The realizable resistive limit: A new concept for mapping geological features spanning a broad range of conductances

Geophysics ◽  
2000 ◽  
Vol 65 (4) ◽  
pp. 1124-1127 ◽  
Author(s):  
Richard S. Smith
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Thin Sheet ◽  
Structural Features ◽  
Secondary Response ◽  
Step Response ◽  
Nonlinear Inversion ◽  
Time Data ◽  
Off Time ◽  
The Ideal

The integral of the step response from zero time to infinite time (the ideal resistive limit) can be used to determine the conductance of the ground, in theory, because the former is directly proportional to the latter. However, in a real time‐domain airborne electromagnetic (AEM) system, it is impossible to measure the step response, or the ideal resistive limit. This is because (1) the off time is finite, being interrupted by the next transmitter pulse; (2) the total effect of all previous transmitter pulses is to reduce the measured response; and (3) the process of removing the primary field during the on time removes a component of the secondary response that has the same shape as the primary response. With a real time‐domain AEM system, it is possible to estimate what is defined as the realizable resistive limit (RRL). The RRL can also be calculated theoretically for a horizontal thin sheet of known conductance. Hence, the measured data can be input into a nonlinear inversion scheme and used to estimate an apparent conductance. RRL is calculated using on‐time data, which is above the noise level between 0.001 S and 100 000 S, so it is possible to map conductances in this eight‐decade range. Traditional methods for deriving conductance use off‐time data only and are restricted to a much smaller range of values (i.e., about two decades). A field example illustrates that, within the resistive areas, the RRL map shows many structural features and lithologies that are not evident on the map of conductance derived using off‐time data. Within the conductive areas, the RRL image shows greater variation; a number of geologically meaningful features are also apparent. Another advantage of RRL images is that artifacts associated with current migration near the edge of conductive features are not as evident as they are in the off‐time‐derived conductance images.

On: “Crone pulse electromagnetic response of a conducting thin horizontal sheet—Theory and application,” by S. S. Rai (GEOPHYSICS, 50, 1350–1354, August 1985).

Geophysics ◽  
1987 ◽  
Vol 52 (3) ◽  
pp. 373-374
Author(s):  
David C. Bartel
Keyword(s):  
Time Domain ◽  
Simple Formula ◽  
Thin Sheet ◽  
Step Response ◽  
Electromagnetic Response ◽  
The Subject ◽  
The Time Domain ◽  
Airborne Em

Rai uses a simple formula for the step response of a conducting, horizontal thin sheet in the time domain and applies it to the Crone pulse electromagnetic (PEM) system. He also uses this formulation to interpret some field results. The idea of an infinite, horizontal, conductive thin sheet is valid in some cases for both ground and airborne EM systems. However, I disagree with some of the derivations of the thin‐sheet equation as presented in the subject paper. The applicability of the study is not questioned; but the interpretation of the field example may be different.

Conductivity‐depth imaging of airborne electromagnetic step‐response data

Geophysics ◽  
1991 ◽  
Vol 56 (1) ◽  
pp. 102-114 ◽  
Author(s):  
J. C. Macnae ◽  
Richard Smith ◽  
B. D. Polzer ◽  
Y. Lamontagne ◽  
P. S. Klinkert
Keyword(s):  
Delay Time ◽  
Thin Sheet ◽  
Mirror Image ◽  
Depth Image ◽  
Step Response ◽  
Thick Sheet ◽  
Time Data ◽  
Data Set ◽  
Section Data ◽  
Image Depth

An adaptation of the Macnae‐Lamontagne method allows transform of airborne step‐response electromagnetic (EM) data to a conductivity‐depth image. The algorithm is based on a nonlinear transformation of the amplitude of the measured response at each delay time to an apparent mirror image depth. Using matrix algebra, the set of mirror image depth‐delay time data pairs can then be used to derive a conductivity section. Data can be efficiently processed on a personal computer at rates faster or comparable to the rate required for collection. Stable conductivity fitting as a function of depth is obtained by damping the matrix inversion by specification of the first‐ and second‐derivative smoothness weights of the fitted conductivity‐depth sounding. Damping parameters may be either fixed or varied along the profile; their choice can be constrained by geologic control. Stability of the process is enhanced by accounting for the transmitter and receiver tilts. The mirror image depth‐delay time data can also be used directly with simple regression to obtain the best‐fitting thin‐sheet and half‐space models. With one novel assumption, the thin‐sheet model can be converted to a thick‐sheet overburden model without prespecification of either its conductivity or thickness. Depending on the geology, these simple models may prove quite useful. The conductivity imaging algorithm has been applied to a test data set collected with the SPECTREM system. The stability and speed of the imaging process were confirmed and have demonstrated airborne EM sounding to depths well over 400 m in an area with quite conductive sediments. Comparing the results with a better resolved image obtained from ground UTEM data shows that the airborne data can adequately define the geometry of the uppermost conductor encountered in the section. The geophysical results are consistent with geologic control and measurements of resistivity obtained from well logs.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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