Chemical remagnetization is a very common phenomenon in sedimentary rocks and developing a greater understanding of the mechanisms has several benefits. Acquisition of a secondary magnetization is usually tangible evidence of a diagenetic event that can be dated by isolation of the chemical remanent magnetization and comparison of the pole position to the apparent polar wander path. This can be important because diagenetic investigations are frequently limited by the difficulty in constraining the time frames in which most past events have occurred. Remagnetization can commonly obscure a primary magnetization; developing a better understanding of remagnetization could improve our ability to uncover primary magnetizations. Many chemical remagnetization mechanisms have been proposed, including those associated with chemical alteration by a number of different fluids (orogenic, basinal and hydrocarbons), burial diagenetic processes (clay diagenesis and maturation of organic matter) or other processes. This paper summarizes our current knowledge of these chemical remagnetization mechanisms, with a focus on examples where there is a connection with chemical alteration.

A new method to evaluate the thermally stimulated depolarization current curves based on the direct signal analysis is first applied here to the fitting of computer‐generated curves to test its validity and accuracy. The method consists in finding the N elementary curves that best fit the experimental spectrum. The adjustment of the τ0 values associated with each energy bin is also included in the fitting. The results are presented for a monoenergetic peak and a peak generated with a Gaussian broadened energy distribution both with a single τ0. Good agreement is also found when a random noise of width σ0 ≤ 0.01 Jmax is added to the data to simulate experimental errors. Experimental complex curves corresponding to the γ and β overlapping relaxations in DGEBA‐EDA epoxy resin system are separated into their fine structure components. The complex band corresponding to water dipoles physisorbed in different sites of the microporosity of bituminous coal is analyzed and different processes are identified. The results are compared to those performed on cleaned peaks. Finally, the TSDC peak corresponding to the glass transition temperature in bisphenol‐A polycarbonate is analyzed and could only be fitted by using a Vogel‐Fulcher expression for the temperature dependence of the relaxation times. © 1994 John Wiley & Sons, Inc.

Hydrocarbon reservoirs are characterized by seismic, welllog, and petrophysical information, which is dissimilar in spatial distribution, scale, and relationship to reservoir properties. We combine this diverse information in a unified inverse-problem formulation using a multiproperty, multiscale model, linking properties statistically by petrophysical relationships and conditioning them to well-log data. Two approaches help us: ͑1͒ Markov-chain Monte Carlo sampling, which generates many reservoir realizations for estimating medium properties and posterior marginal probabilities, and ͑2͒ optimization with a least-squares iterative technique to obtain the most probable model configuration. Our petrophysical model, applied to near-vertical-anglestacked seismic data and well-log data from a gas reservoir, includes a deterministic component, based on a combination of Wyllie and Wood relationships calibrated with the well-log data, and a random component, based on the statistical characterization of the deviations of well-log data from the petrophysical transform. At the petrophysical level, the effects of porosity and saturation on acoustic impedance are coupled; conditioning the inversion to well-log data helps resolve this ambiguity. The combination of well logs, petrophysics, and seismic inversion builds on the corresponding strengths of each type of information, jointly improving ͑1͒ cross resolution of reservoir properties, ͑2͒ vertical resolution of property fields, ͑3͒ compliance to the smooth trend of property fields, and ͑4͒ agreement with well-log data at well positions.