ,θ (Step II), determination of

RNIP,min, RNIP,max and γNIP

3D ZO Stack one five-parametric

search for ψ, θ, γN,

RN,min and RN,max

Step II

Automatic 3D CMP Stack

one three-parametric search for VNMO,min , VNMO,max and γv.

Step I

3D ZO CRS Stack

Stack using the eight paramenters within the projectet Fresnel Zones using the complete CRS

operator

Step IV

Separare le ricerche dei parametri utilizzando sottodomini dei dati

Soluzione possibile grazie

alla formulazione di ipotesi plausibili

( )hHKHhmHKHmmw TzyNIPzy

TTzyNzy

TT ++⎟⎟⎠

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Possibilità di ripetere più volte ogni singolo step

Fig.: Mann et al. 2007

Data-driven stacking parameter determination

Each stacking parameter triple within a given search range defines a hypothetical second-order reflection response. The optimum parameter triple maximizes the coherence between this prediction and the actually measured data.

53

Stacking parameter search:

Pragma'c search: 3 x 1 parameter line search in specific

gathers (Mann et al. 1999) + Cloud = Real-‐5me imaging

One step search:

1 x 3 parameter surface search in prestack data (Garabito et al. 2001)

+ Cloud = High-‐precision imaging

Figs: Mann et al. 2007

CRS stacking result obtained after 4 minutes using 50 CPU

Time domain imaging

Published in: Perroud, H., and Tygel, M., 2005, Velocity estimation by the common-reflection-surface (CRS) method: Using ground-penetrating radar: Geophysics, 70, 1343–1352.

Results GPR data


Conclusions SMARTGEO

Ø  ...minimizes the software and hardware requirements needed to perform a successful SR/GPR data acquisition.

Ø  ...reduces the complexity of data QC and choice of acquisition parameter for less experienced users.

Ø  …provides fast and accurate results by using modern imaging technology and high performance computing.

Enables a wider use of SR/GPR surveys in environmental and earth sciences through Grid technologies

Ø  … facilitates the creation of an integrated geophysical database for environmental studies.

CRS stack

•  Features inherited from conventional stack: –  normal ray concept –  assumption of reflector continuity –  analytical traveltime approximation (2nd order) –  coherence analysis yields stacking parameters –  stack yields simulated zero-offset section

•  Additional features: –  incorporates neighboring CMP gathers –  yields additional stacking parameters –  increases the coverage –  improves reflector continuity and S/N ratio

Basic idea





Basic idea





Technology

SmartGeo/Eiagrid portal (Guido Satta, CRS4)